id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.03225 | Automated Generation of Challenging Multiple-Choice Questions for Vision
Language Model Evaluation | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.CY",
"cs.LG"
] | The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-ended questions, making accurate evaluation difficult due to the variability in natural language responses. To address this, we introduce Auto... | {
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2501.03226 | BoostStep: Boosting mathematical capability of Large Language Models via
improved single-step reasoning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) have demonstrated impressive ability in solving complex mathematical problems with multi-step reasoning and can be further enhanced with well-designed in-context learning (ICL) examples. However, this potential is often constrained by two major challenges in ICL: granularity mismatch and ir... | {
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2501.03227 | When Should Selfish Miners Double-Spend? | [
"cs.CR",
"cs.DC",
"cs.DM",
"cs.IT",
"math.IT",
"math.PR"
] | Although, both double-spending and selfish-mining attacks have been extensively studied since the ``Bitcoin'' whitepaper of Nakamoto and the ``majority is not enough'' paper of Eyal and Sirer, there has been no rigorous stochastic analysis of an attack that combines the two, except for the complicated MDP models. In th... | {
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2501.03228 | LightGNN: Simple Graph Neural Network for Recommendation | [
"cs.IR",
"cs.AI",
"cs.LG"
] | Graph neural networks (GNNs) have demonstrated superior performance in collaborative recommendation through their ability to conduct high-order representation smoothing, effectively capturing structural information within users' interaction patterns. However, existing GNN paradigms face significant challenges in scalab... | {
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2501.03229 | Gaussian Masked Autoencoders | [
"cs.CV",
"cs.AI"
] | This paper explores Masked Autoencoders (MAE) with Gaussian Splatting. While reconstructive self-supervised learning frameworks such as MAE learns good semantic abstractions, it is not trained for explicit spatial awareness. Our approach, named Gaussian Masked Autoencoder, or GMAE, aims to learn semantic abstractions a... | {
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2501.03230 | Video-of-Thought: Step-by-Step Video Reasoning from Perception to
Cognition | [
"cs.AI",
"cs.CV"
] | Existing research of video understanding still struggles to achieve in-depth comprehension and reasoning in complex videos, primarily due to the under-exploration of two key bottlenecks: fine-grained spatial-temporal perceptive understanding and cognitive-level video scene comprehension. This paper bridges the gap by p... | {
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2501.03235 | Neural networks consisting of DNA | [
"physics.bio-ph",
"cond-mat.soft",
"cs.AI",
"cs.NE",
"q-bio.BM",
"q-bio.MN"
] | Neural networks based on soft and biological matter constitute an interesting potential alternative to traditional implementations based on electric circuits. DNA is a particularly promising system in this context due its natural ability to store information. In recent years, researchers have started to construct neura... | {
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2501.03246 | Bridging Auditory Perception and Language Comprehension through
MEG-Driven Encoding Models | [
"q-bio.NC",
"cs.CL",
"cs.LG",
"cs.SD",
"eess.AS",
"eess.SP"
] | Understanding the neural mechanisms behind auditory and linguistic processing is key to advancing cognitive neuroscience. In this study, we use Magnetoencephalography (MEG) data to analyze brain responses to spoken language stimuli. We develop two distinct encoding models: an audio-to-MEG encoder, which uses time-frequ... | {
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2501.03250 | Machine Learning and Deep Learning Techniques used in Cybersecurity and
Digital Forensics: a Review | [
"cs.CR",
"cs.AI"
] | In the paced realms of cybersecurity and digital forensics machine learning (ML) and deep learning (DL) have emerged as game changing technologies that introduce methods to identify stop and analyze cyber risks. This review presents an overview of the ML and DL approaches used in these fields showcasing their advantage... | {
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2501.03254 | Advanced Displacement Magnitude Prediction in Multi-Material Architected
Lattice Structure Beams Using Physics Informed Neural Network Architecture | [
"cs.AI",
"cond-mat.mtrl-sci",
"cs.CE",
"cs.LG",
"cs.NE"
] | This paper proposes an innovative method for predicting deformation in architected lattice structures that combines Physics-Informed Neural Networks (PINNs) with finite element analysis. A thorough study was carried out on FCC-based lattice beams utilizing five different materials (Structural Steel, AA6061, AA7075, Ti6... | {
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2501.03256 | AI-ANNE: (A) (N)eural (N)et for (E)xploration: Transferring Deep
Learning Models onto Microcontrollers and Embedded Systems | [
"cs.LG",
"cs.AI"
] | This working paper explores the integration of neural networks onto resource-constrained embedded systems like a Raspberry Pi Pico / Raspberry Pi Pico 2. A TinyML aproach transfers neural networks directly on these microcontrollers, enabling real-time, low-latency, and energy-efficient inference while maintaining data ... | {
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2501.03257 | Breaking Through the Spike: Spike Window Decoding for Accelerated and
Precise Automatic Speech Recognition | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.SD"
] | Recently, end-to-end automatic speech recognition has become the mainstream approach in both industry and academia. To optimize system performance in specific scenarios, the Weighted Finite-State Transducer (WFST) is extensively used to integrate acoustic and language models, leveraging its capacity to implicitly fuse ... | {
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2501.03259 | Toward Inclusive Educational AI: Auditing Frontier LLMs through a
Multiplexity Lens | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG",
"cs.MA"
] | As large language models (LLMs) like GPT-4 and Llama 3 become integral to educational contexts, concerns are mounting over the cultural biases, power imbalances, and ethical limitations embedded within these technologies. Though generative AI tools aim to enhance learning experiences, they often reflect values rooted i... | {
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2501.03261 | Navigation Variable-based Multi-objective Particle Swarm Optimization
for UAV Path Planning with Kinematic Constraints | [
"cs.RO",
"cs.AI",
"cs.NE"
] | Path planning is essential for unmanned aerial vehicles (UAVs) as it determines the path that the UAV needs to follow to complete a task. This work addresses this problem by introducing a new algorithm called navigation variable-based multi-objective particle swarm optimization (NMOPSO). It first models path planning a... | {
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2501.03262 | REINFORCE++: A Simple and Efficient Approach for Aligning Large Language
Models | [
"cs.CL",
"cs.LG"
] | Reinforcement Learning from Human Feedback (RLHF) has emerged as a critical approach for aligning large language models with human preferences, witnessing rapid algorithmic evolution through methods such as Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO), REINFORCE Leave One-Out (RLOO), ReMax, ... | {
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2501.03264 | Bridge the Inference Gaps of Neural Processes via Expectation
Maximization | [
"cs.LG",
"cs.AI",
"cs.NE"
] | The neural process (NP) is a family of computationally efficient models for learning distributions over functions. However, it suffers from under-fitting and shows suboptimal performance in practice. Researchers have primarily focused on incorporating diverse structural inductive biases, \textit{e.g.} attention or conv... | {
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2501.03265 | Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System
Strategies | [
"cs.LG",
"cs.AI"
] | The emergence of 5G and edge computing hardware has brought about a significant shift in artificial intelligence, with edge AI becoming a crucial technology for enabling intelligent applications. With the growing amount of data generated and stored on edge devices, deploying AI models for local processing and inference... | {
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2501.03266 | LLM Content Moderation and User Satisfaction: Evidence from Response
Refusals in Chatbot Arena | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.HC",
"cs.SI"
] | LLM safety and ethical alignment are widely discussed, but the impact of content moderation on user satisfaction remains underexplored. To address this, we analyze nearly 50,000 Chatbot Arena response-pairs using a novel fine-tuned RoBERTa model, that we trained on hand-labeled data to disentangle refusals due to ethic... | {
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2501.03268 | Heterogeneous Graph Pre-training Based Model for Secure and Efficient
Prediction of Default Risk Propagation among Bond Issuers | [
"cs.LG",
"cs.AI"
] | Efficient prediction of default risk for bond-issuing enterprises is pivotal for maintaining stability and fostering growth in the bond market. Conventional methods usually rely solely on an enterprise's internal data for risk assessment. In contrast, graph-based techniques leverage interconnected corporate information... | {
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2501.03271 | DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich
Paradigm for Direct Preference Optimization | [
"cs.LG",
"cs.AI",
"cs.CL"
] | The rapid rise of large language models (LLMs) has unlocked many applications but also underscores the challenge of aligning them with diverse values and preferences. Direct Preference Optimization (DPO) is central to alignment but constrained by fixed divergences and limited feature transformations. We propose DPO-Ker... | {
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2501.03272 | Backdoor Token Unlearning: Exposing and Defending Backdoors in
Pretrained Language Models | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Supervised fine-tuning has become the predominant method for adapting large pretrained models to downstream tasks. However, recent studies have revealed that these models are vulnerable to backdoor attacks, where even a small number of malicious samples can successfully embed backdoor triggers into the model. While mos... | {
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2501.03273 | Strategic Fusion Optimizes Transformer Compression | [
"cs.LG",
"cs.AI",
"cs.CL"
] | This study investigates transformer model compression by systematically pruning its layers. We evaluated 14 pruning strategies across nine diverse datasets, including 12 strategies based on different signals obtained from layer activations, mutual information, gradients, weights, and attention. To address the limitatio... | {
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2501.03276 | ComMer: a Framework for Compressing and Merging User Data for
Personalization | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | Large Language Models (LLMs) excel at a wide range of tasks, but adapting them to new data, particularly for personalized applications, poses significant challenges due to resource and computational constraints. Existing methods either rely on exposing fresh data to the model through the prompt, which is limited by con... | {
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2501.03277 | HonkaiChat: Companions from Anime that feel alive! | [
"cs.CL"
] | Modern conversational agents, including anime-themed chatbots, are frequently reactive and personality-driven but fail to capture the dynamic nature of human interactions. We propose an event-driven dialogue framework to address these limitations by embedding dynamic events in conversation prompts and fine-tuning model... | {
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2501.03278 | DenseGNN: universal and scalable deeper graph neural networks for
high-performance property prediction in crystals and molecules | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Generative models generate vast numbers of hypothetical materials, necessitating fast, accurate models for property prediction. Graph Neural Networks (GNNs) excel in this domain but face challenges like high training costs, domain adaptation issues, and over-smoothing. We introduce DenseGNN, which employs Dense Connect... | {
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2501.03279 | Revolutionizing Encrypted Traffic Classification with MH-Net: A
Multi-View Heterogeneous Graph Model | [
"cs.CR",
"cs.AI",
"cs.LG"
] | With the growing significance of network security, the classification of encrypted traffic has emerged as an urgent challenge. Traditional byte-based traffic analysis methods are constrained by the rigid granularity of information and fail to fully exploit the diverse correlations between bytes. To address these limita... | {
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2501.03282 | From Aleatoric to Epistemic: Exploring Uncertainty Quantification
Techniques in Artificial Intelligence | [
"cs.AI",
"cs.LG"
] | Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification... | {
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2501.03284 | Sensorformer: Cross-patch attention with global-patch compression is
effective for high-dimensional multivariate time series forecasting | [
"cs.LG"
] | Among the existing Transformer-based multivariate time series forecasting methods, iTransformer, which treats each variable sequence as a token and only explicitly extracts cross-variable dependencies, and PatchTST, which adopts a channel-independent strategy and only explicitly extracts cross-time dependencies, both s... | {
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2501.03286 | Inverse Design of Optimal Stern Shape with Convolutional Neural
Network-based Pressure Distribution | [
"cs.LG",
"physics.flu-dyn"
] | Hull form designing is an iterative process wherein the performance of the hull form needs to be checked via computational fluid dynamics calculations or model experiments. The stern shape has to undergo a process wherein the hull form variations from the pressure distribution analysis results are repeated until the re... | {
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2501.03287 | OpenLKA: an open dataset of lane keeping assist from market autonomous
vehicles | [
"cs.RO",
"cs.CV",
"cs.LG"
] | The Lane Keeping Assist (LKA) system has become a standard feature in recent car models. While marketed as providing auto-steering capabilities, the system's operational characteristics and safety performance remain underexplored, primarily due to a lack of real-world testing and comprehensive data. To fill this gap, w... | {
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2501.03288 | CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps
and Vision Models | [
"cs.SE",
"cs.AI"
] | The rise of large language models (LLMs) like ChatGPT has significantly improved automated code generation, enhancing software development efficiency. However, this introduces challenges in academia, particularly in distinguishing between human-written and LLM-generated code, which complicates issues of academic integr... | {
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2501.03289 | Adaptive Pruning of Pretrained Transformer via Differential Inclusions | [
"cs.LG"
] | Large transformers have demonstrated remarkable success, making it necessary to compress these models to reduce inference costs while preserving their perfor-mance. Current compression algorithms prune transformers at fixed compression ratios, requiring a unique pruning process for each ratio, which results in high com... | {
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2501.03290 | A Decision-Based Heterogenous Graph Attention Network for Multi-Class
Fake News Detection | [
"cs.LG",
"cs.AI",
"cs.SI"
] | A promising tool for addressing fake news detection is Graph Neural Networks (GNNs). However, most existing GNN-based methods rely on binary classification, categorizing news as either real or fake. Additionally, traditional GNN models use a static neighborhood for each node, making them susceptible to issues like over... | {
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2501.03291 | ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient
Fine-tuning | [
"cs.CL"
] | Prompt Tuning (PT) enables the adaptation of Pre-trained Large Language Models (PLMs) to downstream tasks by optimizing a small amount of soft virtual tokens, which are prepended to the input token embeddings. Recently, Decomposed Prompt Tuning (DePT) has demonstrated superior adaptation capabilities by decomposing the... | {
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2501.03292 | Multi-Modal One-Shot Federated Ensemble Learning for Medical Data with
Vision Large Language Model | [
"cs.LG",
"cs.AI"
] | Federated learning (FL) has attracted considerable interest in the medical domain due to its capacity to facilitate collaborative model training while maintaining data privacy. However, conventional FL methods typically necessitate multiple communication rounds, leading to significant communication overhead and delays,... | {
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2501.03295 | A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation
Based on Large Language Models Enhanced by Domain Knowledge Retrieval | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Data-driven soft sensors are crucial in predicting key performance indicators in industrial systems. However, current methods predominantly rely on the supervised learning paradigms of parameter updating, which inherently faces challenges such as high development costs, poor robustness, training instability, and lack o... | {
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2501.03300 | Method of data forward generation with partial differential equations
for machine learning modeling in fluid mechanics | [
"cs.LG",
"physics.flu-dyn"
] | Artificial intelligence (AI) for fluid mechanics has become attractive topic. High-fidelity data is one of most critical issues for the successful applications of AI in fluid mechanics, however, it is expensively obtained or even inaccessible. This study proposes a high-efficient data forward generation method from the... | {
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2501.03301 | Rethinking Byzantine Robustness in Federated Recommendation from Sparse
Aggregation Perspective | [
"cs.CR",
"cs.AI",
"cs.DC",
"cs.LG"
] | To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating a model collaboratively. Unlike FL, FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partia... | {
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2501.03304 | LiLMaps: Learnable Implicit Language Maps | [
"cs.RO",
"cs.LG"
] | One of the current trends in robotics is to employ large language models (LLMs) to provide non-predefined command execution and natural human-robot interaction. It is useful to have an environment map together with its language representation, which can be further utilized by LLMs. Such a comprehensive scene representa... | {
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2501.03305 | Plant Leaf Disease Detection and Classification Using Deep Learning: A
Review and A Proposed System on Bangladesh's Perspective | [
"cs.CV",
"cs.LG"
] | A very crucial part of Bangladeshi people's employment, GDP contribution, and mainly livelihood is agriculture. It plays a vital role in decreasing poverty and ensuring food security. Plant diseases are a serious stumbling block in agricultural production in Bangladesh. At times, humans can't detect the disease from an... | {
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2501.03306 | The Robustness of Spiking Neural Networks in Federated Learning with
Compression Against Non-omniscient Byzantine Attacks | [
"cs.CR",
"cs.DC",
"cs.LG"
] | Spiking Neural Networks (SNNs), which offer exceptional energy efficiency for inference, and Federated Learning (FL), which offers privacy-preserving distributed training, is a rising area of interest that highly beneficial towards Internet of Things (IoT) devices. Despite this, research that tackles Byzantine attacks ... | {
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2501.03324 | Analyzing Bias in Swiss Federal Supreme Court Judgments Using Facebook's
Holistic Bias Dataset: Implications for Language Model Training | [
"cs.CL",
"cs.AI"
] | Natural Language Processing (NLP) is vital for computers to process and respond accurately to human language. However, biases in training data can introduce unfairness, especially in predicting legal judgment. This study focuses on analyzing biases within the Swiss Judgment Prediction Dataset (SJP-Dataset). Our aim is ... | {
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2501.03331 | Global network control from local information | [
"eess.SY",
"cond-mat.dis-nn",
"cs.SY"
] | In the classical control of network systems, the control actions on a node are determined as a function of the states of all nodes in the network. Motivated by applications where the global state cannot be reconstructed in real time due to limitations in the collection, communication, and processing of data, here we in... | {
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2501.03332 | CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous
Interaction Datasets | [
"cs.CV"
] | Challenges in cross-learning involve inhomogeneous or even inadequate amount of training data and lack of resources for retraining large pretrained models. Inspired by transfer learning techniques in NLP, adapters and prefix tuning, this paper presents a new model-agnostic plugin architecture for cross-learning, called... | {
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2501.03336 | Mobile Augmented Reality Framework with Fusional Localization and Pose
Estimation | [
"cs.CV"
] | As a novel way of presenting information, augmented reality (AR) enables people to interact with the physical world in a direct and intuitive way. While there are some mobile AR products implemented with specific hardware at a high cost, the software approaches of AR implementation on mobile platforms(such as smartphon... | {
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2501.03349 | FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for
Lithology Microscopic Image Classification | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Lithology discrimination is a crucial activity in characterizing oil reservoirs, and processing lithology microscopic images is an essential technique for investigating fossils and minerals and geological assessment of shale oil exploration. In this way, Deep Learning (DL) technique is a powerful approach for building ... | {
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2501.03358 | Data integrity vs. inference accuracy in large AIS datasets | [
"cs.CR",
"cs.LG"
] | Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of infer-ence and decision-making in the context of maritime safety, traffic manage-ment and environment... | {
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2501.03360 | Quantum Feature-Empowered Deep Classification for Fast Mangrove Mapping | [
"quant-ph",
"cs.CV",
"eess.IV"
] | A mangrove mapping (MM) algorithm is an essential classification tool for environmental monitoring. The recent literature shows that compared with other index-based MM methods that treat pixels as spatially independent, convolutional neural networks (CNNs) are crucial for leveraging spatial continuity information, lead... | {
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2501.03368 | Detecting Defective Wafers Via Modular Networks | [
"cs.LG"
] | The growing availability of sensors within semiconductor manufacturing processes makes it feasible to detect defective wafers with data-driven models. Without directly measuring the quality of semiconductor devices, they capture the modalities between diverse sensor readings and can be used to predict key quality indic... | {
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2501.03370 | Advanced Machine Learning Techniques for Social Support Detection on
Social Media | [
"cs.CL",
"cs.AI",
"cs.HC",
"cs.LG"
] | The widespread use of social media highlights the need to understand its impact, particularly the role of online social support. This study uses a dataset focused on online social support, which includes binary and multiclass classifications of social support content on social media. The classification of social suppor... | {
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2501.03374 | License Plate Images Generation with Diffusion Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Despite the evident practical importance of license plate recognition (LPR), corresponding research is limited by the volume of publicly available datasets due to privacy regulations such as the General Data Protection Regulation (GDPR). To address this challenge, synthetic data generation has emerged as a promising ap... | {
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2501.03376 | Existential Crisis: A Social Robot's Reason for Being | [
"cs.RO",
"cs.AI",
"cs.HC"
] | As Robots become ever more important in our daily lives there's growing need for understanding how they're perceived by people. This study aims to investigate how the user perception of robots is influenced by displays of personality. Using LLMs and speech to text technology, we designed a within-subject study to compa... | {
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2501.03383 | The Artificial Scientist -- in-transit Machine Learning of Plasma
Simulations | [
"physics.comp-ph",
"cs.DC",
"cs.LG"
] | Increasing HPC cluster sizes and large-scale simulations that produce petabytes of data per run, create massive IO and storage challenges for analysis. Deep learning-based techniques, in particular, make use of these amounts of domain data to extract patterns that help build scientific understanding. Here, we demonstra... | {
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2501.03392 | Over-the-Air Fair Federated Learning via Multi-Objective Optimization | [
"cs.LG",
"cs.AI"
] | In federated learning (FL), heterogeneity among the local dataset distributions of clients can result in unsatisfactory performance for some, leading to an unfair model. To address this challenge, we propose an over-the-air fair federated learning algorithm (OTA-FFL), which leverages over-the-air computation to train f... | {
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} |
2501.03394 | Enhanced Importance Sampling through Latent Space Exploration in
Normalizing Flows | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Importance sampling is a rare event simulation technique used in Monte Carlo simulations to bias the sampling distribution towards the rare event of interest. By assigning appropriate weights to sampled points, importance sampling allows for more efficient estimation of rare events or tails of distributions. However, i... | {
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2501.03397 | DoubleDiffusion: Combining Heat Diffusion with Denoising Diffusion for
Generative Learning on 3D Meshes | [
"cs.CV"
] | This paper proposes DoubleDiffusion, a novel framework that combines heat dissipation diffusion and denoising diffusion for direct generative learning on 3D mesh surfaces. Our approach addresses the challenges of generating continuous signal distributions residing on a curve manifold surface. Unlike previous methods th... | {
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2501.03399 | Compression of 3D Gaussian Splatting with Optimized Feature Planes and
Standard Video Codecs | [
"cs.CV",
"cs.MM"
] | 3D Gaussian Splatting is a recognized method for 3D scene representation, known for its high rendering quality and speed. However, its substantial data requirements present challenges for practical applications. In this paper, we introduce an efficient compression technique that significantly reduces storage overhead b... | {
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2501.03400 | Power System Steady-State Estimation Revisited | [
"math.OC",
"cs.SY",
"eess.SY"
] | In power system steady-state estimation (PSSE), one needs to consider (1) the need for robust statistics, (2) the nonconvex transmission constraints, (3) the fast-varying nature of the inputs, and the corresponding need to track optimal trajectories as closely as possible. In combination, these challenges have not been... | {
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2501.03402 | On the Adversarial Robustness of Benjamini Hochberg | [
"math.ST",
"cs.LG",
"stat.TH"
] | The Benjamini-Hochberg (BH) procedure is widely used to control the false detection rate (FDR) in multiple testing. Applications of this control abound in drug discovery, forensics, anomaly detection, and, in particular, machine learning, ranging from nonparametric outlier detection to out-of-distribution detection and... | {
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2501.03403 | BoundingDocs: a Unified Dataset for Document Question Answering with
Spatial Annotations | [
"cs.CL",
"cs.AI"
] | We present a unified dataset for document Question-Answering (QA), which is obtained combining several public datasets related to Document AI and visually rich document understanding (VRDU). Our main contribution is twofold: on the one hand we reformulate existing Document AI tasks, such as Information Extraction (IE),... | {
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2501.03405 | A Study of the Efficacy of Generative Flow Networks for Robotics and
Machine Fault-Adaptation | [
"cs.RO"
] | Advancements in robotics have opened possibilities to automate tasks in various fields such as manufacturing, emergency response and healthcare. However, a significant challenge that prevents robots from operating in real-world environments effectively is out-of-distribution (OOD) situations, wherein robots encounter u... | {
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2501.03406 | Low-Order Flow Reconstruction and Uncertainty Quantification in
Disturbed Aerodynamics Using Sparse Pressure Measurements | [
"cs.LG",
"physics.flu-dyn"
] | This paper presents a novel machine-learning framework for reconstructing low-order gust-encounter flow field and lift coefficients from sparse, noisy surface pressure measurements. Our study thoroughly investigates the time-varying response of sensors to gust-airfoil interactions, uncovering valuable insights into opt... | {
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2501.03410 | ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models | [
"cs.CV"
] | Building trusted datasets is critical for transparent and responsible Medical AI (MAI) research, but creating even small, high-quality datasets can take years of effort from multidisciplinary teams. This process often delays AI benefits, as human-centric data creation and AI-centric model development are treated as sep... | {
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2501.03413 | SALT: Sales Autocompletion Linked Business Tables Dataset | [
"cs.LG",
"cs.AI",
"cs.DB"
] | Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even ... | {
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2501.03416 | TinySense: A Lighter Weight and More Power-efficient Avionics System for
Flying Insect-scale Robots | [
"cs.RO",
"cs.SY",
"eess.SY"
] | In this paper, we investigate the prospects and challenges of sensor suites in achieving autonomous control for flying insect robots (FIRs) weighing less than a gram. FIRs, owing to their minuscule weight and size, offer unparalleled advantages in terms of material cost and scalability. However, their size introduces c... | {
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2501.03420 | Designing Telepresence Robots to Support Place Attachment | [
"cs.HC",
"cs.RO"
] | People feel attached to places that are meaningful to them, which psychological research calls "place attachment." Place attachment is associated with self-identity, self-continuity, and psychological well-being. Even small cues, including videos, images, sounds, and scents, can facilitate feelings of connection and be... | {
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2501.03430 | A Self-supervised Diffusion Bridge for MRI Reconstruction | [
"eess.IV",
"cs.CV"
] | Diffusion bridges (DBs) are a class of diffusion models that enable faster sampling by interpolating between two paired image distributions. Training traditional DBs for image reconstruction requires high-quality reference images, which limits their applicability to settings where such references are unavailable. We pr... | {
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2501.03432 | Mixture-of-Experts Graph Transformers for Interpretable Particle
Collision Detection | [
"cs.LG",
"hep-ph"
] | The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical techniques for effective interpretation. Neural Networks, including Graph Neural Networks, have shown promise in tasks such as event classification and object identificatio... | {
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2501.03437 | DAMAGE: Detecting Adversarially Modified AI Generated Text | [
"cs.CL"
] | AI humanizers are a new class of online software tools meant to paraphrase and rewrite AI-generated text in a way that allows them to evade AI detection software. We study 19 AI humanizer and paraphrasing tools and qualitatively assess their effects and faithfulness in preserving the meaning of the original text. We sh... | {
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2501.03441 | Finding A Voice: Evaluating African American Dialect Generation for
Chatbot Technology | [
"cs.CL"
] | As chatbots become increasingly integrated into everyday tasks, designing systems that accommodate diverse user populations is crucial for fostering trust, engagement, and inclusivity. This study investigates the ability of contemporary Large Language Models (LLMs) to generate African American Vernacular English (AAVE)... | {
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2501.03443 | Optimization Learning | [
"math.OC",
"cs.AI"
] | This article introduces the concept of optimization learning, a methodology to design optimization proxies that learn the input/output mapping of parametric optimization problems. These optimization proxies are trustworthy by design: they compute feasible solutions to the underlying optimization problems, provide quali... | {
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2501.03445 | Physics-Constrained Generative Artificial Intelligence for Rapid Takeoff
Trajectory Design | [
"cs.LG"
] | To aid urban air mobility (UAM), electric vertical takeoff and landing (eVTOL) aircraft are being targeted. Conventional multidisciplinary analysis and optimization (MDAO) can be expensive, while surrogate-based optimization can struggle with challenging physical constraints. This work proposes physics-constrained gene... | {
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2501.03448 | Optimizing Value of Learning in Task-Oriented Federated Meta-Learning
Systems | [
"cs.LG"
] | Federated Learning (FL) has gained significant attention in recent years due to its distributed nature and privacy preserving benefits. However, a key limitation of conventional FL is that it learns and distributes a common global model to all participants, which fails to provide customized solutions for diverse task r... | {
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2501.03449 | Feasibility of short blocklength Reed-Muller codes for physical layer
security in real environment | [
"cs.IT",
"cs.CR",
"eess.SP",
"math.IT"
] | In this paper, we investigate the application of Reed-Muller (RM) codes for Physical-layer security in a real world wiretap channel scenario. Utilizing software-defined radios (SDRs) in a real indoor environment, we implement a coset coding scheme that leverages the hierarchical structure of RM codes to secure data tra... | {
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2501.03451 | Structure-Preference Enabled Graph Embedding Generation under
Differential Privacy | [
"stat.ML",
"cs.LG",
"cs.SI"
] | Graph embedding generation techniques aim to learn low-dimensional vectors for each node in a graph and have recently gained increasing research attention. Publishing low-dimensional node vectors enables various graph analysis tasks, such as structural equivalence and link prediction. Yet, improper publication opens a ... | {
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2501.03456 | Text to Band Gap: Pre-trained Language Models as Encoders for
Semiconductor Band Gap Prediction | [
"cs.CL",
"cond-mat.mtrl-sci"
] | In this study, we explore the use of a transformer-based language model as an encoder to predict the band gaps of semiconductor materials directly from their text descriptions. Quantum chemistry simulations, including Density Functional Theory (DFT), are computationally intensive and time-consuming, which limits their ... | {
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2501.03458 | Activating Associative Disease-Aware Vision Token Memory for LLM-Based
X-ray Report Generation | [
"eess.IV",
"cs.AI",
"cs.CV"
] | X-ray image based medical report generation achieves significant progress in recent years with the help of the large language model, however, these models have not fully exploited the effective information in visual image regions, resulting in reports that are linguistically sound but insufficient in describing key dis... | {
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2501.03461 | Radar Signal Recognition through Self-Supervised Learning and Domain
Adaptation | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Automatic radar signal recognition (RSR) plays a pivotal role in electronic warfare (EW), as accurately classifying radar signals is critical for informing decision-making processes. Recent advances in deep learning have shown significant potential in improving RSR performance in domains with ample annotated data. Howe... | {
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2501.03462 | ISSR: Iterative Selection with Self-Review for Vocabulary Test
Distractor Generation | [
"cs.CL"
] | Vocabulary acquisition is essential to second language learning, as it underpins all core language skills. Accurate vocabulary assessment is particularly important in standardized exams, where test items evaluate learners' comprehension and contextual use of words. Previous research has explored methods for generating ... | {
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2501.03464 | LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification
and Tagging | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Transformers have set new benchmarks in audio processing tasks, leveraging self-attention mechanisms to capture complex patterns and dependencies within audio data. However, their focus on pairwise interactions limits their ability to process the higher-order relations essential for identifying distinct audio objects. ... | {
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2501.03465 | Extending Internet Access Over LoRa for Internet of Things and Critical
Applications | [
"cs.NI",
"cs.CY",
"cs.SY",
"eess.SY"
] | LoRa bridges the gap between remote locations and mainstream networks, enabling large-scale Internet of Things (IoT) deployments. Despite the recent advancements around LoRa, Internet access over this technology is still largely unexplored. Most existing solutions only handle packets within the local LoRa network and d... | {
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2501.03466 | DGSSA: Domain generalization with structural and stylistic augmentation
for retinal vessel segmentation | [
"eess.IV",
"cs.CV"
] | Retinal vascular morphology is crucial for diagnosing diseases such as diabetes, glaucoma, and hypertension, making accurate segmentation of retinal vessels essential for early intervention. Traditional segmentation methods assume that training and testing data share similar distributions, which can lead to poor perfor... | {
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2501.03467 | FRESHR-GSI: A Generalized Safety Model and Evaluation Framework for
Mobile Robots in Multi-Human Environments | [
"cs.RO",
"cs.HC"
] | Human safety is critical in applications involving close human-robot interactions (HRI) and is a key aspect of physical compatibility between humans and robots. While measures of human safety in HRI exist, these mainly target industrial settings involving robotic manipulators. Less attention has been paid to settings w... | {
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2501.03468 | MTRAG: A Multi-Turn Conversational Benchmark for Evaluating
Retrieval-Augmented Generation Systems | [
"cs.CL",
"cs.AI"
] | Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on multi-turn RAG conversations, where the system is asked to generate a response to a question in the context of a preceding conversation is an important and often overlooked task with several... | {
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2501.03469 | Information-Maximized Soft Variable Discretization for Self-Supervised
Image Representation Learning | [
"cs.CV"
] | Self-supervised learning (SSL) has emerged as a crucial technique in image processing, encoding, and understanding, especially for developing today's vision foundation models that utilize large-scale datasets without annotations to enhance various downstream tasks. This study introduces a novel SSL approach, Informatio... | {
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2501.03471 | Hyperbolic Binary Neural Network | [
"cs.LG",
"cs.CV"
] | Binary Neural Network (BNN) converts full-precision weights and activations into their extreme 1-bit counterparts, making it particularly suitable for deployment on lightweight mobile devices. While binary neural networks are typically formulated as a constrained optimization problem and optimized in the binarized spac... | {
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2501.03475 | Reading with Intent -- Neutralizing Intent | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Queries to large language models (LLMs) can be divided into two parts: the instruction/question and the accompanying context. The context for retrieval-augmented generation (RAG) systems in most benchmarks comes from Wikipedia or Wikipedia-like texts which are written in a neutral and factual tone. However, when RAG sy... | {
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2501.03477 | A study on performance limitations in Federated Learning | [
"cs.LG"
] | Increasing privacy concerns and unrestricted access to data lead to the development of a novel machine learning paradigm called Federated Learning (FL). FL borrows many of the ideas from distributed machine learning, however, the challenges associated with federated learning makes it an interesting engineering problem ... | {
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2501.03479 | Women, Infamous, and Exotic Beings: What Honorific Usages in Wikipedia
Reveal about the Socio-Cultural Norms | [
"cs.CL"
] | Honorifics serve as powerful linguistic markers that reflect social hierarchies and cultural values. This paper presents a large-scale, cross-linguistic exploration of usage of honorific pronouns in Bengali and Hindi Wikipedia articles, shedding light on how socio-cultural factors shape language. Using LLM (GPT-4o), we... | {
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} |
2501.03482 | VOILA: Complexity-Aware Universal Segmentation of CT images by Voxel
Interacting with Language | [
"cs.CV"
] | Satisfactory progress has been achieved recently in universal segmentation of CT images. Following the success of vision-language methods, there is a growing trend towards utilizing text prompts and contrastive learning to develop universal segmentation models. However, there exists a significant imbalance in informati... | {
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} |
2501.03486 | Align-Pro: A Principled Approach to Prompt Optimization for LLM
Alignment | [
"cs.LG",
"cs.AI"
] | The alignment of large language models (LLMs) with human values is critical as these models become increasingly integrated into various societal and decision-making processes. Traditional methods, such as reinforcement learning from human feedback (RLHF), achieve alignment by fine-tuning model parameters, but these app... | {
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} |
2501.03489 | Entropy-Guided Attention for Private LLMs | [
"cs.LG",
"cs.CR"
] | The pervasiveness of proprietary language models has raised critical privacy concerns, necessitating advancements in private inference (PI), where computations are performed directly on encrypted data without revealing users' sensitive information. While PI offers a promising solution, its practical deployment is hinde... | {
"Other": 0,
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} |
2501.03490 | SceneBooth: Diffusion-based Framework for Subject-preserved
Text-to-Image Generation | [
"cs.CV"
] | Due to the demand for personalizing image generation, subject-driven text-to-image generation method, which creates novel renditions of an input subject based on text prompts, has received growing research interest. Existing methods often learn subject representation and incorporate it into the prompt embedding to guid... | {
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} |
2501.03491 | Can LLMs Design Good Questions Based on Context? | [
"cs.CL",
"cs.AI"
] | This paper evaluates questions generated by LLMs from context, comparing them to human-generated questions across six dimensions. We introduce an automated LLM-based evaluation method, focusing on aspects like question length, type, context coverage, and answerability. Our findings highlight unique characteristics of L... | {
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} |
2501.03492 | Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector
Data | [
"cs.LG"
] | Traffic forecasting is a fundamental task in transportation research, however the scope of current research has mainly focused on a single data modality of loop detectors. Recently, the advances in Artificial Intelligence and drone technologies have made possible novel solutions for efficient, accurate and flexible aer... | {
"Other": 0,
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} |
2501.03495 | Textualize Visual Prompt for Image Editing via Diffusion Bridge | [
"cs.CV",
"cs.LG"
] | Visual prompt, a pair of before-and-after edited images, can convey indescribable imagery transformations and prosper in image editing. However, current visual prompt methods rely on a pretrained text-guided image-to-image generative model that requires a triplet of text, before, and after images for retraining over a ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
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"cs.HC": 0,
"cs.IR": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.03496 | A Unified Attack Detection Strategy for Multi-Agent Systems over
Transient and Steady Stages | [
"eess.SY",
"cs.SY"
] | This paper proposes a unified detection strategy against three kinds of attacks for multi-agent systems (MASs) which is applicable to both transient and steady stages. For attacks on the communication layer, a watermarking-based detection scheme with KullbackLeibler (KL) divergence is designed. Different from tradition... | {
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"cs.NE": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.03499 | Can Deep Learning Trigger Alerts from Mobile-Captured Images? | [
"cs.CV",
"cs.AI"
] | Our research presents a comprehensive approach to leveraging mobile camera image data for real-time air quality assessment and recommendation. We develop a regression-based Convolutional Neural Network model and tailor it explicitly for air quality prediction by exploiting the inherent relationship between output param... | {
"Other": 0,
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} |
2501.03503 | Resilient Distributed Control for Uncertain Nonlinear Interconnected
Systems under Network Anomaly | [
"eess.SY",
"cs.SY"
] | We address a distributed adaptive control methodology for nonlinear interconnected systems possibly affected by network anomalies. In the framework of adaptive approximation, the distributed controller and parameter estimator are designed by exploiting a backstepping approach. The stability of the distributed control s... | {
"Other": 0,
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"cs.SY": 1
} |
2501.03507 | An Empirical Study of Accuracy-Robustness Tradeoff and Training
Efficiency in Self-Supervised Learning | [
"cs.CV",
"cs.LG"
] | Self-supervised learning (SSL) has significantly advanced image representation learning, yet efficiency challenges persist, particularly with adversarial training. Many SSL methods require extensive epochs to achieve convergence, a demand further amplified in adversarial settings. To address this inefficiency, we revis... | {
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"cs.SI": 0,
"cs.SY": 0
} |
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