id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.02941 | A Coupled PFEM-DEM Model for Fluid-Granular Flows with Free-Surface
Dynamics Applied to Landslides | [
"cs.CE"
] | Free surface and granular fluid mechanics problems combine the challenges of fluid dynamics with aspects of granular behaviour. This type of problem is particularly relevant in contexts such as the flow of sediments in rivers, the movement of granular soils in reservoirs, or the interactions between a fluid and granula... | {
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2501.02942 | Improved Approximation Algorithms for Low-Rank Problems Using
Semidefinite Optimization | [
"math.OC",
"cs.LG",
"math.PR"
] | Inspired by the impact of the Goemans-Williamson algorithm on combinatorial optimization, we construct an analogous relax-then-sample strategy for low-rank optimization problems. First, for orthogonally constrained quadratic optimization problems, we derive a semidefinite relaxation and a randomized rounding scheme, wh... | {
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2501.02945 | The Tabular Foundation Model TabPFN Outperforms Specialized Time Series
Forecasting Models Based on Simple Features | [
"cs.LG"
] | Foundation models have become popular in forecasting due to their ability to make accurate predictions, even with minimal fine-tuning on specific datasets. In this paper, we demonstrate how the newly released regression variant of TabPFN, a general tabular foundation model, can be applied to time series forecasting. We... | {
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2501.02949 | MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage
Classification | [
"cs.LG",
"eess.SP"
] | Recent advancements in machine learning-based signal analysis, coupled with open data initiatives, have fuelled efforts in automatic sleep stage classification. Despite the proliferation of classification models, few have prioritised reducing model complexity, which is a crucial factor for practical applications. In th... | {
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2501.02950 | Key-value memory in the brain | [
"q-bio.NC",
"cs.AI",
"cs.LG"
] | Classical models of memory in psychology and neuroscience rely on similarity-based retrieval of stored patterns, where similarity is a function of retrieval cues and the stored patterns. While parsimonious, these models do not allow distinct representations for storage and retrieval, despite their distinct computationa... | {
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2501.02955 | MotionBench: Benchmarking and Improving Fine-grained Video Motion
Understanding for Vision Language Models | [
"cs.CV"
] | In recent years, vision language models (VLMs) have made significant advancements in video understanding. However, a crucial capability - fine-grained motion comprehension - remains under-explored in current benchmarks. To address this gap, we propose MotionBench, a comprehensive evaluation benchmark designed to assess... | {
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2501.02961 | A Point Process Model for Optimizing Repeated Personalized Action
Delivery to Users | [
"stat.ML",
"cs.LG"
] | This paper provides a formalism for an important class of causal inference problems inspired by user-advertiser interaction in online advertiser. Then this formalism is specialized to an extension of temporal marked point processes and the neural point processes are suggested as practical solutions to some interesting ... | {
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2501.02962 | SceneVTG++: Controllable Multilingual Visual Text Generation in the Wild | [
"cs.CV"
] | Generating visual text in natural scene images is a challenging task with many unsolved problems. Different from generating text on artificially designed images (such as posters, covers, cartoons, etc.), the text in natural scene images needs to meet the following four key criteria: (1) Fidelity: the generated text sho... | {
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2501.02964 | Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the
Wild | [
"cs.CV",
"cs.AI"
] | Complex visual reasoning remains a key challenge today. Typically, the challenge is tackled using methodologies such as Chain of Thought (COT) and visual instruction tuning. However, how to organically combine these two methodologies for greater success remains unexplored. Also, issues like hallucinations and high trai... | {
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2501.02966 | Human Gaze Boosts Object-Centered Representation Learning | [
"cs.CV",
"cs.LG"
] | Recent self-supervised learning (SSL) models trained on human-like egocentric visual inputs substantially underperform on image recognition tasks compared to humans. These models train on raw, uniform visual inputs collected from head-mounted cameras. This is different from humans, as the anatomical structure of the re... | {
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2501.02968 | FlipedRAG: Black-Box Opinion Manipulation Attacks to Retrieval-Augmented
Generation of Large Language Models | [
"cs.IR"
] | Retrieval-Augmented Generation (RAG) addresses hallucination and real-time constraints by dynamically retrieving relevant information from a knowledge database to supplement the LLMs' input. When presented with a query, RAG selects the most semantically similar texts from its knowledge bases and uses them as context fo... | {
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2501.02969 | LOHA: Direct Graph Spectral Contrastive Learning Between Low-pass and
High-pass Views | [
"cs.LG"
] | Spectral Graph Neural Networks effectively handle graphs with different homophily levels, with low-pass filter mining feature smoothness and high-pass filter capturing differences. When these distinct filters could naturally form two opposite views for self-supervised learning, the commonalities between the counterpart... | {
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2501.02971 | Proof-of-Data: A Consensus Protocol for Collaborative Intelligence | [
"cs.CR",
"cs.AI"
] | Existing research on federated learning has been focused on the setting where learning is coordinated by a centralized entity. Yet the greatest potential of future collaborative intelligence would be unleashed in a more open and democratized setting with no central entity in a dominant role, referred to as "decentraliz... | {
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2501.02973 | HaWoR: World-Space Hand Motion Reconstruction from Egocentric Videos | [
"cs.CV"
] | Despite the advent in 3D hand pose estimation, current methods predominantly focus on single-image 3D hand reconstruction in the camera frame, overlooking the world-space motion of the hands. Such limitation prohibits their direct use in egocentric video settings, where hands and camera are continuously in motion. In t... | {
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2501.02975 | Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular
Balls | [
"cs.LG",
"cs.AI"
] | Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while r... | {
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2501.02976 | STAR: Spatial-Temporal Augmentation with Text-to-Video Models for
Real-World Video Super-Resolution | [
"cs.CV"
] | Image diffusion models have been adapted for real-world video super-resolution to tackle over-smoothing issues in GAN-based methods. However, these models struggle to maintain temporal consistency, as they are trained on static images, limiting their ability to capture temporal dynamics effectively. Integrating text-to... | {
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2501.02977 | CAMP: Collaborative Attention Model with Profiles for Vehicle Routing
Problems | [
"cs.MA",
"cs.AI"
] | The profiled vehicle routing problem (PVRP) is a generalization of the heterogeneous capacitated vehicle routing problem (HCVRP) in which the objective is to optimize the routes of vehicles to serve client demands subject to different vehicle profiles, with each having a preference or constraint on a per-client basis. ... | {
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2501.02979 | Registering Source Tokens to Target Language Spaces in Multilingual
Neural Machine Translation | [
"cs.CL"
] | The multilingual neural machine translation (MNMT) aims for arbitrary translations across multiple languages. Although MNMT-specific models trained by parallel data offer low costs in training and deployment, their performance consistently lags behind that of large language models (LLMs). In this work, we introduce reg... | {
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2501.02981 | CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural
Networks | [
"cs.CR",
"cs.AI",
"cs.NI"
] | Advanced Persistent Threats (APTs) represent a significant challenge in cybersecurity due to their sophisticated and stealthy nature. Traditional Intrusion Detection Systems (IDS) often fall short in detecting these multi-stage attacks. Recently, Graph Neural Networks (GNNs) have been employed to enhance IDS capabiliti... | {
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2501.02982 | A Bio-Inspired Research Paradigm of Collision Perception Neurons
Enabling Neuro-Robotic Integration: The LGMD Case | [
"cs.NE",
"cs.AI",
"q-bio.NC"
] | Compared to human vision, insect visual systems excel at rapid and precise collision detection, despite relying on only tens of thousands of neurons organized through a few neuropils. This efficiency makes them an attractive model system for developing artificial collision-detecting systems. Specifically, researchers h... | {
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2501.02989 | Classifier Weighted Mixture models | [
"stat.ML",
"cs.LG"
] | This paper proposes an extension of standard mixture stochastic models, by replacing the constant mixture weights with functional weights defined using a classifier. Classifier Weighted Mixtures enable straightforward density evaluation, explicit sampling, and enhanced expressivity in variational estimation problems, w... | {
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2501.02990 | SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose
Estimation | [
"cs.CV",
"cs.RO"
] | Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods for surgical instrument pose estimation provide a practical approach to tool tracking, but they often require markers to be attached to the ... | {
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2501.02992 | GLFC: Unified Global-Local Feature and Contrast Learning with
Mamba-Enhanced UNet for Synthetic CT Generation from CBCT | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Generating synthetic Computed Tomography (CT) images from Cone Beam Computed Tomography (CBCT) is desirable for improving the image quality of CBCT. Existing synthetic CT (sCT) generation methods using Convolutional Neural Networks (CNN) and Transformers often face difficulties in effectively capturing both global and ... | {
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2501.02994 | NeuroPMD: Neural Fields for Density Estimation on Product Manifolds | [
"stat.ML",
"cs.LG"
] | We propose a novel deep neural network methodology for density estimation on product Riemannian manifold domains. In our approach, the network directly parameterizes the unknown density function and is trained using a penalized maximum likelihood framework, with a penalty term formed using manifold differential operato... | {
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2501.02997 | CALM: Curiosity-Driven Auditing for Large Language Models | [
"cs.AI",
"cs.CL"
] | Auditing Large Language Models (LLMs) is a crucial and challenging task. In this study, we focus on auditing black-box LLMs without access to their parameters, only to the provided service. We treat this type of auditing as a black-box optimization problem where the goal is to automatically uncover input-output pairs o... | {
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2501.03005 | PiLaMIM: Toward Richer Visual Representations by Integrating Pixel and
Latent Masked Image Modeling | [
"cs.CV"
] | In Masked Image Modeling (MIM), two primary methods exist: Pixel MIM and Latent MIM, each utilizing different reconstruction targets, raw pixels and latent representations, respectively. Pixel MIM tends to capture low-level visual details such as color and texture, while Latent MIM focuses on high-level semantics of an... | {
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2501.03006 | TransPixeler: Advancing Text-to-Video Generation with Transparency | [
"cs.CV"
] | Text-to-video generative models have made significant strides, enabling diverse applications in entertainment, advertising, and education. However, generating RGBA video, which includes alpha channels for transparency, remains a challenge due to limited datasets and the difficulty of adapting existing models. Alpha cha... | {
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2501.03008 | Quality Estimation based Feedback Training for Improving Pronoun
Translation | [
"cs.CL",
"cs.AI"
] | Pronoun translation is a longstanding challenge in neural machine translation (NMT), often requiring inter-sentential context to ensure linguistic accuracy. To address this, we introduce ProNMT, a novel framework designed to enhance pronoun and overall translation quality in context-aware machine translation systems. P... | {
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2501.03012 | Analyzing Fine-tuning Representation Shift for Multimodal LLMs Steering
alignment | [
"cs.AI",
"cs.CL",
"cs.CV"
] | Multimodal LLMs have reached remarkable levels of proficiency in understanding multimodal inputs, driving extensive research to develop increasingly powerful models. However, much less attention has been paid to understanding and explaining the underlying mechanisms of these models. Most existing explainability researc... | {
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2501.03016 | Classification of LCD and self-dual codes over a finite non-unital local
ring | [
"cs.IT",
"math.IT"
] | This work explores LCD and self-dual codes over a noncommutative non-unital ring $ E_p= \langle r,s ~|~ pr =ps=0,~ r^2=r,~ s^2=s,~ rs=r,~ sr=s \rangle$ of order $p^2$ where $p$ is a prime. Initially, we study the monomial equivalence of two free $E_p$-linear codes. In addition, a necessary and sufficient condition is d... | {
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2501.03017 | Convexity in ReLU Neural Networks: beyond ICNNs? | [
"cs.LG"
] | Convex functions and their gradients play a critical role in mathematical imaging, from proximal optimization to Optimal Transport. The successes of deep learning has led many to use learning-based methods, where fixed functions or operators are replaced by learned neural networks. Regardless of their empirical superio... | {
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2501.03018 | Probably Correct Optimal Stable Matching for Two-Sided Markets Under
Uncertainty | [
"cs.LG"
] | We consider a learning problem for the stable marriage model under unknown preferences for the left side of the market. We focus on the centralized case, where at each time step, an online platform matches the agents, and obtains a noisy evaluation reflecting their preferences. Our aim is to quickly identify the stable... | {
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2501.03020 | AC-aware Optimization Framework for Under-Frequency Load Shedding | [
"eess.SY",
"cs.SY"
] | Under-frequency load shedding (UFLS) prevents system collapse during large disturbances. Increased penetration of distributed energy resources (DERs) and reduced system inertia makes it challenging to design a static UFLS scheme, which relies on preset frequency thresholds and load shed fractions to meet design criteri... | {
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2501.03021 | A Trust-Guided Approach to MR Image Reconstruction with Side Information | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Reducing MRI scan times can improve patient care and lower healthcare costs. Many acceleration methods are designed to reconstruct diagnostic-quality images from limited sets of acquired $\textit{k}$-space data. This task can be framed as a linear inverse problem (LIP), where, as a result of undersampling, the forward ... | {
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2501.03026 | Putnam's Critical and Explanatory Tendencies Interpreted from a Machine
Learning Perspective | [
"cs.CY",
"cs.AI",
"cs.LG"
] | Making sense of theory choice in normal and across extraordinary science is central to philosophy of science. The emergence of machine learning models has the potential to act as a wrench in the gears of current debates. In this paper, I will attempt to reconstruct the main movements that lead to and came out of Putnam... | {
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2501.03028 | Enabling Efficient Optimal Control of Reconfigurable Battery Systems by
Unifying System Modeling and Narrowing Search Space | [
"eess.SY",
"cs.SY",
"physics.app-ph"
] | Reconfigurable battery systems (RBSs) are emerging as a promising solution to improving fault tolerance, charge and thermal balance, energy delivery, etc. To optimize these performance metrics, high-dimensional nonlinear integer programming problems need to be formulated and solved. During this process, it is necessary... | {
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2501.03030 | DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration
Models | [
"eess.IV",
"cs.CV"
] | Diffusion models have demonstrated their utility as learned priors for solving various inverse problems. However, most existing approaches are limited to linear inverse problems. This paper exploits the efficient and unsupervised posterior sampling framework of Denoising Diffusion Restoration Models (DDRM) for the solu... | {
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2501.03035 | Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization
Degradation for Mathematical Reasoning | [
"cs.CL",
"cs.AI"
] | Large language models have achieved significant advancements in complex mathematical reasoning benchmarks, such as MATH. However, their substantial computational requirements present challenges for practical deployment. Model quantization has emerged as an effective strategy to reduce memory usage and computational cos... | {
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2501.03038 | Piano Transcription by Hierarchical Language Modeling with Pretrained
Roll-based Encoders | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | Automatic Music Transcription (AMT), aiming to get musical notes from raw audio, typically uses frame-level systems with piano-roll outputs or language model (LM)-based systems with note-level predictions. However, frame-level systems require manual thresholding, while the LM-based systems struggle with long sequences.... | {
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2501.03040 | ChronoSense: Exploring Temporal Understanding in Large Language Models
with Time Intervals of Events | [
"cs.LG",
"cs.CL"
] | Large Language Models (LLMs) have achieved remarkable success in various NLP tasks, yet they still face significant challenges in reasoning and arithmetic. Temporal reasoning, a critical component of natural language understanding, has raised increasing research attention. However, comprehensive testing of Allen's inte... | {
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2501.03041 | Group Shapley with Robust Significance Testing and Its Application to
Bond Recovery Rate Prediction | [
"stat.ML",
"cs.LG"
] | We propose Group Shapley, a metric that extends the classical individual-level Shapley value framework to evaluate the importance of feature groups, addressing the structured nature of predictors commonly found in business and economic data. More importantly, we develop a significance testing procedure based on a three... | {
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2501.03045 | Single-Channel Distance-Based Source Separation for Mobile GPU in
Outdoor and Indoor Environments | [
"eess.AS",
"cs.AI"
] | This study emphasizes the significance of exploring distance-based source separation (DSS) in outdoor environments. Unlike existing studies that primarily focus on indoor settings, the proposed model is designed to capture the unique characteristics of outdoor audio sources. It incorporates advanced techniques, includi... | {
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2501.03049 | Convergence in On-line Learning of Static and Dynamic Systems | [
"eess.SY",
"cs.SY"
] | The paper derives new conditions for global convergence of the adaptive moment generation algorithm when applied for on-line, or equivalently, recursive supervised learning of static and dynamic nonlinear systems. The paper also proposes a difference equation based nonlinear dynamic model, that enforces {\em structure}... | {
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2501.03053 | Dr. Tongue: Sign-Oriented Multi-label Detection for Remote Tongue
Diagnosis | [
"eess.IV",
"cs.CV"
] | Tongue diagnosis is a vital tool in Western and Traditional Chinese Medicine, providing key insights into a patient's health by analyzing tongue attributes. The COVID-19 pandemic has heightened the need for accurate remote medical assessments, emphasizing the importance of precise tongue attribute recognition via teleh... | {
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2501.03054 | A Passive Mechanical Add-on for Treadmill Exercise (P-MATE) in Stroke
Rehabilitation | [
"cs.RO",
"physics.med-ph"
] | Robotic rehabilitation can deliver high-dose gait therapy and improve motor function after a stroke. However, for many devices, high costs and lengthy setup times limit clinical adoption. Thus, we designed, built, and evaluated the Passive Mechanical Add-on for Treadmill Exercise (P-MATE), a low-cost passive end-effect... | {
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2501.03055 | To Analyze and Regulate Human-in-the-loop Learning for Congestion Games | [
"cs.GT",
"cs.AI"
] | In congestion games, selfish users behave myopically to crowd to the shortest paths, and the social planner designs mechanisms to regulate such selfish routing through information or payment incentives. However, such mechanism design requires the knowledge of time-varying traffic conditions and it is the users themselv... | {
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2501.03058 | Survival Analysis Revisited: Understanding and Unifying Poisson,
Exponential, and Cox Models in Fall Risk Analysis | [
"cs.LG",
"cs.AI"
] | This paper explores foundational and applied aspects of survival analysis, using fall risk assessment as a case study. It revisits key time-related probability distributions and statistical methods, including logistic regression, Poisson regression, Exponential regression, and the Cox Proportional Hazards model, offeri... | {
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2501.03059 | Through-The-Mask: Mask-based Motion Trajectories for Image-to-Video
Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We consider the task of Image-to-Video (I2V) generation, which involves transforming static images into realistic video sequences based on a textual description. While recent advancements produce photorealistic outputs, they frequently struggle to create videos with accurate and consistent object motion, especially in ... | {
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2501.03064 | Trust Modeling in Counseling Conversations: A Benchmark Study | [
"cs.CL"
] | In mental health counseling, a variety of earlier studies have focused on dialogue modeling. However, most of these studies give limited to no emphasis on the quality of interaction between a patient and a therapist. The therapeutic bond between a patient and a therapist directly correlates with effective mental health... | {
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2501.03068 | SGLDBench: A Benchmark Suite for Stress-Guided Lightweight 3D Designs | [
"cs.CE"
] | We introduce the Stress-Guided Lightweight Design Benchmark (SGLDBench), a comprehensive benchmark suite to apply and evaluate material layout strategies for generating stiff lightweight designs in 3D domains. SGLDBench provides a seamlessly integrated simulation and analysis framework, providing six reference strategi... | {
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2501.03069 | Benefit evaluation of V2X-enhanced braking in view obstructed crossing
use cases | [
"eess.SY",
"cs.SY"
] | If a crash between two vehicles is imminent, an Automatic Emergency Brake (AEB) is activated to avoid or mitigate the accident. However, the trigger mechanism of the AEB relies on the vehicle's onboard sensors, such as radar and cameras, that require a line of sight to detect the crash opponent. If the line of sight is... | {
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2501.03070 | Slim multi-scale convolutional autoencoder-based reduced-order models
for interpretable features of a complex dynamical system | [
"physics.flu-dyn",
"cs.LG"
] | In recent years, data-driven deep learning models have gained significant interest in the analysis of turbulent dynamical systems. Within the context of reduced-order models (ROMs), convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches. They can learn nonlinear transform... | {
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2501.03072 | OpenTable data with multi-criteria ratings | [
"cs.IR"
] | With the development of recommender systems (RSs), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. Multi-criteria recommender systems (MCRSs) are designed to provide personalized recommendations by considering user preferences in multiple attributes or criteria simulta... | {
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2501.03074 | AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain
Adaptation for Medical Image Segmentation | [
"cs.CV"
] | Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, in medical settings, concerns surrounding data collection and privacy often restrict access to both traini... | {
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2501.03078 | Qinco2: Vector Compression and Search with Improved Implicit Neural
Codebooks | [
"cs.LG"
] | Vector quantization is a fundamental technique for compression and large-scale nearest neighbor search. For high-accuracy operating points, multi-codebook quantization associates data vectors with one element from each of multiple codebooks. An example is residual quantization (RQ), which iteratively quantizes the resi... | {
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2501.03079 | Wheel-GINS: A GNSS/INS Integrated Navigation System with a Wheel-mounted
IMU | [
"cs.RO"
] | A long-term accurate and robust localization system is essential for mobile robots to operate efficiently outdoors. Recent studies have shown the significant advantages of the wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning system. However, it still drifts over extended periods because of the a... | {
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2501.03080 | Joint Secrecy Rate Achieving and Authentication Enhancement via
Tag-based Encoding in Chaotic UAV Communication Environment | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Secure communication is crucial in many emerging systems enabled by unmanned aerial vehicle (UAV) communication networks. To protect legitimate communication in a chaotic UAV environment, where both eavesdropping and jamming become straightforward from multiple adversaries with line-of-sight signal propagation, a new r... | {
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2501.03085 | Personalized Fashion Recommendation with Image Attributes and Aesthetics
Assessment | [
"cs.IR",
"cs.AI"
] | Personalized fashion recommendation is a difficult task because 1) the decisions are highly correlated with users' aesthetic appetite, which previous work frequently overlooks, and 2) many new items are constantly rolling out that cause strict cold-start problems in the popular identity (ID)-based recommendation method... | {
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2501.03088 | Sentiment-guided Commonsense-aware Response Generation for Mental Health
Counseling | [
"cs.CL"
] | The crisis of mental health issues is escalating. Effective counseling serves as a critical lifeline for individuals suffering from conditions like PTSD, stress, etc. Therapists forge a crucial therapeutic bond with clients, steering them towards positivity. Unfortunately, the massive shortage of professionals, high co... | {
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2501.03095 | A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing
Compression in Deep Neural Networks | [
"cs.CV",
"cs.NE"
] | Deep neural networks suffer from storing millions and billions of weights in memory post-training, making challenging memory-intensive models to deploy on embedded devices. The weight-sharing technique is one of the popular compression approaches that use fewer weight values and share across specific connections in the... | {
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2501.03102 | Enhancing Multirotor Drone Efficiency: Exploring Minimum Energy
Consumption Rate of Forward Flight under Varying Payload | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Multirotor unmanned aerial vehicle is a prevailing type of aircraft with wide real-world applications. Energy efficiency is a critical aspect of its performance, determining the range and duration of the missions that can be performed. In this study, we show both analytically and numerically that the optimum of a key e... | {
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2501.03103 | MVP: Multimodal Emotion Recognition based on Video and Physiological
Signals | [
"cs.CV"
] | Human emotions entail a complex set of behavioral, physiological and cognitive changes. Current state-of-the-art models fuse the behavioral and physiological components using classic machine learning, rather than recent deep learning techniques. We propose to fill this gap, designing the Multimodal for Video and Physio... | {
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2501.03112 | LangFair: A Python Package for Assessing Bias and Fairness in Large
Language Model Use Cases | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | Large Language Models (LLMs) have been observed to exhibit bias in numerous ways, potentially creating or worsening outcomes for specific groups identified by protected attributes such as sex, race, sexual orientation, or age. To help address this gap, we introduce LangFair, an open-source Python package that aims to e... | {
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2501.03113 | Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based
Centrality | [
"cs.LG",
"cs.NE"
] | We propose an expressive and efficient approach that combines the strengths of two prominent extensions of Graph Neural Networks (GNNs): Subgraph GNNs and Structural Encodings (SEs). Our approach leverages walk-based centrality measures, both as a powerful form of SE and also as a subgraph selection strategy for Subgra... | {
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2501.03118 | TEE-based Key-Value Stores: a Survey | [
"cs.CR",
"cs.DB"
] | Key-Value Stores (KVSs) are No-SQL databases that store data as key-value pairs and have gained popularity due to their simplicity, scalability, and fast retrieval capabilities. However, storing sensitive data in KVSs requires strong security properties to prevent data leakage and unauthorized tampering. While software... | {
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2501.03119 | From Models to Network Topologies: A Topology Inference Attack in
Decentralized Federated Learning | [
"cs.LG",
"cs.AI"
] | Federated Learning (FL) is widely recognized as a privacy-preserving machine learning paradigm due to its model-sharing mechanism that avoids direct data exchange. However, model training inevitably leaves exploitable traces that can be used to infer sensitive information. In Decentralized FL (DFL), the overlay topolog... | {
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2501.03120 | CAT: Content-Adaptive Image Tokenization | [
"cs.CV"
] | Most existing image tokenizers encode images into a fixed number of tokens or patches, overlooking the inherent variability in image complexity. To address this, we introduce Content-Adaptive Tokenizer (CAT), which dynamically adjusts representation capacity based on the image content and encodes simpler images into fe... | {
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2501.03122 | Normalizing Batch Normalization for Long-Tailed Recognition | [
"cs.CV"
] | In real-world scenarios, the number of training samples across classes usually subjects to a long-tailed distribution. The conventionally trained network may achieve unexpected inferior performance on the rare class compared to the frequent class. Most previous works attempt to rectify the network bias from the data-le... | {
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2501.03124 | PRMBench: A Fine-grained and Challenging Benchmark for Process-Level
Reward Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Process-level Reward Models (PRMs) are crucial for complex reasoning and decision-making tasks, where each intermediate step plays an important role in the reasoning process. Since language models are prone to various types of errors during the reasoning process, PRMs are required to possess nuanced capabilities for de... | {
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2501.03130 | Learning DAGs and Root Causes from Time-Series Data | [
"cs.LG",
"stat.ML"
] | We introduce DAG-TFRC, a novel method for learning directed acyclic graphs (DAGs) from time series with few root causes. By this, we mean that the data are generated by a small number of events at certain, unknown nodes and time points under a structural vector autoregression model. For such data, we (i) learn the DAGs... | {
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2501.03132 | Communication Bounds for the Distributed Experts Problem | [
"cs.LG"
] | In this work, we study the experts problem in the distributed setting where an expert's cost needs to be aggregated across multiple servers. Our study considers various communication models such as the message-passing model and the broadcast model, along with multiple aggregation functions, such as summing and taking t... | {
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2501.03137 | Distributionally Robust Control Synthesis for Stochastic Systems with
Safety and Reach-Avoid Specifications | [
"eess.SY",
"cs.SY"
] | We investigate the problem of synthesizing distributionally robust control policies for stochastic systems under safety and reach-avoid specifications. Using a game-theoretical framework, we consider the setting where the probability distribution of the disturbance at each time step is selected from an ambiguity set de... | {
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2501.03139 | VicSim: Enhancing Victim Simulation with Emotional and Linguistic
Fidelity | [
"cs.CL",
"cs.HC"
] | Scenario-based training has been widely adopted in many public service sectors. Recent advancements in Large Language Models (LLMs) have shown promise in simulating diverse personas to create these training scenarios. However, little is known about how LLMs can be developed to simulate victims for scenario-based traini... | {
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2501.03142 | Co-Activation Graph Analysis of Safety-Verified and Explainable Deep
Reinforcement Learning Policies | [
"cs.AI",
"cs.LG"
] | Deep reinforcement learning (RL) policies can demonstrate unsafe behaviors and are challenging to interpret. To address these challenges, we combine RL policy model checking--a technique for determining whether RL policies exhibit unsafe behaviors--with co-activation graph analysis--a method that maps neural network in... | {
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2501.03145 | Geometry Restoration and Dewarping of Camera-Captured Document Images | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This research focuses on developing a method for restoring the topology of digital images of paper documents captured by a camera, using algorithms for detection, segmentation, geometry restoration, and dewarping. Our methodology employs deep learning (DL) for document outline detection, followed by computer vision (CV... | {
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2501.03151 | Large language models for artificial general intelligence (AGI): A
survey of foundational principles and approaches | [
"cs.AI",
"cs.CV",
"cs.LG"
] | Generative artificial intelligence (AI) systems based on large-scale pretrained foundation models (PFMs) such as vision-language models, large language models (LLMs), diffusion models and vision-language-action (VLA) models have demonstrated the ability to solve complex and truly non-trivial AI problems in a wide varie... | {
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2501.03152 | The Scaling Law for LoRA Base on Mutual Information Upper Bound | [
"cs.LG",
"cs.AI"
] | LoRA (Low-Rank Adaptation) is a widely used model fine-tuning method. In fine-tuning, the law among model performance, model parameters, and data complexity has been a focal issue in the field. Existing methods often leverage external metrics (such as cross-entropy or perplexity) to evaluate model performance. In the f... | {
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2501.03153 | Segment Anything Model for Zero-shot Single Particle Tracking in Liquid
Phase Transmission Electron Microscopy | [
"cs.CV",
"physics.data-an"
] | Liquid phase transmission electron microscopy (LPTEM) offers an unparalleled combination of spatial and temporal resolution, making it a promising tool for single particle tracking at the nanoscale. However, the absence of a standardized framework for identifying and tracking nanoparticles in noisy LPTEM videos has imp... | {
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2501.03156 | Application of $J$-Integral to a Random Elastic Medium | [
"cond-mat.soft",
"cs.CE"
] | This study investigates the use of the $J$-integral to compute the statistics of the energy release rate of a random elastic medium. The spatial variability of the elastic modulus is modeled as a homogeneous lognormal random field. Within the framework of Monte Carlo simulation, a modified contour integral is applied t... | {
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2501.03160 | Statistical Reconstruction For Anisotropic X-ray Dark-Field Tomography | [
"cs.CE"
] | Anisotropic X-ray Dark-Field Tomography (AXDT) is a novel imaging technology that enables the extraction of fiber structures on the micrometer scale, far smaller than standard X-ray Computed Tomography (CT) setups. Directional and structural information is relevant in medical diagnostics and material testing. Compared ... | {
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2501.03162 | Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning | [
"cs.LG",
"eess.SP"
] | Decentralized learning strategies allow a collection of agents to learn efficiently from local data sets without the need for central aggregation or orchestration. Current decentralized learning paradigms typically rely on an averaging mechanism to encourage agreement in the parameter space. We argue that in the contex... | {
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2501.03166 | Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot
In-Context Learning for SQL2Text | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated remarkable performance in various NLP tasks, including semantic parsing, which translates natural language into formal code representations. However, the reverse process, translating code into natural language, termed semantic captioning, has received less attention. This ... | {
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2501.03172 | GLiREL -- Generalist Model for Zero-Shot Relation Extraction | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We introduce GLiREL (Generalist Lightweight model for zero-shot Relation Extraction), an efficient architecture and training paradigm for zero-shot relation classification. Inspired by recent advancements in zero-shot named entity recognition, this work presents an approach to efficiently and accurately predict zero-sh... | {
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2501.03173 | MObI: Multimodal Object Inpainting Using Diffusion Models | [
"cs.CV"
] | Safety-critical applications, such as autonomous driving, require extensive multimodal data for rigorous testing. Methods based on synthetic data are gaining prominence due to the cost and complexity of gathering real-world data but require a high degree of realism and controllability in order to be useful. This paper ... | {
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2501.03176 | Scalable Forward-Forward Algorithm | [
"cs.LG",
"cs.NE"
] | We propose a scalable Forward-Forward (FF) algorithm that eliminates the need for backpropagation by training each layer separately. Unlike backpropagation, FF avoids backward gradients and can be more modular and memory efficient, making it appealing for large networks. We extend FF to modern convolutional architectur... | {
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2501.03181 | FaceSpeak: Expressive and High-Quality Speech Synthesis from Human
Portraits of Different Styles | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Humans can perceive speakers' characteristics (e.g., identity, gender, personality and emotion) by their appearance, which are generally aligned to their voice style. Recently, vision-driven Text-to-speech (TTS) scholars grounded their investigations on real-person faces, thereby restricting effective speech synthesis ... | {
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2501.03182 | Boosting Explainability through Selective Rationalization in Pre-trained
Language Models | [
"cs.CL",
"cs.AI"
] | The widespread application of pre-trained language models (PLMs) in natural language processing (NLP) has led to increasing concerns about their explainability. Selective rationalization is a self-explanatory framework that selects human-intelligible input subsets as rationales for predictions. Recent studies have show... | {
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2501.03183 | Classifier-Guided Captioning Across Modalities | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] | Most current captioning systems use language models trained on data from specific settings, such as image-based captioning via Amazon Mechanical Turk, limiting their ability to generalize to other modality distributions and contexts. This limitation hinders performance in tasks like audio or video captioning, where dif... | {
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2501.03184 | Noise-Robust Target-Speaker Voice Activity Detection Through
Self-Supervised Pretraining | [
"eess.AS",
"cs.LG",
"cs.SD"
] | Target-Speaker Voice Activity Detection (TS-VAD) is the task of detecting the presence of speech from a known target-speaker in an audio frame. Recently, deep neural network-based models have shown good performance in this task. However, training these models requires extensive labelled data, which is costly and time-c... | {
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2501.03187 | Turn-based Multi-Agent Reinforcement Learning Model Checking | [
"cs.AI",
"cs.LG",
"cs.MA"
] | In this paper, we propose a novel approach for verifying the compliance of turn-based multi-agent reinforcement learning (TMARL) agents with complex requirements in stochastic multiplayer games. Our method overcomes the limitations of existing verification approaches, which are inadequate for dealing with TMARL agents ... | {
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} |
2501.03190 | Multimodal Machine Learning Can Predict Videoconference Fluidity and
Enjoyment | [
"cs.LG",
"cs.HC",
"eess.AS",
"eess.IV"
] | Videoconferencing is now a frequent mode of communication in both professional and informal settings, yet it often lacks the fluidity and enjoyment of in-person conversation. This study leverages multimodal machine learning to predict moments of negative experience in videoconferencing. We sampled thousands of short cl... | {
"Other": 0,
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} |
2501.03191 | CLIX: Cross-Lingual Explanations of Idiomatic Expressions | [
"cs.CL"
] | Automated definition generation systems have been proposed to support vocabulary expansion for language learners. The main barrier to the success of these systems is that learners often struggle to understand definitions due to the presence of potentially unfamiliar words and grammar, particularly when non-standard lan... | {
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} |
2501.03200 | The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground
Responses to Long-Form Input | [
"cs.CL"
] | We introduce FACTS Grounding, an online leaderboard and associated benchmark that evaluates language models' ability to generate text that is factually accurate with respect to given context in the user prompt. In our benchmark, each prompt includes a user request and a full document, with a maximum length of 32k token... | {
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} |
2501.03203 | Detecting AI-Generated Text in Educational Content: Leveraging Machine
Learning and Explainable AI for Academic Integrity | [
"cs.CL",
"cs.AI",
"cs.CY"
] | This study seeks to enhance academic integrity by providing tools to detect AI-generated content in student work using advanced technologies. The findings promote transparency and accountability, helping educators maintain ethical standards and supporting the responsible integration of AI in education. A key contributi... | {
"Other": 0,
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"cs.SY": 0
} |
2501.03212 | Leveraging Explainable AI for LLM Text Attribution: Differentiating
Human-Written and Multiple LLMs-Generated Text | [
"cs.CL",
"cs.CY"
] | The development of Generative AI Large Language Models (LLMs) raised the alarm regarding identifying content produced through generative AI or humans. In one case, issues arise when students heavily rely on such tools in a manner that can affect the development of their writing or coding skills. Other issues of plagiar... | {
"Other": 0,
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} |
2501.03218 | Dispider: Enabling Video LLMs with Active Real-Time Interaction via
Disentangled Perception, Decision, and Reaction | [
"cs.CV"
] | Active Real-time interaction with video LLMs introduces a new paradigm for human-computer interaction, where the model not only understands user intent but also responds while continuously processing streaming video on the fly. Unlike offline video LLMs, which analyze the entire video before answering questions, active... | {
"Other": 0,
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} |
2501.03220 | ProTracker: Probabilistic Integration for Robust and Accurate Point
Tracking | [
"cs.CV"
] | In this paper, we propose ProTracker, a novel framework for robust and accurate long-term dense tracking of arbitrary points in videos. The key idea of our method is incorporating probabilistic integration to refine multiple predictions from both optical flow and semantic features for robust short-term and long-term tr... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.03221 | RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet
Transform Projection-based Network | [
"cs.CV"
] | In the domain of 3D object classification, a fundamental challenge lies in addressing the scarcity of labeled data, which limits the applicability of traditional data-intensive learning paradigms. This challenge is particularly pronounced in few-shot learning scenarios, where the objective is to achieve robust generali... | {
"Other": 0,
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"cs.SD": 0,
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} |
2501.03222 | Characterizing the Accuracy-Communication-Privacy Trade-off in
Distributed Stochastic Convex Optimization | [
"cs.LG",
"cs.IT",
"math.IT",
"stat.ML"
] | We consider the problem of differentially private stochastic convex optimization (DP-SCO) in a distributed setting with $M$ clients, where each of them has a local dataset of $N$ i.i.d. data samples from an underlying data distribution. The objective is to design an algorithm to minimize a convex population loss using ... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.03223 | Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac
MRI Segmentation | [
"cs.CV",
"cs.DC",
"cs.LG"
] | Cardiovascular disease (CVD) and cardiac dyssynchrony are major public health problems in the United States. Precise cardiac image segmentation is crucial for extracting quantitative measures that help categorize cardiac dyssynchrony. However, achieving high accuracy often depends on centralizing large datasets from di... | {
"Other": 1,
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"cs.CV": 1,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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