id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.12507 | 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian
Splatting | [
"cs.GR",
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has shown great potential for efficient reconstruction and high-fidelity real-time rendering of complex scenes on consumer hardware. However, due to its rasterization-based formulation, 3DGS is constrained to ideal pinhole cameras and lacks support for secondary lighting effects. Recent met... | {
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2412.12509 | Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge | [
"cs.CL"
] | Large Language Models (LLMs) have become increasingly powerful and ubiquitous, but their stochastic nature poses challenges to the reliability of their outputs. While deterministic settings can improve consistency, they do not guarantee reliability, as a single sample from the model's probability distribution can still... | {
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2412.12510 | Can Large Language Models Understand You Better? An MBTI Personality
Detection Dataset Aligned with Population Traits | [
"cs.CL",
"cs.CY"
] | The Myers-Briggs Type Indicator (MBTI) is one of the most influential personality theories reflecting individual differences in thinking, feeling, and behaving. MBTI personality detection has garnered considerable research interest and has evolved significantly over the years. However, this task tends to be overly opti... | {
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2412.12511 | Invisible Watermarks: Attacks and Robustness | [
"cs.CV"
] | As Generative AI continues to become more accessible, the case for robust detection of generated images in order to combat misinformation is stronger than ever. Invisible watermarking methods act as identifiers of generated content, embedding image- and latent-space messages that are robust to many forms of perturbatio... | {
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2412.12516 | Enhanced Momentum with Momentum Transformers | [
"q-fin.CP",
"cs.LG"
] | The primary objective of this research is to build a Momentum Transformer that is expected to outperform benchmark time-series momentum and mean-reversion trading strategies. We extend the ideas introduced in the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture to equities as t... | {
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2412.12519 | Ambient IoT towards 6G: Standardization, Potentials, and Challenges | [
"cs.IT",
"math.IT"
] | The Ambient Internet of Things (A-IoT) has emerged as a critical direction for achieving effective connectivity as the IoT system evolves to 6G. However, the introduction of A-IoT technologies, particularly involving backscatter modulation, poses numerous challenges for system design and network operations. This paper ... | {
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2412.12520 | Controllability and Tracking of Ensembles: An Optimal Transport Theory
Viewpoint | [
"eess.SY",
"cs.SY"
] | This paper explores the controllability and state tracking of ensembles from the perspective of optimal transport theory. Ensembles, characterized as collections of systems evolving under the same dynamics but with varying initial conditions, are a fundamental concept in control theory and applications. By leveraging o... | {
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2412.12522 | Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust
Text-to-SQL | [
"cs.CL",
"cs.AI"
] | Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets, their accuracy is no... | {
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2412.12525 | CREST: An Efficient Conjointly-trained Spike-driven Framework for
Event-based Object Detection Exploiting Spatiotemporal Dynamics | [
"cs.CV",
"cs.AI"
] | Event-based cameras feature high temporal resolution, wide dynamic range, and low power consumption, which is ideal for high-speed and low-light object detection. Spiking neural networks (SNNs) are promising for event-based object recognition and detection due to their spiking nature but lack efficient training methods... | {
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2412.12527 | When to Speak, When to Abstain: Contrastive Decoding with Abstention | [
"cs.CL"
] | Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks by leveraging pre-trained (i.e., parametric) and external (i.e., contextual) knowledge. While substantial efforts have been made to enhance the utilization of both forms of knowledge, situations in which models lack relevant informati... | {
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2412.12531 | Movable Antenna Aided NOMA: Joint Antenna Positioning, Precoding, and
Decoding Design | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper investigates movable antenna (MA) aided non-orthogonal multiple access (NOMA) for multi-user downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA-enabled users. An optimization problem is formulated to maximize the minimum achievable r... | {
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2412.12532 | Addressing Small and Imbalanced Medical Image Datasets Using Generative
Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K
Sampling | [
"cs.CV",
"cs.AI"
] | The development of accurate medical image classification models is often constrained by privacy concerns and data scarcity for certain conditions, leading to small and imbalanced datasets. To address these limitations, this study explores the use of generative models, such as Denoising Diffusion Probabilistic Models (D... | {
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2412.12538 | A Scalable Approach to Benchmarking the In-Conversation Differential
Diagnostic Accuracy of a Health AI | [
"cs.AI",
"cs.HC"
] | Diagnostic errors in healthcare persist as a critical challenge, with increasing numbers of patients turning to online resources for health information. While AI-powered healthcare chatbots show promise, there exists no standardized and scalable framework for evaluating their diagnostic capabilities. This study introdu... | {
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2412.12540 | Stiefel Flow Matching for Moment-Constrained Structure Elucidation | [
"cs.LG",
"physics.chem-ph"
] | Molecular structure elucidation is a fundamental step in understanding chemical phenomena, with applications in identifying molecules in natural products, lab syntheses, forensic samples, and the interstellar medium. We consider the task of predicting a molecule's all-atom 3D structure given only its molecular formula ... | {
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2412.12541 | LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven
Curriculum Learning | [
"cs.CL",
"cs.AI"
] | While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to specialized models in certain domains, such as grammatical error correction (GEC). Drawing inspiration from the concept of curriculum learni... | {
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2412.12542 | Bots against Bias: Critical Next Steps for Human-Robot Interaction | [
"cs.RO",
"cs.AI",
"cs.HC"
] | We humans are biased - and our robotic creations are biased, too. Bias is a natural phenomenon that drives our perceptions and behavior, including when it comes to socially expressive robots that have humanlike features. Recognizing that we embed bias, knowingly or not, within the design of such robots is crucial to st... | {
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2412.12544 | Seed-CTS: Unleashing the Power of Tree Search for Superior Performance
in Competitive Coding Tasks | [
"cs.AI",
"cs.SE"
] | Competition-level code generation tasks pose significant challenges for current state-of-the-art large language models (LLMs). For example, on the LiveCodeBench-Hard dataset, models such as O1-Mini and O1-Preview achieve pass@1 rates of only 0.366 and 0.143, respectively. While tree search techniques have proven effect... | {
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2412.12547 | A MARL Based Multi-Target Tracking Algorithm Under Jamming Against Radar | [
"eess.SY",
"cs.SY",
"eess.SP"
] | Unmanned aerial vehicles (UAVs) have played an increasingly important role in military operations and social life. Among all application scenarios, multi-target tracking tasks accomplished by UAV swarms have received extensive attention. However, when UAVs use radar to track targets, the tracking performance can be sev... | {
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2412.12550 | Consistent Diffusion: Denoising Diffusion Model with Data-Consistent
Training for Image Restoration | [
"cs.CV"
] | In this work, we address the limitations of denoising diffusion models (DDMs) in image restoration tasks, particularly the shape and color distortions that can compromise image quality. While DDMs have demonstrated a promising performance in many applications such as text-to-image synthesis, their effectiveness in imag... | {
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2412.12552 | SAModified: A Foundation Model-Based Zero-Shot Approach for Refining
Noisy Land-Use Land-Cover Maps | [
"cs.CV",
"cs.AI"
] | Land-use and land cover (LULC) analysis is critical in remote sensing, with wide-ranging applications across diverse fields such as agriculture, utilities, and urban planning. However, automating LULC map generation using machine learning is rendered challenging due to noisy labels. Typically, the ground truths (e.g. E... | {
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2412.12555 | Parameters Optimization of Pair Trading Algorithm | [
"cs.CE"
] | Pair trading is a market-neutral quantitative trading strategy that exploits price anomalies between two correlated assets. By taking simultaneous long and short positions, it generates profits based on relative price movements, independent of overall market trends. This study explores the mathematical foundations of p... | {
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2412.12559 | EXIT: Context-Aware Extractive Compression for Enhancing
Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.IR"
] | We introduce EXIT, an extractive context compression framework that enhances both the effectiveness and efficiency of retrieval-augmented generation (RAG) in question answering (QA). Current RAG systems often struggle when retrieval models fail to rank the most relevant documents, leading to the inclusion of more conte... | {
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2412.12561 | Tell Me What to Track: Infusing Robust Language Guidance for Enhanced
Referring Multi-Object Tracking | [
"cs.CV",
"cs.AI"
] | Referring multi-object tracking (RMOT) is an emerging cross-modal task that aims to localize an arbitrary number of targets based on a language expression and continuously track them in a video. This intricate task involves reasoning on multi-modal data and precise target localization with temporal association. However... | {
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2412.12562 | Efficient Oriented Object Detection with Enhanced Small Object
Recognition in Aerial Images | [
"cs.CV"
] | Achieving a balance between computational efficiency and detection accuracy in the realm of rotated bounding box object detection within aerial imagery is a significant challenge. While prior research has aimed at creating lightweight models that enhance computational performance and feature extraction, there remains a... | {
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2412.12563 | Task-Agnostic Language Model Watermarking via High Entropy Passthrough
Layers | [
"cs.CL"
] | In the era of costly pre-training of large language models, ensuring the intellectual property rights of model owners, and insuring that said models are responsibly deployed, is becoming increasingly important. To this end, we propose model watermarking via passthrough layers, which are added to existing pre-trained ne... | {
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2412.12564 | Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with
Large Language Models | [
"cs.CL"
] | Aspect-based sentiment analysis (ABSA), a sequence labeling task, has attracted increasing attention in multilingual contexts. While previous research has focused largely on fine-tuning or training models specifically for ABSA, we evaluate large language models (LLMs) under zero-shot conditions to explore their potenti... | {
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2412.12565 | PBVS 2024 Solution: Self-Supervised Learning and Sampling Strategies for
SAR Classification in Extreme Long-Tail Distribution | [
"cs.CV"
] | The Multimodal Learning Workshop (PBVS 2024) aims to improve the performance of automatic target recognition (ATR) systems by leveraging both Synthetic Aperture Radar (SAR) data, which is difficult to interpret but remains unaffected by weather conditions and visible light, and Electro-Optical (EO) data for simultaneou... | {
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2412.12566 | ITP: Instance-Aware Test Pruning for Out-of-Distribution Detection | [
"cs.CV"
] | Out-of-distribution (OOD) detection is crucial for ensuring the reliable deployment of deep models in real-world scenarios. Recently, from the perspective of over-parameterization, a series of methods leveraging weight sparsification techniques have shown promising performance. These methods typically focus on selectin... | {
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2412.12567 | FCMR: Robust Evaluation of Financial Cross-Modal Multi-Hop Reasoning | [
"cs.CL"
] | Real-world decision-making often requires integrating and reasoning over information from multiple modalities. While recent multimodal large language models (MLLMs) have shown promise in such tasks, their ability to perform multi-hop reasoning across diverse sources remains insufficiently evaluated. Existing benchmarks... | {
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2412.12569 | Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport | [
"cs.CL"
] | Lexical semantic change detection aims to identify shifts in word meanings over time. While existing methods using embeddings from a diachronic corpus pair estimate the degree of change for target words, they offer limited insight into changes at the level of individual usage instances. To address this, we apply Unbala... | {
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2412.12571 | ChatDiT: A Training-Free Baseline for Task-Agnostic Free-Form Chatting
with Diffusion Transformers | [
"cs.CV"
] | Recent research arXiv:2410.15027 arXiv:2410.23775 has highlighted the inherent in-context generation capabilities of pretrained diffusion transformers (DiTs), enabling them to seamlessly adapt to diverse visual tasks with minimal or no architectural modifications. These capabilities are unlocked by concatenating self-a... | {
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2412.12572 | License Plate Detection and Character Recognition Using Deep Learning
and Font Evaluation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | License plate detection (LPD) is essential for traffic management, vehicle tracking, and law enforcement but faces challenges like variable lighting and diverse font types, impacting accuracy. Traditionally reliant on image processing and machine learning, the field is now shifting towards deep learning for its robust ... | {
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2412.12575 | SIDE: Socially Informed Drought Estimation Toward Understanding Societal
Impact Dynamics of Environmental Crisis | [
"cs.SI",
"cs.AI"
] | Drought has become a critical global threat with significant societal impact. Existing drought monitoring solutions primarily focus on assessing drought severity using quantitative measurements, overlooking the diverse societal impact of drought from human-centric perspectives. Motivated by the collective intelligence ... | {
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2412.12583 | Process-Supervised Reward Models for Verifying Clinical Note Generation:
A Scalable Approach Guided by Domain Expertise | [
"cs.CL"
] | Process-supervised reward models (PRMs), which verify large language model (LLM) outputs step-by-step, have achieved significant success in mathematical and coding problems. However, their application to other domains remains largely unexplored. In this work, we train a PRM to provide step-level reward signals for clin... | {
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2412.12587 | Distributed satellite information networks: Architecture, enabling
technologies, and trends | [
"cs.IT",
"cs.AI",
"cs.NI",
"math.IT"
] | Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current sa... | {
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2412.12588 | PerSphere: A Comprehensive Framework for Multi-Faceted Perspective
Retrieval and Summarization | [
"cs.CL"
] | As online platforms and recommendation algorithms evolve, people are increasingly trapped in echo chambers, leading to biased understandings of various issues. To combat this issue, we have introduced PerSphere, a benchmark designed to facilitate multi-faceted perspective retrieval and summarization, thus breaking free... | {
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2412.12590 | Integrated Sensing and Communications in Downlink FDD MIMO without CSI
Feedback | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean... | {
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2412.12591 | LLMs are Also Effective Embedding Models: An In-depth Overview | [
"cs.CL"
] | Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-sca... | {
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2412.12594 | A Simple and Efficient Baseline for Zero-Shot Generative Classification | [
"cs.CV"
] | Large diffusion models have become mainstream generative models in both academic studies and industrial AIGC applications. Recently, a number of works further explored how to employ the power of large diffusion models as zero-shot classifiers. While recent zero-shot diffusion-based classifiers have made performance adv... | {
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2412.12596 | OpenViewer: Openness-Aware Multi-View Learning | [
"cs.CV",
"stat.AP",
"stat.ML"
] | Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of m... | {
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2412.12597 | Distribution-Free Uncertainty Quantification in Mechanical Ventilation
Treatment: A Conformal Deep Q-Learning Framework | [
"cs.LG"
] | Mechanical Ventilation (MV) is a critical life-support intervention in intensive care units (ICUs). However, optimal ventilator settings are challenging to determine because of the complexity of balancing patient-specific physiological needs with the risks of adverse outcomes that impact morbidity, mortality, and healt... | {
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2412.12602 | Don't Yell at Your Robot: Physical Correction as the Collaborative
Interface for Language Model Powered Robots | [
"cs.RO",
"cs.HC"
] | We present a novel approach for enhancing human-robot collaboration using physical interactions for real-time error correction of large language model (LLM) powered robots. Unlike other methods that rely on verbal or text commands, the robot leverages an LLM to proactively executes 6 DoF linear Dynamical System (DS) co... | {
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2412.12603 | RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image
Classification | [
"cs.CV"
] | Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model compression. However, existing methods rarely take into account the specificity of remote sensing image... | {
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2412.12605 | An Advantage-based Optimization Method for Reinforcement Learning in
Large Action Space | [
"cs.LG",
"cs.AI"
] | Reinforcement learning tasks in real-world scenarios often involve large, high-dimensional action spaces, leading to challenges such as convergence difficulties, instability, and high computational complexity. It is widely acknowledged that traditional value-based reinforcement learning algorithms struggle to address t... | {
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2412.12606 | Multi-Dimensional Insights: Benchmarking Real-World Personalization in
Large Multimodal Models | [
"cs.AI",
"cs.CL",
"cs.CV"
] | The rapidly developing field of large multimodal models (LMMs) has led to the emergence of diverse models with remarkable capabilities. However, existing benchmarks fail to comprehensively, objectively and accurately evaluate whether LMMs align with the diverse needs of humans in real-world scenarios. To bridge this ga... | {
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2412.12609 | MultiLingPoT: Enhancing Mathematical Reasoning with Multilingual Program
Fine-tuning | [
"cs.CL"
] | Program-of-Thought (PoT), which aims to use programming language instead of natural language as an intermediate step in reasoning, is an important way for LLMs to solve mathematical problems. Since different programming languages excel in different areas, it is natural to use the most suitable language for solving spec... | {
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2412.12612 | Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised
generation-verification framework | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | Graph databases like Neo4j are gaining popularity for handling complex, interconnected data, over traditional relational databases in modeling and querying relationships. While translating natural language into SQL queries is well-researched, generating Cypher queries for Neo4j remains relatively underexplored. In this... | {
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2412.12616 | Macroscopic stress, couple stress and flux tensors derived through
energetic equivalence from microscopic continuous and discrete heterogeneous
finite representative volumes | [
"cs.CE",
"cond-mat.other"
] | This paper presents a rigorous derivation of equations to evaluate the macroscopic stress tensor, the couple stress tensor, and the flux vector equivalent to underlying microscopic fields in continuous and discrete heterogeneous systems with independent displacements and rotations. Contrary to the classical asymptotic ... | {
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2412.12617 | PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly
Detection | [
"cs.CV"
] | Point cloud anomaly detection under the anomaly-free setting poses significant challenges as it requires accurately capturing the features of 3D normal data to identify deviations indicative of anomalies. Current efforts focus on devising reconstruction tasks, such as acquiring normal data representations by restoring ... | {
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2412.12619 | Phoneme-Level Feature Discrepancies: A Key to Detecting Sophisticated
Speech Deepfakes | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Recent advancements in text-to-speech and speech conversion technologies have enabled the creation of highly convincing synthetic speech. While these innovations offer numerous practical benefits, they also cause significant security challenges when maliciously misused. Therefore, there is an urgent need to detect thes... | {
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2412.12620 | Multi-Domain Features Guided Supervised Contrastive Learning for Radar
Target Detection | [
"cs.CV"
] | Detecting small targets in sea clutter is challenging due to dynamic maritime conditions. Existing solutions either model sea clutter for detection or extract target features based on clutter-target echo differences, including statistical and deep features. While more common, the latter often excels in controlled scena... | {
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2412.12621 | Jailbreaking? One Step Is Enough! | [
"cs.CL"
] | Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. Examining jailbreak prompts helps uncover the shortcomings of LLMs. However, current jailbreak methods and the target model's defenses are engaged in an indep... | {
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2412.12622 | Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in
Large-scale Networks | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Managing mixed traffic comprising human-driven and robot vehicles (RVs) across large-scale networks presents unique challenges beyond single-intersection control. This paper proposes a reinforcement learning framework for coordinating mixed traffic across multiple interconnected intersections. Our key contribution is a... | {
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2412.12626 | Improving the Transferability of 3D Point Cloud Attack via
Spectral-aware Admix and Optimization Designs | [
"cs.CV",
"cs.CR"
] | Deep learning models for point clouds have shown to be vulnerable to adversarial attacks, which have received increasing attention in various safety-critical applications such as autonomous driving, robotics, and surveillance. Existing 3D attackers generally design various attack strategies in the white-box setting, re... | {
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2412.12627 | Make Imagination Clearer! Stable Diffusion-based Visual Imagination for
Multimodal Machine Translation | [
"cs.CL"
] | Visual information has been introduced for enhancing machine translation (MT), and its effectiveness heavily relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations. In this paper, we introduce a stable diffusion-based imagination network into a multimodal large la... | {
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2412.12628 | Dense Audio-Visual Event Localization under Cross-Modal Consistency and
Multi-Temporal Granularity Collaboration | [
"cs.CV"
] | In the field of audio-visual learning, most research tasks focus exclusively on short videos. This paper focuses on the more practical Dense Audio-Visual Event Localization (DAVEL) task, advancing audio-visual scene understanding for longer, untrimmed videos. This task seeks to identify and temporally pinpoint all even... | {
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2412.12629 | a2z-1 for Multi-Disease Detection in Abdomen-Pelvis CT: External
Validation and Performance Analysis Across 21 Conditions | [
"eess.IV",
"cs.AI",
"cs.CV"
] | We present a comprehensive evaluation of a2z-1, an artificial intelligence (AI) model designed to analyze abdomen-pelvis CT scans for 21 time-sensitive and actionable findings. Our study focuses on rigorous assessment of the model's performance and generalizability. Large-scale retrospective analysis demonstrates an av... | {
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2412.12632 | What External Knowledge is Preferred by LLMs? Characterizing and
Exploring Chain of Evidence in Imperfect Context | [
"cs.CL",
"cs.AI"
] | Incorporating external knowledge into large language models (LLMs) has emerged as a promising approach to mitigate outdated knowledge and hallucination in LLMs. However, external knowledge is often imperfect. In addition to useful knowledge, external knowledge is rich in irrelevant or misinformation in the context that... | {
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2412.12636 | TrainMover: Efficient ML Training Live Migration with No Memory Overhead | [
"cs.DC",
"cs.AI",
"cs.LG",
"cs.PF"
] | Machine learning training has emerged as one of the most prominent workloads in modern data centers. These training jobs are large-scale, long-lasting, and tightly coupled, and are often disrupted by various events in the cluster such as failures, maintenance, and job scheduling. To handle these events, we rely on cold... | {
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2412.12639 | Falcon: Faster and Parallel Inference of Large Language Models through
Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree | [
"cs.CL",
"cs.AI"
] | Striking an optimal balance between minimal drafting latency and high speculation accuracy to enhance the inference speed of Large Language Models remains a significant challenge in speculative decoding. In this paper, we introduce Falcon, an innovative semi-autoregressive speculative decoding framework fashioned to au... | {
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2412.12640 | Building Gradient Bridges: Label Leakage from Restricted Gradient
Sharing in Federated Learning | [
"cs.LG",
"cs.CR"
] | The growing concern over data privacy, the benefits of utilizing data from diverse sources for model training, and the proliferation of networked devices with enhanced computational capabilities have all contributed to the rise of federated learning (FL). The clients in FL collaborate to train a global model by uploadi... | {
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2412.12641 | Lagrangian Index Policy for Restless Bandits with Average Reward | [
"cs.LG",
"cs.AI",
"math.OC",
"math.PR"
] | We study the Lagrangian Index Policy (LIP) for restless multi-armed bandits with long-run average reward. In particular, we compare the performance of LIP with the performance of the Whittle Index Policy (WIP), both heuristic policies known to be asymptotically optimal under certain natural conditions. Even though in m... | {
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2412.12642 | RDPI: A Refine Diffusion Probability Generation Method for
Spatiotemporal Data Imputation | [
"cs.LG",
"cs.AI"
] | Spatiotemporal data imputation plays a crucial role in various fields such as traffic flow monitoring, air quality assessment, and climate prediction. However, spatiotemporal data collected by sensors often suffer from temporal incompleteness, and the sparse and uneven distribution of sensors leads to missing data in t... | {
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2412.12643 | LLM-based Discriminative Reasoning for Knowledge Graph Question
Answering | [
"cs.CL"
] | Large language models (LLMs) based on generative pre-trained Transformer have achieved remarkable performance on knowledge graph question-answering (KGQA) tasks. However, LLMs often produce ungrounded subgraph planning or reasoning results in KGQA due to the hallucinatory behavior brought by the generative paradigm, wh... | {
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2412.12644 | iPrOp: Interactive Prompt Optimization for Large Language Models with a
Human in the Loop | [
"cs.CL"
] | Prompt engineering has made significant contributions to the era of large language models, yet its effectiveness depends on the skills of a prompt author. Automatic prompt optimization can support the prompt development process, but requires annotated data. This paper introduces $\textit{iPrOp}$, a novel Interactive Pr... | {
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2412.12648 | Exploring AI-Enabled Cybersecurity Frameworks: Deep-Learning Techniques,
GPU Support, and Future Enhancements | [
"cs.CR",
"cs.AI",
"cs.DC",
"cs.LG"
] | Traditional rule-based cybersecurity systems have proven highly effective against known malware threats. However, they face challenges in detecting novel threats. To address this issue, emerging cybersecurity systems are incorporating AI techniques, specifically deep-learning algorithms, to enhance their ability to det... | {
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2412.12649 | ClustEm4Ano: Clustering Text Embeddings of Nominal Textual Attributes
for Microdata Anonymization | [
"cs.CL",
"cs.AI"
] | This work introduces ClustEm4Ano, an anonymization pipeline that can be used for generalization and suppression-based anonymization of nominal textual tabular data. It automatically generates value generalization hierarchies (VGHs) that, in turn, can be used to generalize attributes in quasi-identifiers. The pipeline l... | {
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2412.12650 | Neural-Network-Driven Reward Prediction as a Heuristic: Advancing
Q-Learning for Mobile Robot Path Planning | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Q-learning is a widely used reinforcement learning technique for solving path planning problems. It primarily involves the interaction between an agent and its environment, enabling the agent to learn an optimal strategy that maximizes cumulative rewards. Although many studies have reported the effectiveness of Q-learn... | {
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2412.12651 | Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph
Convolution Network for sEEG SOZ Identification | [
"cs.LG",
"cs.AI",
"eess.SP",
"q-bio.NC"
] | Diagnosing seizure onset zone (SOZ) is a challenge in neurosurgery, where stereoelectroencephalography (sEEG) serves as a critical technique. In sEEG SOZ identification, the existing studies focus solely on the intra-patient representation of epileptic information, overlooking the general features of epilepsy across pa... | {
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2412.12654 | CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental
Learning | [
"cs.CV"
] | Few-Shot Class-Incremental Learning (FSCIL) defines a practical but challenging task where models are required to continuously learn novel concepts with only a few training samples. Due to data scarcity, existing FSCIL methods resort to training a backbone with abundant base data and then keeping it frozen afterward. H... | {
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2412.12656 | DriveTester: A Unified Platform for Simulation-Based Autonomous Driving
Testing | [
"cs.SE",
"cs.AI"
] | Simulation-based testing plays a critical role in evaluating the safety and reliability of autonomous driving systems (ADSs). However, one of the key challenges in ADS testing is the complexity of preparing and configuring simulation environments, particularly in terms of compatibility and stability between the simulat... | {
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2412.12660 | SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation | [
"cs.CV"
] | Recently, developing unified medical image segmentation models gains increasing attention, especially with the advent of the Segment Anything Model (SAM). SAM has shown promising binary segmentation performance in natural domains, however, transferring it to the medical domain remains challenging, as medical images oft... | {
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2412.12661 | MedMax: Mixed-Modal Instruction Tuning for Training Biomedical
Assistants | [
"cs.AI",
"cs.CL",
"cs.CV"
] | Recent advancements in mixed-modal generative models have enabled flexible integration of information across image-text content. These models have opened new avenues for developing unified biomedical assistants capable of analyzing biomedical images, answering complex questions about them, and predicting the impact of ... | {
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2412.12667 | A Two-Fold Patch Selection Approach for Improved 360-Degree Image
Quality Assessment | [
"cs.CV",
"cs.LG"
] | This article presents a novel approach to improving the accuracy of 360-degree perceptual image quality assessment (IQA) through a two-fold patch selection process. Our methodology combines visual patch selection with embedding similarity-based refinement. The first stage focuses on selecting patches from 360-degree im... | {
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2412.12669 | Adaptive Prototype Replay for Class Incremental Semantic Segmentation | [
"cs.CV"
] | Class incremental semantic segmentation (CISS) aims to segment new classes during continual steps while preventing the forgetting of old knowledge. Existing methods alleviate catastrophic forgetting by replaying distributions of previously learned classes using stored prototypes or features. However, they overlook a cr... | {
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2412.12672 | Structural Pruning via Spatial-aware Information Redundancy for Semantic
Segmentation | [
"cs.CV"
] | In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoption. The filter pruning method for structured network slimming offers a direct and effective solution for the reduction of segmentation networ... | {
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2412.12674 | Train More Parameters But Mind Their Placement: Insights into Language
Adaptation with PEFT | [
"cs.CL"
] | Smaller LLMs still face significant challenges even in medium-resourced languages, particularly when it comes to language-specific knowledge -- a problem not easily resolved with machine-translated data. In this case study on Icelandic, we aim to enhance the generation performance of an LLM by specialising it using uns... | {
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2412.12675 | ShotVL: Human-Centric Highlight Frame Retrieval via Language Queries | [
"cs.CV"
] | Existing works on human-centric video understanding typically focus on analyzing specific moment or entire videos. However, many applications require higher precision at the frame level. In this work, we propose a novel task, BestShot, which aims to locate highlight frames within human-centric videos via language queri... | {
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2412.12679 | Detecting Document-level Paraphrased Machine Generated Content:
Mimicking Human Writing Style and Involving Discourse Features | [
"cs.CL"
] | The availability of high-quality APIs for Large Language Models (LLMs) has facilitated the widespread creation of Machine-Generated Content (MGC), posing challenges such as academic plagiarism and the spread of misinformation. Existing MGC detectors often focus solely on surface-level information, overlooking implicit ... | {
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2412.12681 | Everyday AR through AI-in-the-Loop | [
"cs.HC",
"cs.AI",
"cs.LG"
] | This workshop brings together experts and practitioners from augmented reality (AR) and artificial intelligence (AI) to shape the future of AI-in-the-loop everyday AR experiences. With recent advancements in both AR hardware and AI capabilities, we envision that everyday AR -- always-available and seamlessly integrated... | {
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2412.12683 | ShiftedBronzes: Benchmarking and Analysis of Domain Fine-Grained
Classification in Open-World Settings | [
"cs.CV"
] | In real-world applications across specialized domains, addressing complex out-of-distribution (OOD) challenges is a common and significant concern. In this study, we concentrate on the task of fine-grained bronze ware dating, a critical aspect in the study of ancient Chinese history, and developed a benchmark dataset n... | {
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2412.12685 | SemStereo: Semantic-Constrained Stereo Matching Network for Remote
Sensing | [
"cs.CV"
] | Semantic segmentation and 3D reconstruction are two fundamental tasks in remote sensing, typically treated as separate or loosely coupled tasks. Despite attempts to integrate them into a unified network, the constraints between the two heterogeneous tasks are not explicitly modeled, since the pioneering studies either ... | {
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2412.12686 | XTransplant: A Probe into the Upper Bound Performance of Multilingual
Capability and Culture Adaptability in LLMs via Mutual Cross-lingual
Feed-forward Transplantation | [
"cs.CL"
] | Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely due to their English-centric pretraining data. To address this imbalance, we propose a probing method named XTransplant that explores cross-lingual latent interactions via cross-lingual feed-for... | {
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2412.12687 | Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large
Language Models | [
"cs.LG",
"cs.DC",
"cs.IT",
"cs.NI",
"eess.SP",
"math.IT"
] | This paper studies a hybrid language model (HLM) architecture that integrates a small language model (SLM) operating on a mobile device with a large language model (LLM) hosted at the base station (BS) of a wireless network. The HLM token generation process follows the speculative inference principle: the SLM's vocabul... | {
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2412.12688 | UniEntrezDB: Large-scale Gene Ontology Annotation Dataset and Evaluation
Benchmarks with Unified Entrez Gene Identifiers | [
"cs.DB"
] | Gene studies are crucial for fields such as protein structure prediction, drug discovery, and cancer genomics, yet they face challenges in fully utilizing the vast and diverse information available. Gene studies require clean, factual datasets to ensure reliable results. Ontology graphs, neatly organized domain termino... | {
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2412.12693 | SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through
Hierarchical Evaluation | [
"cs.CV",
"cs.AI"
] | Current vision-language models may grasp basic spatial cues and simple directions (e.g. left, right, front, back), but struggle with the multi-dimensional spatial reasoning necessary for human-like understanding and real-world applications. To address this gap, we develop SPHERE (Spatial Perception and Hierarchical Eva... | {
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2412.12694 | An Adaptive Balance Search Based Complementary Heterogeneous Particle
Swarm Optimization Architecture | [
"cs.NE"
] | A series of modified cognitive-only particle swarm optimization (PSO) algorithms effectively mitigate premature convergence by constructing distinct vectors for different particles. However, the underutilization of these constructed vectors hampers convergence accuracy. In this paper, an adaptive balance search based c... | {
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2412.12696 | ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking
Neural Networks for Class Incremental Learning | [
"cs.CV"
] | Inspired by the human brain's ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental Learning (CIL). Our comparative experiments reveal that limited datasets introduce biases in logits distributions among tasks. Fixed featur... | {
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2412.12698 | Audio Array-Based 3D UAV Trajectory Estimation with LiDAR
Pseudo-Labeling | [
"cs.RO",
"cs.SD",
"eess.AS"
] | As small unmanned aerial vehicles (UAVs) become increasingly prevalent, there is growing concern regarding their impact on public safety and privacy, highlighting the need for advanced tracking and trajectory estimation solutions. In response, this paper introduces a novel framework that utilizes audio array for 3D UAV... | {
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2412.12700 | ParMod: A Parallel and Modular Framework for Learning Non-Markovian
Tasks | [
"cs.LG",
"cs.AI"
] | The commonly used Reinforcement Learning (RL) model, MDPs (Markov Decision Processes), has a basic premise that rewards depend on the current state and action only. However, many real-world tasks are non-Markovian, which has long-term memory and dependency. The reward sparseness problem is further amplified in non-Mark... | {
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} |
2412.12701 | Trigger$^3$: Refining Query Correction via Adaptive Model Selector | [
"cs.CL"
] | In search scenarios, user experience can be hindered by erroneous queries due to typos, voice errors, or knowledge gaps. Therefore, query correction is crucial for search engines. Current correction models, usually small models trained on specific data, often struggle with queries beyond their training scope or those r... | {
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} |
2412.12704 | MapExpert: Online HD Map Construction with Simple and Efficient Sparse
Map Element Expert | [
"cs.CV"
] | Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models; however, these methods often overlook the distinct characteristics of different non-cubic map... | {
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} |
2412.12706 | More Tokens, Lower Precision: Towards the Optimal Token-Precision
Trade-off in KV Cache Compression | [
"cs.CL"
] | As large language models (LLMs) process increasing context windows, the memory usage of KV cache has become a critical bottleneck during inference. The mainstream KV compression methods, including KV pruning and KV quantization, primarily focus on either token or precision dimension separately. However, these works lea... | {
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} |
2412.12709 | Accelerating lensed quasar discovery and modeling with physics-informed
variational autoencoders | [
"astro-ph.GA",
"astro-ph.CO",
"astro-ph.IM",
"cs.CV",
"cs.LG"
] | Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and the characteristics of quasar hosts. However, detecting them in astronomical images is difficult due to the prevalence of non-lensing objects. To address this challenge, we ... | {
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} |
2412.12710 | Enhancing Naturalness in LLM-Generated Utterances through Disfluency
Insertion | [
"cs.CL"
] | Disfluencies are a natural feature of spontaneous human speech but are typically absent from the outputs of Large Language Models (LLMs). This absence can diminish the perceived naturalness of synthesized speech, which is an important criteria when building conversational agents that aim to mimick human behaviours. We ... | {
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} |
2412.12716 | Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds | [
"cs.CV",
"cs.RO"
] | Compact UAV systems, while advancing delivery and surveillance, pose significant security challenges due to their small size, which hinders detection by traditional methods. This paper presents a cost-effective, unsupervised UAV detection method using spatial-temporal sequence processing to fuse multiple LiDAR scans fo... | {
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} |
2412.12718 | ASAP: Advancing Semantic Alignment Promotes Multi-Modal Manipulation
Detecting and Grounding | [
"cs.CV",
"cs.MM"
] | We present ASAP, a new framework for detecting and grounding multi-modal media manipulation (DGM4).Upon thorough examination, we observe that accurate fine-grained cross-modal semantic alignment between the image and text is vital for accurately manipulation detection and grounding. While existing DGM4 methods pay rare... | {
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} |
2412.12722 | Defending LVLMs Against Vision Attacks through Partial-Perception
Supervision | [
"cs.CV",
"cs.AI",
"cs.CR"
] | Recent studies have raised significant concerns regarding the vulnerability of Large Vision Language Models (LVLMs) to maliciously injected or perturbed input images, which can mislead their responses. Existing defense methods show that such vision attacks are sensitive to image modifications especially cropping, using... | {
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} |
2412.12725 | RaCFormer: Towards High-Quality 3D Object Detection via Query-based
Radar-Camera Fusion | [
"cs.CV"
] | We propose Radar-Camera fusion transformer (RaCFormer) to boost the accuracy of 3D object detection by the following insight. The Radar-Camera fusion in outdoor 3D scene perception is capped by the image-to-BEV transformation--if the depth of pixels is not accurately estimated, the naive combination of BEV features act... | {
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} |
2412.12731 | SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum
Algorithms and Neuro-Fuzzy Systems | [
"cs.CL",
"quant-ph"
] | Sentiment analysis is an essential component of natural language processing, used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning and neuro... | {
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} |
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