id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.12050 | Exploring Semantic Consistency and Style Diversity for Domain
Generalized Semantic Segmentation | [
"cs.CV"
] | Domain Generalized Semantic Segmentation (DGSS) seeks to utilize source domain data exclusively to enhance the generalization of semantic segmentation across unknown target domains. Prevailing studies predominantly concentrate on feature normalization and domain randomization, these approaches exhibit significant limit... | {
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2412.12061 | Virtual Agent-Based Communication Skills Training to Facilitate Health
Persuasion Among Peers | [
"cs.HC",
"cs.CL",
"cs.CY"
] | Many laypeople are motivated to improve the health behavior of their family or friends but do not know where to start, especially if the health behavior is potentially stigmatizing or controversial. We present an approach that uses virtual agents to coach community-based volunteers in health counseling techniques, such... | {
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2412.12062 | Semi-automated analysis of audio-recorded lessons: The case of teachers'
engaging messages | [
"cs.CL",
"cs.CY"
] | Engaging messages delivered by teachers are a key aspect of the classroom discourse that influences student outcomes. However, improving this communication is challenging due to difficulties in obtaining observations. This study presents a methodology for efficiently extracting actual observations of engaging messages ... | {
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2412.12063 | Revelations: A Decidable Class of POMDPs with Omega-Regular Objectives | [
"cs.AI",
"cs.LO",
"cs.SY",
"eess.SY"
] | Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making. We are interested in constructing algorithms with theoretical guarantees to determine whether the agent has a strategy ensuring a given specification with probability 1. This well-studied proble... | {
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2412.12064 | Development of a Compact High-Voltage Functional Electrical Stimulation
Device | [
"eess.SY",
"cs.SY"
] | This report details the design and development of a compact high-voltage functional electrical stimulation (FES) device. Unlike conventional FES systems, the proposed design prioritizes user comfort by leveraging rapid switching times to effectively activate muscles while minimizing stimulation of pain receptors. The d... | {
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2412.12068 | SPADE: Spectroscopic Photoacoustic Denoising using an Analytical and
Data-free Enhancement Framework | [
"cs.CV"
] | Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, sPA imaging is highly susceptible to noise, ... | {
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2412.12072 | Making FETCH! Happen: Finding Emergent Dog Whistles Through Common
Habitats | [
"cs.CL"
] | WARNING: This paper contains content that maybe upsetting or offensive to some readers. Dog whistles are coded expressions with dual meanings: one intended for the general public (outgroup) and another that conveys a specific message to an intended audience (ingroup). Often, these expressions are used to convey controv... | {
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2412.12074 | Extrapolating Jet Radiation with Autoregressive Transformers | [
"hep-ph",
"cs.LG"
] | Generative networks are an exciting tool for fast LHC event generation. Usually, they are used to generate configurations with a fixed number of particles. Autoregressive transformers allow us to generate events with variable numbers of particles, very much in line with the physics of QCD jet radiation. We show how the... | {
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2412.12075 | CG-Bench: Clue-grounded Question Answering Benchmark for Long Video
Understanding | [
"cs.CV"
] | Most existing video understanding benchmarks for multimodal large language models (MLLMs) focus only on short videos. The limited number of benchmarks for long video understanding often rely solely on multiple-choice questions (MCQs). However, because of the inherent limitation of MCQ-based evaluation and the increasin... | {
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2412.12077 | CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole
Slide Image Analysis in Computational Pathology | [
"cs.CV"
] | The emergence of large multimodal models (LMMs) has brought significant advancements to pathology. Previous research has primarily focused on separately training patch-level and whole-slide image (WSI)-level models, limiting the integration of learned knowledge across patches and WSIs, and resulting in redundant models... | {
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2412.12079 | UniLoc: Towards Universal Place Recognition Using Any Single Modality | [
"cs.CV"
] | To date, most place recognition methods focus on single-modality retrieval. While they perform well in specific environments, cross-modal methods offer greater flexibility by allowing seamless switching between map and query sources. It also promises to reduce computation requirements by having a unified model, and ach... | {
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2412.12083 | IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and
Illuminations | [
"cs.CV"
] | Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics. Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs, while still st... | {
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2412.12087 | Instruction-based Image Manipulation by Watching How Things Move | [
"cs.CV"
] | This paper introduces a novel dataset construction pipeline that samples pairs of frames from videos and uses multimodal large language models (MLLMs) to generate editing instructions for training instruction-based image manipulation models. Video frames inherently preserve the identity of subjects and scenes, ensuring... | {
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2412.12089 | Stabilizing Reinforcement Learning in Differentiable Multiphysics
Simulation | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.RO"
] | Recent advances in GPU-based parallel simulation have enabled practitioners to collect large amounts of data and train complex control policies using deep reinforcement learning (RL), on commodity GPUs. However, such successes for RL in robotics have been limited to tasks sufficiently simulated by fast rigid-body dynam... | {
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2412.12091 | Wonderland: Navigating 3D Scenes from a Single Image | [
"cs.CV"
] | This paper addresses a challenging question: How can we efficiently create high-quality, wide-scope 3D scenes from a single arbitrary image? Existing methods face several constraints, such as requiring multi-view data, time-consuming per-scene optimization, low visual quality in backgrounds, and distorted reconstructio... | {
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2412.12092 | No More Tuning: Prioritized Multi-Task Learning with Lagrangian
Differential Multiplier Methods | [
"cs.LG",
"cs.IR"
] | Given the ubiquity of multi-task in practical systems, Multi-Task Learning (MTL) has found widespread application across diverse domains. In real-world scenarios, these tasks often have different priorities. For instance, In web search, relevance is often prioritized over other metrics, such as click-through rates or u... | {
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2412.12093 | CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View
Diffusion Models | [
"cs.CV"
] | Reconstructing photorealistic and dynamic portrait avatars from images is essential to many applications including advertising, visual effects, and virtual reality. Depending on the application, avatar reconstruction involves different capture setups and constraints $-$ for example, visual effects studios use camera ar... | {
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2412.12094 | SepLLM: Accelerate Large Language Models by Compressing One Segment into
One Separator | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due to their quadratic complexity. In this work, we have identified a key ... | {
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2412.12095 | Causal Diffusion Transformers for Generative Modeling | [
"cs.CV"
] | We introduce Causal Diffusion as the autoregressive (AR) counterpart of Diffusion models. It is a next-token(s) forecasting framework that is friendly to both discrete and continuous modalities and compatible with existing next-token prediction models like LLaMA and GPT. While recent works attempt to combine diffusion ... | {
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2412.12096 | PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting | [
"cs.CV"
] | With the advent of portable 360{\deg} cameras, panorama has gained significant attention in applications like virtual reality (VR), virtual tours, robotics, and autonomous driving. As a result, wide-baseline panorama view synthesis has emerged as a vital task, where high resolution, fast inference, and memory efficienc... | {
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2412.12098 | MaxInfoRL: Boosting exploration in reinforcement learning through
information gain maximization | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected exploration, i.e., select random sequences of actions. Exploration can also be directed using intrinsic rewards, such as curio... | {
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2412.12101 | InterPLM: Discovering Interpretable Features in Protein Language Models
via Sparse Autoencoders | [
"q-bio.BM",
"cs.AI",
"cs.LG",
"q-bio.QM"
] | Protein language models (PLMs) have demonstrated remarkable success in protein modeling and design, yet their internal mechanisms for predicting structure and function remain poorly understood. Here we present a systematic approach to extract and analyze interpretable features from PLMs using sparse autoencoders (SAEs)... | {
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2412.12102 | Distributed Collaborative Inference System in Next-Generation Networks
and Communication | [
"cs.NI",
"cs.AI"
] | With the rapid advancement of artificial intelligence, generative artificial intelligence (GAI) has taken a leading role in transforming data processing methods. However, the high computational demands of GAI present challenges for devices with limited resources. As we move towards the sixth generation of mobile networ... | {
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2412.12103 | Empathic Coupling of Homeostatic States for Intrinsic Prosociality | [
"cs.MA",
"cs.AI",
"cs.NE"
] | When regarding the suffering of others, we often experience personal distress and feel compelled to help. Inspired by living systems, we investigate the emergence of prosocial behavior among autonomous agents that are motivated by homeostatic self-regulation. We perform multi-agent reinforcement learning, treating each... | {
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2412.12104 | Reservoir Computing Generalized | [
"nlin.CD",
"cs.LG"
] | A physical neural network (PNN) has both the strong potential to solve machine learning tasks and intrinsic physical properties, such as high-speed computation and energy efficiency. Reservoir computing (RC) is an excellent framework for implementing an information processing system with a dynamical system by attaching... | {
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2412.12107 | Generative AI Literacy: Twelve Defining Competencies | [
"cs.HC",
"cs.AI"
] | This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal consi... | {
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2412.12108 | Responsible AI Governance: A Response to UN Interim Report on Governing
AI for Humanity | [
"cs.CY",
"cs.AI"
] | This report presents a comprehensive response to the United Nation's Interim Report on Governing Artificial Intelligence (AI) for Humanity. It emphasizes the transformative potential of AI in achieving the Sustainable Development Goals (SDGs) while acknowledging the need for robust governance to mitigate associated ris... | {
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2412.12110 | Enhancing the conformal predictability of context-aware recommendation
systems by using Deep Autoencoders | [
"cs.IR",
"cs.LG"
] | In the field of Recommender Systems (RS), neural collaborative filtering represents a significant milestone by combining matrix factorization and deep neural networks to achieve promising results. Traditional methods like matrix factorization often rely on linear models, limiting their capability to capture complex int... | {
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2412.12111 | Voice Biomarker Analysis and Automated Severity Classification of
Dysarthric Speech in a Multilingual Context | [
"cs.SD",
"cs.CL",
"eess.AS"
] | Dysarthria, a motor speech disorder, severely impacts voice quality, pronunciation, and prosody, leading to diminished speech intelligibility and reduced quality of life. Accurate assessment is crucial for effective treatment, but traditional perceptual assessments are limited by their subjectivity and resource intensi... | {
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2412.12112 | Generative Modeling of Neural Dynamics via Latent Stochastic
Differential Equations | [
"q-bio.NC",
"cs.LG"
] | We propose a probabilistic framework for developing computational models of biological neural systems. In this framework, physiological recordings are viewed as discrete-time partial observations of an underlying continuous-time stochastic dynamical system which implements computations through its state evolution. To m... | {
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2412.12115 | Rashomon effect in Educational Research: Why More is Better Than One for
Measuring the Importance of the Variables? | [
"cs.CY",
"cs.LG"
] | This study explores how the Rashomon effect influences variable importance in the context of student demographics used for academic outcomes prediction. Our research follows the way machine learning algorithms are employed in Educational Data Mining, focusing on highlighting the so-called Rashomon effect. The study use... | {
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2412.12116 | AI in Education: Rationale, Principles, and Instructional Implications | [
"cs.CY",
"cs.AI"
] | This study examines the integration of generative AI in schools, assessing its benefits and risks. As AI use by students grows, it's crucial to understand its impact on learning and teaching practices. Generative AI, like ChatGPT, can create human-like content, prompting questions about its educational role. The articl... | {
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2412.12118 | High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in
Youth Athletes: A Smartwatch-Based Screening Platform | [
"eess.SP",
"cs.LG"
] | Sudden Cardiac Arrest (SCA) is the leading cause of death among athletes of all age levels worldwide. Current prescreening methods for cardiac risk factors are largely ineffective, and implementing the International Olympic Committee recommendation for 12-lead ECG screening remains prohibitively expensive. To address t... | {
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2412.12119 | Mastering Board Games by External and Internal Planning with Language
Models | [
"cs.AI",
"cs.CL",
"cs.LG"
] | While large language models perform well on a range of complex tasks (e.g., text generation, question answering, summarization), robust multi-step planning and reasoning remains a considerable challenge for them. In this paper we show that search-based planning can significantly improve LLMs' playing strength across se... | {
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2412.12121 | NLLG Quarterly arXiv Report 09/24: What are the most influential current
AI Papers? | [
"cs.DL",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | The NLLG (Natural Language Learning & Generation) arXiv reports assist in navigating the rapidly evolving landscape of NLP and AI research across cs.CL, cs.CV, cs.AI, and cs.LG categories. This fourth installment captures a transformative period in AI history - from January 1, 2023, following ChatGPT's debut, through S... | {
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2412.12122 | Inverse design of potential metastructures inspired from Indian medieval
architectural elements | [
"cs.LG",
"cs.AI",
"eess.SP"
] | In this study, we immerse in the intricate world of patterns, examining the structural details of Indian medieval architecture for the discovery of motifs with great application potential from the mechanical metastructure perspective. The motifs that specifically engrossed us are derived from the tomb of I'timad-ud-Dau... | {
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2412.12123 | Investigating the Convergence of Sigmoid-Based Fuzzy General Grey
Cognitive Maps | [
"eess.SY",
"cs.SY"
] | The Fuzzy General Grey Cognitive Map (FGGCM) and Fuzzy Grey Cognitive Map (FGCM) extend the Fuzzy Cognitive Map (FCM) by integrating uncertainty from multiple interval data or fuzzy numbers. Despite extensive studies on the convergence of FCM and FGCM, the convergence behavior of FGGCM under sigmoid activation function... | {
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2412.12126 | Seamless Optical Cloud Computing across Edge-Metro Network for
Generative AI | [
"cs.DC",
"cs.CV",
"cs.LG",
"eess.IV",
"eess.SP"
] | The rapid advancement of generative artificial intelligence (AI) in recent years has profoundly reshaped modern lifestyles, necessitating a revolutionary architecture to support the growing demands for computational power. Cloud computing has become the driving force behind this transformation. However, it consumes sig... | {
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2412.12129 | SceneDiffuser: Efficient and Controllable Driving Simulation
Initialization and Rollout | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Realistic and interactive scene simulation is a key prerequisite for autonomous vehicle (AV) development. In this work, we present SceneDiffuser, a scene-level diffusion prior designed for traffic simulation. It offers a unified framework that addresses two key stages of simulation: scene initialization, which involves... | {
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2412.12131 | Technical Insights on Blockchain's Role in Financial Systems | [
"cs.CR",
"cs.CE",
"cs.ET"
] | This research provides a critical analysis regarding the way blockchain is being implemented in the financial industry, highlighting its vital role in promoting green finance, guaranteeing compliance with regulations, improving supply chain finance, boosting decentralized finance (DeFi), and strengthening the Internet ... | {
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2412.12139 | ECGtizer: a fully automated digitizing and signal recovery pipeline for
electrocardiograms | [
"eess.SP",
"cs.LG"
] | Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analysis. This study introduces ECGtizer, an open-source, fully automated tool designed to digitize paper ECGs and recover signals lost during storage. ECGtizer ... | {
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2412.12140 | Frontier AI systems have surpassed the self-replicating red line | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | Successful self-replication under no human assistance is the essential step for AI to outsmart the human beings, and is an early signal for rogue AIs. That is why self-replication is widely recognized as one of the few red line risks of frontier AI systems. Nowadays, the leading AI corporations OpenAI and Google evalua... | {
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2412.12143 | Harnessing Transfer Learning from Swahili: Advancing Solutions for
Comorian Dialects | [
"cs.CL",
"cs.AI",
"cs.LG"
] | If today some African languages like Swahili have enough resources to develop high-performing Natural Language Processing (NLP) systems, many other languages spoken on the continent are still lacking such support. For these languages, still in their infancy, several possibilities exist to address this critical lack of ... | {
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2412.12144 | Automatic Item Generation for Personality Situational Judgment Tests
with Large Language Models | [
"cs.CL",
"cs.AI"
] | Personality assessment, particularly through situational judgment tests (SJTs), is a vital tool for psychological research, talent selection, and educational evaluation. This study explores the potential of GPT-4, a state-of-the-art large language model (LLM), to automate the generation of personality situational judgm... | {
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2412.12145 | Na'vi or Knave: Jailbreaking Language Models via Metaphorical Avatars | [
"cs.CL",
"cs.AI"
] | Metaphor serves as an implicit approach to convey information, while enabling the generalized comprehension of complex subjects. However, metaphor can potentially be exploited to bypass the safety alignment mechanisms of Large Language Models (LLMs), leading to the theft of harmful knowledge. In our study, we introduce... | {
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2412.12146 | Generative Modeling and Data Augmentation for Power System Production
Simulation | [
"eess.SY",
"cs.AI",
"cs.LG",
"cs.SY"
] | As a key component of power system production simulation, load forecasting is critical for the stable operation of power systems. Machine learning methods prevail in this field. However, the limited training data can be a challenge. This paper proposes a generative model-assisted approach for load forecasting under sma... | {
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2412.12147 | Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in
Continuous Control | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Generalizing across robot embodiments and tasks is crucial for adaptive robotic systems. Modular policy learning approaches adapt to new embodiments but are limited to specific tasks, while few-shot imitation learning (IL) approaches often focus on a single embodiment. In this paper, we introduce a few-shot behavior cl... | {
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2412.12148 | How to Choose a Threshold for an Evaluation Metric for Large Language
Models | [
"stat.ML",
"cs.CL",
"cs.LG",
"q-fin.ST",
"stat.AP"
] | To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a methodology to identify a robust threshold on these metrics even though there are many serious implications of an incorrect choice of the thres... | {
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2412.12149 | MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment
Detection via Synchronous and Attentive Fusion | [
"cs.LG",
"cs.AI",
"cs.CV"
] | The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to ... | {
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2412.12150 | Rethinking Comprehensive Benchmark for Chart Understanding: A
Perspective from Scientific Literature | [
"cs.CL",
"cs.CV",
"cs.LG"
] | Scientific Literature charts often contain complex visual elements, including multi-plot figures, flowcharts, structural diagrams and etc. Evaluating multimodal models using these authentic and intricate charts provides a more accurate assessment of their understanding abilities. However, existing benchmarks face limit... | {
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2412.12151 | SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration | [
"cs.LG",
"cs.AI",
"cs.SE"
] | The tool-use ability of Large Language Models (LLMs) has a profound impact on a wide range of industrial applications. However, LLMs' self-control and calibration capability in appropriately using tools remains understudied. The problem is consequential as it raises potential risks of degraded performance and poses a t... | {
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2412.12152 | GraphTool-Instruction: Revolutionizing Graph Reasoning in LLMs through
Decomposed Subtask Instruction | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs) have been demonstrated to possess the capabilities to understand fundamental graph properties and address various graph reasoning tasks. Existing methods fine-tune LLMs to understand and execute graph reasoning tasks by specially designed task instructions. However, these Text-Instruction m... | {
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2412.12153 | Revisiting Weight Averaging for Model Merging | [
"cs.LG",
"cs.AI"
] | Model merging aims to build a multi-task learner by combining the parameters of individually fine-tuned models without additional training. While a straightforward approach is to average model parameters across tasks, this often results in suboptimal performance due to interference among parameters across tasks. In thi... | {
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2412.12154 | PyOD 2: A Python Library for Outlier Detection with LLM-powered Model
Selection | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream analysis, recommendation systems, and social network moderation. Among open-source libraries for outlier detection, the Python Outlier Detectio... | {
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2412.12155 | Adapting Unsigned Graph Neural Networks for Signed Graphs: A Few-Shot
Prompt Tuning Approach | [
"cs.LG",
"cs.AI"
] | Signed Graph Neural Networks (SGNNs) are powerful tools for signed graph representation learning but struggle with limited generalization and heavy dependence on labeled data. While recent advancements in "graph pre-training and prompt tuning" have reduced label dependence in Graph Neural Networks (GNNs) and improved t... | {
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2412.12156 | Deep Distributed Optimization for Large-Scale Quadratic Programming | [
"math.OC",
"cs.LG",
"cs.MA"
] | Quadratic programming (QP) forms a crucial foundation in optimization, encompassing a broad spectrum of domains and serving as the basis for more advanced algorithms. Consequently, as the scale and complexity of modern applications continue to grow, the development of efficient and reliable QP algorithms is becoming in... | {
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2412.12157 | What Makes In-context Learning Effective for Mathematical Reasoning: A
Theoretical Analysis | [
"cs.CL",
"cs.AI"
] | Owing to the capability of in-context learning, large language models (LLMs) have shown impressive performance across diverse mathematical reasoning benchmarks. However, we find that few-shot demonstrations can sometimes bring negative performance and their effectiveness on LLMs' reasoning abilities remains unreliable.... | {
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2412.12158 | Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge
Hypergraph Representation | [
"cs.LG",
"cs.AI"
] | Knowledge hypergraphs generalize knowledge graphs using hyperedges to connect multiple entities and depict complicated relations. Existing methods either transform hyperedges into an easier-to-handle set of binary relations or view hyperedges as isolated and ignore their adjacencies. Both approaches have information lo... | {
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2412.12159 | Personalized Sleep Staging Leveraging Source-free Unsupervised Domain
Adaptation | [
"cs.LG",
"cs.AI"
] | Sleep staging is crucial for assessing sleep quality and diagnosing related disorders. Recent deep learning models for automatic sleep staging using polysomnography often suffer from poor generalization to new subjects because they are trained and tested on the same labeled datasets, overlooking individual differences.... | {
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2412.12160 | Climate Aware Deep Neural Networks (CADNN) for Wind Power Simulation | [
"cs.LG",
"cs.AI"
] | Wind power forecasting plays a critical role in modern energy systems, facilitating the integration of renewable energy sources into the power grid. Accurate prediction of wind energy output is essential for managing the inherent intermittency of wind power, optimizing energy dispatch, and ensuring grid stability. This... | {
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2412.12161 | Discover Physical Concepts and Equations with Machine Learning | [
"cs.LG",
"cond-mat.dis-nn",
"cs.AI",
"physics.comp-ph"
] | Machine learning can uncover physical concepts or physical equations when prior knowledge from another one is available. However, in many cases, these two aspects are coupled and cannot be discovered independently. We extend SciNet, which is a neural network architecture that simulates the human physical reasoning proc... | {
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2412.12163 | Towards LLM-based optimization compilers. Can LLMs learn how to apply a
single peephole optimization? Reasoning is all LLMs need! | [
"cs.LG",
"cs.AI",
"cs.PL"
] | Large Language Models (LLMs) have demonstrated great potential in various language processing tasks, and recent studies have explored their application in compiler optimizations. However, all these studies focus on the conventional open-source LLMs, such as Llama2, which lack enhanced reasoning mechanisms. In this stud... | {
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2412.12164 | GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake
News Detection | [
"cs.LG",
"cs.AI"
] | Multimodal fake news detection often involves modelling heterogeneous data sources, such as vision and language. Existing detection methods typically rely on fusion effectiveness and cross-modal consistency to model the content, complicating understanding how each modality affects prediction accuracy. Additionally, the... | {
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2412.12165 | Multimodal Approaches to Fair Image Classification: An Ethical
Perspective | [
"cs.CV",
"cs.AI",
"cs.CY",
"cs.LG"
] | In the rapidly advancing field of artificial intelligence, machine perception is becoming paramount to achieving increased performance. Image classification systems are becoming increasingly integral to various applications, ranging from medical diagnostics to image generation; however, these systems often exhibit harm... | {
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2412.12166 | Performance of a large language model-Artificial Intelligence based
chatbot for counseling patients with sexually transmitted infections and
genital diseases | [
"cs.CL",
"cs.AI"
] | Introduction: Global burden of sexually transmitted infections (STIs) is rising out of proportion to specialists. Current chatbots like ChatGPT are not tailored for handling STI-related concerns out of the box. We developed Otiz, an Artificial Intelligence-based (AI-based) chatbot platform designed specifically for STI... | {
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2412.12167 | Greek2MathTex: A Greek Speech-to-Text Framework for LaTeX Equations
Generation | [
"cs.CL",
"cs.AI",
"eess.AS"
] | In the vast majority of the academic and scientific domains, LaTeX has established itself as the de facto standard for typesetting complex mathematical equations and formulae. However, LaTeX's complex syntax and code-like appearance present accessibility barriers for individuals with disabilities, as well as those unfa... | {
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2412.12168 | A Decomposition Modeling Framework for Seasonal Time-Series Forecasting | [
"cs.LG",
"cs.AI"
] | Seasonal time series exhibit intricate long-term dependencies, posing a significant challenge for accurate future prediction. This paper introduces the Multi-scale Seasonal Decomposition Model (MSSD) for seasonal time-series forecasting. Initially, leveraging the inherent periodicity of seasonal time series, we decompo... | {
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2412.12169 | Regulation of Language Models With Interpretability Will Likely Result
In A Performance Trade-Off | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Regulation is increasingly cited as the most important and pressing concern in machine learning. However, it is currently unknown how to implement this, and perhaps more importantly, how it would effect model performance alongside human collaboration if actually realized. In this paper, we attempt to answer these quest... | {
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2412.12170 | PickLLM: Context-Aware RL-Assisted Large Language Model Routing | [
"cs.LG",
"cs.AI"
] | Recently, the number of off-the-shelf Large Language Models (LLMs) has exploded with many open-source options. This creates a diverse landscape regarding both serving options (e.g., inference on local hardware vs remote LLM APIs) and model heterogeneous expertise. However, it is hard for the user to efficiently optimiz... | {
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2412.12171 | AI Adoption to Combat Financial Crime: Study on Natural Language
Processing in Adverse Media Screening of Financial Services in English and
Bangla multilingual interpretation | [
"cs.CL",
"cs.AI"
] | This document explores the potential of employing Artificial Intelligence (AI), specifically Natural Language Processing (NLP), to strengthen the detection and prevention of financial crimes within the Mobile Financial Services(MFS) of Bangladesh with multilingual scenario. The analysis focuses on the utilization of NL... | {
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2412.12173 | A NotSo Simple Way to Beat Simple Bench | [
"cs.CL",
"cs.AI"
] | This paper presents a novel framework for enhancing reasoning capabilities in large language models (LLMs) by leveraging iterative reasoning and feedback-driven methodologies. Building on the limitations identified in the SimpleBench benchmark, a dataset designed to evaluate logical coherence and real-world reasoning, ... | {
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2412.12175 | Explore Theory of Mind: Program-guided adversarial data generation for
theory of mind reasoning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Do large language models (LLMs) have theory of mind? A plethora of papers and benchmarks have been introduced to evaluate if current models have been able to develop this key ability of social intelligence. However, all rely on limited datasets with simple patterns that can potentially lead to problematic blind spots i... | {
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2412.12176 | SprayCraft: Graph-Based Route Optimization for Variable Rate Precision
Spraying | [
"cs.OH",
"cs.SY",
"eess.SY"
] | To efficiently manage plant diseases, Agriculture Cyber-Physical Systems (A-CPS) have been developed to detect and localize disease infestations by integrating the Internet of Agro-Things (IoAT). By the nature of plant and pathogen interactions, the spread of a disease appears as a focus with density of infected plants... | {
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2412.12177 | Model-diff: A Tool for Comparative Study of Language Models in the Input
Space | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Comparing two (large) language models (LMs) side-by-side and pinpointing their prediction similarities and differences on the same set of inputs are crucial in many real-world scenarios, e.g., one can test if a licensed model was potentially plagiarized by another. Traditional analysis compares the LMs' outputs on some... | {
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2412.12178 | Activation Sparsity Opportunities for Compressing General Large Language
Models | [
"cs.LG",
"cs.AI"
] | Deploying local AI models, such as Large Language Models (LLMs), to edge devices can substantially enhance devices' independent capabilities, alleviate the server's burden, and lower the response time. Owing to these tremendous potentials, many big tech companies have released several lightweight Small Language Models ... | {
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2412.12183 | Adopting Explainable-AI to investigate the impact of urban morphology
design on energy and environmental performance in dry-arid climates | [
"cs.LG",
"cs.AI",
"cs.CY"
] | In rapidly urbanizing regions, designing climate-responsive urban forms is crucial for sustainable development, especially in dry arid-climates where urban morphology has a significant impact on energy consumption and environmental performance. This study advances urban morphology evaluation by combining Urban Building... | {
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2412.12185 | Graph Similarity Computation via Interpretable Neural Node Alignment | [
"cs.LG",
"cs.AI"
] | \Graph similarity computation is an essential task in many real-world graph-related applications such as retrieving the similar drugs given a query chemical compound or finding the user's potential friends from the social network database. Graph Edit Distance (GED) and Maximum Common Subgraphs (MCS) are the two commonl... | {
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2412.12187 | Clustering Time-Snapshots of Temporal Networks: From Synthetic Data to
Real-World Applications | [
"cs.SI",
"math.DS"
] | The evolution of many dynamical systems that describe relationships or interactions between objects can be effectively modeled by temporal networks, which are typically represented as a sequence of static network snapshots. In this paper, we introduce a novel random walk based approach that can identify clusters of tim... | {
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2412.12188 | Predicting Internet Connectivity in Schools: A Feasibility Study
Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.SI"
] | Internet connectivity in schools is critical to provide students with the digital literary skills necessary to compete in modern economies. In order for governments to effectively implement digital infrastructure development in schools, accurate internet connectivity information is required. However, traditional survey... | {
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2412.12189 | Multi-Surrogate-Teacher Assistance for Representation Alignment in
Fingerprint-based Indoor Localization | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Despite remarkable progress in knowledge transfer across visual and textual domains, extending these achievements to indoor localization, particularly for learning transferable representations among Received Signal Strength (RSS) fingerprint datasets, remains a challenge. This is due to inherent discrepancies among the... | {
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2412.12190 | iMoT: Inertial Motion Transformer for Inertial Navigation | [
"cs.LG",
"cs.AI",
"cs.RO"
] | We propose iMoT, an innovative Transformer-based inertial odometry method that retrieves cross-modal information from motion and rotation modalities for accurate positional estimation. Unlike prior work, during the encoding of the motion context, we introduce Progressive Series Decoupler at the beginning of each encode... | {
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} |
2412.12191 | Vehicle Detection and Classification for Toll collection using YOLOv11
and Ensemble OCR | [
"cs.CV"
] | Traditional automated toll collection systems depend on complex hardware configurations, that require huge investments in installation and maintenance. This research paper presents an innovative approach to revolutionize automated toll collection by using a single camera per plaza with the YOLOv11 computer vision archi... | {
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2412.12192 | No Free Lunch for Defending Against Prefilling Attack by In-Context
Learning | [
"cs.CR",
"cs.AI"
] | The security of Large Language Models (LLMs) has become an important research topic since the emergence of ChatGPT. Though there have been various effective methods to defend against jailbreak attacks, prefilling attacks remain an unsolved and popular threat against open-sourced LLMs. In-Context Learning (ICL) offers a... | {
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2412.12194 | BlockDoor: Blocking Backdoor Based Watermarks in Deep Neural Networks | [
"cs.CR",
"cs.LG"
] | Adoption of machine learning models across industries have turned Neural Networks (DNNs) into a prized Intellectual Property (IP), which needs to be protected from being stolen or being used without authorization. This topic gave rise to multiple watermarking schemes, through which, one can establish the ownership of a... | {
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2412.12196 | TrendSim: Simulating Trending Topics in Social Media Under Poisoning
Attacks with LLM-based Multi-agent System | [
"cs.SI",
"cs.AI"
] | Trending topics have become a significant part of modern social media, attracting users to participate in discussions of breaking events. However, they also bring in a new channel for poisoning attacks, resulting in negative impacts on society. Therefore, it is urgent to study this critical problem and develop effectiv... | {
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2412.12197 | Anti-bullying Adaptive Cruise Control: A proactive right-of-way
protection approach | [
"eess.SY",
"cs.RO",
"cs.SY"
] | The current Adaptive Cruise Control (ACC) systems are vulnerable to "road bully" such as cut-ins. This paper proposed an Anti-bullying Adaptive Cruise Control (AACC) approach with proactive right-of-way protection ability. It bears the following features: i) with the enhanced capability of preventing bullying from cut-... | {
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2412.12198 | Pop-out vs. Glue: A Study on the pre-attentive and focused attention
stages in Visual Search tasks | [
"q-bio.NC",
"cs.AI",
"stat.ME"
] | This study explores visual search asymmetry and the detection process between parallel and serial search strategies, building upon Treisman's Feature Integration Theory [3]. Our experiment examines how easy it is to locate an oblique line among vertical distractors versus a vertical line among oblique distractors, a fr... | {
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2412.12201 | Embracing Large Language Models in Traffic Flow Forecasting | [
"cs.LG",
"cs.AI"
] | Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods been proposed. Existing efforts mainly focus on capturing and utilizing spatio-temporal dependencies... | {
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} |
2412.12202 | A multi-theoretical kernel-based approach to social network-based
recommendation | [
"cs.SI",
"cs.IR",
"cs.LG"
] | Recommender systems are a critical component of e-commercewebsites. The rapid development of online social networking services provides an opportunity to explore social networks together with information used in traditional recommender systems, such as customer demographics, product characteristics, and transactions. I... | {
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} |
2412.12204 | SEE: Sememe Entanglement Encoding for Transformer-bases Models
Compression | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Transformer-based large language models exhibit groundbreaking capabilities, but their storage and computational costs are prohibitively high, limiting their application in resource-constrained scenarios. An effective approach is to eliminate redundant model parameters and computational costs while incorporating effici... | {
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} |
2412.12206 | Provably Secure Robust Image Steganography via Cross-Modal Error
Correction | [
"cs.MM",
"cs.CR",
"cs.CV"
] | The rapid development of image generation models has facilitated the widespread dissemination of generated images on social networks, creating favorable conditions for provably secure image steganography. However, existing methods face issues such as low quality of generated images and lack of semantic control in the g... | {
"Other": 1,
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"cs.SY": 0
} |
2412.12208 | AI-Driven Innovations in Volumetric Video Streaming: A Review | [
"cs.CV"
] | Recent efforts to enhance immersive and interactive user experiences have driven the development of volumetric video, a form of 3D content that enables 6 DoF. Unlike traditional 2D content, volumetric content can be represented in various ways, such as point clouds, meshes, or neural representations. However, due to it... | {
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} |
2412.12212 | Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image
Prompts with Text Summarization | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Text-to-image models are vulnerable to the stepwise "Divide-and-Conquer Attack" (DACA) that utilize a large language model to obfuscate inappropriate content in prompts by wrapping sensitive text in a benign narrative. To mitigate stepwise DACA attacks, we propose a two-layer method involving text summarization followe... | {
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} |
2412.12213 | The AI Black-Scholes: Finance-Informed Neural Network | [
"cs.LG",
"q-fin.CP",
"stat.ML"
] | In the realm of option pricing, existing models are typically classified into principle-driven methods, such as solving partial differential equations (PDEs) that pricing function satisfies, and data-driven approaches, such as machine learning (ML) techniques that parameterize the pricing function directly. While princ... | {
"Other": 0,
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} |
2412.12214 | DLSOM: A Deep learning-based strategy for liver cancer subtyping | [
"q-bio.GN",
"cs.LG"
] | Liver cancer is a leading cause of cancer-related mortality worldwide, with its high genetic heterogeneity complicating diagnosis and treatment. This study introduces DLSOM, a deep learning framework utilizing stacked autoencoders to analyze the complete somatic mutation landscape of 1,139 liver cancer samples, coverin... | {
"Other": 0,
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} |
2412.12215 | Imagined Speech State Classification for Robust Brain-Computer Interface | [
"cs.LG",
"cs.AI",
"cs.SD",
"eess.AS"
] | This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Specifically, we evaluated conventional machine learning techniques such as CSP-SVM and LDA-SVM classifiers alongside deep learning architectur... | {
"Other": 0,
"cs.AI": 1,
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} |
2412.12216 | SitPose: Real-Time Detection of Sitting Posture and Sedentary Behavior
Using Ensemble Learning With Depth Sensor | [
"cs.CV",
"cs.LG"
] | Poor sitting posture can lead to various work-related musculoskeletal disorders (WMSDs). Office employees spend approximately 81.8% of their working time seated, and sedentary behavior can result in chronic diseases such as cervical spondylosis and cardiovascular diseases. To address these health concerns, we present S... | {
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.12217 | Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts,
Challenges, and Mitigation Strategies | [
"cs.CR",
"cs.LG"
] | Deep learning (DL) has significantly transformed cybersecurity, enabling advancements in malware detection, botnet identification, intrusion detection, user authentication, and encrypted traffic analysis. However, the rise of adversarial examples (AE) poses a critical challenge to the robustness and reliability of DL-b... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2412.12218 | Accelerating Sparse Graph Neural Networks with Tensor Core Optimization | [
"cs.LG",
"cs.AR"
] | Graph neural networks (GNNs) have seen extensive application in domains such as social networks, bioinformatics, and recommendation systems. However, the irregularity and sparsity of graph data challenge traditional computing methods, which are insufficient to meet the performance demands of GNNs. Recent research has e... | {
"Other": 1,
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} |
2412.12219 | Are Large Language Models Useful for Time Series Data Analysis? | [
"cs.LG",
"cs.AI"
] | Time series data plays a critical role across diverse domains such as healthcare, energy, and finance, where tasks like classification, anomaly detection, and forecasting are essential for informed decision-making. Recently, large language models (LLMs) have gained prominence for their ability to handle complex data an... | {
"Other": 0,
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"cs.SY": 0
} |
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