id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.10390 | Towards an Environmental Ethics of Artificial Intelligence | [
"cs.CY",
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] | In recent years, much research has been dedicated to uncovering the environmental impact of Artificial Intelligence (AI), showing that training and deploying AI systems require large amounts of energy and resources, and the outcomes of AI may lead to decisions and actions that may negatively impact the environment. Thi... | {
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2501.10391 | Developing an Ontology for AI Act Fundamental Rights Impact Assessments | [
"cs.CY",
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] | The recently published EU Artificial Intelligence Act (AI Act) is a landmark regulation that regulates the use of AI technologies. One of its novel requirements is the obligation to conduct a Fundamental Rights Impact Assessment (FRIA), where organisations in the role of deployers must assess the risks of their AI syst... | {
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2501.10392 | Ion Transmitter for Molecular Communication | [
"cs.ET",
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] | Molecular communication (MC) is an emerging paradigm that takes inspiration from biological processes, enabling communication at the nanoscale and facilitating the development of the Internet of Bio-Nano Things (IoBNT). Traditional models of MC often rely on idealized assumptions that overlook practical challenges rela... | {
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2501.10395 | Towards General Purpose Robots at Scale: Lifelong Learning and Learning
to Use Memory | [
"cs.LG",
"cs.AI",
"cs.RO"
] | The widespread success of artificial intelligence in fields like natural language processing and computer vision has not yet fully transferred to robotics, where progress is hindered by the lack of large-scale training data and the complexity of real-world tasks. To address this, many robot learning researchers are pus... | {
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2501.10396 | AI-Powered Urban Transportation Digital Twin: Methods and Applications | [
"eess.SY",
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"cs.CY",
"cs.NI",
"cs.SY"
] | We present a survey paper on methods and applications of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its "eyes," which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in it... | {
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2501.10401 | Custom Loss Functions in Fuel Moisture Modeling | [
"stat.AP",
"cs.LG",
"stat.ML"
] | Fuel moisture content (FMC) is a key predictor for wildfire rate of spread (ROS). Machine learning models of FMC are being used more in recent years, augmenting or replacing traditional physics-based approaches. Wildfire rate of spread (ROS) has a highly nonlinear relationship with FMC, where small differences in dry f... | {
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2501.10404 | Automated Detection of Epileptic Spikes and Seizures Incorporating a
Novel Spatial Clustering Prior | [
"eess.SP",
"cs.LG"
] | A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal's intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, ... | {
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2501.10408 | Leveraging Cross-Attention Transformer and Multi-Feature Fusion for
Cross-Linguistic Speech Emotion Recognition | [
"eess.AS",
"cs.CL",
"cs.SD"
] | Speech Emotion Recognition (SER) plays a crucial role in enhancing human-computer interaction. Cross-Linguistic SER (CLSER) has been a challenging research problem due to significant variability in linguistic and acoustic features of different languages. In this study, we propose a novel approach HuMP-CAT, which combin... | {
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2501.10413 | Cooperative Search and Track of Rogue Drones using Multiagent
Reinforcement Learning | [
"cs.MA",
"cs.AI",
"cs.RO",
"cs.SY",
"eess.SY"
] | This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities. While current interception technologies focus mainly on the jamming/spoofing tasks, the challenges of effectively locating and tracking rogue drones have not received adequate attention. Solving this pro... | {
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2501.10415 | Making Software FAIR: A machine-assisted workflow for the research
software lifecycle | [
"cs.DL",
"cs.IR",
"cs.LG",
"cs.SE"
] | A key issue hindering discoverability, attribution and reusability of open research software is that its existence often remains hidden within the manuscript of research papers. For these resources to become first-class bibliographic records, they first need to be identified and subsequently registered with persistent ... | {
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2501.10421 | CodEv: An Automated Grading Framework Leveraging Large Language Models
for Consistent and Constructive Feedback | [
"cs.CY",
"cs.AI",
"cs.HC"
] | Grading programming assignments is crucial for guiding students to improve their programming skills and coding styles. This study presents an automated grading framework, CodEv, which leverages Large Language Models (LLMs) to provide consistent and constructive feedback. We incorporate Chain of Thought (CoT) prompting ... | {
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2501.10423 | Do we actually understand the impact of renewables on electricity
prices? A causal inference approach | [
"stat.AP",
"cs.LG"
] | The energy transition is profoundly reshaping electricity market dynamics. It makes it essential to understand how renewable energy generation actually impacts electricity prices, among all other market drivers. These insights are critical to design policies and market interventions that ensure affordable, reliable, an... | {
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2501.10425 | Delay Neural Networks (DeNN) for exploiting temporal information in
event-based datasets | [
"cs.NE",
"cs.LG"
] | In Deep Neural Networks (DNN) and Spiking Neural Networks (SNN), the information of a neuron is computed based on the sum of the amplitudes (weights) of the electrical potentials received in input from other neurons. We propose here a new class of neural networks, namely Delay Neural Networks (DeNN), where the informat... | {
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2501.10428 | Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by
Level of Detail (LOD) Theory | [
"eess.SP",
"cs.HC",
"cs.LG"
] | Objective: This study explores a novel deep learning approach for EEG analysis and perceptual state guidance, inspired by Level of Detail (LOD) theory. The goal is to improve perceptual state identification accuracy and advance personalized psychological therapy. Methods: Portable EEG devices and music rhythm signals w... | {
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2501.10429 | Recent Advances of 6G Ultra-Massive MIMO Technologies in Spatial and
Beam Domains | [
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | To explore the full potential of ultra-massive multiple-input multiple-output (MIMO) communication systems, it is fundamental to understand new ultra-massive MIMO channel characteristics and establish pervasive channel models. On this basis, large dimensional spatial-temporal transmission and random access technologies... | {
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2501.10430 | Prediction Model of Aqua Fisheries Using IoT Devices | [
"cs.LG",
"cs.AR",
"cs.SY",
"eess.SY"
] | Aquaculture involves cultivating marine and freshwater organisms, with real-time monitoring of aquatic parameters being crucial in fish farming. This thesis proposes an IoT-based framework using sensors and Arduino for efficient monitoring and control of water quality. Different sensors including pH, temperature, and t... | {
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2501.10431 | Quantum Annealing for Robust Principal Component Analysis | [
"cs.ET",
"cs.LG",
"quant-ph",
"stat.ML"
] | Principal component analysis is commonly used for dimensionality reduction, feature extraction, denoising, and visualization. The most commonly used principal component analysis method is based upon optimization of the L2-norm, however, the L2-norm is known to exaggerate the contribution of errors and outliers. When op... | {
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2501.10435 | Robust Hybrid Classical-Quantum Transfer Learning Model for Text
Classification Using GPT-Neo 125M with LoRA & SMOTE Enhancement | [
"cs.LG",
"quant-ph"
] | This research introduces a hybrid classical-quantum framework for text classification, integrating GPT-Neo 125M with Low-Rank Adaptation (LoRA) and Synthetic Minority Over-sampling Technique (SMOTE) using quantum computing backends. While the GPT-Neo 125M baseline remains the best-performing model, the implementation o... | {
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2501.10436 | A flatness-based predictive controller for six-degrees of freedom
spacecraft rendezvous | [
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"cs.SY"
] | This work presents a closed-loop guidance algorithm for six-degrees of freedom spacecraft rendezvous with a passive target flying in an eccentric orbit. The main assumption is that the chaser vehicle has an attitude control system, based on reaction wheels, providing the necessary torque to change its orientation where... | {
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2501.10437 | Chance-constrained Model Predictive Control for Near Rectilinear Halo
Orbit spacecraft rendezvous | [
"eess.SY",
"cs.SY"
] | This work presents a robust Model Predictive Controller (MPC) to solve the problem of spacecraft rendezvous in the context of the restricted three-body problem (R3BP) as will be required to dock with space stations in cislunar space. The employed methodology is both valid for chemical and electric thrusters. By exploit... | {
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2501.10438 | Event-Based Impulsive Control for Spacecraft Rendezvous Hovering Phases | [
"eess.SY",
"cs.SY"
] | This work presents an event-triggered controller for spacecraft rendezvous hovering phases. The goal is to maintain the chaser within a bounded region with respect to the target. The main assumption is that the chaser vehicle has impulsive thrusters. These are assumed to be orientable at any direction and are constrain... | {
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2501.10440 | Median of Means Sampling for the Keister Function | [
"stat.ME",
"cs.LG",
"cs.NA",
"math.NA",
"stat.CO",
"stat.ML"
] | This study investigates the performance of median-of-means sampling compared to traditional mean-of-means sampling for computing the Keister function integral using Randomized Quasi-Monte Carlo (RQMC) methods. The research tests both lattice points and digital nets as point distributions across dimensions 2, 3, 5, and ... | {
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2501.10441 | A Review of Detection, Evolution, and Data Reconstruction Strategies for
False Data Injection Attacks in Power Cyber-Physical Systems | [
"cs.CR",
"cs.SY",
"eess.SY"
] | The integration of information and physical systems in modern power grids has heightened vulnerabilities to False Data Injection Attacks (FDIAs), threatening the secure operation of power cyber-physical systems (CPS). This paper reviews FDIA detection, evolution, and data reconstruction strategies, highlighting cross-d... | {
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2501.10443 | Monetary Evolution: How Societies Shaped Money from Antiquity to
Cryptocurrencies | [
"cs.CR",
"cs.CE",
"econ.GN",
"q-fin.EC"
] | With the growing popularity and rising value of cryptocurrencies, skepticism surrounding this groundbreaking innovation persists. Many financial and business experts argue that the value created in the cryptocurrency realm resembles the generation of currency from thin air. However, a historical analysis of the fundame... | {
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2501.10446 | Optimizing a multi-state cold-standby system with multiple vacations in
the repair and loss of units | [
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"stat.ME"
] | A complex multi-state redundant system with preventive maintenance subject to multiple events is considered. The online unit can undergo several types of failures: internal and those provoked by external shocks. Multiple degradation levels are assumed so as internal and external. Degradation levels are observed by rand... | {
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2501.10447 | A Predictive Cooperative Collision Avoidance for Multi-Robot Systems
Using Control Barrier Function | [
"cs.SY",
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] | Control barrier function (CBF)-based methods provide the minimum modification necessary to formally guarantee safety in the context of quadratic programming, and strict safety guarantee for safety critical systems. However, most CBF-related derivatives myopically focus on present safety at each time step, a reasoning o... | {
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2501.10448 | Towards Lightweight Time Series Forecasting: a Patch-wise Transformer
with Weak Data Enriching | [
"cs.LG",
"cs.AI"
] | Patch-wise Transformer based time series forecasting achieves superior accuracy. However, this superiority relies heavily on intricate model design with massive parameters, rendering both training and inference expensive, thus preventing their deployments on edge devices with limited resources and low latency requireme... | {
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2501.10451 | Automating Credit Card Limit Adjustments Using Machine Learning | [
"cs.LG"
] | Venezuelan banks have historically made credit card limit adjustment decisions manually through committees. However, since the number of credit card holders in Venezuela is expected to increase in the upcoming months due to economic improvements, manual decisions are starting to become unfeasible. In this project, a ma... | {
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2501.10453 | Uncovering Bias in Foundation Models: Impact, Testing, Harm, and
Mitigation | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Bias in Foundation Models (FMs) - trained on vast datasets spanning societal and historical knowledge - poses significant challenges for fairness and equity across fields such as healthcare, education, and finance. These biases, rooted in the overrepresentation of stereotypes and societal inequalities in training data,... | {
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2501.10454 | Spatio-Temporal Graph Convolutional Networks: Optimised Temporal
Architecture | [
"cs.LG",
"stat.ML"
] | Spatio-Temporal graph convolutional networks were originally introduced with CNNs as temporal blocks for feature extraction. Since then LSTM temporal blocks have been proposed and shown to have promising results. We propose a novel architecture combining both CNN and LSTM temporal blocks and then provide an empirical c... | {
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2501.10455 | PhyDeformer: High-Quality Non-Rigid Garment Registration with
Physics-Awareness | [
"cs.CV",
"cs.GR"
] | We present PhyDeformer, a new deformation method for high-quality garment mesh registration. It operates in two phases: In the first phase, a garment grading is performed to achieve a coarse 3D alignment between the mesh template and the target mesh, accounting for proportional scaling and fit (e.g. length, size). Then... | {
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2501.10459 | Efficient Traffic Prediction Through Spatio-Temporal Distillation | [
"cs.LG",
"cs.CE"
] | Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations through a graph-based message-passing framework. Although GNNs have shown great promise in handling traffic datasets, their deployment in real... | {
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2501.10461 | A Framework for Mining Collectively-Behaving Bots in MMORPGs | [
"cs.LG",
"cs.AI"
] | In MMORPGs (Massively Multiplayer Online Role-Playing Games), abnormal players (bots) using unauthorized automated programs to carry out pre-defined behaviors systematically and repeatedly are commonly observed. Bots usually engage in these activities to gain in-game money, which they eventually trade for real money ou... | {
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2501.10462 | BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal
Scene Generation | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | With the widespread use of virtual reality applications, 3D scene generation has become a new challenging research frontier. 3D scenes have highly complex structures and need to ensure that the output is dense, coherent, and contains all necessary structures. Many current 3D scene generation methods rely on pre-trained... | {
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2501.10463 | GLow -- A Novel, Flower-Based Simulated Gossip Learning Strategy | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Fully decentralized learning algorithms are still in an early stage of development. Creating modular Gossip Learning strategies is not trivial due to convergence challenges and Byzantine faults intrinsic in systems of decentralized nature. Our contribution provides a novel means to simulate custom Gossip Learning syste... | {
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2501.10464 | Adapting Beyond the Depth Limit: Counter Strategies in Large Imperfect
Information Games | [
"cs.GT",
"cs.AI"
] | We study the problem of adapting to a known sub-rational opponent during online play while remaining robust to rational opponents. We focus on large imperfect-information (zero-sum) games, which makes it impossible to inspect the whole game tree at once and necessitates the use of depth-limited search. However, all exi... | {
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2501.10465 | The Mathematics of Artificial Intelligence | [
"math.OC",
"cs.AI"
] | This overview article highlights the critical role of mathematics in artificial intelligence (AI), emphasizing that mathematics provides tools to better understand and enhance AI systems. Conversely, AI raises new problems and drives the development of new mathematics at the intersection of various fields. This article... | {
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2501.10466 | Improving the Efficiency of Self-Supervised Adversarial Training through
Latent Clustering-Based Selection | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.CV"
] | Compared with standard learning, adversarially robust learning is widely recognized to demand significantly more training examples. Recent works propose the use of self-supervised adversarial training (SSAT) with external or synthetically generated unlabeled data to enhance model robustness. However, SSAT requires a su... | {
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2501.10467 | Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for
AI-Driven Cybersecurity | [
"cs.CR",
"cs.AI",
"cs.CY",
"cs.SE"
] | This paper critically examines the evolving ethical and regulatory challenges posed by the integration of artificial intelligence (AI) in cybersecurity. We trace the historical development of AI regulation, highlighting major milestones from theoretical discussions in the 1940s to the implementation of recent global fr... | {
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2501.10470 | Off-policy Evaluation for Payments at Adyen | [
"cs.LG",
"cs.IR"
] | This paper demonstrates the successful application of Off-Policy Evaluation (OPE) to accelerate recommender system development and optimization at Adyen, a global leader in financial payment processing. Facing the limitations of traditional A/B testing, which proved slow, costly, and often inconclusive, we integrated O... | {
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2501.10471 | Village-Net Clustering: A Rapid approach to Non-linear Unsupervised
Clustering of High-Dimensional Data | [
"cs.LG",
"q-bio.QM",
"stat.ML"
] | Clustering large high-dimensional datasets with diverse variable is essential for extracting high-level latent information from these datasets. Here, we developed an unsupervised clustering algorithm, we call "Village-Net". Village-Net is specifically designed to effectively cluster high-dimension data without priori k... | {
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2501.10474 | Poxel: Voxel Reconstruction for 3D Printing | [
"cs.GR",
"cs.CV"
] | Recent advancements in 3D reconstruction, especially through neural rendering approaches like Neural Radiance Fields (NeRF) and Plenoxel, have led to high-quality 3D visualizations. However, these methods are optimized for digital environments and employ view-dependent color models (RGB) and 2D splatting techniques, wh... | {
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2501.10476 | Revisiting Rogers' Paradox in the Context of Human-AI Interaction | [
"cs.AI",
"cs.LG"
] | Humans learn about the world, and how to act in the world, in many ways: from individually conducting experiments to observing and reproducing others' behavior. Different learning strategies come with different costs and likelihoods of successfully learning more about the world. The choice that any one individual makes... | {
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2501.10479 | Lossless Compression of Vector IDs for Approximate Nearest Neighbor
Search | [
"cs.LG",
"cs.DB",
"cs.IR"
] | Approximate nearest neighbor search for vectors relies on indexes that are most often accessed from RAM. Therefore, storage is the factor limiting the size of the database that can be served from a machine. Lossy vector compression, i.e., embedding quantization, has been applied extensively to reduce the size of indexe... | {
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2501.10481 | Using Domain Knowledge with Deep Learning to Solve Applied Inverse
Problems | [
"cs.LG",
"cond-mat.mtrl-sci",
"cs.CE"
] | Advancements in deep learning have improved the ability to model complex, nonlinear relationships, such as those encountered in complex material inverse problems. However, the effectiveness of these methods often depends on large datasets, which are not always available. In this study, the incorporation of domain-speci... | {
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2501.10482 | Simulation of Random LR Fuzzy Intervals | [
"stat.ML",
"cs.LG",
"cs.LO",
"math.PR",
"stat.CO",
"stat.OT"
] | Random fuzzy variables join the modeling of the impreciseness (due to their ``fuzzy part'') and randomness. Statistical samples of such objects are widely used, and their direct, numerically effective generation is therefore necessary. Usually, these samples consist of triangular or trapezoidal fuzzy numbers. In this p... | {
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2501.10483 | ArxEval: Evaluating Retrieval and Generation in Language Models for
Scientific Literature | [
"cs.CL",
"cs.AI"
] | Language Models [LMs] are now playing an increasingly large role in information generation and synthesis; the representation of scientific knowledge in these systems needs to be highly accurate. A prime challenge is hallucination; that is, generating apparently plausible but actually false information, including invent... | {
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2501.10484 | Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study
of ChatGPT and Claude | [
"cs.CY",
"cs.AI"
] | Recent advances in Large Language Models (LLMs) have enabled human-like responses across various tasks, raising questions about their ethical decision-making capabilities and potential biases. This study investigates protected attributes in LLMs through systematic evaluation of their responses to ethical dilemmas. Usin... | {
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2501.10486 | Enhancing the Reliability in Machine Learning for Gravitational Wave
Parameter Estimation with Attention-Based Models | [
"astro-ph.IM",
"cs.LG",
"gr-qc"
] | We introduce a technique to enhance the reliability of gravitational wave parameter estimation results produced by machine learning. We develop two independent machine learning models based on the Vision Transformer to estimate effective spin and chirp mass from spectrograms of gravitational wave signals from binary bl... | {
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2501.10487 | Tabular-TX: Theme-Explanation Structure-based Table Summarization via
In-Context Learning | [
"cs.CL",
"cs.AI"
] | This paper proposes a Theme-Explanation Structure-based Table Summarization (Tabular-TX) pipeline designed to efficiently process table data. Tabular-TX preprocesses table data by focusing on highlighted cells and then generates summary sentences structured with a Theme Part in the form of adverbial phrases followed by... | {
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2501.10492 | ACCEPT: Diagnostic Forecasting of Battery Degradation Through
Contrastive Learning | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Modeling lithium-ion battery (LIB) degradation offers significant cost savings and enhances the safety and reliability of electric vehicles (EVs) and battery energy storage systems (BESS). Whilst data-driven methods have received great attention for forecasting degradation, they often demonstrate limited generalization... | {
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2501.10496 | Extension of Symmetrized Neural Network Operators with Fractional and
Mixed Activation Functions | [
"stat.ML",
"cs.LG"
] | We propose a novel extension to symmetrized neural network operators by incorporating fractional and mixed activation functions. This study addresses the limitations of existing models in approximating higher-order smooth functions, particularly in complex and high-dimensional spaces. Our framework introduces a fractio... | {
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2501.10499 | Learning More With Less: Sample Efficient Dynamics Learning and
Model-Based RL for Loco-Manipulation | [
"cs.RO"
] | Combining the agility of legged locomotion with the capabilities of manipulation, loco-manipulation platforms have the potential to perform complex tasks in real-world applications. To this end, state-of-the-art quadrupeds with attached manipulators, such as the Boston Dynamics Spot, have emerged to provide a capable a... | {
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} |
2501.10513 | ConfigBot: Adaptive Resource Allocation for Robot Applications in
Dynamic Environments | [
"cs.RO"
] | The growing use of autonomous mobile service robots (AMSRs) in dynamic environments requires flexible management of compute resources to optimize the performance of diverse tasks such as navigation, localization, perception, and so on. Current robot deployments, which oftentimes rely on static configurations (of the OS... | {
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2501.10514 | Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT
Public Bus Transit | [
"cs.LG",
"cs.AI"
] | Bus transit plays a vital role in urban public transportation but often struggles to provide accurate and reliable departure times. This leads to delays, passenger dissatisfaction, and decreased ridership, particularly in transit-dependent areas. A major challenge lies in the discrepancy between actual and scheduled bu... | {
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2501.10523 | Multiclass Queue Scheduling Under Slowdown: An Approximate Dynamic
Programming Approach | [
"math.OC",
"cs.SY",
"eess.SY"
] | In many service systems, especially those in healthcare, customer waiting times can result in increased service requirements. Such service slowdowns can significantly impact system performance. Therefore, it is important to properly account for their impact when designing scheduling policies. Scheduling under wait-depe... | {
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2501.10525 | DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids | [
"cs.SD",
"cs.LG",
"eess.AS",
"eess.SP"
] | The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a `one-size-fits-all' approach, which aims to train a single, monolithic architecture that generalises across different noises ... | {
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2501.10526 | Solving Sparse Finite Element Problems on Neuromorphic Hardware | [
"cs.NE",
"cs.AI",
"cs.LG",
"cs.NA",
"math.NA"
] | We demonstrate that scalable neuromorphic hardware can implement the finite element method, which is a critical numerical method for engineering and scientific discovery. Our approach maps the sparse interactions between neighboring finite elements to small populations of neurons that dynamically update according to th... | {
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2501.10529 | A Tensor Low-Rank Approximation for Value Functions in Multi-Task
Reinforcement Learning | [
"cs.LG"
] | In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order t... | {
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2501.10533 | A Unified Comparative Study with Generalized Conformity Scores for
Multi-Output Conformal Regression | [
"stat.ML",
"cs.LG"
] | Conformal prediction provides a powerful framework for constructing distribution-free prediction regions with finite-sample coverage guarantees. While extensively studied in univariate settings, its extension to multi-output problems presents additional challenges, including complex output dependencies and high computa... | {
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2501.10534 | 4bit-Quantization in Vector-Embedding for RAG | [
"cs.LG",
"cs.AI"
] | Retrieval-augmented generation (RAG) is a promising technique that has shown great potential in addressing some of the limitations of large language models (LLMs). LLMs have two major limitations: they can contain outdated information due to their training data, and they can generate factually inaccurate responses, a p... | {
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2501.10538 | Universality of Benign Overfitting in Binary Linear Classification | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | The practical success of deep learning has led to the discovery of several surprising phenomena. One of these phenomena, that has spurred intense theoretical research, is ``benign overfitting'': deep neural networks seem to generalize well in the over-parametrized regime even though the networks show a perfect fit to n... | {
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2501.10540 | DPERC: Direct Parameter Estimation for Mixed Data | [
"stat.ML",
"cs.LG"
] | The covariance matrix is a foundation in numerous statistical and machine-learning applications such as Principle Component Analysis, Correlation Heatmap, etc. However, missing values within datasets present a formidable obstacle to accurately estimating this matrix. While imputation methods offer one avenue for addres... | {
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2501.10542 | Improved IR-based Bug Localization with Intelligent Relevance Feedback | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Software bugs pose a significant challenge during development and maintenance, and practitioners spend nearly 50% of their time dealing with bugs. Many existing techniques adopt Information Retrieval (IR) to localize a reported bug using textual and semantic relevance between bug reports and source code. However, they ... | {
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2501.10543 | FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for
Prescriptive Process Monitoring | [
"cs.LG",
"cs.AI"
] | We present a novel 5-step framework called Fine-Tuned Offline Reinforcement Learning Augmented Process Sequence Optimization (FORLAPS), which aims to identify optimal execution paths in business processes using reinforcement learning. We implemented this approach on real-life event logs from our case study an energy re... | {
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2501.10546 | Scalable Machine Learning Training Infrastructure for Online Ads
Recommendation and Auction Scoring Modeling at Google | [
"cs.DC",
"cs.AI",
"cs.LG"
] | Large-scale Ads recommendation and auction scoring models at Google scale demand immense computational resources. While specialized hardware like TPUs have improved linear algebra computations, bottlenecks persist in large-scale systems. This paper proposes solutions for three critical challenges that must be addressed... | {
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2501.10547 | HyperCam: Low-Power Onboard Computer Vision for IoT Cameras | [
"cs.CV",
"cs.LG",
"cs.NE",
"eess.IV"
] | We present HyperCam, an energy-efficient image classification pipeline that enables computer vision tasks onboard low-power IoT camera systems. HyperCam leverages hyperdimensional computing to perform training and inference efficiently on low-power microcontrollers. We implement a low-power wireless camera platform usi... | {
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2501.10548 | Diffusion Models in Recommendation Systems: A Survey | [
"cs.IR"
] | Recommender systems remain an essential topic due to its wide application in various domains and the business potential behind them. With the rise of deep learning, common solutions have leveraged neural networks to facilitate collaborative filtering, and some have turned to generative adversarial networks to augment t... | {
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2501.10555 | Towards Data-Centric AI: A Comprehensive Survey of Traditional,
Reinforcement, and Generative Approaches for Tabular Data Transformation | [
"cs.LG",
"cs.AI"
] | Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric AI, improving data quality and representation has become essential for enhancing model performance, particularly in applications centered a... | {
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2501.10557 | MurkySky: Analyzing News Reliability on Bluesky | [
"cs.SI"
] | Bluesky has recently emerged as a lively competitor to Twitter/X for a platform for public discourse and news sharing. Most of the research on Bluesky so far has focused on characterizing its adoption due to migration. There has been less interest on characterizing the properties of Bluesky as a platform for news shari... | {
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2501.10560 | Picachv: Formally Verified Data Use Policy Enforcement for Secure Data
Analytics | [
"cs.CR",
"cs.DB",
"cs.PL"
] | Ensuring the proper use of sensitive data in analytics under complex privacy policies is an increasingly critical challenge. Many existing approaches lack portability, verifiability, and scalability across diverse data processing frameworks. We introduce Picachv, a novel security monitor that automatically enforces dat... | {
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} |
2501.10561 | Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation
via Uncertainty Quantification | [
"cs.RO"
] | Autonomous surgical robots are a promising solution to the increasing demand for surgery amid a shortage of surgeons. Recent work has proposed learning-based approaches for the autonomous manipulation of soft tissue. However, due to variability in tissue geometries and stiffnesses, these methods do not always perform o... | {
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2501.10562 | On the Benefits of Instance Decomposition in Video Prediction Models | [
"cs.CV"
] | Video prediction is a crucial task for intelligent agents such as robots and autonomous vehicles, since it enables them to anticipate and act early on time-critical incidents. State-of-the-art video prediction methods typically model the dynamics of a scene jointly and implicitly, without any explicit decomposition int... | {
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2501.10573 | The Geometry of Tokens in Internal Representations of Large Language
Models | [
"cs.CL",
"cs.LG"
] | We investigate the relationship between the geometry of token embeddings and their role in the next token prediction within transformer models. An important aspect of this connection uses the notion of empirical measure, which encodes the distribution of token point clouds across transformer layers and drives the evolu... | {
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2501.10576 | AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics
of AI | [
"cs.CY",
"cs.AI",
"cs.LG"
] | In this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety... | {
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2501.10579 | AI Technicians: Developing Rapid Occupational Training Methods for a
Competitive AI Workforce | [
"cs.CY",
"cs.AI"
] | The accelerating pace of developments in Artificial Intelligence~(AI) and the increasing role that technology plays in society necessitates substantial changes in the structure of the workforce. Besides scientists and engineers, there is a need for a very large workforce of competent AI technicians (i.e., maintainers, ... | {
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2501.10582 | Adapting Large Language Models for Character-based Augmentative and
Alternative Communication | [
"cs.CL",
"cs.HC"
] | Users of Augmentative and Alternative Communication (AAC) may write letter-by-letter via an interface that uses a character language model. However, most state-of-the-art large pretrained language models predict subword tokens of variable length. We investigate how to practically use such models to make accurate and ef... | {
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2501.10592 | Analytical Models of Frequency and Voltage in Large-Scale All-Inverter
Power Systems | [
"eess.SY",
"cs.SY"
] | Low-order frequency response models for power systems have a decades-long history in optimization and control problems such as unit commitment, economic dispatch, and wide-area control. With a few exceptions, these models are built upon the Newtonian mechanics of synchronous generators, assuming that the frequency dyna... | {
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2501.10593 | ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference
and Assistance | [
"cs.AI",
"cs.LG"
] | Autonomous agents' interactions with humans are increasingly focused on adapting to their changing preferences in order to improve assistance in real-world tasks. Effective agents must learn to accurately infer human goals, which are often hidden, to collaborate well. However, existing Multi-Agent Reinforcement Learnin... | {
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2501.10594 | Accurate and thermodynamically consistent hydrogen equation of state for
planetary modeling with flow matching | [
"astro-ph.EP",
"cond-mat.mtrl-sci",
"cs.LG",
"physics.comp-ph"
] | Accurate determination of the equation of state of dense hydrogen is essential for understanding gas giants. Currently, there is still no consensus on methods for calculating its entropy, which play a fundamental role and can result in qualitatively different predictions for Jupiter's interior. Here, we investigate var... | {
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2501.10598 | Solving Finite-Horizon MDPs via Low-Rank Tensors | [
"cs.LG"
] | We study the problem of learning optimal policies in finite-horizon Markov Decision Processes (MDPs) using low-rank reinforcement learning (RL) methods. In finite-horizon MDPs, the policies, and therefore the value functions (VFs) are not stationary. This aggravates the challenges of high-dimensional MDPs, as they suff... | {
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2501.10600 | High Resolution Tree Height Mapping of the Amazon Forest using Planet
NICFI Images and LiDAR-Informed U-Net Model | [
"cs.CV"
] | Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the mean tree canopy height in the Amazon forest from Planet NICFI im... | {
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2501.10604 | When language and vision meet road safety: leveraging multimodal large
language models for video-based traffic accident analysis | [
"cs.CV",
"cs.AI",
"cs.CL"
] | The increasing availability of traffic videos functioning on a 24/7/365 time scale has the great potential of increasing the spatio-temporal coverage of traffic accidents, which will help improve traffic safety. However, analyzing footage from hundreds, if not thousands, of traffic cameras in a 24/7/365 working protoco... | {
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} |
2501.10605 | Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement
Learning | [
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | We present Wasserstein Adaptive Value Estimation for Actor-Critic (WAVE), an approach to enhance stability in deep reinforcement learning through adaptive Wasserstein regularization. Our method addresses the inherent instability of actor-critic algorithms by incorporating an adaptively weighted Wasserstein regularizati... | {
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2501.10606 | Differentiable Adversarial Attacks for Marked Temporal Point Processes | [
"cs.LG",
"cs.CR",
"stat.ML"
] | Marked temporal point processes (MTPPs) have been shown to be extremely effective in modeling continuous time event sequences (CTESs). In this work, we present adversarial attacks designed specifically for MTPP models. A key criterion for a good adversarial attack is its imperceptibility. For objects such as images or ... | {
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2501.10607 | On the Optimality of Random Partial Sphere Coverings in High Dimensions | [
"math.MG",
"cs.IT",
"math.FA",
"math.IT"
] | Given $N$ geodesic caps on the normalized unit sphere in $\mathbb{R}^d$, and whose total surface area sums to one, what is the maximal surface area their union can cover? We show that when these caps have equal surface area, as both the dimension $d$ and the number of caps $N$ tend to infinity, the maximum proportion c... | {
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2501.10609 | Universal Discrete Filtering with Lookahead or Delay | [
"eess.SP",
"cs.IT",
"math.IT"
] | We consider the universal discrete filtering problem, where an input sequence generated by an unknown source passes through a discrete memoryless channel, and the goal is to estimate its components based on the output sequence with limited lookahead or delay. We propose and establish the universality of a family of sch... | {
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2501.10610 | Automated Water Irrigation System | [
"eess.SY",
"cs.SY"
] | This paper presents the design and implementation of an automated water irrigation system aimed at optimizing plant care through precision moisture monitoring and controlled water delivery. The system uses a capacitive soil moisture sensor, an ADC (analog-to-digital converter), and a relay-driven water pump to ensure p... | {
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2501.10615 | Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation | [
"cs.CV"
] | Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases. While deep learning techniques have significantly improved aorta segmentation, they remain challenging due to the intricate multiscale structure and the complexity of the surrounding tissues. This paper presen... | {
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.10617 | Mutual Regression Distance | [
"cs.LG",
"stat.ML"
] | The maximum mean discrepancy and Wasserstein distance are popular distance measures between distributions and play important roles in many machine learning problems such as metric learning, generative modeling, domain adaption, and clustering. However, since they are functions of pair-wise distances between data points... | {
"Other": 0,
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"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.10621 | RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating
Proximal Hyperspectral Leaf Sensing | [
"cs.RO",
"cs.CV"
] | Driven by the need to address labor shortages and meet the demands of a rapidly growing population, robotic automation has become a critical component in precision agriculture. Leaf-level hyperspectral spectroscopy is shown to be a powerful tool for phenotyping, monitoring crop health, identifying essential nutrients w... | {
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.10625 | Assessing Markov Property in Driving Behaviors: Insights from
Statistical Tests | [
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ME"
] | The Markov property serves as a foundational assumption in most existing work on vehicle driving behavior, positing that future states depend solely on the current state, not the series of preceding states. This study validates the Markov properties of vehicle trajectories for both Autonomous Vehicles (AVs) and Human-d... | {
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.10627 | AI/ML Based Detection and Categorization of Covert Communication in IPv6
Network | [
"cs.CR",
"cs.AI",
"cs.LG",
"cs.NI"
] | The flexibility and complexity of IPv6 extension headers allow attackers to create covert channels or bypass security mechanisms, leading to potential data breaches or system compromises. The mature development of machine learning has become the primary detection technology option used to mitigate covert communication ... | {
"Other": 1,
"cs.AI": 1,
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"cs.CR": 1,
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"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.10629 | Prompt-Enabled Large AI Models for CSI Feedback | [
"cs.IT",
"eess.SP",
"math.IT"
] | Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy through novel architectures, the underlying mechanisms of AI-based CSI feedback remain unclear. This study investigates these mechanisms by an... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
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"cs.CV": 0,
"cs.CY": 0,
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"cs.IR": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
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} |
2501.10630 | Exploring the Potential of Large Language Models for Massive MIMO CSI
Feedback | [
"cs.IT",
"eess.SP",
"math.IT"
] | Large language models (LLMs) have achieved remarkable success across a wide range of tasks, particularly in natural language processing and computer vision. This success naturally raises an intriguing yet unexplored question: Can LLMs be harnessed to tackle channel state information (CSI) compression and feedback in ma... | {
"Other": 0,
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"cs.IR": 0,
"cs.IT": 1,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.10636 | Efficient and Safe Trajectory Planning for Autonomous Agricultural
Vehicle Headland Turning in Cluttered Orchard Environments | [
"cs.RO"
] | Autonomous agricultural vehicles (AAVs), including field robots and autonomous tractors, are becoming essential in modern farming by improving efficiency and reducing labor costs. A critical task in AAV operations is headland turning between crop rows. This task is challenging in orchards with limited headland space, i... | {
"Other": 0,
"cs.AI": 0,
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"cs.CL": 0,
"cs.CR": 0,
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"cs.IR": 0,
"cs.IT": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.10637 | HOPS: High-order Polynomials with Self-supervised Dimension Reduction
for Load Forecasting | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Load forecasting is a fundamental task in smart grid. Many techniques have been applied to developing load forecasting models. Due to the challenges such as the Curse of Dimensionality, overfitting, and limited computing resources, multivariate higher-order polynomial models have received limited attention in load fore... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
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"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.10638 | A Resource-Efficient Training Framework for Remote Sensing Text--Image
Retrieval | [
"cs.CV",
"cs.IR"
] | Remote sensing text--image retrieval (RSTIR) aims to retrieve the matched remote sensing (RS) images from the database according to the descriptive text. Recently, the rapid development of large visual-language pre-training models provides new insights for RSTIR. Nevertheless, as the complexity of models grows in RSTIR... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
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"cs.HC": 0,
"cs.IR": 1,
"cs.IT": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.10639 | Latent-space adversarial training with post-aware calibration for
defending large language models against jailbreak attacks | [
"cs.CR",
"cs.CL"
] | Ensuring safety alignment has become a critical requirement for large language models (LLMs), particularly given their widespread deployment in real-world applications. However, LLMs remain susceptible to jailbreak attacks, which exploit system vulnerabilities to bypass safety measures and generate harmful outputs. Alt... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
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"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
} |
2501.10640 | ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning | [
"cs.CV",
"cs.DC"
] | Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have dominated the field of Computer Vision (CV). Graph Neural Networks (GNN) have performed remarkably well across diverse domains because they can represent complex relationships via unstructured graphs. However, the applicability of GNNs for visual ta... | {
"Other": 1,
"cs.AI": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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