id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.05638 | ELMTEX: Fine-Tuning Large Language Models for Structured Clinical
Information Extraction. A Case Study on Clinical Reports | cs.CL cs.AI | Europe's healthcare systems require enhanced interoperability and
digitalization, driving a demand for innovative solutions to process legacy
clinical data. This paper presents the results of our project, which aims to
leverage Large Language Models (LLMs) to extract structured information from
unstructured clinical ... |
2502.05640 | ETHEREAL: Energy-efficient and High-throughput Inference using
Compressed Tsetlin Machine | cs.LG | The Tsetlin Machine (TM) is a novel alternative to deep neural networks
(DNNs). Unlike DNNs, which rely on multi-path arithmetic operations, a TM
learns propositional logic patterns from data literals using Tsetlin automata.
This fundamental shift from arithmetic to logic underpinning makes TM suitable
for empowering... |
2502.05641 | Generating Physically Realistic and Directable Human Motions from
Multi-Modal Inputs | cs.RO cs.AI | This work focuses on generating realistic, physically-based human behaviors
from multi-modal inputs, which may only partially specify the desired motion.
For example, the input may come from a VR controller providing arm motion and
body velocity, partial key-point animation, computer vision applied to videos,
or even... |
2502.05643 | Design of optimal repetitive control based on EID estimator with
adaptive periodic event-triggered mechanism for linear systems subjected to
exogenous disturbances | eess.SY cs.SY | The periodic signal tracking and the unknown disturbance rejection under
limited communication resources are main important issues in many physical
systems and practical applications. The control of such systems has some
challenges such as time-varying delay, unknown external disturbances, structure
uncertainty, and ... |
2502.05649 | Gender Bias in Instruction-Guided Speech Synthesis Models | cs.CL cs.LG eess.AS | Recent advancements in controllable expressive speech synthesis, especially
in text-to-speech (TTS) models, have allowed for the generation of speech with
specific styles guided by textual descriptions, known as style prompts. While
this development enhances the flexibility and naturalness of synthesized
speech, ther... |
2502.05650 | Incongruence Identification in Eyewitness Testimony | cs.CL | Incongruence detection in eyewitness narratives is critical for understanding
the reliability of testimonies, yet traditional approaches often fail to
address the nuanced inconsistencies inherent in such accounts. In this paper,
we introduce a novel task of incongruence detection in eyewitness testimonies.
Given a pa... |
2502.05651 | KMI: A Dataset of Korean Motivational Interviewing Dialogues for
Psychotherapy | cs.CL cs.AI | The increasing demand for mental health services has led to the rise of
AI-driven mental health chatbots, though challenges related to privacy, data
collection, and expertise persist. Motivational Interviewing (MI) is gaining
attention as a theoretical basis for boosting expertise in the development of
these chatbots... |
2502.05652 | An inpainting approach to manipulate asymmetry in pre-operative breast
images | cs.CV | One of the most frequent modalities of breast cancer treatment is surgery.
Breast surgery can cause visual alterations to the breasts, due to scars and
asymmetries. To enable an informed choice of treatment, the patient must be
adequately informed of the aesthetic outcomes of each treatment plan. In this
work, we pro... |
2502.05654 | Evaluating the Techno-Economic Viability of a Solar PV-Wind Turbine
Hybrid System with Battery Storage for an Electric Vehicle Charging Station
in Khobar, Saudi Arabia | eess.SY cs.SY | The main aim of this investigation is to replicate and enhance a sustainable
hybrid energy structure that combines solar photovoltaic, wind turbines,
battery storage. The study employs the Homer simulation model to evaluate the
scaling, cost, and control strategy of this hybrid power system. This work
primarily focus... |
2502.05656 | Flowing Through Layers: A Continuous Dynamical Systems Perspective on
Transformers | cs.LG math.DS | We show that the standard discrete update rule of transformer layers can be
naturally interpreted as a forward Euler discretization of a continuous
dynamical system. Our Transformer Flow Approximation Theorem demonstrates that,
under standard Lipschitz continuity assumptions, token representations converge
uniformly ... |
2502.05660 | Evaluating Vision-Language Models for Emotion Recognition | cs.CV cs.CL | Large Vision-Language Models (VLMs) have achieved unprecedented success in
several objective multimodal reasoning tasks. However, to further enhance their
capabilities of empathetic and effective communication with humans, improving
how VLMs process and understand emotions is crucial. Despite significant
research att... |
2502.05664 | CODESIM: Multi-Agent Code Generation and Problem Solving through
Simulation-Driven Planning and Debugging | cs.CL cs.AI | Large Language Models (LLMs) have made significant strides in code generation
and problem solving. Current approaches employ external tool-based iterative
debuggers that use compiler or other tool-based runtime feedback to refine
coarse programs generated by various methods. However, the effectiveness of
these approa... |
2502.05667 | Online Controller Synthesis for Robot Collision Avoidance: A Case Study | cs.RO cs.SY eess.SY | The inherent uncertainty of dynamic environments poses significant challenges
for modeling robot behavior, particularly in tasks such as collision avoidance.
This paper presents an online controller synthesis framework tailored for
robots equipped with deep learning-based perception components, with a focus on
addres... |
2502.05668 | The late-stage training dynamics of (stochastic) subgradient descent on
homogeneous neural networks | cs.LG cs.NE math.OC stat.ML | We analyze the implicit bias of constant step stochastic subgradient descent
(SGD). We consider the setting of binary classification with homogeneous neural
networks - a large class of deep neural networks with ReLU-type activation
functions such as MLPs and CNNs without biases. We interpret the dynamics of
normalize... |
2502.05669 | Rigid Body Adversarial Attacks | cs.CV cs.GR | Due to their performance and simplicity, rigid body simulators are often used
in applications where the objects of interest can considered very stiff.
However, no material has infinite stiffness, which means there are potentially
cases where the non-zero compliance of the seemingly rigid object can cause a
significan... |
2502.05670 | Language Models Largely Exhibit Human-like Constituent Ordering
Preferences | cs.CL cs.AI | Though English sentences are typically inflexible vis-\`a-vis word order,
constituents often show far more variability in ordering. One prominent theory
presents the notion that constituent ordering is directly correlated with
constituent weight: a measure of the constituent's length or complexity. Such
theories are ... |
2502.05672 | On the Convergence and Stability of Upside-Down Reinforcement Learning,
Goal-Conditioned Supervised Learning, and Online Decision Transformers | stat.ML cs.AI cs.LG cs.NE cs.SY eess.SY | This article provides a rigorous analysis of convergence and stability of
Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised
Learning and Online Decision Transformers. These algorithms performed
competitively across various benchmarks, from games to robotic tasks, but their
theoretical understan... |
2502.05673 | The Evolution of Dataset Distillation: Toward Scalable and Generalizable
Solutions | cs.CV | Dataset distillation, which condenses large-scale datasets into compact
synthetic representations, has emerged as a critical solution for training
modern deep learning models efficiently. While prior surveys focus on
developments before 2023, this work comprehensively reviews recent advances,
emphasizing scalability ... |
2502.05675 | Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning | cs.CL | Reasoning abilities of LLMs have been a key focus in recent years. One
challenging reasoning domain with interesting nuances is legal reasoning, which
requires careful application of rules, and precedents while balancing deductive
and analogical reasoning, and conflicts between rules. Although there have been
a few w... |
2502.05676 | Generalized Venn and Venn-Abers Calibration with Applications in
Conformal Prediction | stat.ML cs.LG stat.ME | Ensuring model calibration is critical for reliable predictions, yet popular
distribution-free methods, such as histogram binning and isotonic regression,
provide only asymptotic guarantees. We introduce a unified framework for Venn
and Venn-Abers calibration, generalizing Vovk's binary classification approach
to arb... |
2502.05677 | Surprise Potential as a Measure of Interactivity in Driving Scenarios | cs.RO cs.LG | Validating the safety and performance of an autonomous vehicle (AV) requires
benchmarking on real-world driving logs. However, typical driving logs contain
mostly uneventful scenarios with minimal interactions between road users.
Identifying interactive scenarios in real-world driving logs enables the
curation of dat... |
2502.05679 | Federated Learning with Reservoir State Analysis for Time Series Anomaly
Detection | cs.LG | With a growing data privacy concern, federated learning has emerged as a
promising framework to train machine learning models without sharing locally
distributed data. In federated learning, local model training by multiple
clients and model integration by a server are repeated only through model
parameter sharing. M... |
2502.05684 | Machine Unlearning via Information Theoretic Regularization | cs.LG cs.AI cs.IT math.IT stat.ML | How can we effectively remove or "unlearn" undesirable information, such as
specific features or individual data points, from a learning outcome while
minimizing utility loss and ensuring rigorous guarantees? We introduce a
mathematical framework based on information-theoretic regularization to address
both feature a... |
2502.05685 | Mobile Application Threats and Security | cs.CR cs.AI | The movement to mobile computing solutions provides flexibility to different
users whether it is a business user, a student, or even providing entertainment
to children and adults of all ages. Due to these emerging technologies mobile
users are unable to safeguard private information in a very effective way and
cyber... |
2502.05690 | Managing Geological Uncertainty in Critical Mineral Supply Chains: A
POMDP Approach with Application to U.S. Lithium Resources | cs.AI econ.GN q-fin.EC | The world is entering an unprecedented period of critical mineral demand,
driven by the global transition to renewable energy technologies and electric
vehicles. This transition presents unique challenges in mineral resource
development, particularly due to geological uncertainty-a key characteristic
that traditional... |
2502.05691 | Consistent sampling of Paley-Wiener functions on graphons | eess.SP cs.IT math.CO math.IT | We study sampling methods for Paley-Wiener functions on graphons, thereby
adapting and generalizing methods initially developed for graphs to the graphon
setting. We then derive conditions under which such a sampling estimate is
consistent with graphon convergence.
|
2502.05692 | Variational integrators for optimal control of foldable drones | math.OC cs.NA cs.SY eess.SY math.NA | Numerical methods that preserves geometric invariants of the system such as
energy, momentum and symplectic form, are called geometric integrators. These
include variational integrators as an important subclass of geometric
integrators. The general idea for those variational integrators is to
discretize Hamilton's pr... |
2502.05693 | Vertical Vibratory Transport of Grasped Parts Using Impacts | cs.RO | In this paper, we use impact-induced acceleration in conjunction with
periodic stick-slip to successfully and quickly transport parts vertically
against gravity. We show analytically that vertical vibratory transport is more
difficult than its horizontal counterpart, and provide guidelines for achieving
optimal verti... |
2502.05694 | Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study
of Gemini, LLaMA and ChatGPT | cs.CL cs.AI cs.LG | This study investigates the performance of various large language models
(LLMs) on zero-shot end-to-end relation extraction (RE) in Chinese, a task that
integrates entity recognition and relation extraction without requiring
annotated data. While LLMs show promise for RE, most prior work focuses on
English or assumes... |
2502.05695 | Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models
for Wireless Networks | cs.MM cs.AI cs.CV cs.LG eess.IV | This paper proposes a novel framework for real-time adaptive-bitrate video
streaming by integrating latent diffusion models (LDMs) within the FFmpeg
techniques. This solution addresses the challenges of high bandwidth usage,
storage inefficiencies, and quality of experience (QoE) degradation associated
with tradition... |
2502.05696 | Implicit Physics-aware Policy for Dynamic Manipulation of Rigid Objects
via Soft Body Tools | cs.RO | Recent advancements in robot tool use have unlocked their usage for novel
tasks, yet the predominant focus is on rigid-body tools, while the
investigation of soft-body tools and their dynamic interaction with rigid
bodies remains unexplored. This paper takes a pioneering step towards dynamic
one-shot soft tool use fo... |
2502.05699 | Context information can be more important than reasoning for time series
forecasting with a large language model | cs.LG cs.AI | With the evolution of large language models (LLMs), there is growing interest
in leveraging LLMs for time series tasks. In this paper, we explore the
characteristics of LLMs for time series forecasting by considering various
existing and proposed prompting techniques. Forecasting for both short and long
time series w... |
2502.05701 | TOKON: TOKenization-Optimized Normalization for time series analysis
with a large language model | cs.LG | While large language models have rapidly evolved towards general artificial
intelligence, their versatility in analyzing time series data remains limited.
To address this limitation, we propose a novel normalization technique that
considers the inherent nature of tokenization. The proposed
Tokenization-Optimized Norm... |
2502.05702 | Graph Neural Networks for Efficient AC Power Flow Prediction in Power
Grids | eess.SY cs.SY | This paper proposes a novel approach using Graph Neural Networks (GNNs) to
solve the AC Power Flow problem in power grids. AC OPF is essential for
minimizing generation costs while meeting the operational constraints of the
grid. Traditional solvers struggle with scalability, especially in large
systems with renewabl... |
2502.05704 | Rethinking Word Similarity: Semantic Similarity through Classification
Confusion | cs.CL cs.AI | Word similarity has many applications to social science and cultural
analytics tasks like measuring meaning change over time and making sense of
contested terms. Yet traditional similarity methods based on cosine similarity
between word embeddings cannot capture the context-dependent, asymmetrical,
polysemous nature ... |
2502.05706 | TD(0) Learning converges for Polynomial mixing and non-linear functions | stat.ML cs.LG | Theoretical work on Temporal Difference (TD) learning has provided
finite-sample and high-probability guarantees for data generated from Markov
chains. However, these bounds typically require linear function approximation,
instance-dependent step sizes, algorithmic modifications, and restrictive
mixing rates. We pres... |
2502.05708 | GWRF: A Generalizable Wireless Radiance Field for Wireless Signal
Propagation Modeling | cs.NI cs.LG | We present Generalizable Wireless Radiance Fields (GWRF), a framework for
modeling wireless signal propagation at arbitrary 3D transmitter and receiver
positions. Unlike previous methods that adapt vanilla Neural Radiance Fields
(NeRF) from the optical to the wireless signal domain, requiring extensive
per-scene trai... |
2502.05709 | Flow-based Conformal Prediction for Multi-dimensional Time Series | cs.LG stat.ML | Conformal prediction for time series presents two key challenges: (1)
leveraging sequential correlations in features and non-conformity scores and
(2) handling multi-dimensional outcomes. We propose a novel conformal
prediction method to address these two key challenges by integrating
Transformer and Normalizing Flow... |
2502.05710 | SSDD-GAN: Single-Step Denoising Diffusion GAN for Cochlear Implant
Surgical Scene Completion | cs.CV | Recent deep learning-based image completion methods, including both
inpainting and outpainting, have demonstrated promising results in restoring
corrupted images by effectively filling various missing regions. Among these,
Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic
Models (DDPMs) hav... |
2502.05713 | 4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised
Disease Progression Modelling of Idiopathic Pulmonary Fibrosis | eess.IV cs.AI cs.CV cs.LG | Understanding the progression trajectories of diseases is crucial for early
diagnosis and effective treatment planning. This is especially vital for
life-threatening conditions such as Idiopathic Pulmonary Fibrosis (IPF), a
chronic, progressive lung disease with a prognosis comparable to many cancers.
Computed tomogr... |
2502.05714 | Proving the Coding Interview: A Benchmark for Formally Verified Code
Generation | cs.SE cs.AI cs.LG cs.LO | We introduce the Formally Verified Automated Programming Progress Standards,
or FVAPPS, a benchmark of 4715 samples for writing programs and proving their
correctness, the largest formal verification benchmark, including 1083 curated
and quality controlled samples. Previously, APPS provided a benchmark and
dataset fo... |
2502.05718 | Using agent-based models and EXplainable Artificial Intelligence (XAI)
to simulate social behaviors and policy intervention scenarios: A case study
of private well users in Ireland | cs.CY cs.LG | Around 50 percent of Irelands rural population relies on unregulated private
wells vulnerable to agricultural runoff and untreated wastewater. High national
rates of Shiga toxin-producing Escherichia coli (STEC) and other waterborne
illnesses have been linked to well water exposure. Periodic well testing is
essential... |
2502.05719 | Extended Histogram-based Outlier Score (EHBOS) | cs.LG cs.AI stat.ML | Histogram-Based Outlier Score (HBOS) is a widely used outlier or anomaly
detection method known for its computational efficiency and simplicity.
However, its assumption of feature independence limits its ability to detect
anomalies in datasets where interactions between features are critical. In this
paper, we propos... |
2502.05720 | Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented
One-Max-Search | cs.DS cs.AI | One-max search is a classic problem in online decision-making, in which a
trader acts on a sequence of revealed prices and accepts one of them
irrevocably to maximise its profit. The problem has been studied both in
probabilistic and in worst-case settings, notably through competitive analysis,
and more recently in l... |
2502.05722 | Explainable and Class-Revealing Signal Feature Extraction via Scattering
Transform and Constrained Zeroth-Order Optimization | cs.LG eess.SP math.OC stat.ML | We propose a new method to extract discriminant and explainable features from
a particular machine learning model, i.e., a combination of the scattering
transform and the multiclass logistic regression. Although this model is
well-known for its ability to learn various signal classes with high
classification rate, it... |
2502.05724 | Rethinking Link Prediction for Directed Graphs | cs.LG cs.AI | Link prediction for directed graphs is a crucial task with diverse real-world
applications. Recent advances in embedding methods and Graph Neural Networks
(GNNs) have shown promising improvements. However, these methods often lack a
thorough analysis of embedding expressiveness and suffer from ineffective
benchmarks ... |
2502.05725 | Predictive Coresets | stat.CO cs.LG | Modern data analysis often involves massive datasets with hundreds of
thousands of observations, making traditional inference algorithms
computationally prohibitive. Coresets are selection methods designed to choose
a smaller subset of observations while maintaining similar learning
performance. Conventional coreset ... |
2502.05726 | Improving Environment Novelty Quantification for Effective Unsupervised
Environment Design | cs.LG stat.ML | Unsupervised Environment Design (UED) formalizes the problem of autocurricula
through interactive training between a teacher agent and a student agent. The
teacher generates new training environments with high learning potential,
curating an adaptive curriculum that strengthens the student's ability to
handle unseen ... |
2502.05727 | Impact of Data Poisoning Attacks on Feasibility and Optimality of Neural
Power System Optimizers | cs.LG | The increased integration of clean yet stochastic energy resources and the
growing number of extreme weather events are narrowing the decision-making
window of power grid operators. This time constraint is fueling a plethora of
research on Machine Learning-, or ML-, based optimization proxies. While
finding a fast so... |
2502.05728 | Hierarchical Equivariant Policy via Frame Transfer | cs.RO | Recent advances in hierarchical policy learning highlight the advantages of
decomposing systems into high-level and low-level agents, enabling efficient
long-horizon reasoning and precise fine-grained control. However, the interface
between these hierarchy levels remains underexplored, and existing hierarchical
metho... |
2502.05729 | BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting | cs.CL | This paper introduces BnTTS (Bangla Text-To-Speech), the first framework for
Bangla speaker adaptation-based TTS, designed to bridge the gap in Bangla
speech synthesis using minimal training data. Building upon the XTTS
architecture, our approach integrates Bangla into a multilingual TTS pipeline,
with modifications ... |
2502.05735 | Towards Autonomous Experimentation: Bayesian Optimization over Problem
Formulation Space for Accelerated Alloy Development | eess.SY cs.CE cs.LG cs.SY math.OC stat.ML | Accelerated discovery in materials science demands autonomous systems capable
of dynamically formulating and solving design problems. In this work, we
introduce a novel framework that leverages Bayesian optimization over a problem
formulation space to identify optimal design formulations in line with
decision-maker p... |
2502.05738 | Performance Analysis of Traditional VQA Models Under Limited
Computational Resources | cs.CV | In real-world applications where computational resources are limited,
effectively integrating visual and textual information for Visual Question
Answering (VQA) presents significant challenges. This paper investigates the
performance of traditional models under computational constraints, focusing on
enhancing VQA per... |
2502.05739 | Mitigating Sensitive Information Leakage in LLMs4Code through Machine
Unlearning | cs.CR cs.AI cs.SE | Large Language Models for Code (LLMs4Code) excel at code generation tasks,
yielding promise to release developers from huge software development burdens.
Nonetheless, these models have been shown to suffer from the significant
privacy risks due to the potential leakage of sensitive information embedded
during trainin... |
2502.05740 | RECOVER: Designing a Large Language Model-based Remote Patient
Monitoring System for Postoperative Gastrointestinal Cancer Care | cs.HC cs.AI | Cancer surgery is a key treatment for gastrointestinal (GI) cancers, a group
of cancers that account for more than 35% of cancer-related deaths worldwide,
but postoperative complications are unpredictable and can be life-threatening.
In this paper, we investigate how recent advancements in large language models
(LLMs... |
2502.05741 | Linear Attention Modeling for Learned Image Compression | cs.CV | Recent years, learned image compression has made tremendous progress to
achieve impressive coding efficiency. Its coding gain mainly comes from
non-linear neural network-based transform and learnable entropy modeling.
However, most of recent focuses have been solely on a strong backbone, and few
studies consider the ... |
2502.05742 | An Evolutionary Game With the Game Transitions Based on the Markov
Process | cs.SI physics.soc-ph | The psychology of the individual is continuously changing in nature, which
has a significant influence on the evolutionary dynamics of populations. To
study the influence of the continuously changing psychology of individuals on
the behavior of populations, in this paper, we consider the game transitions of
individua... |
2502.05743 | Understanding Representation Dynamics of Diffusion Models via
Low-Dimensional Modeling | cs.LG cs.CV | This work addresses the critical question of why and when diffusion models,
despite being designed for generative tasks, can excel at learning high-quality
representations in a self-supervised manner. To address this, we develop a
mathematical framework based on a low-dimensional data model and posterior
estimation, ... |
2502.05744 | DISCD: Distributed Lossy Semantic Communication for Logical Deduction of
Hypothesis | cs.IT math.IT | In this paper, we address hypothesis testing in a distributed network of
nodes, where each node has only partial information about the State of the
World (SotW) and is tasked with determining which hypothesis, among a given
set, is most supported by the data available within the node. However, due to
each node's limi... |
2502.05749 | UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal
Control | cs.CV cs.AI cs.SY eess.SY | Recent advances in diffusion bridge models leverage Doob's $h$-transform to
establish fixed endpoints between distributions, demonstrating promising
results in image translation and restoration tasks. However, these approaches
frequently produce blurred or excessively smoothed image details and lack a
comprehensive t... |
2502.05752 | PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based
Implicit Neural Map | cs.RO cs.CV cs.GR | Robots require high-fidelity reconstructions of their environment for
effective operation. Such scene representations should be both, geometrically
accurate and photorealistic to support downstream tasks. While this can be
achieved by building distance fields from range sensors and radiance fields
from cameras, the s... |
2502.05755 | Filter, Obstruct and Dilute: Defending Against Backdoor Attacks on
Semi-Supervised Learning | cs.LG | Recent studies have verified that semi-supervised learning (SSL) is
vulnerable to data poisoning backdoor attacks. Even a tiny fraction of
contaminated training data is sufficient for adversaries to manipulate up to
90\% of the test outputs in existing SSL methods. Given the emerging threat of
backdoor attacks design... |
2502.05756 | Exploring Visual Embedding Spaces Induced by Vision Transformers for
Online Auto Parts Marketplaces | cs.CV cs.LG | This study examines the capabilities of the Vision Transformer (ViT) model in
generating visual embeddings for images of auto parts sourced from online
marketplaces, such as Craigslist and OfferUp. By focusing exclusively on
single-modality data, the analysis evaluates ViT's potential for detecting
patterns indicativ... |
2502.05759 | Reinforced Lifelong Editing for Language Models | cs.CL | Large language models (LLMs) acquire information from pre-training corpora,
but their stored knowledge can become inaccurate or outdated over time. Model
editing addresses this challenge by modifying model parameters without
retraining, and prevalent approaches leverage hypernetworks to generate these
parameter updat... |
2502.05761 | 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised
Anomaly | cs.CV | Industrial anomaly detection achieves progress thanks to datasets such as
MVTec-AD and VisA. However, they suffer from limitations in terms of the number
of defect samples, types of defects, and availability of real-world scenes.
These constraints inhibit researchers from further exploring the performance of
industri... |
2502.05765 | Privacy-Preserving Dataset Combination | cs.LG cs.CR cs.CY | Access to diverse, high-quality datasets is crucial for machine learning
model performance, yet data sharing remains limited by privacy concerns and
competitive interests, particularly in regulated domains like healthcare. This
dynamic especially disadvantages smaller organizations that lack resources to
purchase dat... |
2502.05768 | Cooperative Optimization of Grid-Edge Cyber and Physical Resources for
Resilient Power System Operation | eess.SY cs.SY | The cooperative operation of grid-edge power and energy resources is crucial
to improving the resilience of power systems during contingencies. However,
given the complex cyber-physical nature of power grids, it is hard to respond
timely with limited costs for deploying additional cyber and/or phyiscal
resources, suc... |
2502.05769 | Digital Twin Buildings: 3D Modeling, GIS Integration, and Visual
Descriptions Using Gaussian Splatting, ChatGPT/Deepseek, and Google Maps
Platform | cs.CV | Urban digital twins are virtual replicas of cities that use multi-source data
and data analytics to optimize urban planning, infrastructure management, and
decision-making. Towards this, we propose a framework focused on the
single-building scale. By connecting to cloud mapping platforms such as Google
Map Platforms ... |
2502.05772 | Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language
Model Guardrails | cs.CV cs.AI | Vision Large Language Models (VLLMs) integrate visual data processing,
expanding their real-world applications, but also increasing the risk of
generating unsafe responses. In response, leading companies have implemented
Multi-Layered safety defenses, including alignment training, safety system
prompts, and content m... |
2502.05773 | PIPA: Preference Alignment as Prior-Informed Statistical Estimation | cs.LG cs.AI stat.ML | Offline preference alignment for language models such as Direct Preference
Optimization (DPO) is favored for its effectiveness and simplicity, eliminating
the need for costly reinforcement learning. Various offline algorithms have
been developed for different data settings, yet they lack a unified
understanding.
In... |
2502.05775 | Implicit Communication of Contextual Information in Human-Robot
Collaboration | cs.RO cs.HC | Implicit communication is crucial in human-robot collaboration (HRC), where
contextual information, such as intentions, is conveyed as implicatures,
forming a natural part of human interaction. However, enabling robots to
appropriately use implicit communication in cooperative tasks remains
challenging. My research a... |
2502.05776 | Dynamic Pricing in the Linear Valuation Model using Shape Constraints | stat.ML cs.LG | We propose a shape-constrained approach to dynamic pricing for censored data
in the linear valuation model that eliminates the need for tuning parameters
commonly required in existing methods. Previous works have addressed the
challenge of unknown market noise distribution F using strategies ranging from
kernel metho... |
2502.05777 | Predictive Crash Analytics for Traffic Safety using Deep Learning | cs.LG cs.AI | Traditional automated crash analysis systems heavily rely on static
statistical models and historical data, requiring significant manual
interpretation and lacking real-time predictive capabilities. This research
presents an innovative approach to traffic safety analysis through the
integration of ensemble learning m... |
2502.05779 | A 3D Multimodal Feature for Infrastructure Anomaly Detection | cs.CV | Ageing structures require periodic inspections to identify structural
defects. Previous work has used geometric distortions to locate cracks in
synthetic masonry bridge point clouds but has struggled to detect small cracks.
To address this limitation, this study proposes a novel 3D multimodal feature,
3DMulti-FPFHI, ... |
2502.05780 | GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial
Latent Generation | cs.LG | Despite graph neural networks' (GNNs) great success in modelling
graph-structured data, out-of-distribution (OOD) test instances still pose a
great challenge for current GNNs. One of the most effective techniques to
detect OOD nodes is to expose the detector model with an additional OOD
node-set, yet the extra OOD in... |
2502.05783 | WatchGuardian: Enabling User-Defined Personalized Just-in-Time
Intervention on Smartwatch | cs.HC cs.AI cs.LG | While just-in-time interventions (JITIs) have effectively targeted common
health behaviors, individuals often have unique needs to intervene in personal
undesirable actions that can negatively affect physical, mental, and social
well-being. We present WatchGuardian, a smartwatch-based JITI system that
empowers users ... |
2502.05784 | Propagation of Chaos for Mean-Field Langevin Dynamics and its
Application to Model Ensemble | stat.ML cs.LG | Mean-field Langevin dynamics (MFLD) is an optimization method derived by
taking the mean-field limit of noisy gradient descent for two-layer neural
networks in the mean-field regime. Recently, the propagation of chaos (PoC) for
MFLD has gained attention as it provides a quantitative characterization of the
optimizati... |
2502.05788 | EPBC-YOLOv8: An efficient and accurate improved YOLOv8 underwater
detector based on an attention mechanism | cs.CV cs.AI | In this study, we enhance underwater target detection by integrating channel
and spatial attention into YOLOv8's backbone, applying Pointwise Convolution in
FasterNeXt for the FasterPW model, and leveraging Weighted Concat in a
BiFPN-inspired WFPN structure for improved cross-scale connections and
robustness. Utilizi... |
2502.05790 | I3S: Importance Sampling Subspace Selection for Low-Rank Optimization in
LLM Pretraining | cs.LG | Low-rank optimization has emerged as a promising approach to enabling
memory-efficient training of large language models (LLMs). Existing low-rank
optimization methods typically project gradients onto a low-rank subspace,
reducing the memory cost of storing optimizer states. A key challenge in these
methods is identi... |
2502.05792 | AToM: Adaptive Theory-of-Mind-Based Human Motion Prediction in Long-Term
Human-Robot Interactions | cs.RO | Humans learn from observations and experiences to adjust their behaviours
towards better performance. Interacting with such dynamic humans is
challenging, as the robot needs to predict the humans accurately for safe and
efficient operations. Long-term interactions with dynamic humans have not been
extensively studied... |
2502.05793 | On Reference (In-)Determinacy in Natural Language Inference | cs.CL | We revisit the reference determinacy (RD) assumption in the task of natural
language inference (NLI), i.e., the premise and hypothesis are assumed to refer
to the same context when human raters annotate a label. While RD is a practical
assumption for constructing a new NLI dataset, we observe that current NLI
models,... |
2502.05794 | Structural Perturbation in Large Language Model Representations through
Recursive Symbolic Regeneration | cs.CL | Symbolic perturbations offer a novel approach for influencing neural
representations without requiring direct modification of model parameters. The
recursive regeneration of symbolic structures introduces structured variations
in latent embeddings, leading to controlled shifts in attention dynamics and
lexical divers... |
2502.05795 | The Curse of Depth in Large Language Models | cs.LG cs.AI | In this paper, we introduce the Curse of Depth, a concept that highlights,
explains, and addresses the recent observation in modern Large Language
Models(LLMs) where nearly half of the layers are less effective than expected.
We first confirm the wide existence of this phenomenon across the most popular
families of L... |
2502.05800 | MicroViT: A Vision Transformer with Low Complexity Self Attention for
Edge Device | cs.CV | The Vision Transformer (ViT) has demonstrated state-of-the-art performance in
various computer vision tasks, but its high computational demands make it
impractical for edge devices with limited resources. This paper presents
MicroViT, a lightweight Vision Transformer architecture optimized for edge
devices by signifi... |
2502.05802 | Kalman Filter-Based Distributed Gaussian Process for Unknown Scalar
Field Estimation in Wireless Sensor Networks | cs.MA cs.RO | In this letter, we propose an online scalar field estimation algorithm of
unknown environments using a distributed Gaussian process (DGP) framework in
wireless sensor networks (WSNs). While the kernel-based Gaussian process (GP)
has been widely employed for estimating unknown scalar fields, its centralized
nature is ... |
2502.05803 | FlashCheck: Exploration of Efficient Evidence Retrieval for Fast
Fact-Checking | cs.IR | The advances in digital tools have led to the rampant spread of
misinformation. While fact-checking aims to combat this, manual fact-checking
is cumbersome and not scalable. It is essential for automated fact-checking to
be efficient for aiding in combating misinformation in real-time and at the
source. Fact-checking... |
2502.05806 | Divide-and-Conquer: Tree-structured Strategy with Answer Distribution
Estimator for Goal-Oriented Visual Dialogue | cs.CV | Goal-oriented visual dialogue involves multi-round interaction between
artificial agents, which has been of remarkable attention due to its wide
applications. Given a visual scene, this task occurs when a Questioner asks an
action-oriented question and an Answerer responds with the intent of letting
the Questioner kn... |
2502.05807 | Devil is in the Details: Density Guidance for Detail-Aware Generation
with Flow Models | cs.LG | Diffusion models have emerged as a powerful class of generative models,
capable of producing high-quality images by mapping noise to a data
distribution. However, recent findings suggest that image likelihood does not
align with perceptual quality: high-likelihood samples tend to be smooth, while
lower-likelihood one... |
2502.05812 | Multi-Agent Reinforcement Learning in Wireless Distributed Networks for
6G | cs.IT cs.SY eess.SY math.IT | The introduction of intelligent interconnectivity between the physical and
human worlds has attracted great attention for future sixth-generation (6G)
networks, emphasizing massive capacity, ultra-low latency, and unparalleled
reliability. Wireless distributed networks and multi-agent reinforcement
learning (MARL), b... |
2502.05815 | Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional
Neural Network Models | eess.IV cs.CV cs.LG | Alzheimer's disease is an untreatable, progressive brain disorder that slowly
robs people of their memory, thinking abilities, and ultimately their capacity
to complete even the most basic tasks. Among older adults, it is the most
frequent cause of dementia. Although there is presently no treatment for
Alzheimer's di... |
2502.05817 | DreamFLEX: Learning Fault-Aware Quadrupedal Locomotion Controller for
Anomaly Situation in Rough Terrains | cs.RO cs.SY eess.SY | Recent advances in quadrupedal robots have demonstrated impressive agility
and the ability to traverse diverse terrains. However, hardware issues, such as
motor overheating or joint locking, may occur during long-distance walking or
traversing through rough terrains leading to locomotion failures. Although
several st... |
2502.05819 | Stacked Intelligent Metasurface Enabled Near-Field Multiuser
Beamfocusing in the Wave Domain | cs.IT eess.SP math.IT | Intelligent surfaces represent a breakthrough technology capable of
customizing the wireless channel cost-effectively. However, the existing works
generally focus on planar wavefront, neglecting near-field spherical wavefront
characteristics caused by large array aperture and high operation frequencies
in the teraher... |
2502.05822 | HCMRM: A High-Consistency Multimodal Relevance Model for Search Ads | cs.IR | Search advertising is essential for merchants to reach the target users on
short video platforms. Short video ads aligned with user search intents are
displayed through relevance matching and bid ranking mechanisms. This paper
focuses on improving query-to-video relevance matching to enhance the
effectiveness of rank... |
2502.05824 | Aerial Reliable Collaborative Communications for Terrestrial Mobile
Users via Evolutionary Multi-Objective Deep Reinforcement Learning | cs.NE | Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base
stations (BSs) to improve terrestrial communications. However, the limited
onboard energy and antenna power of a UAV restrict its communication range and
transmission capability. To address these limitations, this work employs
collaborative bea... |
2502.05825 | Delta -- Contrastive Decoding Mitigates Text Hallucinations in Large
Language Models | cs.CL cs.AI | Large language models (LLMs) demonstrate strong capabilities in natural
language processing but remain prone to hallucinations, generating factually
incorrect or fabricated content. This issue undermines their reliability,
particularly in high-stakes domains such as healthcare and legal advisory. To
address this chal... |
2502.05826 | MindCraft: Revolutionizing Education through AI-Powered Personalized
Learning and Mentorship for Rural India | cs.CY cs.AI cs.ET | MindCraft is a modern platform designed to revolutionize education in rural
India by leveraging Artificial Intelligence (AI) to create personalized
learning experiences, provide mentorship, and foster resource-sharing. In a
country where access to quality education is deeply influenced by geography and
socio economic... |
2502.05827 | HyGEN: Regularizing Negative Hyperedge Generation for Accurate Hyperedge
Prediction | cs.SI cs.AI | Hyperedge prediction is a fundamental task to predict future high-order
relations based on the observed network structure. Existing hyperedge
prediction methods, however, suffer from the data sparsity problem. To
alleviate this problem, negative sampling methods can be used, which leverage
non-existing hyperedges as ... |
2502.05832 | Compressing Model with Few Class-Imbalance Samples: An
Out-of-Distribution Expedition | cs.LG cs.AI cs.CV | In recent years, as a compromise between privacy and performance, few-sample
model compression has been widely adopted to deal with limited data resulting
from privacy and security concerns. However, when the number of available
samples is extremely limited, class imbalance becomes a common and tricky
problem. Achiev... |
2502.05833 | Machine learning-based hybrid dynamic modeling and economic predictive
control of carbon capture process for ship decarbonization | eess.SY cs.SY | Implementing carbon capture technology on-board ships holds promise as a
solution to facilitate the reduction of carbon intensity in international
shipping, as mandated by the International Maritime Organization. In this work,
we address the energy-efficient operation of shipboard carbon capture processes
by proposin... |
2502.05835 | Contrastive Representation Distillation via Multi-Scale Feature
Decoupling | cs.CV cs.AI | Knowledge distillation is a technique aimed at enhancing the performance of a
smaller student network without increasing its parameter size by transferring
knowledge from a larger, pre-trained teacher network. Previous approaches have
predominantly focused on distilling global feature information while
overlooking th... |
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