id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.17271 | Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning | [
"cs.LG"
] | Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, existing models struggle to effectively capture dependencies from multiple perspectives, limiting their ability to model complex data. To address... | {
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2412.17279 | Learning from Mistakes: Self-correct Adversarial Training for Chinese
Unnatural Text Correction | [
"cs.CL"
] | Unnatural text correction aims to automatically detect and correct spelling errors or adversarial perturbation errors in sentences. Existing methods typically rely on fine-tuning or adversarial training to correct errors, which have achieved significant success. However, these methods exhibit poor generalization perfor... | {
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2412.17280 | Coupled differential-algebraic equations framework for modeling
six-degree-of-freedom flight dynamics of asymmetric fixed-wing aircraft | [
"eess.SY",
"cs.SC",
"cs.SY"
] | This study presents a comprehensive mathematical framework for modeling the flight dynamics of a six-degree-of-freedom fixed-wing aircraft as a rigid body with three control surfaces: rudder, elevators, and ailerons. The framework consists of 35 differential-algebraic equations (DAEs) and requires 30 constants to be sp... | {
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2412.17281 | Non-Convex Tensor Recovery from Local Measurements | [
"cs.LG"
] | Motivated by the settings where sensing the entire tensor is infeasible, this paper proposes a novel tensor compressed sensing model, where measurements are only obtained from sensing each lateral slice via mutually independent matrices. Leveraging the low tubal rank structure, we reparameterize the unknown tensor ${\b... | {
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2412.17282 | LMD-PGN: Cross-Modal Knowledge Distillation from First-Person-View
Images to Third-Person-View BEV Maps for Universal Point Goal Navigation | [
"cs.RO"
] | Point goal navigation (PGN) is a mapless navigation approach that trains robots to visually navigate to goal points without relying on pre-built maps. Despite significant progress in handling complex environments using deep reinforcement learning, current PGN methods are designed for single-robot systems, limiting thei... | {
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2412.17283 | Emerging Microelectronic Materials by Design: Navigating Combinatorial
Design Space with Scarce and Dispersed Data | [
"cond-mat.mtrl-sci",
"cs.CE",
"cs.LG"
] | The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular interest are emerging materials that display exceptional physical properties, making them promising candidates in energy-efficient microelectr... | {
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2412.17284 | Towards Unsupervised Model Selection for Domain Adaptive Object
Detection | [
"cs.CV"
] | Evaluating the performance of deep models in new scenarios has drawn increasing attention in recent years. However, while it is possible to collect data from new scenarios, the annotations are not always available. Existing DAOD methods often rely on validation or test sets on the target domain for model selection, whi... | {
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2412.17285 | Enabling Time-series Foundation Model for Building Energy Forecasting
via Contrastive Curriculum Learning | [
"cs.LG",
"cs.AI"
] | Advances in time-series forecasting are driving a shift from conventional machine learning models to foundation models (FMs) that are trained with generalized knowledge. However, existing FMs still perform poorly in the energy fields, such as building energy forecasting (BEF). This paper studies the adaptation of FM to... | {
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2412.17287 | LLM4AD: A Platform for Algorithm Design with Large Language Model | [
"cs.AI"
] | We introduce LLM4AD, a unified Python platform for algorithm design (AD) with large language models (LLMs). LLM4AD is a generic framework with modularized blocks for search methods, algorithm design tasks, and LLM interface. The platform integrates numerous key methods and supports a wide range of algorithm design task... | {
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2412.17288 | Multi-Modal Grounded Planning and Efficient Replanning For Learning
Embodied Agents with A Few Examples | [
"cs.RO",
"cs.AI"
] | Learning a perception and reasoning module for robotic assistants to plan steps to perform complex tasks based on natural language instructions often requires large free-form language annotations, especially for short high-level instructions. To reduce the cost of annotation, large language models (LLMs) are used as a ... | {
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2412.17290 | Free-viewpoint Human Animation with Pose-correlated Reference Selection | [
"cs.CV"
] | Diffusion-based human animation aims to animate a human character based on a source human image as well as driving signals such as a sequence of poses. Leveraging the generative capacity of diffusion model, existing approaches are able to generate high-fidelity poses, but struggle with significant viewpoint changes, es... | {
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2412.17292 | AV-EmoDialog: Chat with Audio-Visual Users Leveraging Emotional Cues | [
"cs.CV",
"cs.AI",
"cs.HC"
] | In human communication, both verbal and non-verbal cues play a crucial role in conveying emotions, intentions, and meaning beyond words alone. These non-linguistic information, such as facial expressions, eye contact, voice tone, and pitch, are fundamental elements of effective interactions, enriching conversations by ... | {
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2412.17295 | Friends-MMC: A Dataset for Multi-modal Multi-party Conversation
Understanding | [
"cs.CL"
] | Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used applications. Compared with the traditional multi-modal conversations, MMC requires stronger character-centered understanding abilities as ... | {
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2412.17297 | Revisiting Multimodal Fusion for 3D Anomaly Detection from an
Architectural Perspective | [
"cs.CV"
] | Existing efforts to boost multimodal fusion of 3D anomaly detection (3D-AD) primarily concentrate on devising more effective multimodal fusion strategies. However, little attention was devoted to analyzing the role of multimodal fusion architecture (topology) design in contributing to 3D-AD. In this paper, we aim to br... | {
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2412.17301 | Dynamic Scheduling Strategies for Resource Optimization in Computing
Environments | [
"cs.DC",
"cs.AI"
] | The rapid development of cloud-native architecture has promoted the widespread application of container technology, but the optimization problems in container scheduling and resource management still face many challenges. This paper proposes a container scheduling method based on multi-objective optimization, which aim... | {
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2412.17302 | Neural Spatial-Temporal Tensor Representation for Infrared Small Target
Detection | [
"cs.CV"
] | Optimization-based approaches dominate infrared small target detection as they leverage infrared imagery's intrinsic low-rankness and sparsity. While effective for single-frame images, they struggle with dynamic changes in multi-frame scenarios as traditional spatial-temporal representations often fail to adapt. To add... | {
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2412.17303 | When Focus Enhances Utility: Target Range LDP Frequency Estimation and
Unknown Item Discovery | [
"cs.CR",
"cs.DB"
] | Local Differential Privacy (LDP) protocols enable the collection of randomized client messages for data analysis, without the necessity of a trusted data curator. Such protocols have been successfully deployed in real-world scenarios by major tech companies like Google, Apple, and Microsoft. In this paper, we propose a... | {
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2412.17304 | On the Feasibility of Vision-Language Models for Time-Series
Classification | [
"cs.AI"
] | We build upon time-series classification by leveraging the capabilities of Vision Language Models (VLMs). We find that VLMs produce competitive results after two or less epochs of fine-tuning. We develop a novel approach that incorporates graphical data representations as images in conjunction with numerical data. This... | {
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2412.17305 | Exploiting Label Skewness for Spiking Neural Networks in Federated
Learning | [
"cs.LG",
"cs.CV"
] | The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive data collected by these devices. To address data privacy concerns when deploying SNNs on edge device... | {
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2412.17306 | Multiple Consistency-guided Test-Time Adaptation for Contrastive
Audio-Language Models with Unlabeled Audio | [
"cs.SD",
"cs.CV",
"eess.AS"
] | One fascinating aspect of pre-trained Audio-Language Models (ALMs) learning is their impressive zero-shot generalization capability and test-time adaptation (TTA) methods aiming to improve domain performance without annotations. However, previous test time adaptation (TTA) methods for ALMs in zero-shot classification t... | {
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2412.17310 | Popularity Estimation and New Bundle Generation using Content and
Context based Embeddings | [
"cs.IR",
"cs.AI"
] | Recommender systems create enormous value for businesses and their consumers. They increase revenue for businesses while improving the consumer experience by recommending relevant products amidst huge product base. Product bundling is an exciting development in the field of product recommendations. It aims at generatin... | {
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2412.17312 | Improving Pareto Set Learning for Expensive Multi-objective Optimization
via Stein Variational Hypernetworks | [
"cs.LG",
"cs.NE",
"stat.ML"
] | Expensive multi-objective optimization problems (EMOPs) are common in real-world scenarios where evaluating objective functions is costly and involves extensive computations or physical experiments. Current Pareto set learning methods for such problems often rely on surrogate models like Gaussian processes to approxima... | {
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2412.17314 | Collaborative Optimization in Financial Data Mining Through Deep
Learning and ResNeXt | [
"cs.LG",
"q-fin.CP"
] | This study proposes a multi-task learning framework based on ResNeXt, aiming to solve the problem of feature extraction and task collaborative optimization in financial data mining. Financial data usually has the complex characteristics of high dimensionality, nonlinearity, and time series, and is accompanied by potent... | {
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2412.17315 | CodeV: Issue Resolving with Visual Data | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these tasks. While recent approaches have made progress on this task, they focus on textual data within is... | {
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2412.17316 | Fast Gradient Computation for RoPE Attention in Almost Linear Time | [
"cs.LG",
"cs.AI",
"cs.CC",
"cs.CL"
] | The Rotary Position Embedding (RoPE) mechanism has become a powerful enhancement to the Transformer architecture, which enables models to capture token relationships when encoding positional information. However, the RoPE mechanisms make the computations of attention mechanisms more complicated, which makes efficient a... | {
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2412.17317 | Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect
Prediction | [
"cs.LG",
"cs.SE"
] | Cross-Project Defect Prediction (CPDP) poses a non-trivial challenge to construct a reliable defect predictor by leveraging data from other projects, particularly when data owners are concerned about data privacy. In recent years, Federated Learning (FL) has become an emerging paradigm to guarantee privacy information ... | {
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2412.17321 | Assessing Human Editing Effort on LLM-Generated Texts via
Compression-Based Edit Distance | [
"cs.CL",
"cs.AI"
] | Assessing the extent of human edits on texts generated by Large Language Models (LLMs) is crucial to understanding the human-AI interactions and improving the quality of automated text generation systems. Existing edit distance metrics, such as Levenshtein, BLEU, ROUGE, and TER, often fail to accurately measure the eff... | {
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2412.17323 | xPatch: Dual-Stream Time Series Forecasting with Exponential
Seasonal-Trend Decomposition | [
"cs.LG",
"cs.AI"
] | In recent years, the application of transformer-based models in time-series forecasting has received significant attention. While often demonstrating promising results, the transformer architecture encounters challenges in fully exploiting the temporal relations within time series data due to its attention mechanism. I... | {
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2412.17325 | Feature Based Methods in Domain Adaptation for Object Detection: A
Review Paper | [
"cs.CV"
] | Domain adaptation, a pivotal branch of transfer learning, aims to enhance the performance of machine learning models when deployed in target domains with distinct data distributions. This is particularly critical for object detection tasks, where domain shifts (caused by factors such as lighting conditions, viewing ang... | {
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2412.17330 | EcoSearch: A Constant-Delay Best-First Search Algorithm for Program
Synthesis | [
"cs.LG",
"cs.AI",
"cs.PL"
] | Many approaches to program synthesis perform a combinatorial search within a large space of programs to find one that satisfies a given specification. To tame the search space blowup, previous works introduced probabilistic and neural approaches to guide this combinatorial search by inducing heuristic cost functions. B... | {
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2412.17331 | Uncertainty-Participation Context Consistency Learning for
Semi-supervised Semantic Segmentation | [
"cs.CV"
] | Semi-supervised semantic segmentation has attracted considerable attention for its ability to mitigate the reliance on extensive labeled data. However, existing consistency regularization methods only utilize high certain pixels with prediction confidence surpassing a fixed threshold for training, failing to fully leve... | {
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2412.17332 | A Dual-Perspective Metaphor Detection Framework Using Large Language
Models | [
"cs.CL"
] | Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often ... | {
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2412.17333 | Broadband Ground Motion Synthesis by Diffusion Model with Minimal
Condition | [
"cs.LG",
"cs.AI",
"physics.geo-ph"
] | Earthquakes are rare. Hence there is a fundamental call for reliable methods to generate realistic ground motion data for data-driven approaches in seismology. Recent GAN-based methods fall short of the call, as the methods either require special information such as geological traits or generate subpar waveforms that f... | {
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2412.17334 | Complete Implementation of WXF Chinese Chess Rules | [
"cs.AI"
] | Unlike repetitions in Western Chess where all repetitions are draws, repetitions in Chinese Chess could result in a win, draw, or loss depending on the kind of repetition being made by both players. One of the biggest hurdles facing Chinese Chess application development is a proper system for judging games correctly. T... | {
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2412.17336 | APEX$^2$: Adaptive and Extreme Summarization for Personalized Knowledge
Graphs | [
"cs.LG",
"cs.AI",
"cs.DB",
"cs.SC"
] | Knowledge graphs (KGs), which store an extensive number of relational facts, serve various applications. Recently, personalized knowledge graphs (PKGs) have emerged as a solution to optimize storage costs by customizing their content to align with users' specific interests within particular domains. In the real world, ... | {
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2412.17337 | Neural-MCRL: Neural Multimodal Contrastive Representation Learning for
EEG-based Visual Decoding | [
"cs.CV"
] | Decoding neural visual representations from electroencephalogram (EEG)-based brain activity is crucial for advancing brain-machine interfaces (BMI) and has transformative potential for neural sensory rehabilitation. While multimodal contrastive representation learning (MCRL) has shown promise in neural decoding, existi... | {
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2412.17338 | Enhancing Topic Interpretability for Neural Topic Modeling through
Topic-wise Contrastive Learning | [
"cs.AI"
] | Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment... | {
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2412.17339 | MineAgent: Towards Remote-Sensing Mineral Exploration with Multimodal
Large Language Models | [
"cs.AI",
"cs.CL"
] | Remote-sensing mineral exploration is critical for identifying economically viable mineral deposits, yet it poses significant challenges for multimodal large language models (MLLMs). These include limitations in domain-specific geological knowledge and difficulties in reasoning across multiple remote-sensing images, fu... | {
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2412.17342 | Dynamics of Collective Information Processing for Risk Encoding in
Social Networks during Crises | [
"cs.SI",
"physics.data-an"
] | Online social networks are increasingly being utilized for collective sense making and information processing in disasters. However, the underlying mechanisms that shape the dynamics of collective intelligence in online social networks during disasters is not fully understood. To bridge this gap, we examine the mechani... | {
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2412.17343 | End-to-end Generative Spatial-Temporal Ultrasonic Odometry and Mapping
Framework | [
"cs.RO"
] | Performing simultaneous localization and mapping (SLAM) in low-visibility conditions, such as environments filled with smoke, dust and transparent objets, has long been a challenging task. Sensors like cameras and Light Detection and Ranging (LiDAR) are significantly limited under these conditions, whereas ultrasonic s... | {
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2412.17344 | Reinforcement Learning with a Focus on Adjusting Policies to Reach
Targets | [
"cs.LG"
] | The objective of a reinforcement learning agent is to discover better actions through exploration. However, typical exploration techniques aim to maximize rewards, often incurring high costs in both exploration and learning processes. We propose a novel deep reinforcement learning method, which prioritizes achieving an... | {
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2412.17345 | On the Power and Limitations of Examples for Description Logic Concepts | [
"cs.LO",
"cs.LG"
] | Labeled examples (i.e., positive and negative examples) are an attractive medium for communicating complex concepts. They are useful for deriving concept expressions (such as in concept learning, interactive concept specification, and concept refinement) as well as for illustrating concept expressions to a user or doma... | {
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2412.17346 | FFA Sora, video generation as fundus fluorescein angiography simulator | [
"cs.CV",
"cs.AI"
] | Fundus fluorescein angiography (FFA) is critical for diagnosing retinal vascular diseases, but beginners often struggle with image interpretation. This study develops FFA Sora, a text-to-video model that converts FFA reports into dynamic videos via a Wavelet-Flow Variational Autoencoder (WF-VAE) and a diffusion transfo... | {
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2412.17347 | Three-Class Text Sentiment Analysis Based on LSTM | [
"cs.CL",
"cs.CE",
"cs.LG"
] | Sentiment analysis is a crucial task in natural language processing (NLP) with applications in public opinion monitoring, market research, and beyond. This paper introduces a three-class sentiment classification method for Weibo comments using Long Short-Term Memory (LSTM) networks to discern positive, neutral, and neg... | {
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2412.17348 | ORIGAMI: A generative transformer architecture for predictions from
semi-structured data | [
"cs.LG"
] | Despite the popularity and widespread use of semi-structured data formats such as JSON, end-to-end supervised learning applied directly to such data remains underexplored. We present ORIGAMI (Object RepresentatIon via Generative Autoregressive ModellIng), a transformer-based architecture that directly processes nested ... | {
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2412.17350 | DiffFormer: a Differential Spatial-Spectral Transformer for
Hyperspectral Image Classification | [
"cs.CV"
] | Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information. In this work, we propose the Differential Spatial-Spectral Transformer (DiffFormer), a novel framework designed to address the inherent chall... | {
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2412.17351 | The evolution of cooperation in spatial public goods game with tolerant
punishment based on reputation threshold | [
"cs.SI",
"physics.soc-ph"
] | Reputation and punishment are significant guidelines for regulating individual behavior in human society, and those with a good reputation are more likely to be imitated by others. In addition, society imposes varying degrees of punishment for behaviors that harm the interests of groups with different reputations. Howe... | {
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2412.17352 | Efficacy of Full-Packet Encryption in Mitigating Protocol Detection for
Evasive Virtual Private Networks | [
"cs.CR",
"cs.LG",
"cs.NI"
] | Full-packet encryption is a technique used by modern evasive Virtual Private Networks (VPNs) to avoid protocol-based flagging from censorship models by disguising their traffic as random noise on the network. Traditional methods for censoring full-packet-encryption based VPN protocols requires assuming a substantial am... | {
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2412.17355 | Bi-Directional Multi-Scale Graph Dataset Condensation via Information
Bottleneck | [
"cs.LG",
"cs.DB"
] | Dataset condensation has significantly improved model training efficiency, but its application on devices with different computing power brings new requirements for different data sizes. Thus, condensing multiple scale graphs simultaneously is the core of achieving efficient training in different on-device scenarios. E... | {
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2412.17356 | Optimal Multi-Level ASK Modulations for RIS-Assisted Communications with
Energy-Based Noncoherent Reception | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper investigates the performance of one- and two-sided amplitude shift keying (ASK) modulations in noncoherent single-input single-output (SISO) wireless communication systems assisted by a reconfigurable intelligent surface (RIS). Novel noncoherent receiver structures are proposed based on the energy of the rec... | {
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2412.17358 | Risk-Sensitive Orbital Debris Collision Avoidance using Distributionally
Robust Chance Constraints | [
"eess.SY",
"cs.RO",
"cs.SY"
] | The exponential increase in orbital debris and active satellites will lead to congested orbits, necessitating more frequent collision avoidance maneuvers by satellites. To minimize fuel consumption while ensuring the safety of satellites, enforcing a chance constraint, which poses an upper bound in collision probabilit... | {
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2412.17360 | Tiered Acquisition for Constrained Bayesian Optimization: An Application
to Analog Circuits | [
"cs.NE"
] | Analog circuit design can be considered as an optimization problem with the targeted circuit specifications as constraints. When stringent circuit specifications are considered, it is desired to have an optimization methodology that adapts well to heavily constrained search spaces. To this end, we propose a novel Bayes... | {
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2412.17361 | An Experimental Evaluation of Japanese Tokenizers for Sentiment-Based
Text Classification | [
"cs.CL"
] | This study investigates the performance of three popular tokenization tools: MeCab, Sudachi, and SentencePiece, when applied as a preprocessing step for sentiment-based text classification of Japanese texts. Using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, we evaluate two traditional machine lear... | {
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2412.17364 | Efficient fine-tuning methodology of text embedding models for
information retrieval: contrastive learning penalty (clp) | [
"cs.IR",
"cs.AI"
] | Text embedding models play a crucial role in natural language processing, particularly in information retrieval, and their importance is further highlighted with the recent utilization of RAG (Retrieval- Augmented Generation). This study presents an efficient fine-tuning methodology encompassing data selection, loss fu... | {
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2412.17365 | Boosting LLM via Learning from Data Iteratively and Selectively | [
"cs.CL",
"cs.AI"
] | Datasets nowadays are generally constructed from multiple sources and using different synthetic techniques, making data de-noising and de-duplication crucial before being used for post-training. In this work, we propose to perform instruction tuning by iterative data selection (\ApproachName{}). We measure the quality ... | {
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2412.17366 | FlowMamba: Learning Point Cloud Scene Flow with Global Motion
Propagation | [
"cs.CV"
] | Scene flow methods based on deep learning have achieved impressive performance. However, current top-performing methods still struggle with ill-posed regions, such as extensive flat regions or occlusions, due to insufficient local evidence. In this paper, we propose a novel global-aware scene flow estimation network wi... | {
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2412.17373 | FRTP: Federating Route Search Records to Enhance Long-term Traffic
Prediction | [
"cs.AI"
] | Accurate traffic prediction, especially predicting traffic conditions several days in advance is essential for intelligent transportation systems (ITS). Such predictions enable mid- and long-term traffic optimization, which is crucial for efficient transportation planning. However, the inclusion of diverse external fea... | {
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2412.17374 | Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark | [
"cs.IR"
] | Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention. However, current research in MSR faces two significant challenges that hinder the field's development: the absence of uniform procedures for m... | {
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2412.17376 | How Green Can AI Be? A Study of Trends in Machine Learning Environmental
Impacts | [
"cs.LG",
"cs.CY"
] | The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consumption and environmental impacts associated with AI, possibly shifting impacts from the use phase to the manufacturing phase in the life-cyc... | {
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2412.17377 | A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild
High-Difficulty Motions | [
"cs.CV",
"cs.AI"
] | Extracting physically plausible 3D human motion from videos is a critical task. Although existing simulation-based motion imitation methods can enhance the physical quality of daily motions estimated from monocular video capture, extending this capability to high-difficulty motions remains an open challenge. This can b... | {
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2412.17378 | Balanced 3DGS: Gaussian-wise Parallelism Rendering with Fine-Grained
Tiling | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) is increasingly attracting attention in both academia and industry owing to its superior visual quality and rendering speed. However, training a 3DGS model remains a time-intensive task, especially in load imbalance scenarios where workload diversity among pixels and Gaussian spheres causes... | {
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2412.17383 | Interweaving Memories of a Siamese Large Language Model | [
"cs.CL"
] | Parameter-efficient fine-tuning (PEFT) methods optimize large language models (LLMs) by modifying or introducing a small number of parameters to enhance alignment with downstream tasks. However, they can result in catastrophic forgetting, where LLMs prioritize new knowledge at the expense of comprehensive world knowled... | {
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2412.17387 | Singular Value Scaling: Efficient Generative Model Compression via
Pruned Weights Refinement | [
"cs.CV",
"cs.AI"
] | While pruning methods effectively maintain model performance without extra training costs, they often focus solely on preserving crucial connections, overlooking the impact of pruned weights on subsequent fine-tuning or distillation, leading to inefficiencies. Moreover, most compression techniques for generative models... | {
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2412.17390 | PointVoxelFormer -- Reviving point cloud networks for 3D medical imaging | [
"cs.CV"
] | Point clouds are a very efficient way to represent volumetric data in medical imaging. First, they do not occupy resources for empty spaces and therefore can avoid trade-offs between resolution and field-of-view for voxel-based 3D convolutional networks (CNNs) - leading to smaller and robust models. Second, they provid... | {
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2412.17395 | WarriorCoder: Learning from Expert Battles to Augment Code Large
Language Models | [
"cs.CL"
] | Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges for data collection and annotation. To address this, current methods often design various data flywheels to collect complex code instructions... | {
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2412.17397 | Towards Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search
Boosted Reasoning via Iterative Preference Learning | [
"cs.LG",
"cs.CV"
] | With current state-of-the-art approaches aimed at enhancing the reasoning capabilities of Large Language Models(LLMs) through iterative preference learning inspired by AlphaZero, we propose to further enhance the step-wise reasoning capabilities through intrinsic self-correction to some extent. Our work leverages step-... | {
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2412.17401 | Learning Dynamic Local Context Representations for Infrared Small Target
Detection | [
"cs.CV"
] | Infrared small target detection (ISTD) is challenging due to complex backgrounds, low signal-to-clutter ratios, and varying target sizes and shapes. Effective detection relies on capturing local contextual information at the appropriate scale. However, small-kernel CNNs have limited receptive fields, leading to false a... | {
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2412.17404 | BrainMAP: Learning Multiple Activation Pathways in Brain Networks | [
"cs.AI"
] | Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and human behavior. To enhance analysis and comprehension of brain activity, Graph Neural Networks (GNNs) have been widely applied to the analysis ... | {
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2412.17405 | Impact of Evidence Theory Uncertainty on Training Object Detection
Models | [
"cs.CV"
] | This paper investigates the use of Evidence Theory to enhance the training efficiency of object detection models by incorporating uncertainty into the feedback loop. In each training iteration, during the validation phase, Evidence Theory is applied to establish a relationship between ground truth labels and prediction... | {
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2412.17408 | Just What You Desire: Constrained Timeline Summarization with
Self-Reflection for Enhanced Relevance | [
"cs.CL"
] | Given news articles about an entity, such as a public figure or organization, timeline summarization (TLS) involves generating a timeline that summarizes the key events about the entity. However, the TLS task is too underspecified, since what is of interest to each reader may vary, and hence there is not a single ideal... | {
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2412.17411 | Pretraining with random noise for uncertainty calibration | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Uncertainty calibration, the process of aligning confidence with accuracy, is a hallmark of human intelligence. However, most machine learning models struggle to achieve this alignment, particularly when the training dataset is small relative to the network's capacity. Here, we demonstrate that uncertainty calibration ... | {
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2412.17412 | Silencer: Robust Community Detection by Silencing of Noisy Pixels | [
"cs.SI"
] | Real-world networks carry all kinds of noise, resulting in numerous challenges for community detection. Further improving the performance and robustness of community detection has attracted significant attention. This paper considers edge noise, which causes edges in the network to be added or removed. Existing methods... | {
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2412.17414 | Spatio-Temporal Electromagnetic Kernel Learning for Channel Prediction | [
"eess.SP",
"cs.IT",
"math.IT"
] | Accurate channel prediction is essential for addressing channel aging caused by user mobility. However, the actual channel variations over time are highly complex in high-mobility scenarios, which makes it difficult for existing predictors to obtain future channels accurately. The low accuracy of channel predictors lea... | {
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2412.17415 | VidCtx: Context-aware Video Question Answering with Image Models | [
"cs.CV",
"cs.AI",
"cs.MM"
] | To address computational and memory limitations of Large Multimodal Models in the Video Question-Answering task, several recent methods extract textual representations per frame (e.g., by captioning) and feed them to a Large Language Model (LLM) that processes them to produce the final response. However, in this way, t... | {
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2412.17417 | Multimodal Preference Data Synthetic Alignment with Reward Model | [
"cs.CV"
] | Multimodal large language models (MLLMs) have significantly advanced tasks like caption generation and visual question answering by integrating visual and textual data. However, they sometimes produce misleading or hallucinate content due to discrepancies between their pre-training data and real user prompts. Existing ... | {
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2412.17427 | Measuring Contextual Informativeness in Child-Directed Text | [
"cs.CL"
] | To address an important gap in creating children's stories for vocabulary enrichment, we investigate the automatic evaluation of how well stories convey the semantics of target vocabulary words, a task with substantial implications for generating educational content. We motivate this task, which we call measuring conte... | {
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2412.17432 | Neural Continuous-Time Supermartingale Certificates | [
"eess.SY",
"cs.AI",
"cs.SY"
] | We introduce for the first time a neural-certificate framework for continuous-time stochastic dynamical systems. Autonomous learning systems in the physical world demand continuous-time reasoning, yet existing learnable certificates for probabilistic verification assume discretization of the time continuum. Inspired by... | {
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2412.17438 | Markov Process-Based Graph Convolutional Networks for Entity
Classification in Knowledge Graphs | [
"cs.AI",
"cs.LG"
] | Despite the vast amount of information encoded in Knowledge Graphs (KGs), information about the class affiliation of entities remains often incomplete. Graph Convolutional Networks (GCNs) have been shown to be effective predictors of complete information about the class affiliation of entities in KGs. However, these mo... | {
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2412.17440 | The Role of XAI in Transforming Aeronautics and Aerospace Systems | [
"cs.AI"
] | Recent advancements in Artificial Intelligence (AI) have transformed decision-making in aeronautics and aerospace. These advancements in AI have brought with them the need to understand the reasons behind the predictions generated by AI systems and models, particularly by professionals in these sectors. In this context... | {
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2412.17449 | Applying LLM and Topic Modelling in Psychotherapeutic Contexts | [
"cs.LG",
"cs.AI"
] | This study explores the use of Large language models to analyze therapist remarks in a psychotherapeutic setting. The paper focuses on the application of BERTopic, a machine learning-based topic modeling tool, to the dialogue of two different groups of therapists (classical and modern), which makes it possible to ident... | {
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2412.17451 | Diving into Self-Evolving Training for Multimodal Reasoning | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | Reasoning ability is essential for Large Multimodal Models (LMMs). In the absence of multimodal chain-of-thought annotated data, self-evolving training, where the model learns from its own outputs, has emerged as an effective and scalable approach for enhancing reasoning abilities. Despite its growing usage, a comprehe... | {
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2412.17452 | A Temporal Convolutional Network-based Approach for Network Intrusion
Detection | [
"cs.CR",
"cs.LG"
] | Network intrusion detection is critical for securing modern networks, yet the complexity of network traffic poses significant challenges to traditional methods. This study proposes a Temporal Convolutional Network(TCN) model featuring a residual block architecture with dilated convolutions to capture dependencies in ne... | {
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2412.17455 | Learning from Summarized Data: Gaussian Process Regression with Sample
Quasi-Likelihood | [
"stat.ML",
"cs.LG"
] | Gaussian process regression is a powerful Bayesian nonlinear regression method. Recent research has enabled the capture of many types of observations using non-Gaussian likelihoods. To deal with various tasks in spatial modeling, we benefit from this development. Difficulties still arise when we can only access summari... | {
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2412.17456 | Developmental Predictive Coding Model for Early Infancy Mono and
Bilingual Vocal Continual Learning | [
"cs.AI",
"cs.CL"
] | Understanding how infants perceive speech sounds and language structures is still an open problem. Previous research in artificial neural networks has mainly focused on large dataset-dependent generative models, aiming to replicate language-related phenomena such as ''perceptual narrowing''. In this paper, we propose a... | {
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2412.17458 | Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly
Detection | [
"cs.CV",
"cs.AI"
] | Unsupervised anomaly detection methods can identify surface defects in industrial images by leveraging only normal samples for training. Due to the risk of overfitting when learning from a single class, anomaly synthesis strategies are introduced to enhance detection capability by generating artificial anomalies. Howev... | {
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2412.17462 | Sampling-Based Constrained Motion Planning with Products of Experts | [
"cs.RO"
] | We present a novel approach to enhance the performance of sampling-based Model Predictive Control (MPC) in constrained optimization by leveraging products of experts. Our methodology divides the main problem into two components: one focused on optimality and the other on feasibility. By combining the solutions from eac... | {
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2412.17464 | CALLIC: Content Adaptive Learning for Lossless Image Compression | [
"cs.CV",
"eess.IV"
] | Learned lossless image compression has achieved significant advancements in recent years. However, existing methods often rely on training amortized generative models on massive datasets, resulting in sub-optimal probability distribution estimation for specific testing images during encoding process. To address this ch... | {
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2412.17468 | Line Graph Vietoris-Rips Persistence Diagram for Topological Graph
Representation Learning | [
"cs.LG",
"cs.AI",
"math.AT"
] | While message passing graph neural networks result in informative node embeddings, they may suffer from describing the topological properties of graphs. To this end, node filtration has been widely used as an attempt to obtain the topological information of a graph using persistence diagrams. However, these attempts ha... | {
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2412.17472 | Collective dynamics behind success | [
"physics.soc-ph",
"cs.SI"
] | Understanding the collective dynamics behind the success of ideas, products, behaviors, and social actors is critical for decision-making across diverse contexts, including hiring, funding, career choices, and the design of interventions for social change. Methodological advances and the increasing availability of big ... | {
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2412.17477 | Predicting Satisfied User and Machine Ratio for Compressed Images: A
Unified Approach | [
"cs.CV",
"cs.MM"
] | Nowadays, high-quality images are pursued by both humans for better viewing experience and by machines for more accurate visual analysis. However, images are usually compressed before being consumed, decreasing their quality. It is meaningful to predict the perceptual quality of compressed images for both humans and ma... | {
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2412.17478 | Signal Transformation for Effective Multi-Channel Signal Processing | [
"eess.SP",
"cs.AI"
] | Electroencephalography (EEG) is an non-invasive method to record the electrical activity of the brain. The EEG signals are low bandwidth and recorded from multiple electrodes simultaneously in a time synchronized manner. Typical EEG signal processing involves extracting features from all the individual channels separat... | {
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} |
2412.17481 | A Survey on LLM-based Multi-Agent System: Recent Advances and New
Frontiers in Application | [
"cs.CL",
"cs.MA"
] | LLM-based Multi-Agent Systems ( LLM-MAS ) have become a research hotspot since the rise of large language models (LLMs). However, with the continuous influx of new related works, the existing reviews struggle to capture them comprehensively. This paper presents a comprehensive survey of these studies. We first discuss ... | {
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2412.17483 | A Silver Bullet or a Compromise for Full Attention? A Comprehensive
Study of Gist Token-based Context Compression | [
"cs.CL"
] | In this work, we provide a thorough investigation of gist-based context compression methods to improve long-context processing in large language models. We focus on two key questions: (1) How well can these methods replace full attention models? and (2) What potential failure patterns arise due to compression? Through ... | {
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2412.17484 | Power- and Fragmentation-aware Online Scheduling for GPU Datacenters | [
"cs.DC",
"cs.AI"
] | The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs, energy demands, and the environmental footprint of large-scale computing infrastructures. This work addresses the online scheduling problem ... | {
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} |
2412.17486 | Is ChatGPT Massively Used by Students Nowadays? A Survey on the Use of
Large Language Models such as ChatGPT in Educational Settings | [
"cs.CY",
"cs.AI"
] | The rapid adoption of Generative AI (GenAI) based on Large Language Models (LLMs) such as ChatGPT has recently and profoundly impacted education, offering transformative opportunities while raising significant concerns. In this study we present the results of a survey that investigates how 395 students aged 13 to 25 ye... | {
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} |
2412.17487 | DeepMF: Deep Motion Factorization for Closed-Loop Safety-Critical
Driving Scenario Simulation | [
"cs.AI",
"cs.LG"
] | Safety-critical traffic scenarios are of great practical relevance to evaluating the robustness of autonomous driving (AD) systems. Given that these long-tail events are extremely rare in real-world traffic data, there is a growing body of work dedicated to the automatic traffic scenario generation. However, nearly all... | {
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} |
2412.17490 | A Toolkit for Virtual Reality Data Collection | [
"cs.HC",
"cs.AI",
"cs.LG"
] | Due to the still relatively low number of users, acquiring large-scale and multidimensional virtual reality datasets remains a significant challenge. Consequently, VR datasets comparable in size to state-of-the-art collections in natural language processing or computer vision are rare or absent. However, the availabili... | {
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} |
2412.17496 | Guided Real Image Dehazing using YCbCr Color Space | [
"cs.CV"
] | Image dehazing, particularly with learning-based methods, has gained significant attention due to its importance in real-world applications. However, relying solely on the RGB color space often fall short, frequently leaving residual haze. This arises from two main issues: the difficulty in obtaining clear textural fea... | {
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} |
2412.17498 | DRT: Deep Reasoning Translation via Long Chain-of-Thought | [
"cs.CL",
"cs.AI"
] | Recently, O1-like models have emerged as representative examples, illustrating the effectiveness of long chain-of-thought (CoT) in reasoning tasks such as math and coding tasks. In this paper, we introduce DRT, an attempt to bring the success of long CoT to neural machine translation (MT). Specifically, in view of the ... | {
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} |
2412.17499 | Improving the Noise Estimation of Latent Neural Stochastic Differential
Equations | [
"cs.LG",
"stat.ML"
] | Latent neural stochastic differential equations (SDEs) have recently emerged as a promising approach for learning generative models from stochastic time series data. However, they systematically underestimate the noise level inherent in such data, limiting their ability to capture stochastic dynamics accurately. We inv... | {
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} |
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