id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.11132 | Advanced technology in railway track monitoring using the GPR Technique:
A Review | [
"cs.LG",
"cs.CV",
"eess.IV"
] | Subsurface evaluation of railway tracks is crucial for safe operation, as it allows for the early detection and remediation of potential structural weaknesses or defects that could lead to accidents or derailments. Ground Penetrating Radar (GPR) is an electromagnetic survey technique as advanced non-destructive technol... | {
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2501.11133 | A Simultaneous Decoding Approach to Joint State and Message
Communications | [
"cs.IT",
"math.IT"
] | The capacity-distortion (C-D) trade-offs for joint state and message communications (JSMC) over single- and multi-user channels are investigated, where the transmitters have access to generalized state information and feedback while the receivers jointly decode the messages and estimate the channel state. A coding sche... | {
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2501.11135 | Playing the Lottery With Concave Regularizers for Sparse Trainable
Neural Networks | [
"cs.LG",
"cs.AI"
] | The design of sparse neural networks, i.e., of networks with a reduced number of parameters, has been attracting increasing research attention in the last few years. The use of sparse models may significantly reduce the computational and storage footprint in the inference phase. In this context, the lottery ticket hypo... | {
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2501.11136 | A Novel Switch-Type Policy Network for Resource Allocation Problems:
Technical Report | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Deep Reinforcement Learning (DRL) has become a powerful tool for developing control policies in queueing networks, but the common use of Multi-layer Perceptron (MLP) neural networks in these applications has significant drawbacks. MLP architectures, while versatile, often suffer from poor sample efficiency and a tenden... | {
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2501.11139 | Community Detection for Contextual-LSBM: Theoretical Limitations of
Misclassification Rate and Efficient Algorithms | [
"stat.ML",
"cs.LG"
] | The integration of network information and node attribute information has recently gained significant attention in the community detection literature. In this work, we consider community detection in the Contextual Labeled Stochastic Block Model (CLSBM), where the network follows an LSBM and node attributes follow a Ga... | {
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2501.11140 | CLOFAI: A Dataset of Real And Fake Image Classification Tasks for
Continual Learning | [
"cs.CV",
"cs.AI"
] | The rapid advancement of generative AI models capable of creating realistic media has led to a need for classifiers that can accurately distinguish between genuine and artificially-generated images. A significant challenge for these classifiers emerges when they encounter images from generative models that are not repr... | {
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2501.11141 | Kilometer-Scale E3SM Land Model Simulation over North America | [
"cs.CE"
] | The development of a kilometer-scale E3SM Land Model (km-scale ELM) is an integral part of the E3SM project, which seeks to advance energy-related Earth system science research with state-of-the-art modeling and simulation capabilities on exascale computing systems. Through the utilization of high-fidelity data product... | {
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2501.11145 | Blockchain and Stablecoin Integration for Crowdfunding: A framework for
enhanced efficiency, security, and liquidity | [
"cs.CE"
] | Crowdfunding platforms face high transaction fees, need for more transparency, and trust deficits. These issues deter contributors and entrepreneurs from effectively leveraging crowdfunding for innovation and growth. Blockchain technology introduces decentralization, security, and efficiency to address these limitation... | {
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2501.11149 | CART-MPC: Coordinating Assistive Devices for Robot-Assisted Transferring
with Multi-Agent Model Predictive Control | [
"cs.RO"
] | Bed-to-wheelchair transferring is a ubiquitous activity of daily living (ADL), but especially challenging for caregiving robots with limited payloads. We develop a novel algorithm that leverages the presence of other assistive devices: a Hoyer sling and a wheelchair for coarse manipulation of heavy loads, alongside a r... | {
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2501.11153 | Efficient Frame Extraction: A Novel Approach Through Frame Similarity
and Surgical Tool Tracking for Video Segmentation | [
"cs.CV"
] | The interest in leveraging Artificial Intelligence (AI) for surgical procedures to automate analysis has witnessed a significant surge in recent years. One of the primary tools for recording surgical procedures and conducting subsequent analyses, such as performance assessment, is through videos. However, these operati... | {
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2501.11154 | Modelling of automotive steel fatigue lifetime by machine learning
method | [
"cs.LG",
"cs.NE"
] | In the current study, the fatigue life of QSTE340TM steel was modelled using a machine learning method, namely, a neural network. This problem was solved by a Multi-Layer Perceptron (MLP) neural network with a 3-75-1 architecture, which allows the prediction of the crack length based on the number of load cycles N, the... | {
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2501.11159 | LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR data | [
"cs.CV",
"cs.AR",
"eess.IV"
] | This paper presents LiFT, a lightweight, fully quantized 3D object detection algorithm for LiDAR data, optimized for real-time inference on FPGA platforms. Through an in-depth analysis of FPGA-specific limitations, we identify a set of FPGA-induced constraints that shape the algorithm's design. These include a computat... | {
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2501.11161 | Modeling Attention during Dimensional Shifts with Counterfactual and
Delayed Feedback | [
"cs.LG"
] | Attention can be used to inform choice selection in contextual bandit tasks even when context features have not been previously experienced. One example of this is in dimensional shifts, where additional feature values are introduced and the relationship between features and outcomes can either be static or variable. A... | {
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2501.11162 | Query Repairs | [
"cs.DB"
] | We formalize and study the problem of repairing database queries based on user feedback in the form of a collection of labeled examples. We propose a framework based on the notion of a proximity pre-order, and we investigate and compare query repairs for conjunctive queries (CQs) using different such pre-orders. The pr... | {
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2501.11165 | Structure and Context of Retweet Coordination in the 2022 U.S. Midterm
Elections | [
"cs.SI",
"cs.CY"
] | The ability to detect coordinated activity in communication networks is an ongoing challenge. Prior approaches emphasize considering any activity exceeding a specific threshold of similarity to be coordinated. However, identifying such a threshold is often arbitrary and can be difficult to distinguish from grassroots o... | {
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2501.11166 | AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in
Hindi-English Code-Mixed Conversations | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In this study, we introduce a solution to the SemEval 2024 Task 10 on subtask 1, dedicated to Emotion Recognition in Conversation (ERC) in code-mixed Hindi-English conversations. ERC in code-mixed conversations presents unique challenges, as existing models are typically trained on monolingual datasets and may not perf... | {
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2501.11167 | Federated Testing (FedTest): A New Scheme to Enhance Convergence and
Mitigate Adversarial Attacks in Federating Learning | [
"cs.LG",
"cs.IT",
"math.IT"
] | Federated Learning (FL) has emerged as a significant paradigm for training machine learning models. This is due to its data-privacy-preserving property and its efficient exploitation of distributed computational resources. This is achieved by conducting the training process in parallel at distributed users. However, tr... | {
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2501.11168 | DeepEyeNet: Adaptive Genetic Bayesian Algorithm Based Hybrid
ConvNeXtTiny Framework For Multi-Feature Glaucoma Eye Diagnosis | [
"cs.CV",
"eess.SP"
] | Glaucoma is a leading cause of irreversible blindness worldwide, emphasizing the critical need for early detection and intervention. In this paper, we present DeepEyeNet, a novel and comprehensive framework for automated glaucoma detection using retinal fundus images. Our approach integrates advanced image standardizat... | {
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2501.11170 | AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair
Analysis | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. Conversely, Subtask 2 extends the analysis to ... | {
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2501.11171 | Counteracting temporal attacks in Video Copy Detection | [
"cs.CV",
"cs.AI",
"cs.IR",
"cs.LG",
"cs.MM"
] | Video Copy Detection (VCD) plays a crucial role in copyright protection and content verification by identifying duplicates and near-duplicates in large-scale video databases. The META AI Challenge on video copy detection provided a benchmark for evaluating state-of-the-art methods, with the Dual-level detection approac... | {
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2501.11175 | ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large
Vision-Language Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The growing popularity of Contrastive Language-Image Pretraining (CLIP) has led to its widespread application in various visual downstream tasks. To enhance CLIP's effectiveness and versatility, efficient few-shot adaptation techniques have been widely adopted. Among these approaches, training-free methods, particularl... | {
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2501.11178 | Conditional Feature Importance with Generative Modeling Using
Adversarial Random Forests | [
"stat.ML",
"cs.LG"
] | This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance assesses the impact of a feature on a prediction model's performance given the information of other features. Model-agnostic post hoc methods to... | {
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2501.11183 | Can Safety Fine-Tuning Be More Principled? Lessons Learned from
Cybersecurity | [
"cs.CR",
"cs.AI",
"cs.LG"
] | As LLMs develop increasingly advanced capabilities, there is an increased need to minimize the harm that could be caused to society by certain model outputs; hence, most LLMs have safety guardrails added, for example via fine-tuning. In this paper, we argue the position that current safety fine-tuning is very similar t... | {
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2501.11188 | Global Attitude Synchronization for Multi-agent Systems on SO(3) | [
"eess.SY",
"cs.SY"
] | In this paper, we address the problem of attitude synchronization for a group of rigid body systems evolving on SO(3). The interaction among these systems is modeled through an undirected, connected, and acyclic graph topology. First, we present an almost global continuous distributed attitude synchronization scheme wi... | {
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2501.11190 | Reinforcement Learning Based Goodput Maximization with Quantized
Feedback in URLLC | [
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT"
] | This paper presents a comprehensive system model for goodput maximization with quantized feedback in Ultra-Reliable Low-Latency Communication (URLLC), focusing on dynamic channel conditions and feedback schemes. The study investigates a communication system, where the receiver provides quantized channel state informati... | {
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2501.11196 | Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer
learning | [
"eess.IV",
"cs.CV"
] | Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study, we present an enhanced ResUNet architecture for automatic brain tumor segmentation, integrating an EfficientNetB0 encoder, a channel attention mechanism, and an Atrous S... | {
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2501.11197 | Q-RESTORE: Quantum-Driven Framework for Resilient and Equitable
Transportation Network Restoration | [
"cs.MA",
"cs.ET"
] | Efficient and socially equitable restoration of transportation networks post disasters is crucial for community resilience and access to essential services. The ability to rapidly recover critical infrastructure can significantly mitigate the impacts of disasters, particularly in underserved communities where prolonged... | {
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2501.11199 | Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data
Generation | [
"cs.CL"
] | Accurate classification of clinical text often requires fine-tuning pre-trained language models, a process that is costly and time-consuming due to the need for high-quality data and expert annotators. Synthetic data generation offers an alternative, though pre-trained models may not capture the syntactic diversity of ... | {
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2501.11202 | Online Hybrid-Belief POMDP with Coupled Semantic-Geometric Models and
Semantic Safety Awareness | [
"cs.RO"
] | Robots operating in complex and unknown environments frequently require geometric-semantic representations of the environment to safely perform their tasks. While inferring the environment, they must account for many possible scenarios when planning future actions. Since objects' class types are discrete and the robot'... | {
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2501.11203 | Advancing Oyster Phenotype Segmentation with Multi-Network Ensemble and
Multi-Scale mechanism | [
"cs.CV"
] | Phenotype segmentation is pivotal in analysing visual features of living organisms, enhancing our understanding of their characteristics. In the context of oysters, meat quality assessment is paramount, focusing on shell, meat, gonad, and muscle components. Traditional manual inspection methods are time-consuming and s... | {
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2501.11211 | Ditto: Accelerating Diffusion Model via Temporal Value Similarity | [
"cs.AR",
"cs.CV",
"cs.LG"
] | Diffusion models achieve superior performance in image generation tasks. However, it incurs significant computation overheads due to its iterative structure. To address these overheads, we analyze this iterative structure and observe that adjacent time steps in diffusion models exhibit high value similarity, leading to... | {
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2501.11213 | Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine
Learning Approach | [
"cs.LG"
] | This paper presents a risk analysis of flowlines in the oil and gas sector using Geographic Information Systems (GIS) and machine learning (ML). Flowlines, vital conduits transporting oil, gas, and water from wellheads to surface facilities, often face under-assessment compared to transmission pipelines. This study add... | {
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2501.11214 | Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware
Spatiotemporal Graph Neural Networks: A Chicago Case Study | [
"cs.LG"
] | Urban prediction tasks, such as forecasting traffic flow, temperature, and crime rates, are crucial for efficient urban planning and management. However, existing Spatiotemporal Graph Neural Networks (ST-GNNs) often rely solely on accuracy, overlooking spatial and demographic disparities in their predictions. This over... | {
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2501.11216 | TigerVector: Supporting Vector Search in Graph Databases for Advanced
RAGs | [
"cs.DB"
] | In this paper, we introduce TigerVector, a system that integrates vector search and graph query within TigerGraph, a Massively Parallel Processing (MPP) native graph database. We extend the vertex attribute type with the embedding type. To support fast vector search, we devise an MPP index framework that interoperates ... | {
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2501.11218 | Leveraging GANs For Active Appearance Models Optimized Model Fitting | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Generative Adversarial Networks (GANs) have gained prominence in refining model fitting tasks in computer vision, particularly in domains involving deformable models like Active Appearance Models (AAMs). This paper explores the integration of GANs to enhance the AAM fitting process, addressing challenges in optimizing ... | {
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2501.11219 | Zero-determinant strategies in repeated continuously-relaxed games | [
"physics.soc-ph",
"cs.MA"
] | Mixed extension has played an important role in game theory, especially in the proof of the existence of Nash equilibria in strategic form games. Mixed extension can be regarded as continuous relaxation of a strategic form game. Recently, in repeated games, a class of behavior strategies, called zero-determinant strate... | {
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2501.11221 | Finding Reproducible and Prognostic Radiomic Features in Variable Slice
Thickness Contrast Enhanced CT of Colorectal Liver Metastases | [
"eess.IV",
"cs.CV"
] | Establishing the reproducibility of radiomic signatures is a critical step in the path to clinical adoption of quantitative imaging biomarkers; however, radiomic signatures must also be meaningfully related to an outcome of clinical importance to be of value for personalized medicine. In this study, we analyze both the... | {
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2501.11222 | An Imbalanced Learning-based Sampling Method for Physics-informed Neural
Networks | [
"cs.LG",
"stat.ML"
] | This paper introduces Residual-based Smote (RSmote), an innovative local adaptive sampling technique tailored to improve the performance of Physics-Informed Neural Networks (PINNs) through imbalanced learning strategies. Traditional residual-based adaptive sampling methods, while effective in enhancing PINN accuracy, o... | {
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2501.11223 | Reasoning Language Models: A Blueprint | [
"cs.AI",
"cs.CL"
] | Reasoning language models (RLMs), also known as Large Reasoning Models (LRMs), such as OpenAI's o1 and o3, DeepSeek-V3, and Alibaba's QwQ, have redefined AI's problem-solving capabilities by extending LLMs with advanced reasoning mechanisms. Yet, their high costs, proprietary nature, and complex architectures - uniquel... | {
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2501.11225 | CNN-based TEM image denoising from first principles | [
"cond-mat.mtrl-sci",
"cs.CV",
"eess.IV"
] | Transmission electron microscope (TEM) images are often corrupted by noise, hindering their interpretation. To address this issue, we propose a deep learning-based approach using simulated images. Using density functional theory calculations with a set of pseudo-atomic orbital basis sets, we generate highly accurate gr... | {
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2501.11226 | Local Limits of Small World Networks | [
"math.PR",
"cs.DS",
"cs.SI",
"math.CO"
] | Small-world networks, known for their high local clustering and short average path lengths, are a fundamental structure in many real-world systems, including social, biological, and technological networks. We apply the theory of local convergence (Benjamini-Schramm convergence) to derive the limiting behavior of the lo... | {
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2501.11229 | Successive Interference Cancellation-aided Diffusion Models for Joint
Channel Estimation and Data Detection in Low Rank Channel Scenarios | [
"cs.CV",
"cs.IT",
"eess.SP",
"math.IT"
] | This paper proposes a novel joint channel-estimation and source-detection algorithm using successive interference cancellation (SIC)-aided generative score-based diffusion models. Prior work in this area focuses on massive MIMO scenarios, which are typically characterized by full-rank channels, and fail in low-rank cha... | {
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2501.11230 | Optimum Power-Subcarrier Allocation and Time-Sharing in Multicarrier
NOMA Uplink | [
"eess.SP",
"cs.IT",
"math.IT"
] | Currently used resource allocation methods for uplink multicarrier non-orthogonal multiple access (MC-NOMA) systems have multiple shortcomings. Current approaches either allocate the same power across all subcarriers to a user, or use heuristic-based near-far, strong channel-weak channel user grouping to assign the dec... | {
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2501.11231 | KPL: Training-Free Medical Knowledge Mining of Vision-Language Models | [
"cs.CV"
] | Visual Language Models such as CLIP excel in image recognition due to extensive image-text pre-training. However, applying the CLIP inference in zero-shot classification, particularly for medical image diagnosis, faces challenges due to: 1) the inadequacy of representing image classes solely with single category names;... | {
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2501.11233 | PlotEdit: Natural Language-Driven Accessible Chart Editing in PDFs via
Multimodal LLM Agents | [
"cs.IR",
"cs.CL",
"cs.MA"
] | Chart visualizations, while essential for data interpretation and communication, are predominantly accessible only as images in PDFs, lacking source data tables and stylistic information. To enable effective editing of charts in PDFs or digital scans, we present PlotEdit, a novel multi-agent framework for natural langu... | {
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2501.11236 | A New Formulation of Lipschitz Constrained With Functional Gradient
Learning for GANs | [
"cs.CV",
"cs.LG"
] | This paper introduces a promising alternative method for training Generative Adversarial Networks (GANs) on large-scale datasets with clear theoretical guarantees. GANs are typically learned through a minimax game between a generator and a discriminator, which is known to be empirically unstable. Previous learning para... | {
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2501.11238 | WSSM: Geographic-enhanced hierarchical state-space model for global
station weather forecast | [
"cs.LG",
"cs.AI",
"physics.ao-ph"
] | Global Station Weather Forecasting (GSWF), a prominent meteorological research area, is pivotal in providing timely localized weather predictions. Despite the progress existing models have made in the overall accuracy of the GSWF, executing high-precision extreme event prediction still presents a substantial challenge.... | {
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2501.11240 | Fast instance-specific algorithm configuration with graph neural network | [
"cs.LG"
] | Combinatorial optimization (CO) problems are pivotal across various industrial applications, where the speed of solving these problems is crucial. Improving the performance of CO solvers across diverse input instances requires fine-tuning solver parameters for each instance. However, this tuning process is time-consumi... | {
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2501.11241 | Irony in Emojis: A Comparative Study of Human and LLM Interpretation | [
"cs.CL",
"cs.CV",
"cs.SI"
] | Emojis have become a universal language in online communication, often carrying nuanced and context-dependent meanings. Among these, irony poses a significant challenge for Large Language Models (LLMs) due to its inherent incongruity between appearance and intent. This study examines the ability of GPT-4o to interpret ... | {
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2501.11246 | Unlocking the Potential: A Novel Tool for Assessing Untapped
Micro-Pumped Hydro Energy Storage Systems in Michigan | [
"eess.SY",
"cs.SY"
] | This study presents an innovative tool designed to unlock the potential of Michigan's lakes and dams for applications such as water resource management and renewable energy generation. Given Michigan's relatively flat landscape, the focus is on systems that could serve as micro-hydro energy storage solutions. To ensure... | {
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2501.11247 | Multivariate Wireless Link Quality Prediction Based on Pre-trained Large
Language Models | [
"cs.LG",
"cs.NI"
] | Accurate and reliable link quality prediction (LQP) is crucial for optimizing network performance, ensuring communication stability, and enhancing user experience in wireless communications. However, LQP faces significant challenges due to the dynamic and lossy nature of wireless links, which are influenced by interfer... | {
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2501.11249 | Enhancing SAR Object Detection with Self-Supervised Pre-training on
Masked Auto-Encoders | [
"cs.CV"
] | Supervised fine-tuning methods (SFT) perform great efficiency on artificial intelligence interpretation in SAR images, leveraging the powerful representation knowledge from pre-training models. Due to the lack of domain-specific pre-trained backbones in SAR images, the traditional strategies are loading the foundation ... | {
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2501.11252 | Constant Optimization Driven Database System Testing | [
"cs.SE",
"cs.DB",
"cs.PL"
] | Logic bugs are bugs that can cause database management systems (DBMSs) to silently produce incorrect results for given queries. Such bugs are severe, because they can easily be overlooked by both developers and users, and can cause applications that rely on the DBMSs to malfunction. In this work, we propose Constant-Op... | {
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2501.11253 | How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks? | [
"eess.IV",
"cs.CV"
] | The pre-training and fine-tuning paradigm has become prominent in transfer learning. For example, if the model is pre-trained on ImageNet and then fine-tuned to PASCAL, it can significantly outperform that trained on PASCAL from scratch. While ImageNet pre-training has shown enormous success, it is formed in 2D, and th... | {
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2501.11255 | Bounding the Settling Time of Finite-Time Stable Systems using Sum of
Squares | [
"math.OC",
"cs.SY",
"eess.SY"
] | Finite-time stability (FTS) of a differential equation guarantees that solutions reach a given equilibrium point in finite time, where the time of convergence depends on the initial state of the system. For traditional stability notions such as exponential stability, the convex optimization framework of Sum-of-Squares ... | {
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2501.11258 | Enhancing Uncertainty Estimation in Semantic Segmentation via
Monte-Carlo Frequency Dropout | [
"cs.CV",
"cs.LG",
"eess.IV",
"stat.ML"
] | Monte-Carlo (MC) Dropout provides a practical solution for estimating predictive distributions in deterministic neural networks. Traditional dropout, applied within the signal space, may fail to account for frequency-related noise common in medical imaging, leading to biased predictive estimates. A novel approach exten... | {
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2501.11260 | A Survey of World Models for Autonomous Driving | [
"cs.RO",
"cs.CV"
] | Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling, fundamentally transforming how vehicles interpret dynamic scenes and execute safe decision-making. In particular, world models have emerged as a linchpin technology, offering high-fidelity representations of the driving... | {
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2501.11263 | Towards Loss-Resilient Image Coding for Unstable Satellite Networks | [
"cs.CV",
"eess.IV"
] | Geostationary Earth Orbit (GEO) satellite communication demonstrates significant advantages in emergency short burst data services. However, unstable satellite networks, particularly those with frequent packet loss, present a severe challenge to accurate image transmission. To address it, we propose a loss-resilient im... | {
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2501.11264 | Code Readability in the Age of Large Language Models: An Industrial Case
Study from Atlassian | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Programmers spend a significant amount of time reading code during the software development process. This trend is amplified by the emergence of large language models (LLMs) that automatically generate code. However, little is known about the readability of the LLM-generated code and whether it is still important from ... | {
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2501.11265 | A Metric Topology of Deep Learning for Data Classification | [
"cs.LG",
"stat.ML"
] | Empirically, Deep Learning (DL) has demonstrated unprecedented success in practical applications. However, DL remains by and large a mysterious "black-box", spurring recent theoretical research to build its mathematical foundations. In this paper, we investigate DL for data classification through the prism of metric to... | {
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2501.11267 | Communication-Efficient Federated Learning by Quantized Variance
Reduction for Heterogeneous Wireless Edge Networks | [
"cs.DC",
"cs.LG"
] | Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead caused by frequent and costly server-device synchronization. Notably, most existing communication-e... | {
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2501.11268 | Sparse L0-norm based Kernel-free Quadratic Surface Support Vector
Machines | [
"cs.LG",
"stat.ML"
] | Kernel-free quadratic surface support vector machine (SVM) models have gained significant attention in machine learning. However, introducing a quadratic classifier increases the model's complexity by quadratically expanding the number of parameters relative to the dimensionality of the data, exacerbating overfitting. ... | {
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2501.11269 | Can xLLMs Understand the Structure of Dialog? Exploring Multilingual
Response Generation in Complex Scenarios | [
"cs.CL"
] | Multilingual research has garnered increasing attention, especially in the domain of dialogue systems. The rapid advancements in large language models (LLMs) have fueled the demand for high-performing multilingual models. However, two major challenges persist: the scarcity of high-quality multilingual datasets and the ... | {
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2501.11270 | Spatiotemporal Air Quality Mapping in Urban Areas Using Sparse Sensor
Data, Satellite Imagery, Meteorological Factors, and Spatial Features | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Monitoring air pollution is crucial for protecting human health from exposure to harmful substances. Traditional methods of air quality monitoring, such as ground-based sensors and satellite-based remote sensing, face limitations due to high deployment costs, sparse sensor coverage, and environmental interferences. To ... | {
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2501.11273 | Multi-round, Chain-of-thought Post-editing for Unfaithful Summaries | [
"cs.CL"
] | Recent large language models (LLMs) have demonstrated a remarkable ability to perform natural language understanding and generation tasks. In this work, we investigate the use of LLMs for evaluating faithfulness in news summarization, finding that it achieves a strong correlation with human judgments. We further invest... | {
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2501.11275 | Higher Order Approximation Rates for ReLU CNNs in Korobov Spaces | [
"cs.LG",
"cs.NA",
"math.NA"
] | This paper investigates the $L_p$ approximation error for higher order Korobov functions using deep convolutional neural networks (CNNs) with ReLU activation. For target functions having a mixed derivative of order m+1 in each direction, we improve classical approximation rate of second order to (m+1)-th order (modulo ... | {
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2501.11276 | ITCFN: Incomplete Triple-Modal Co-Attention Fusion Network for Mild
Cognitive Impairment Conversion Prediction | [
"eess.IV",
"cs.CV"
] | Alzheimer's disease (AD) is a common neurodegenerative disease among the elderly. Early prediction and timely intervention of its prodromal stage, mild cognitive impairment (MCI), can decrease the risk of advancing to AD. Combining information from various modalities can significantly improve predictive accuracy. Howev... | {
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2501.11280 | Empirical Bayes Estimation for Lasso-Type Regularizers: Analysis of
Automatic Relevance Determination | [
"math.ST",
"cs.IT",
"cs.LG",
"math.IT",
"stat.TH"
] | This paper focuses on linear regression models with non-conjugate sparsity-inducing regularizers such as lasso and group lasso. Although empirical Bayes approach enables us to estimate the regularization parameter, little is known on the properties of the estimators. In particular, there are many unexplained aspects re... | {
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2501.11282 | Several classes of linear codes with few weights derived from Weil sums | [
"cs.IT",
"math.IT"
] | Linear codes with few weights have applications in secret sharing, authentication codes, association schemes and strongly regular graphs. In this paper, several classes of $t$-weight linear codes over ${\mathbb F}_{q}$ are presented with the defining sets given by the intersection, difference and union of two certain s... | {
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2501.11283 | Large Language Model Agents for Radio Map Generation and Wireless
Network Planning | [
"cs.IT",
"math.IT"
] | Using commercial software for radio map generation and wireless network planning often require complex manual operations, posing significant challenges in terms of scalability, adaptability, and user-friendliness, due to heavy manual operations. To address these issues, we propose an automated solution that employs lar... | {
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2501.11284 | RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning
Systems? | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Can scaling transform reasoning? In this work, we explore the untapped potential of scaling Long Chain-of-Thought (Long-CoT) data to 1000k samples, pioneering the development of a slow-thinking model, RedStar. Through extensive experiments with various LLMs and different sizes, we uncover the ingredients for specializa... | {
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2501.11288 | PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues | [
"cs.CV"
] | Multi-object tracking (MOT) is a rising topic in video processing technologies and has important application value in consumer electronics. Currently, tracking-by-detection (TBD) is the dominant paradigm for MOT, which performs target detection and association frame by frame. However, the association performance of TBD... | {
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2501.11292 | Advancing Multi-Party Dialogue Systems with Speaker-ware Contrastive
Learning | [
"cs.CL"
] | Dialogue response generation has made significant progress, but most research has focused on dyadic dialogue. In contrast, multi-party dialogues involve more participants, each potentially discussing different topics, making the task more complex. Current methods often rely on graph neural networks to model dialogue co... | {
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2501.11293 | A Machine Learning Framework for Handling Unreliable Absence Label and
Class Imbalance for Marine Stinger Beaching Prediction | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Bluebottles (\textit{Physalia} spp.) are marine stingers resembling jellyfish, whose presence on Australian beaches poses a significant public risk due to their venomous nature. Understanding the environmental factors driving bluebottles ashore is crucial for mitigating their impact, and machine learning tools are to d... | {
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2501.11299 | MIFNet: Learning Modality-Invariant Features for Generalizable
Multimodal Image Matching | [
"cs.CV"
] | Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness agains... | {
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2501.11301 | Question-to-Question Retrieval for Hallucination-Free Knowledge Access:
An Approach for Wikipedia and Wikidata Question Answering | [
"cs.CL",
"cs.AI"
] | This paper introduces an approach to question answering over knowledge bases like Wikipedia and Wikidata by performing "question-to-question" matching and retrieval from a dense vector embedding store. Instead of embedding document content, we generate a comprehensive set of questions for each logical content unit usin... | {
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2501.11305 | Generalizable Spectral Embedding with an Application to UMAP | [
"cs.LG",
"stat.ML"
] | Spectral Embedding (SE) is a popular method for dimensionality reduction, applicable across diverse domains. Nevertheless, its current implementations face three prominent drawbacks which curtail its broader applicability: generalizability (i.e., out-of-sample extension), scalability, and eigenvectors separation. In th... | {
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2501.11306 | Collaborative Imputation of Urban Time Series through Cross-city
Meta-learning | [
"cs.LG",
"cs.AI"
] | Urban time series, such as mobility flows, energy consumption, and pollution records, encapsulate complex urban dynamics and structures. However, data collection in each city is impeded by technical challenges such as budget limitations and sensor failures, necessitating effective data imputation techniques that can en... | {
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2501.11309 | Finer-CAM: Spotting the Difference Reveals Finer Details for Visual
Explanation | [
"cs.CV",
"cs.AI"
] | Class activation map (CAM) has been widely used to highlight image regions that contribute to class predictions. Despite its simplicity and computational efficiency, CAM often struggles to identify discriminative regions that distinguish visually similar fine-grained classes. Prior efforts address this limitation by in... | {
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2501.11310 | Anomaly Detection for Industrial Applications, Its Challenges,
Solutions, and Future Directions: A Review | [
"cs.CV"
] | Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintains the quality and optimizes the efficiency in production processes across diverse industrial tasks, including advanced manufacturing and aerospace engineering. Traditional ... | {
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2501.11311 | A2SB: Audio-to-Audio Schrodinger Bridges | [
"cs.SD",
"cs.LG",
"eess.AS"
] | Audio in the real world may be perturbed due to numerous factors, causing the audio quality to be degraded. The following work presents an audio restoration model tailored for high-res music at 44.1kHz. Our model, Audio-to-Audio Schrodinger Bridges (A2SB), is capable of both bandwidth extension (predicting high-frequen... | {
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2501.11313 | Asymptotically Optimal Aperiodic and Periodic Sequence Sets with Low
Ambiguity Zone Through Locally Perfect Nonlinear Functions | [
"cs.IT",
"math.IT"
] | Low ambiguity zone (LAZ) sequences play a crucial role in modern integrated sensing and communication (ISAC) systems. In this paper, we introduce a novel class of functions known as locally perfect nonlinear functions (LPNFs). By utilizing LPNFs and interleaving techniques, we propose three new classes of both periodic... | {
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2501.11318 | Nested Annealed Training Scheme for Generative Adversarial Networks | [
"cs.CV",
"cs.LG"
] | Recently, researchers have proposed many deep generative models, including generative adversarial networks(GANs) and denoising diffusion models. Although significant breakthroughs have been made and empirical success has been achieved with the GAN, its mathematical underpinnings remain relatively unknown. This paper fo... | {
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2501.11319 | StyleSSP: Sampling StartPoint Enhancement for Training-free
Diffusion-based Method for Style Transfer | [
"cs.CV"
] | Training-free diffusion-based methods have achieved remarkable success in style transfer, eliminating the need for extensive training or fine-tuning. However, due to the lack of targeted training for style information extraction and constraints on the content image layout, training-free methods often suffer from layout... | {
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2501.11323 | Physics-Informed Machine Learning for Efficient Reconfigurable
Intelligent Surface Design | [
"cs.LG",
"eess.SP",
"physics.app-ph",
"stat.ML"
] | Reconfigurable intelligent surface (RIS) is a two-dimensional periodic structure integrated with a large number of reflective elements, which can manipulate electromagnetic waves in a digital way, offering great potentials for wireless communication and radar detection applications. However, conventional RIS designs hi... | {
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2501.11325 | CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On
with Temporal Concatenation | [
"cs.CV",
"cs.AI"
] | Virtual try-on (VTON) technology has gained attention due to its potential to transform online retail by enabling realistic clothing visualization of images and videos. However, most existing methods struggle to achieve high-quality results across image and video try-on tasks, especially in long video scenarios. In thi... | {
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2501.11326 | The "Law" of the Unconscious Contrastive Learner: Probabilistic
Alignment of Unpaired Modalities | [
"cs.LG",
"stat.ML"
] | While internet-scale data often comes in pairs (e.g., audio/image, image/text), we often want to perform inferences over modalities unseen together in the training data (e.g., audio/text). Empirically, this can often be addressed by learning multiple contrastive embedding spaces between existing modality pairs, implici... | {
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2501.11333 | A Dynamic Improvement Framework for Vehicular Task Offloading | [
"eess.SY",
"cs.NI",
"cs.SY"
] | In this paper, the task offloading from vehicles with random velocities is optimized via a novel dynamic improvement framework. Particularly, in a vehicular network with multiple vehicles and base stations (BSs), computing tasks of vehicles are offloaded via BSs to an edge server. Due to the random velocities, the exac... | {
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2501.11335 | Few-shot Policy (de)composition in Conversational Question Answering | [
"cs.CL",
"cs.AI"
] | The task of policy compliance detection (PCD) is to determine if a scenario is in compliance with respect to a set of written policies. In a conversational setting, the results of PCD can indicate if clarifying questions must be asked to determine compliance status. Existing approaches usually claim to have reasoning c... | {
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} |
2501.11338 | Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted
Projection on Principal Components | [
"eess.SY",
"cs.SY"
] | This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers' behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the ne... | {
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2501.11340 | GenVidBench: A Challenging Benchmark for Detecting AI-Generated Video | [
"cs.CV"
] | The rapid advancement of video generation models has made it increasingly challenging to distinguish AI-generated videos from real ones. This issue underscores the urgent need for effective AI-generated video detectors to prevent the dissemination of false information through such videos. However, the development of hi... | {
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2501.11341 | Lee and Seung (2000)'s Algorithms for Non-negative Matrix Factorization:
A Supplementary Proof Guide | [
"math.NA",
"cs.LG",
"cs.NA"
] | Lee and Seung (2000) introduced numerical solutions for non-negative matrix factorization (NMF) using iterative multiplicative update algorithms. These algorithms have been actively utilized as dimensionality reduction tools for high-dimensional non-negative data and learning algorithms for artificial neural networks. ... | {
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} |
2501.11342 | Disentangled Modeling of Preferences and Social Influence for Group
Recommendation | [
"cs.IR"
] | The group recommendation (GR) aims to suggest items for a group of users in social networks. Existing work typically considers individual preferences as the sole factor in aggregating group preferences. Actually, social influence is also an important factor in modeling users' contributions to the final group decision. ... | {
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} |
2501.11347 | EndoChat: Grounded Multimodal Large Language Model for Endoscopic
Surgery | [
"cs.CV"
] | Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making. In the context of robotic-assisted surgery, MLLMs can serve as effective tools for surgical training and guidance. However, there is still a lack of MLLMs specialized for surgica... | {
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} |
2501.11350 | Adaptive parameters identification for nonlinear dynamics using deep
permutation invariant networks | [
"cs.LG"
] | The promising outcomes of dynamical system identification techniques, such as SINDy [Brunton et al. 2016], highlight their advantages in providing qualitative interpretability and extrapolation compared to non-interpretable deep neural networks [Rudin 2019]. These techniques suffer from parameter updating in real-time ... | {
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} |
2501.11351 | Automatic Labelling & Semantic Segmentation with 4D Radar Tensors | [
"cs.CV",
"eess.SP"
] | In this paper, an automatic labelling process is presented for automotive datasets, leveraging on complementary information from LiDAR and camera. The generated labels are then used as ground truth with the corresponding 4D radar data as inputs to a proposed semantic segmentation network, to associate a class label to ... | {
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} |
2501.11353 | Accelerating Data Access for Single Node in Distributed Storage Systems
via MDS Codes | [
"cs.IT",
"math.IT"
] | Maximum distance separable (MDS) array codes are widely employed in modern distributed storage systems to provide high data reliability with small storage overhead. Compared with the data access latency of the entire file, the data access latency of a single node in a distributed storage system is equally important. In... | {
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} |
2501.11354 | Towards Advancing Code Generation with Large Language Models: A Research
Roadmap | [
"cs.SE",
"cs.AI"
] | Recently, we have witnessed the rapid development of large language models, which have demonstrated excellent capabilities in the downstream task of code generation. However, despite their potential, LLM-based code generation still faces numerous technical and evaluation challenges, particularly when embedded in real-w... | {
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2501.11357 | On the Dimension of Pullback Attractors in Recurrent Neural Networks | [
"math.DS",
"cs.AI",
"cs.LG"
] | Recurrent Neural Networks (RNNs) are high-dimensional state space models capable of learning functions on sequence data. Recently, it has been conjectured that reservoir computers, a particular class of RNNs, trained on observations of a dynamical systems can be interpreted as embeddings. This result has been establish... | {
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} |
2501.11360 | Federated Learning with Sample-level Client Drift Mitigation | [
"cs.LG",
"cs.AI"
] | Federated Learning (FL) suffers from severe performance degradation due to the data heterogeneity among clients. Existing works reveal that the fundamental reason is that data heterogeneity can cause client drift where the local model update deviates from the global one, and thus they usually tackle this problem from t... | {
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} |
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