id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.02219 | Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised
Learning | [
"cs.LG",
"cs.AI",
"cs.IT",
"math.IT"
] | Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (non-IID) nature of data among clients. In this paper, we propose a novel approach, diffusion model-based data synthesis aided FSSL (DDSA-FSS... | {
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2501.02221 | CORD: Generalizable Cooperation via Role Diversity | [
"cs.AI",
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"cs.MA"
] | Cooperative multi-agent reinforcement learning (MARL) aims to develop agents that can collaborate effectively. However, most cooperative MARL methods overfit training agents, making learned policies not generalize well to unseen collaborators, which is a critical issue for real-world deployment. Some methods attempt to... | {
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2501.02226 | Knowledge Graph Retrieval-Augmented Generation for LLM-based
Recommendation | [
"cs.IR"
] | Recommender systems have become increasingly vital in our daily lives, helping to alleviate the problem of information overload across various user-oriented online services. The emergence of Large Language Models (LLMs) has yielded remarkable achievements, demonstrating their potential for the development of next-gener... | {
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2501.02227 | tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation
and Its Application in Medical Image Segmentation | [
"eess.IV",
"cs.CV"
] | Transfer learning, by leveraging knowledge from pre-trained models, has significantly enhanced the performance of target tasks. However, as deep neural networks scale up, full fine-tuning introduces substantial computational and storage challenges in resource-constrained environments, limiting its widespread adoption. ... | {
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2501.02232 | Distillation-Enhanced Physical Adversarial Attacks | [
"cs.CV"
] | The study of physical adversarial patches is crucial for identifying vulnerabilities in AI-based recognition systems and developing more robust deep learning models. While recent research has focused on improving patch stealthiness for greater practical applicability, achieving an effective balance between stealth and ... | {
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2501.02235 | Survey on Question Answering over Visually Rich Documents: Methods,
Challenges, and Trends | [
"cs.CL"
] | Using Large Language Models (LLMs) for Visually-rich Document Understanding (VrDU) has significantly improved performance on tasks requiring both comprehension and generation, such as question answering, albeit introducing new challenges. This survey explains how VrDU models enhanced by LLMs function, covering methods ... | {
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2501.02237 | Financial Named Entity Recognition: How Far Can LLM Go? | [
"cs.CL",
"cs.AI"
] | The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and ... | {
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2501.02241 | Interpretable Load Forecasting via Representation Learning of
Geo-distributed Meteorological Factors | [
"cs.LG",
"cs.AI"
] | Meteorological factors (MF) are crucial in day-ahead load forecasting as they significantly influence the electricity consumption behaviors of consumers. Numerous studies have incorporated MF into the load forecasting model to achieve higher accuracy. Selecting MF from one representative location or the averaged MF as ... | {
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2501.02242 | Encircling General 2-D Boundaries by Mobile Robots with Collision
Avoidance: A Vector Field Guided Approach | [
"cs.RO",
"cs.SY",
"eess.SY"
] | The ability to automatically encircle boundaries with mobile robots is crucial for tasks such as border tracking and object enclosing. Previous research has primarily focused on regular boundaries, often assuming that their geometric equations are known in advance, which is not often the case in practice. In this paper... | {
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2501.02260 | MagicFace: High-Fidelity Facial Expression Editing with Action-Unit
Control | [
"cs.CV"
] | We address the problem of facial expression editing by controling the relative variation of facial action-unit (AU) from the same person. This enables us to edit this specific person's expression in a fine-grained, continuous and interpretable manner, while preserving their identity, pose, background and detailed facia... | {
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2501.02263 | The Convergence of Blockchain Technology and Islamic Economics:
Decentralized Solutions for Shariah-Compliant Finance | [
"cs.CR",
"cs.CE",
"cs.ET"
] | This paper provides a brief overview of the ongoing financial revolution, which extends beyond the emergence of cryptocurrencies as a digital medium of exchange. At its core, this revolution is driven by a paradigm shift rooted in the technological advancements of blockchain and the foundational principles of Islamic e... | {
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2501.02264 | Unsupervised Class Generation to Expand Semantic Segmentation Datasets | [
"cs.CV"
] | Semantic segmentation is a computer vision task where classification is performed at a pixel level. Due to this, the process of labeling images for semantic segmentation is time-consuming and expensive. To mitigate this cost there has been a surge in the use of synthetically generated data -- usually created using simu... | {
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2501.02266 | LLMzSz{\L}: a comprehensive LLM benchmark for Polish | [
"cs.CL",
"cs.AI"
] | This article introduces the first comprehensive benchmark for the Polish language at this scale: LLMzSz{\L} (LLMs Behind the School Desk). It is based on a coherent collection of Polish national exams, including both academic and professional tests extracted from the archives of the Polish Central Examination Board. It... | {
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2501.02267 | Towards a constructive framework for control theory | [
"math.OC",
"cs.AI",
"cs.SY",
"eess.SY"
] | This work presents a framework for control theory based on constructive analysis to account for discrepancy between mathematical results and their implementation in a computer, also referred to as computational uncertainty. In control engineering, the latter is usually either neglected or considered submerged into some... | {
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2501.02268 | What Kind of Visual Tokens Do We Need? Training-free Visual Token
Pruning for Multi-modal Large Language Models from the Perspective of Graph | [
"cs.CV",
"cs.AI"
] | Recent Multimodal Large Language Models(MLLMs) often use a large number of visual tokens to compensate their visual shortcoming, leading to excessive computation and obvious visual redundancy. In this paper, we investigate what kind of visual tokens are needed for MLLMs, and reveal that both foreground and background t... | {
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2501.02269 | TDM: Temporally-Consistent Diffusion Model for All-in-One Real-World
Video Restoration | [
"cs.CV"
] | In this paper, we propose the first diffusion-based all-in-one video restoration method that utilizes the power of a pre-trained Stable Diffusion and a fine-tuned ControlNet. Our method can restore various types of video degradation with a single unified model, overcoming the limitation of standard methods that require... | {
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2501.02270 | Efficient Video-Based ALPR System Using YOLO and Visual Rhythm | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Automatic License Plate Recognition (ALPR) involves extracting vehicle license plate information from image or a video capture. These systems have gained popularity due to the wide availability of low-cost surveillance cameras and advances in Deep Learning. Typically, video-based ALPR systems rely on multiple frames to... | {
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2501.02271 | Securing Integrated Sensing and Communication Against a Mobile
Adversary: A Stackelberg Game with Deep Reinforcement Learning | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we study a secure integrated sensing and communication (ISAC) system employing a full-duplex base station with sensing capabilities against a mobile proactive adversarial target$\unicode{x2014}$a malicious unmanned aerial vehicle (M-UAV). We develop a game-theoretic model to enhance communication securit... | {
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2501.02273 | Digital Deep Joint Source-Channel Coding with Blind Training for
Adaptive Modulation and Power Control | [
"eess.SP",
"cs.IT",
"math.IT"
] | This paper proposes a novel digital deep joint source-channel coding (DeepJSCC) framework that achieves robust performance across diverse communication environments without requiring extensive retraining and prior knowledge of communication environments. Traditional digital DeepJSCC techniques often face challenges in ... | {
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2501.02278 | An experimental comparison of tree-data structures for connectivity
queries on fully-dynamic undirected graphs (Extended Version) | [
"cs.DB"
] | During the past decades significant efforts have been made to propose data structures for answering connectivity queries on fully dynamic graphs, i.e., graphs with frequent insertions and deletions of edges. However, a comprehensive understanding of how these data structures perform in practice is missing, since not al... | {
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2501.02279 | Stochastic Generalized Dynamic Games with Coupled Chance Constraints | [
"eess.SY",
"cs.SY"
] | Designing multi-agent systems with safety constraints and uncertain dynamics is a challenging problem. This paper studies a stochastic dynamic non-cooperative game with coupling safety chance constraints. The uncertainty is assumed to satisfy a concentration of measure property. Firstly, due to the non-convexity of cha... | {
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2501.02280 | On Symmetries in Analytic Input-Output Systems | [
"eess.SY",
"cs.SY"
] | There are many notions of symmetry for state space models. They play a role in understanding when systems are time reversible, provide a system theoretic interpretation of thermodynamics, and have applications in certain stabilization and optimal control problems. The earliest form of symmetry for analytic input-output... | {
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2501.02285 | Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud
Embedding | [
"cs.CV",
"cs.AI"
] | Hyperbolic spaces allow for more efficient modeling of complex, hierarchical structures, which is particularly beneficial in tasks involving multi-modal data. Although hyperbolic geometries have been proven effective for language-image pre-training, their capabilities to unify language, image, and 3D Point Cloud modali... | {
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2501.02287 | Deep Learning-Driven Segmentation of Ischemic Stroke Lesions Using
Multi-Channel MRI | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Ischemic stroke, caused by cerebral vessel occlusion, presents substantial challenges in medical imaging due to the variability and subtlety of stroke lesions. Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing and managing ischemic stroke, yet existing segmentation techniques often fail to accurately ... | {
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2501.02288 | Making the Peers' Subjective Well-being Visible Impairs
Cooperator-centered Experimental Social Networks | [
"cs.SI",
"physics.soc-ph"
] | Past experiments show that reputation or the knowledge of peers' past cooperation can enhance cooperation in human social networks. On the other hand, the knowledge of peers' wealth undermines cooperativeness, and that of peers' interconnectedness and network structure does not affect it. However, it is unknown if maki... | {
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2501.02295 | Explicit vs. Implicit: Investigating Social Bias in Large Language
Models through Self-Reflection | [
"cs.CL"
] | Large Language Models (LLMs) have been shown to exhibit various biases and stereotypes in their generated content. While extensive research has investigated bias in LLMs, prior work has predominantly focused on explicit bias, leaving the more nuanced implicit biases largely unexplored. This paper presents a systematic ... | {
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2501.02298 | Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds
for Score-Based Generative Models in W2-distance | [
"stat.ML",
"cs.LG"
] | Score-based Generative Models (SGMs) aim to sample from a target distribution by learning score functions using samples perturbed by Gaussian noise. Existing convergence bounds for SGMs in the $\mathcal{W}_2$-distance rely on stringent assumptions about the data distribution. In this work, we present a novel framework ... | {
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2501.02299 | The parenthood effect in urban mobility | [
"physics.soc-ph",
"cs.IT",
"math.IT",
"physics.data-an"
] | The modelling of human mobility is vital for the understanding of the complexity of urban dynamics and guiding effective interventions to improve quality of life. Traditional modelling approaches focus on `average citizens,' which overlook the multitude of experiences from distinct sociodemographic groups. Recent studi... | {
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2501.02300 | Diabetic Retinopathy Detection Using CNN with Residual Block with DCGAN | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Diabetic Retinopathy (DR) is a major cause of blindness worldwide, caused by damage to the blood vessels in the retina due to diabetes. Early detection and classification of DR are crucial for timely intervention and preventing vision loss. This work proposes an automated system for DR detection using Convolutional Neu... | {
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2501.02303 | Design and Benchmarking of A Multi-Modality Sensor for Robotic
Manipulation with GAN-Based Cross-Modality Interpretation | [
"cs.RO",
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] | In this paper, we present the design and benchmark of an innovative sensor, ViTacTip, which fulfills the demand for advanced multi-modal sensing in a compact design. A notable feature of ViTacTip is its transparent skin, which incorporates a `see-through-skin' mechanism. This mechanism aims at capturing detailed object... | {
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2501.02309 | Multi-Satellite Beam Hopping and Power Allocation Using Deep
Reinforcement Learning | [
"eess.SY",
"cs.SY"
] | In non-geostationary orbit (NGSO) satellite communication systems, effectively utilizing beam hopping (BH) technology is crucial for addressing uneven traffic demands. However, optimizing beam scheduling and resource allocation in multi-NGSO BH scenarios remains a significant challenge. This paper proposes a multi-NGSO... | {
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2501.02311 | Analysis of Fluorescence Telescope Data Using Machine Learning Methods | [
"astro-ph.IM",
"cs.LG"
] | Fluorescence telescopes are among the key instruments used for studying ultra-high energy cosmic rays in all modern experiments. We use model data for a small ground-based telescope EUSO-TA to try some methods of machine learning and neural networks for recognizing tracks of extensive air showers in its data and for re... | {
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2501.02313 | DiffGraph: Heterogeneous Graph Diffusion Model | [
"cs.LG",
"cs.AI",
"cs.IR"
] | Recent advances in Graph Neural Networks (GNNs) have revolutionized graph-structured data modeling, yet traditional GNNs struggle with complex heterogeneous structures prevalent in real-world scenarios. Despite progress in handling heterogeneous interactions, two fundamental challenges persist: noisy data significantly... | {
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2501.02314 | RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave
Imaging Radar | [
"cs.CV"
] | 3D object detection is crucial for Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS). However, most 3D detectors prioritize detection accuracy, often overlooking network inference speed in practical applications. In this paper, we propose RadarNeXt, a real-time and reliable 3D object detector based ... | {
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2501.02325 | Revisiting Compactness for District Plans | [
"physics.soc-ph",
"cs.CV"
] | Modern sampling methods create ensembles of district maps that score well on discrete compactness scores, whereas the Polsby-Popper and other shape-based scores remain highly relevant for building fair maps and litigating unfair ones. The aim of this paper is twofold. First, we introduce population-weighted versions of... | {
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2501.02330 | SR-Reward: Taking The Path More Traveled | [
"cs.LG",
"cs.AI"
] | In this paper, we propose a novel method for learning reward functions directly from offline demonstrations. Unlike traditional inverse reinforcement learning (IRL), our approach decouples the reward function from the learner's policy, eliminating the adversarial interaction typically required between the two. This res... | {
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2501.02333 | On The Causal Network Of Face-selective Regions In Human Brain During
Movie Watching | [
"q-bio.NC",
"cs.LG",
"eess.IV"
] | Understanding the causal interactions in simple brain tasks, such as face detection, remains a challenging and ambiguous process for researchers. In this study, we address this issue by employing a novel causal discovery method -- Directed Acyclic Graphs via M-matrices for Acyclicity (DAGMA) -- to investigate the causa... | {
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2501.02334 | Validity Arguments For Constructed Response Scoring Using Generative
Artificial Intelligence Applications | [
"cs.CL",
"cs.AI",
"cs.CY"
] | The rapid advancements in large language models and generative artificial intelligence (AI) capabilities are making their broad application in the high-stakes testing context more likely. Use of generative AI in the scoring of constructed responses is particularly appealing because it reduces the effort required for ha... | {
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2501.02335 | Connecting the Unconnectable through Feedback | [
"cs.IT",
"eess.SP",
"math.IT"
] | Reliable uplink connectivity remains a persistent challenge for IoT devices, particularly those at the cell edge, due to their limited transmit power and single-antenna configurations. This paper introduces a novel framework aimed at connecting the unconnectable, leveraging real-time feedback from access points (APs) t... | {
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2501.02336 | AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM
Inference | [
"cs.CL",
"cs.AI"
] | Long-context large language models (LLMs) inference is increasingly critical, motivating a number of studies devoted to alleviating the substantial storage and computational costs in such scenarios. Layer-wise skipping methods are promising optimizations but rarely explored in long-context inference. We observe that ex... | {
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2501.02338 | Evaluation of the Code Generation Capabilities of ChatGPT 4: A
Comparative Analysis in 19 Programming Languages | [
"cs.SE",
"cs.AI"
] | This bachelor's thesis examines the capabilities of ChatGPT 4 in code generation across 19 programming languages. The study analyzed solution rates across three difficulty levels, types of errors encountered, and code quality in terms of runtime and memory efficiency through a quantitative experiment. A total of 188 pr... | {
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2501.02341 | UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude
Mobility | [
"cs.RO",
"cs.AI"
] | Low-altitude mobility, exemplified by unmanned aerial vehicles (UAVs), has introduced transformative advancements across various domains, like transportation, logistics, and agriculture. Leveraging flexible perspectives and rapid maneuverability, UAVs extend traditional systems' perception and action capabilities, garn... | {
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2501.02342 | Optimizing Small Language Models for In-Vehicle Function-Calling | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.HC"
] | We propose a holistic approach for deploying Small Language Models (SLMs) as function-calling agents within vehicles as edge devices, offering a more flexible and robust alternative to traditional rule-based systems. By leveraging SLMs, we simplify vehicle control mechanisms and enhance the user experience. Given the i... | {
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2501.02344 | Accurate Crop Yield Estimation of Blueberries using Deep Learning and
Smart Drones | [
"cs.CV"
] | We present an AI pipeline that involves using smart drones equipped with computer vision to obtain a more accurate fruit count and yield estimation of the number of blueberries in a field. The core components are two object-detection models based on the YOLO deep learning architecture: a Bush Model that is able to dete... | {
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2501.02346 | Exploring the Capabilities and Limitations of Large Language Models for
Radiation Oncology Decision Support | [
"physics.med-ph",
"cs.AI"
] | Thanks to the rapidly evolving integration of LLMs into decision-support tools, a significant transformation is happening across large-scale systems. Like other medical fields, the use of LLMs such as GPT-4 is gaining increasing interest in radiation oncology as well. An attempt to assess GPT-4's performance in radiati... | {
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2501.02348 | Thinking with Many Minds: Using Large Language Models for
Multi-Perspective Problem-Solving | [
"cs.CL",
"cs.HC"
] | Complex problem-solving requires cognitive flexibility--the capacity to entertain multiple perspectives while preserving their distinctiveness. This flexibility replicates the "wisdom of crowds" within a single individual, allowing them to "think with many minds." While mental simulation enables imagined deliberation, ... | {
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2501.02349 | Revelio: A Real-World Screen-Camera Communication System with Visually
Imperceptible Data Embedding | [
"cs.MM",
"cs.CR",
"cs.CV",
"cs.IT",
"cs.NI",
"math.IT"
] | We present `Revelio', a real-world screen-camera communication system leveraging temporal flicker fusion in the OKLAB color space. Using spatially-adaptive flickering and encoding information in pixel region shapes, Revelio achieves visually imperceptible data embedding while remaining robust against noise, asynchronic... | {
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2501.02352 | GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and
Deep Learning | [
"cs.CR",
"cs.AI",
"cs.CV",
"cs.LG"
] | The increasing reliance on Global Navigation Satellite Systems (GNSS), particularly the Global Positioning System (GPS), underscores the urgent need to safeguard these technologies against malicious threats such as spoofing and jamming. As the backbone for positioning, navigation, and timing (PNT) across various applic... | {
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2501.02353 | Reweighting Improves Conditional Risk Bounds | [
"cs.LG",
"stat.ML"
] | In this work, we study the weighted empirical risk minimization (weighted ERM) schema, in which an additional data-dependent weight function is incorporated when the empirical risk function is being minimized. We show that under a general ``balanceable" Bernstein condition, one can design a weighted ERM estimator to ac... | {
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2501.02354 | PrivDPR: Synthetic Graph Publishing with Deep PageRank under
Differential Privacy | [
"cs.DB",
"cs.CR"
] | The objective of privacy-preserving synthetic graph publishing is to safeguard individuals' privacy while retaining the utility of original data. Most existing methods focus on graph neural networks under differential privacy (DP), and yet two fundamental problems in generating synthetic graphs remain open. First, the ... | {
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2501.02355 | CorrFill: Enhancing Faithfulness in Reference-based Inpainting with
Correspondence Guidance in Diffusion Models | [
"cs.CV"
] | In the task of reference-based image inpainting, an additional reference image is provided to restore a damaged target image to its original state. The advancement of diffusion models, particularly Stable Diffusion, allows for simple formulations in this task. However, existing diffusion-based methods often lack explic... | {
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2501.02356 | When is the Computation of a Feature Attribution Method Tractable? | [
"cs.LG",
"stat.ML"
] | Feature attribution methods have become essential for explaining machine learning models. Many popular approaches, such as SHAP and Banzhaf values, are grounded in power indices from cooperative game theory, which measure the contribution of features to model predictions. This work studies the computational complexity ... | {
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2501.02361 | Context Aware Lemmatization and Morphological Tagging Method in Turkish | [
"cs.CL",
"cs.AI"
] | The smallest part of a word that defines the word is called a word root. Word roots are used to increase success in many applications since they simplify the word. In this study, the lemmatization model, which is a word root finding method, and the morphological tagging model, which predicts the grammatical knowledge o... | {
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2501.02362 | Easing Optimization Paths: a Circuit Perspective | [
"cs.LG",
"eess.SP",
"stat.ML"
] | Gradient descent is the method of choice for training large artificial intelligence systems. As these systems become larger, a better understanding of the mechanisms behind gradient training would allow us to alleviate compute costs and help steer these systems away from harmful behaviors. To that end, we suggest utili... | {
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2501.02363 | V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust
Collaborative 3D Object Detection | [
"cs.CV",
"cs.MA"
] | In V2X collaborative perception, the domain gaps between heterogeneous nodes pose a significant challenge for effective information fusion. Pose errors arising from latency and GPS localization noise further exacerbate the issue by leading to feature misalignment. To overcome these challenges, we propose V2X-DGPE, a hi... | {
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2501.02364 | Understanding How Nonlinear Layers Create Linearly Separable Features
for Low-Dimensional Data | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Deep neural networks have attained remarkable success across diverse classification tasks. Recent empirical studies have shown that deep networks learn features that are linearly separable across classes. However, these findings often lack rigorous justifications, even under relatively simple settings. In this work, we... | {
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2501.02368 | Enhancing Workplace Productivity and Well-being Using AI Agent | [
"cs.AI",
"cs.HC"
] | This paper discusses the use of Artificial Intelligence (AI) to enhance workplace productivity and employee well-being. By integrating machine learning (ML) techniques with neurobiological data, the proposed approaches ensure alignment with human ethical standards through value alignment models and Hierarchical Reinfor... | {
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2501.02369 | Predicting two-dimensional spatiotemporal chaotic patterns with
optimized high-dimensional hybrid reservoir computing | [
"cs.LG",
"nlin.CD"
] | As an alternative approach for predicting complex dynamical systems where physics-based models are no longer reliable, reservoir computing (RC) has gained popularity. The hybrid approach is considered an interesting option for improving the prediction performance of RC. The idea is to combine a knowledge-based model (K... | {
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2501.02370 | Prepending or Cross-Attention for Speech-to-Text? An Empirical
Comparison | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Following the remarkable success of Large Language Models (LLMs) in NLP tasks, there is increasing interest in extending their capabilities to speech -- the most common form of communication. The most widespread approach to integrating speech into LLMs is dense feature prepending (DFP), which prepends the projected spe... | {
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2501.02373 | BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors | [
"cs.LG",
"cs.CR"
] | Task arithmetic in large-scale pre-trained models enables flexible adaptation to diverse downstream tasks without extensive re-training. By leveraging task vectors (TVs), users can perform modular updates to pre-trained models through simple arithmetic operations like addition and subtraction. However, this flexibility... | {
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2501.02376 | Generalizable Origin Identification for Text-Guided Image-to-Image
Diffusion Models | [
"cs.CV"
] | Text-guided image-to-image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights, and evading content tracing. This motivates us to introduce ... | {
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2501.02378 | A ghost mechanism: An analytical model of abrupt learning | [
"cs.LG",
"q-bio.NC",
"stat.ML"
] | \emph{Abrupt learning} is commonly observed in neural networks, where long plateaus in network performance are followed by rapid convergence to a desirable solution. Yet, despite its common occurrence, the complex interplay of task, network architecture, and learning rule has made it difficult to understand the underly... | {
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2501.02379 | Tensor-GaLore: Memory-Efficient Training via Gradient Tensor
Decomposition | [
"cs.LG"
] | We present Tensor-GaLore, a novel method for efficient training of neural networks with higher-order tensor weights. Many models, particularly those used in scientific computing, employ tensor-parameterized layers to capture complex, multidimensional relationships. When scaling these methods to high-resolution problems... | {
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2501.02385 | Guiding Medical Vision-Language Models with Explicit Visual Prompts:
Framework Design and Comprehensive Exploration of Prompt Variations | [
"cs.CV",
"cs.CL"
] | While mainstream vision-language models (VLMs) have advanced rapidly in understanding image level information, they still lack the ability to focus on specific areas designated by humans. Rather, they typically rely on large volumes of high-quality image-text paired data to learn and generate posterior attention maps. ... | {
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2501.02392 | Syntactic Evolution in Language Usage | [
"cs.CL",
"cs.AI"
] | This research aims to investigate the dynamic nature of linguistic style throughout various stages of life, from post teenage to old age. By employing linguistic analysis tools and methodologies, the study will delve into the intricacies of how individuals adapt and modify their language use over time. The research use... | {
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2501.02393 | Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers | [
"cs.LG",
"cond-mat.mes-hall",
"cond-mat.mtrl-sci",
"cs.AI",
"cs.CL"
] | We present an approach to modifying Transformer architectures by integrating graph-aware relational reasoning into the attention mechanism, merging concepts from graph neural networks and language modeling. Building on the inherent connection between attention and graph theory, we reformulate the Transformer's attentio... | {
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2501.02401 | iTARGET: Interpretable Tailored Age Regression for Grouped Epigenetic
Traits | [
"q-bio.GN",
"cs.AI"
] | Accurately predicting chronological age from DNA methylation patterns is crucial for advancing biological age estimation. However, this task is made challenging by Epigenetic Correlation Drift (ECD) and Heterogeneity Among CpGs (HAC), which reflect the dynamic relationship between methylation and age across different l... | {
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2501.02406 | Zero-Shot Statistical Tests for LLM-Generated Text Detection using
Finite Sample Concentration Inequalities | [
"stat.ML",
"cs.AI",
"cs.CL",
"cs.IT",
"cs.LG",
"math.IT"
] | Verifying the provenance of content is crucial to the function of many organizations, e.g., educational institutions, social media platforms, firms, etc. This problem is becoming increasingly difficult as text generated by Large Language Models (LLMs) becomes almost indistinguishable from human-generated content. In ad... | {
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2501.02407 | Anonymization by Design of Language Modeling | [
"cs.CL",
"cs.CR",
"cs.LG"
] | Rapid advances in Natural Language Processing (NLP) have revolutionized many fields, including healthcare. However, these advances raise significant privacy concerns, especially when models specialized on sensitive data can memorize and then expose and regurgitate confidential information. This paper presents a privacy... | {
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2501.02408 | GenTREC: The First Test Collection Generated by Large Language Models
for Evaluating Information Retrieval Systems | [
"cs.IR"
] | Building test collections for Information Retrieval evaluation has traditionally been a resource-intensive and time-consuming task, primarily due to the dependence on manual relevance judgments. While various cost-effective strategies have been explored, the development of such collections remains a significant challen... | {
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2501.02409 | Interpretable Neural ODEs for Gene Regulatory Network Discovery under
Perturbations | [
"cs.LG",
"cs.AI",
"cs.CE",
"q-bio.MN",
"stat.ME"
] | Modern high-throughput biological datasets with thousands of perturbations provide the opportunity for large-scale discovery of causal graphs that represent the regulatory interactions between genes. Differentiable causal graphical models have been proposed to infer a gene regulatory network (GRN) from large scale inte... | {
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2501.02410 | JammingSnake: A follow-the-leader continuum robot with variable
stiffness based on fiber jamming | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Follow-the-leader (FTL) motion is essential for continuum robots operating in fragile and confined environments. It allows the robot to exert minimal force on its surroundings, reducing the risk of damage. This paper presents a novel design of a snake-like robot capable of achieving FTL motion by integrating fiber jamm... | {
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2501.02411 | Transfer learning via Regularized Linear Discriminant Analysis | [
"stat.ML",
"cs.LG"
] | Linear discriminant analysis is a widely used method for classification. However, the high dimensionality of predictors combined with small sample sizes often results in large classification errors. To address this challenge, it is crucial to leverage data from related source models to enhance the classification perfor... | {
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2501.02413 | Semantic foundations of equality saturation | [
"cs.PL",
"cs.DB"
] | Equality saturation is an emerging technique for program and query optimization developed in the programming language community. It performs term rewriting over an E-graph, a data structure that compactly represents a program space. Despite its popularity, the theory of equality saturation lags behind the practice. In ... | {
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2501.02414 | Journey into Automation: Image-Derived Pavement Texture Extraction and
Evaluation | [
"cs.CV",
"cs.LG"
] | Mean texture depth (MTD) is pivotal in assessing the skid resistance of asphalt pavements and ensuring road safety. This study focuses on developing an automated system for extracting texture features and evaluating MTD based on pavement images. The contributions of this work are threefold: firstly, it proposes an econ... | {
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2501.02421 | Fastest Mixing Reversible Markov Chain: Clique Lifted Graphs and
Subgraphs | [
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | Markov chains are one of the well-known tools for modeling and analyzing stochastic systems. At the same time, they are used for constructing random walks that can achieve a given stationary distribution. This paper is concerned with determining the transition probabilities that optimize the mixing time of the reversib... | {
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2501.02423 | Scaling Laws for Floating Point Quantization Training | [
"cs.LG",
"cs.AR",
"cs.CL"
] | Low-precision training is considered an effective strategy for reducing both training and downstream inference costs. Previous scaling laws for precision mainly focus on integer quantization, which pay less attention to the constituents in floating-point quantization and thus cannot well fit the LLM losses in this scen... | {
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} |
2501.02427 | MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal
Guidance | [
"cs.CV"
] | Neural Representations for Videos (NeRV) has emerged as a promising implicit neural representation (INR) approach for video analysis, which represents videos as neural networks with frame indexes as inputs. However, NeRV-based methods are time-consuming when adapting to a large number of diverse videos, as each video r... | {
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2501.02428 | Framework for lung CT image segmentation based on UNet++ | [
"eess.IV",
"cs.CV"
] | Recently, the state-of-art models for medical image segmentation is U-Net and their variants. These networks, though succeeding in deriving notable results, ignore the practical problem hanging over the medical segmentation field: overfitting and small dataset. The over-complicated deep neural networks unnecessarily ex... | {
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2501.02429 | Citation Structural Diversity: A Novel and Concise Metric Combining
Structure and Semantics for Literature Evaluation | [
"cs.IR"
] | As academic research becomes increasingly diverse, traditional literature evaluation methods face significant limitations,particularly in capturing the complexity of academic dissemination and the multidimensional impacts of literature. To address these challenges, this paper introduces a novel literature evaluation mo... | {
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2501.02430 | FOLDER: Accelerating Multi-modal Large Language Models with Enhanced
Performance | [
"cs.CV"
] | Recently, Multi-modal Large Language Models (MLLMs) have shown remarkable effectiveness for multi-modal tasks due to their abilities to generate and understand cross-modal data. However, processing long sequences of visual tokens extracted from visual backbones poses a challenge for deployment in real-time applications... | {
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2501.02432 | Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural
Language Understanding | [
"cs.CL"
] | Dataset pruning aims to select a subset of a dataset for efficient model training. While data efficiency in natural language processing has primarily focused on within-corpus scenarios during model pre-training, efficient dataset pruning for task-specific fine-tuning across diverse datasets remains challenging due to v... | {
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2501.02434 | Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model
for Metaphor Mapping Identification | [
"cs.CL"
] | Metaphors play a crucial role in human communication, yet their comprehension remains a significant challenge for natural language processing (NLP) due to the cognitive complexity involved. According to Conceptual Metaphor Theory (CMT), metaphors map a target domain onto a source domain, and understanding this mapping ... | {
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2501.02436 | An Analysis Framework for Understanding Deep Neural Networks Based on
Network Dynamics | [
"cs.LG",
"nlin.CD",
"stat.ML"
] | Advancing artificial intelligence demands a deeper understanding of the mechanisms underlying deep learning. Here, we propose a straightforward analysis framework based on the dynamics of learning models. Neurons are categorized into two modes based on whether their transformation functions preserve order. This categor... | {
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2501.02438 | Efficient Deployment of Large Language Models on Resource-constrained
Devices | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.DC"
] | Deploying Large Language Models (LLMs) on resource-constrained (or weak) devices presents significant challenges due to limited resources and heterogeneous data distribution. To address the data concern, it is necessary to fine-tune LLMs using on-device private data for various downstream tasks. While Federated Learnin... | {
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2501.02441 | A Statistical Hypothesis Testing Framework for Data Misappropriation
Detection in Large Language Models | [
"stat.ML",
"cs.AI",
"cs.CL",
"cs.CR",
"cs.LG",
"math.ST",
"stat.TH"
] | Large Language Models (LLMs) are rapidly gaining enormous popularity in recent years. However, the training of LLMs has raised significant privacy and legal concerns, particularly regarding the inclusion of copyrighted materials in their training data without proper attribution or licensing, which falls under the broad... | {
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} |
2501.02442 | Unsupervised Search for Ethnic Minorities' Medical Segmentation Training
Set | [
"cs.CV"
] | This article investigates the critical issue of dataset bias in medical imaging, with a particular emphasis on racial disparities caused by uneven population distribution in dataset collection. Our analysis reveals that medical segmentation datasets are significantly biased, primarily influenced by the demographic comp... | {
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2501.02446 | RTLMarker: Protecting LLM-Generated RTL Copyright via a Hardware
Watermarking Framework | [
"cs.CR",
"cs.AI"
] | Recent advances of large language models in the field of Verilog generation have raised several ethical and security concerns, such as code copyright protection and dissemination of malicious code. Researchers have employed watermarking techniques to identify codes generated by large language models. However, the exist... | {
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2501.02447 | MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin
Lesion Segmentation | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Denoising Diffusion Models (DDMs) are widely used for high-quality image generation and medical image segmentation but often rely on Unet-based architectures, leading to high computational overhead, especially with high-resolution images. This work proposes three NCA-based improvements for diffusion-based medical image... | {
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2501.02448 | Understand, Solve and Translate: Bridging the Multilingual Mathematical
Reasoning Gap | [
"cs.CL"
] | Large language models (LLMs) demonstrate exceptional performance on complex reasoning tasks. However, despite their strong reasoning capabilities in high-resource languages (e.g., English and Chinese), a significant performance gap persists in other languages. To investigate this gap in Korean, we introduce HRM8K, a be... | {
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} |
2501.02450 | GCP: Guarded Collaborative Perception with Spatial-Temporal Aware
Malicious Agent Detection | [
"cs.CV"
] | Collaborative perception significantly enhances autonomous driving safety by extending each vehicle's perception range through message sharing among connected and autonomous vehicles. Unfortunately, it is also vulnerable to adversarial message attacks from malicious agents, resulting in severe performance degradation. ... | {
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} |
2501.02451 | Enhancing Contrastive Learning for Retinal Imaging via Adjusted
Augmentation Scales | [
"cs.CV",
"cs.AI"
] | Contrastive learning, a prominent approach within self-supervised learning, has demonstrated significant effectiveness in developing generalizable models for various applications involving natural images. However, recent research indicates that these successes do not necessarily extend to the medical imaging domain. In... | {
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} |
2501.02453 | Blockage-Aware UAV-Assisted Wireless Data Harvesting With Building
Avoidance | [
"cs.IT",
"eess.SP",
"math.IT"
] | Unmanned aerial vehicles (UAVs) offer dynamic trajectory control, enabling them to avoid obstacles and establish line-of-sight (LoS) wireless channels with ground nodes (GNs), unlike traditional ground-fixed base stations. This study addresses the joint optimization of scheduling and three-dimensional (3D) trajectory p... | {
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} |
2501.02456 | Keeping Score: A Quantitative Analysis of How the CHI Community
Appreciates Its Milestones | [
"cs.HC",
"cs.SI"
] | The ACM CHI Conference has a tradition of citing its intellectual heritage. At the same time, we know CHI is highly diverse and evolving. In this highly dynamic context, it is not clear how the CHI community continues to appreciate its milestones (within and outside of CHI). We present an investigation into how the com... | {
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} |
2501.02458 | Neural Reflectance Fields for Radio-Frequency Ray Tracing | [
"cs.CV",
"cs.LG",
"cs.NI",
"eess.SP"
] | Ray tracing is widely employed to model the propagation of radio-frequency (RF) signal in complex environment. The modelling performance greatly depends on how accurately the target scene can be depicted, including the scene geometry and surface material properties. The advances in computer vision and LiDAR make scene ... | {
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} |
2501.02460 | Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large
Language Models in Medical Applications | [
"cs.CL"
] | Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer health inquiry support. However, they often generate hallucinations due to limited medical knowledge. Incorporating external knowledge is ther... | {
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} |
2501.02461 | FedRSClip: Federated Learning for Remote Sensing Scene Classification
Using Vision-Language Models | [
"cs.CV",
"cs.AI"
] | Remote sensing data is often distributed across multiple institutions, and due to privacy concerns and data-sharing restrictions, leveraging large-scale datasets in a centralized training framework is challenging. Federated learning offers a promising solution by enabling collaborative model training across distributed... | {
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} |
2501.02464 | Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera | [
"cs.CV",
"cs.AI",
"cs.RO"
] | While recent depth estimation methods exhibit strong zero-shot generalization, achieving accurate metric depth across diverse camera types-particularly those with large fields of view (FoV) such as fisheye and 360-degree cameras-remains a significant challenge. This paper presents Depth Any Camera (DAC), a powerful zer... | {
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} |
2501.02465 | EOG Communication Interface for Quadriplegics: Prototype & Signal
Processing | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Electrooculography (EOG) is an electrophysiological signal that determines the human eye orientation and is therefore widely used in Human Tracking Interfaces (HCI). The purpose of this project is to develop a communication method for quadriplegic patients using EOG signals aimed at text and voice generation. The syste... | {
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"cs.SY": 1
} |
2501.02467 | DeTrack: In-model Latent Denoising Learning for Visual Object Tracking | [
"cs.CV"
] | Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding bo... | {
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} |
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