id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.09220 | USDRL: Unified Skeleton-Based Dense Representation Learning with
Multi-Grained Feature Decorrelation | [
"cs.CV"
] | Contrastive learning has achieved great success in skeleton-based representation learning recently. However, the prevailing methods are predominantly negative-based, necessitating additional momentum encoder and memory bank to get negative samples, which increases the difficulty of model training. Furthermore, these me... | {
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2412.09222 | Building a Privacy Web with SPIDEr -- Secure Pipeline for Information
De-Identification with End-to-End Encryption | [
"cs.CR",
"cs.IT",
"math.IT"
] | Data de-identification makes it possible to glean insights from data while preserving user privacy. The use of Trusted Execution Environments (TEEs) allow for the execution of de-identification applications on the cloud without the need for a user to trust the third-party application provider. In this paper, we present... | {
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2412.09223 | CSSDH: An Ontology for Social Determinants of Health to Operational
Continuity of Care Data Interoperability | [
"cs.LO",
"cs.AI"
] | The rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monitor their health and share information with healthcare professionals as needed. However, creating an efficient care plan management system requires more than just analyzing ... | {
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2412.09224 | DASK: Distribution Rehearsing via Adaptive Style Kernel Learning for
Exemplar-Free Lifelong Person Re-Identification | [
"cs.CV"
] | Lifelong person re-identification (LReID) is an important but challenging task that suffers from catastrophic forgetting due to significant domain gaps between training steps. Existing LReID approaches typically rely on data replay and knowledge distillation to mitigate this issue. However, data replay methods compromi... | {
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2412.09229 | UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set
Object Detection Framework | [
"cs.CV"
] | We tackle the challenging problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images. The main difficulty arises from the absence of supervision for these unknown classes, making it challenging to distinguish them from the background. Existing OSOD detectors ei... | {
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2412.09230 | Foundation Models and Adaptive Feature Selection: A Synergistic Approach
to Video Question Answering | [
"cs.CV",
"cs.AI"
] | This paper tackles the intricate challenge of video question-answering (VideoQA). Despite notable progress, current methods fall short of effectively integrating questions with video frames and semantic object-level abstractions to create question-aware video representations. We introduce Local-Global Question Aware Vi... | {
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2412.09232 | Uplift modeling with continuous treatments: A predict-then-optimize
approach | [
"cs.LG"
] | The goal of uplift modeling is to recommend actions that optimize specific outcomes by determining which entities should receive treatment. One common approach involves two steps: first, an inference step that estimates conditional average treatment effects (CATEs), and second, an optimization step that ranks entities ... | {
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2412.09237 | LMAgent: A Large-scale Multimodal Agents Society for Multi-user
Simulation | [
"cs.AI"
] | The believable simulation of multi-user behavior is crucial for understanding complex social systems. Recently, large language models (LLMs)-based AI agents have made significant progress, enabling them to achieve human-like intelligence across various tasks. However, real human societies are often dynamic and complex,... | {
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2412.09238 | Disturbance-Adaptive Data-Driven Predictive Control: Trading Comfort
Violations for Savings in Building Climate Control | [
"eess.SY",
"cs.SY"
] | Model Predictive Control (MPC) has demonstrated significant potential in improving energy efficiency in building climate control, outperforming traditional controllers commonly used in modern building management systems. Among MPC variants, data-driven predictive control (DPC) offers the advantage of modeling building ... | {
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2412.09240 | VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation
with Unsupervised Domain Adaptation | [
"cs.CV",
"cs.AI"
] | Segmentation models are typically constrained by the categories defined during training. To address this, researchers have explored two independent approaches: adapting Vision-Language Models (VLMs) and leveraging synthetic data. However, VLMs often struggle with granularity, failing to disentangle fine-grained concept... | {
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2412.09243 | SPRec: Self-Play to Debias LLM-based Recommendation | [
"cs.IR"
] | Large language models (LLMs) have attracted significant attention in recommendation systems. Current work primarily applies supervised fine-tuning (SFT) to adapt the model for recommendation tasks. However, SFT on positive examples only limits the model's ability to align with user preference. To address this, research... | {
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2412.09247 | Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by
Utilizing Generative LLMs | [
"cs.CL"
] | Satire detection is essential for accurately extracting opinions from textual data and combating misinformation online. However, the lack of diverse corpora for satire leads to the problem of stylistic bias which impacts the models' detection performances. This study proposes a debiasing approach for satire detection, ... | {
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2412.09250 | GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning | [
"cs.LG",
"math.GT",
"stat.ML"
] | Fine-tuning large language models (LLMs) is computationally intensive because it requires updating all parameters. Low-Rank Adaptation (LoRA) improves efficiency by modifying only a subset of weights but introduces a trade-off between expressivity and computational cost: lower ranks reduce resources but limit expressiv... | {
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2412.09254 | When Can Memorization Improve Fairness? | [
"cs.LG",
"cs.CY"
] | We study to which extent additive fairness metrics (statistical parity, equal opportunity and equalized odds) can be influenced in a multi-class classification problem by memorizing a subset of the population. We give explicit expressions for the bias resulting from memorization in terms of the label and group membersh... | {
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2412.09258 | FD2-Net: Frequency-Driven Feature Decomposition Network for
Infrared-Visible Object Detection | [
"cs.CV"
] | Infrared-visible object detection (IVOD) seeks to harness the complementary information in infrared and visible images, thereby enhancing the performance of detectors in complex environments. However, existing methods often neglect the frequency characteristics of complementary information, such as the abundant high-fr... | {
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2412.09261 | Single-View Graph Contrastive Learning with Soft Neighborhood Awareness | [
"cs.LG"
] | Most graph contrastive learning (GCL) methods heavily rely on cross-view contrast, thus facing several concomitant challenges, such as the complexity of designing effective augmentations, the potential for information loss between views, and increased computational costs. To mitigate reliance on cross-view contrasts, w... | {
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2412.09262 | LatentSync: Audio Conditioned Latent Diffusion Models for Lip Sync | [
"cs.CV"
] | We present LatentSync, an end-to-end lip sync framework based on audio conditioned latent diffusion models without any intermediate motion representation, diverging from previous diffusion-based lip sync methods based on pixel space diffusion or two-stage generation. Our framework can leverage the powerful capabilities... | {
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2412.09263 | First Train to Generate, then Generate to Train: UnitedSynT5 for
Few-Shot NLI | [
"cs.CL",
"cs.AI"
] | Natural Language Inference (NLI) tasks require identifying the relationship between sentence pairs, typically classified as entailment, contradiction, or neutrality. While the current state-of-the-art (SOTA) model, Entailment Few-Shot Learning (EFL), achieves a 93.1% accuracy on the Stanford Natural Language Inference ... | {
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2412.09264 | Speeding up approximate MAP by applying domain knowledge about relevant
variables | [
"cs.AI"
] | The MAP problem in Bayesian networks is notoriously intractable, even when approximated. In an earlier paper we introduced the Most Frugal Explanation heuristic approach to solving MAP, by partitioning the set of intermediate variables (neither observed nor part of the MAP variables) into a set of relevant variables, w... | {
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2412.09265 | Score and Distribution Matching Policy: Advanced Accelerated Visuomotor
Policies via Matched Distillation | [
"cs.RO",
"cs.LG",
"stat.ML"
] | Visual-motor policy learning has advanced with architectures like diffusion-based policies, known for modeling complex robotic trajectories. However, their prolonged inference times hinder high-frequency control tasks requiring real-time feedback. While consistency distillation (CD) accelerates inference, it introduces... | {
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2412.09269 | Towards Understanding the Robustness of LLM-based Evaluations under
Perturbations | [
"cs.CL",
"cs.AI"
] | Traditional evaluation metrics like BLEU and ROUGE fall short when capturing the nuanced qualities of generated text, particularly when there is no single ground truth. In this paper, we explore the potential of Large Language Models (LLMs), specifically Google Gemini 1, to serve as automatic evaluators for non-standar... | {
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2412.09276 | Text-Video Multi-Grained Integration for Video Moment Montage | [
"cs.CV"
] | The proliferation of online short video platforms has driven a surge in user demand for short video editing. However, manually selecting, cropping, and assembling raw footage into a coherent, high-quality video remains laborious and time-consuming. To accelerate this process, we focus on a user-friendly new task called... | {
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2412.09278 | Towards a Multimodal Large Language Model with Pixel-Level Insight for
Biomedicine | [
"cs.CV",
"cs.AI"
] | In recent years, Multimodal Large Language Models (MLLM) have achieved notable advancements, demonstrating the feasibility of developing an intelligent biomedical assistant. However, current biomedical MLLMs predominantly focus on image-level understanding and restrict interactions to textual commands, thus limiting th... | {
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2412.09279 | Multi-Aircraft Scheduling Optimization in Urban Environments | [
"eess.SY",
"cs.SY"
] | With the increasing development of intelligent transportation systems and advancements in aviation technology, the concept of Advanced Air Mobility (AAM) is gaining attention. This study aims to improve operational safety and service quality within Urban Air Mobility (UAM) through a trajectory-based operation (TBO). A ... | {
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2412.09280 | Learning to Solve Domain-Specific Calculation Problems with
Knowledge-Intensive Programs Generator | [
"cs.CL"
] | Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs. But it still requires professional knowledge to facilitate the expertise for some domain-specific tasks. In this paper, we investigate into knowledge-intensive calculation problems. We find that the math problems to be ch... | {
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2412.09282 | CRVQ: Channel-Relaxed Vector Quantization for Extreme Compression of
LLMs | [
"cs.LG",
"cs.CL"
] | Powerful large language models (LLMs) are increasingly expected to be deployed with lower computational costs, enabling their capabilities on resource-constrained devices. Post-training quantization (PTQ) has emerged as a star approach to achieve this ambition, with best methods compressing weights to less than 2 bit o... | {
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2412.09283 | InstanceCap: Improving Text-to-Video Generation via Instance-aware
Structured Caption | [
"cs.CV",
"cs.AI"
] | Text-to-video generation has evolved rapidly in recent years, delivering remarkable results. Training typically relies on video-caption paired data, which plays a crucial role in enhancing generation performance. However, current video captions often suffer from insufficient details, hallucinations and imprecise motion... | {
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2412.09286 | Learning Novel Skills from Language-Generated Demonstrations | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes a skill-learning framework that enables robots to acquire novel skills from natural la... | {
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2412.09289 | Optimising TinyML with Quantization and Distillation of Transformer and
Mamba Models for Indoor Localisation on Edge Devices | [
"cs.LG",
"cs.SE"
] | This paper proposes small and efficient machine learning models (TinyML) for resource-constrained edge devices, specifically for on-device indoor localisation. Typical approaches for indoor localisation rely on centralised remote processing of data transmitted from lower powered devices such as wearables. However, ther... | {
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2412.09292 | Transfer Learning of RSSI to Improve Indoor Localisation Performance | [
"cs.LG"
] | With the growing demand for health monitoring systems, in-home localisation is essential for tracking patient conditions. The unique spatial characteristics of each house required annotated data for Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI)-based monitoring system. However, collecting annotat... | {
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2412.09296 | GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with
Rhythmic Poses and Realistic Expression | [
"cs.CV"
] | Audio-driven talking head generation necessitates seamless integration of audio and visual data amidst the challenges posed by diverse input portraits and intricate correlations between audio and facial motions. In response, we propose a robust framework GoHD designed to produce highly realistic, expressive, and contro... | {
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2412.09308 | Dynamic Prompt Allocation and Tuning for Continual Test-Time Adaptation | [
"cs.LG"
] | Continual test-time adaptation (CTTA) has recently emerged to adapt a pre-trained source model to continuously evolving target distributions, which accommodates the dynamic nature of real-world environments. To mitigate the risk of catastrophic forgetting in CTTA, existing methods typically incorporate explicit regular... | {
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2412.09311 | Advancing Attribution-Based Neural Network Explainability through
Relative Absolute Magnitude Layer-Wise Relevance Propagation and
Multi-Component Evaluation | [
"cs.CV",
"cs.AI"
] | Recent advancement in deep-neural network performance led to the development of new state-of-the-art approaches in numerous areas. However, the black-box nature of neural networks often prohibits their use in areas where model explainability and model transparency are crucial. Over the years, researchers proposed many ... | {
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2412.09315 | Beware of Metacognitive Laziness: Effects of Generative Artificial
Intelligence on Learning Motivation, Processes, and Performance | [
"cs.AI",
"cs.HC"
] | With the continuous development of technological and educational innovation, learners nowadays can obtain a variety of support from agents such as teachers, peers, education technologies, and recently, generative artificial intelligence such as ChatGPT. The concept of hybrid intelligence is still at a nascent stage, an... | {
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2412.09317 | Multimodal Sentiment Analysis based on Video and Audio Inputs | [
"cs.SD",
"cs.AI",
"cs.CV",
"cs.MM",
"eess.AS"
] | Despite the abundance of current researches working on the sentiment analysis from videos and audios, finding the best model that gives the highest accuracy rate is still considered a challenge for researchers in this field. The main objective of this paper is to prove the usability of emotion recognition models that t... | {
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2412.09318 | Benchmarking LLMs for Mimicking Child-Caregiver Language in Interaction | [
"cs.CL",
"cs.AI"
] | LLMs can generate human-like dialogues, yet their ability to simulate early child-adult interactions remains largely unexplored. In this paper, we examined how effectively LLMs can capture the distinctive features of child-caregiver language in interaction, using both static and interactive benchmarking methods. We fou... | {
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2412.09319 | FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot
Medical Image Segmentation | [
"cs.CV"
] | Existing few-shot medical image segmentation (FSMIS) models fail to address a practical issue in medical imaging: the domain shift caused by different imaging techniques, which limits the applicability to current FSMIS tasks. To overcome this limitation, we focus on the cross-domain few-shot medical image segmentation ... | {
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2412.09323 | T-SVG: Text-Driven Stereoscopic Video Generation | [
"cs.CV"
] | The advent of stereoscopic videos has opened new horizons in multimedia, particularly in extended reality (XR) and virtual reality (VR) applications, where immersive content captivates audiences across various platforms. Despite its growing popularity, producing stereoscopic videos remains challenging due to the techni... | {
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2412.09324 | Are Conditional Latent Diffusion Models Effective for Image Restoration? | [
"cs.CV"
] | Recent advancements in image restoration increasingly employ conditional latent diffusion models (CLDMs). While these models have demonstrated notable performance improvements in recent years, this work questions their suitability for IR tasks. CLDMs excel in capturing high-level semantic correlations, making them effe... | {
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2412.09328 | Auto-Regressive Moving Diffusion Models for Time Series Forecasting | [
"cs.LG",
"cs.AI",
"stat.AP"
] | Time series forecasting (TSF) is essential in various domains, and recent advancements in diffusion-based TSF models have shown considerable promise. However, these models typically adopt traditional diffusion patterns, treating TSF as a noise-based conditional generation task. This approach neglects the inherent conti... | {
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2412.09329 | Towards Open-Vocabulary Video Semantic Segmentation | [
"cs.MM",
"cs.AI"
] | Semantic segmentation in videos has been a focal point of recent research. However, existing models encounter challenges when faced with unfamiliar categories. To address this, we introduce the Open Vocabulary Video Semantic Segmentation (OV-VSS) task, designed to accurately segment every pixel across a wide range of o... | {
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2412.09330 | Computer-Aided Osteoporosis Diagnosis Using Transfer Learning with
Enhanced Features from Stacked Deep Learning Modules | [
"eess.IV",
"cs.CV"
] | Knee osteoporosis weakens the bone tissue in the knee joint, increasing fracture risk. Early detection through X-ray images enables timely intervention and improved patient outcomes. While some researchers have focused on diagnosing knee osteoporosis through manual radiology evaluation and traditional machine learning ... | {
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2412.09331 | Physics-Driven Autoregressive State Space Models for Medical Image
Reconstruction | [
"eess.IV",
"cs.CV"
] | Medical image reconstruction from undersampled acquisitions is an ill-posed problem that involves inversion of the imaging operator linking measurement and image domains. In recent years, physics-driven (PD) models have gained prominence in learning-based reconstruction given their enhanced balance between efficiency a... | {
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2412.09333 | MaskTerial: A Foundation Model for Automated 2D Material Flake Detection | [
"cs.CV",
"cond-mat.mtrl-sci",
"eess.IV"
] | The detection and classification of exfoliated two-dimensional (2D) material flakes from optical microscope images can be automated using computer vision algorithms. This has the potential to increase the accuracy and objectivity of classification and the efficiency of sample fabrication, and it allows for large-scale ... | {
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2412.09335 | Does Low Spoilage Under Cold Conditions Foster Cultural Complexity
During the Foraging Era? -- A Theoretical and Computational Inquiry | [
"cs.CY",
"cs.AI",
"econ.GN",
"q-fin.EC"
] | Human cultural complexity did not arise in a vacuum. Scholars in the humanities and social sciences have long debated how ecological factors, such as climate and resource availability, enabled early hunter-gatherers to allocate time and energy beyond basic subsistence tasks. This paper presents a formal, interdisciplin... | {
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2412.09337 | RTCUDB: Building Databases with RT Processors | [
"cs.DB"
] | A spectrum of new hardware has been studied to accelerate database systems in the past decade. Specifically, CUDA cores are known to benefit from the fast development of GPUs and make notable performance improvements. The state-of-the-art GPU-based implementation, i.e., Crystal, can achieve up to 61 times higher perfor... | {
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2412.09341 | Training LayoutLM from Scratch for Efficient Named-Entity Recognition in
the Insurance Domain | [
"cs.CL"
] | Generic pre-trained neural networks may struggle to produce good results in specialized domains like finance and insurance. This is due to a domain mismatch between training data and downstream tasks, as in-domain data are often scarce due to privacy constraints. In this work, we compare different pre-training strategi... | {
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2412.09342 | Diffusion Predictive Control with Constraints | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY"
] | Diffusion models have recently gained popularity for policy learning in robotics due to their ability to capture high-dimensional and multimodal distributions. However, diffusion policies are inherently stochastic and typically trained offline, limiting their ability to handle unseen and dynamic conditions where novel ... | {
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2412.09346 | Quantitative Evaluation of Motif Sets in Time Series | [
"cs.LG",
"cs.CV"
] | Time Series Motif Discovery (TSMD), which aims at finding recurring patterns in time series, is an important task in numerous application domains, and many methods for this task exist. These methods are usually evaluated qualitatively. A few metrics for quantitative evaluation, where discovered motifs are compared to s... | {
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2412.09349 | DisPose: Disentangling Pose Guidance for Controllable Human Image
Animation | [
"cs.CV"
] | Controllable human image animation aims to generate videos from reference images using driving videos. Due to the limited control signals provided by sparse guidance (e.g., skeleton pose), recent works have attempted to introduce additional dense conditions (e.g., depth map) to ensure motion alignment. However, such st... | {
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2412.09353 | Causal Graphical Models for Vision-Language Compositional Understanding | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.MM"
] | Recent work has empirically shown that Vision-Language Models (VLMs) struggle to fully understand the compositional properties of the human language, usually modeling an image caption as a "bag of words". As a result, they perform poorly on compositional tasks, which require a deeper understanding of the different enti... | {
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2412.09355 | Stop Relearning: Model Reuse via Feature Distribution Analysis for
Incremental Entity Resolution | [
"cs.DB"
] | Entity resolution is essential for data integration, facilitating analytics and insights from complex systems. Multi-source and incremental entity resolution address the challenges of integrating diverse and dynamic data, which is common in real-world scenarios. A critical question is how to classify matches and non-ma... | {
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2412.09362 | Falcon-UI: Understanding GUI Before Following User Instructions | [
"cs.CL"
] | Pursuing human-like interaction for Graphical User Interface (GUI) agents requires understanding the GUI context and following user instructions. However, existing works typically couple these two aspects and focus more on instruct-following abilities, while ignoring the importance of understanding the GUI context. In ... | {
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2412.09369 | Distribution free uncertainty quantification in neuroscience-inspired
deep operators | [
"stat.ML",
"cs.LG"
] | Energy-efficient deep learning algorithms are essential for a sustainable future and feasible edge computing setups. Spiking neural networks (SNNs), inspired from neuroscience, are a positive step in the direction of achieving the required energy efficiency. However, in a bid to lower the energy requirements, accuracy ... | {
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2412.09370 | Word Sense Linking: Disambiguating Outside the Sandbox | [
"cs.CL",
"cs.AI"
] | Word Sense Disambiguation (WSD) is the task of associating a word in a given context with its most suitable meaning among a set of possible candidates. While the task has recently witnessed renewed interest, with systems achieving performances above the estimated inter-annotator agreement, at the time of writing it sti... | {
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2412.09376 | A comprehensive interpretable machine learning framework for Mild
Cognitive Impairment and Alzheimer's disease diagnosis | [
"cs.LG"
] | An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The dataset used comprises volumetric measurements from brain MRI and genetic data from healthy individuals an... | {
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2412.09378 | From Bench to Bedside: A Review of Clinical Trials in Drug Discovery and
Development | [
"cs.CY",
"cs.CL"
] | Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application. During the development of new drugs, clinical trials are used not only to evaluate the safety and efficacy of the drug but also to explore its dosage, treatment regimens, and pote... | {
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2412.09379 | Hybrid variable spiking graph neural networks for energy-efficient
scientific machine learning | [
"cs.LG"
] | Graph-based representations for samples of computational mechanics-related datasets can prove instrumental when dealing with problems like irregular domains or molecular structures of materials, etc. To effectively analyze and process such datasets, deep learning offers Graph Neural Networks (GNNs) that utilize techniq... | {
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2412.09380 | Diffusion Model with Representation Alignment for Protein Inverse
Folding | [
"cs.LG",
"cs.AI"
] | Protein inverse folding is a fundamental problem in bioinformatics, aiming to recover the amino acid sequences from a given protein backbone structure. Despite the success of existing methods, they struggle to fully capture the intricate inter-residue relationships critical for accurate sequence prediction. We propose ... | {
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2412.09383 | Neural Text Normalization for Luxembourgish using Real-Life Variation
Data | [
"cs.CL"
] | Orthographic variation is very common in Luxembourgish texts due to the absence of a fully-fledged standard variety. Additionally, developing NLP tools for Luxembourgish is a difficult task given the lack of annotated and parallel data, which is exacerbated by ongoing standardization. In this paper, we propose the firs... | {
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2412.09385 | AI Predicts AGI: Leveraging AGI Forecasting and Peer Review to Explore
LLMs' Complex Reasoning Capabilities | [
"cs.AI"
] | We tasked 16 state-of-the-art large language models (LLMs) with estimating the likelihood of Artificial General Intelligence (AGI) emerging by 2030. To assess the quality of these forecasts, we implemented an automated peer review process (LLM-PR). The LLMs' estimates varied widely, ranging from 3% (Reka- Core) to 47.6... | {
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2412.09386 | Multi-Stage Segmentation and Cascade Classification Methods for
Improving Cardiac MRI Analysis | [
"eess.IV",
"cs.CV",
"cs.LG"
] | The segmentation and classification of cardiac magnetic resonance imaging are critical for diagnosing heart conditions, yet current approaches face challenges in accuracy and generalizability. In this study, we aim to further advance the segmentation and classification of cardiac magnetic resonance images by introducin... | {
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2412.09387 | Distributed Intelligent System Architecture for UAV-Assisted Monitoring
of Wind Energy Infrastructure | [
"cs.RO",
"cs.AI",
"cs.SY",
"eess.SY"
] | With the rapid development of green energy, the efficiency and reliability of wind turbines are key to sustainable renewable energy production. For that reason, this paper presents a novel intelligent system architecture designed for the dynamic collection and real-time processing of visual data to detect defects in wi... | {
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2412.09388 | All You Need in Knowledge Distillation Is a Tailored Coordinate System | [
"cs.CV",
"cs.AI"
] | Knowledge Distillation (KD) is essential in transferring dark knowledge from a large teacher to a small student network, such that the student can be much more efficient than the teacher but with comparable accuracy. Existing KD methods, however, rely on a large teacher trained specifically for the target task, which i... | {
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2412.09389 | UFO: Enhancing Diffusion-Based Video Generation with a Uniform Frame
Organizer | [
"cs.CV",
"cs.AI"
] | Recently, diffusion-based video generation models have achieved significant success. However, existing models often suffer from issues like weak consistency and declining image quality over time. To overcome these challenges, inspired by aesthetic principles, we propose a non-invasive plug-in called Uniform Frame Organ... | {
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2412.09399 | A Geometry-Aware Message Passing Neural Network for Modeling
Aerodynamics over Airfoils | [
"cs.LG"
] | Computational modeling of aerodynamics is a key problem in aerospace engineering, often involving flows interacting with solid objects such as airfoils. Deep surrogate models have emerged as purely data-driven approaches that learn direct mappings from simulation conditions to solutions based on either simulation or ex... | {
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2412.09401 | SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos | [
"cs.CV"
] | In this paper, we introduce SLAM3R, a novel and effective monocular RGB SLAM system for real-time and high-quality dense 3D reconstruction. SLAM3R provides an end-to-end solution by seamlessly integrating local 3D reconstruction and global coordinate registration through feed-forward neural networks. Given an input vid... | {
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2412.09402 | MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease
Recognition from Fundus Images | [
"cs.CV"
] | Existing multi-modal learning methods on fundus and OCT images mostly require both modalities to be available and strictly paired for training and testing, which appears less practical in clinical scenarios. To expand the scope of clinical applications, we formulate a novel setting, "OCT-enhanced disease recognition fr... | {
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2412.09404 | Opinion de-polarization of social networks with GNNs | [
"cs.SI",
"cs.LG"
] | Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests that social media are highly polarized. A phenomenon that is commonly observed is the echo chamber structure, where users are organized in polarized communities and form connection... | {
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2412.09405 | Learned Compression for Compressed Learning | [
"eess.IV",
"cs.CV",
"cs.LG",
"eess.AS",
"eess.SP"
] | Modern sensors produce increasingly rich streams of high-resolution data. Due to resource constraints, machine learning systems discard the vast majority of this information via resolution reduction. Compressed-domain learning allows models to operate on compact latent representations, allowing higher effective resolut... | {
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2412.09407 | Uncommon Belief in Rationality | [
"cs.AI",
"cs.MA"
] | Common knowledge/belief in rationality is the traditional standard assumption in analysing interaction among agents. This paper proposes a graph-based language for capturing significantly more complicated structures of higher-order beliefs that agents might have about the rationality of the other agents. The two main c... | {
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2412.09411 | Resilience for Regular Path Queries: Towards a Complexity Classification | [
"cs.DB"
] | The resilience problem for a query and an input set or bag database is to compute the minimum number of facts to remove from the database to make the query false. In this paper, we study how to compute the resilience of Regular Path Queries (RPQs) over graph databases. Our goal is to characterize the regular languages ... | {
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2412.09413 | Imitate, Explore, and Self-Improve: A Reproduction Report on
Slow-thinking Reasoning Systems | [
"cs.AI",
"cs.CL"
] | Recently, slow-thinking reasoning systems, such as o1, have demonstrated remarkable capabilities in solving complex reasoning tasks. These systems typically engage in an extended thinking process before responding to a query, allowing them to generate more thorough, accurate, and well-reasoned solutions. These systems ... | {
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2412.09415 | Text Generation Models for Luxembourgish with Limited Data: A Balanced
Multilingual Strategy | [
"cs.CL"
] | This paper addresses the challenges in developing language models for less-represented languages, with a focus on Luxembourgish. Despite its active development, Luxembourgish faces a digital data scarcity, exacerbated by Luxembourg's multilingual context. We propose a novel text generation model based on the T5 archite... | {
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2412.09416 | Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical
Ability Assessment of LLM-Powered AI Tutors | [
"cs.CL"
] | In this paper, we investigate whether current state-of-the-art large language models (LLMs) are effective as AI tutors and whether they demonstrate pedagogical abilities necessary for good AI tutoring in educational dialogues. Previous efforts towards evaluation have been limited to subjective protocols and benchmarks.... | {
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2412.09417 | Reinforcement Learning Within the Classical Robotics Stack: A Case Study
in Robot Soccer | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge. Model-free reinforcement learning (RL) is a promising approach to learning decision-making in such domains, however, end-to-end RL in complex environments is often intractable. To ... | {
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2412.09418 | NExT-LF: A Novel Operational Modal Analysis Method via Tangential
Interpolation | [
"eess.SY",
"cs.SY"
] | Operational Modal Analysis (OMA) is vital for identifying modal parameters under real-world conditions, yet existing methods often face challenges with noise sensitivity and stability. This work introduces NExT-LF, a novel method that combines the well-known Natural Excitation Technique (NExT) with the Loewner Framewor... | {
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2412.09420 | Mixture of neural fields for heterogeneous reconstruction in cryo-EM | [
"cs.LG"
] | Cryo-electron microscopy (cryo-EM) is an experimental technique for protein structure determination that images an ensemble of macromolecules in near-physiological contexts. While recent advances enable the reconstruction of dynamic conformations of a single biomolecular complex, current methods do not adequately model... | {
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2412.09423 | Data Efficient Prediction of excited-state properties using Quantum
Neural Networks | [
"quant-ph",
"cs.LG"
] | Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more resource-intensive than calculating their ground state counterparts. We present a quantum machine learning model that predicts excited-state p... | {
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2412.09424 | Slope Considered Online Nonlinear Trajectory Planning with Differential
Energy Model for Autonomous Driving | [
"cs.RO"
] | Achieving energy-efficient trajectory planning for autonomous driving remains a challenge due to the limitations of model-agnostic approaches. This study addresses this gap by introducing an online nonlinear programming trajectory optimization framework that integrates a differentiable energy model into autonomous syst... | {
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2412.09427 | A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon
LiDAR data | [
"eess.IV",
"cs.CV"
] | Single-photon avalanche diodes (SPADs) are advanced sensors capable of detecting individual photons and recording their arrival times with picosecond resolution using time-correlated Single-Photon Counting detection techniques. They are used in various applications, such as LiDAR, and can capture high-speed sequences o... | {
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2412.09428 | Multimodal Music Generation with Explicit Bridges and Retrieval
Augmentation | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fusion. Despite their effectiveness in other modalities, their application in multimodal music generation faces challenges of data scarcity, w... | {
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2412.09429 | From Intention To Implementation: Automating Biomedical Research via
LLMs | [
"cs.MA",
"cs.AI",
"cs.CL"
] | Conventional biomedical research is increasingly labor-intensive due to the exponential growth of scientific literature and datasets. Artificial intelligence (AI), particularly Large Language Models (LLMs), has the potential to revolutionize this process by automating various steps. Still, significant challenges remain... | {
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2412.09433 | Solving Multiagent Path Finding on Highly Centralized Networks | [
"cs.CC",
"cs.AI"
] | The Mutliagent Path Finding (MAPF) problem consists of identifying the trajectories that a set of agents should follow inside a given network in order to reach their desired destinations as soon as possible, but without colliding with each other. We aim to minimize the maximum time any agent takes to reach their goal, ... | {
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2412.09439 | Towards Robust and Fair Vision Learning in Open-World Environments | [
"cs.CV"
] | The dissertation presents four key contributions toward fairness and robustness in vision learning. First, to address the problem of large-scale data requirements, the dissertation presents a novel Fairness Domain Adaptation approach derived from two major novel research findings of Bijective Maximum Likelihood and Fai... | {
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2412.09440 | Learning to Adapt: Bio-Inspired Gait Strategies for Versatile Quadruped
Locomotion | [
"cs.RO"
] | Deep reinforcement learning (DRL) has revolutionised quadruped robot locomotion, but existing control frameworks struggle to generalise beyond their training-induced observational scope, resulting in limited adaptability. In contrast, animals achieve exceptional adaptability through gait transition strategies, diverse ... | {
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2412.09441 | MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental
Learning | [
"cs.LG",
"cs.CV"
] | Class-Incremental Learning (CIL) requires models to continually acquire knowledge of new classes without forgetting old ones. Despite Pre-trained Models (PTMs) have shown excellent performance in CIL, catastrophic forgetting still occurs as the model learns new concepts. Existing work seeks to utilize lightweight compo... | {
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2412.09442 | ATPrompt: Textual Prompt Learning with Embedded Attributes | [
"cs.CV"
] | Textual-based prompt learning methods primarily employ multiple learnable soft prompts and hard class tokens in a cascading manner as text prompt inputs, aiming to align image and text (category) spaces for downstream tasks. However, current training is restricted to aligning images with predefined known categories and... | {
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2412.09444 | Search Strategy Generation for Branch and Bound Using Genetic
Programming | [
"cs.LG"
] | Branch-and-Bound (B\&B) is an exact method in integer programming that recursively divides the search space into a tree. During the resolution process, determining the next subproblem to explore within the tree-known as the search strategy-is crucial. Hand-crafted heuristics are commonly used, but none are effective ov... | {
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2412.09445 | Embeddings are all you need! Achieving High Performance Medical Image
Classification through Training-Free Embedding Analysis | [
"eess.IV",
"cs.CV"
] | Developing artificial intelligence (AI) and machine learning (ML) models for medical imaging typically involves extensive training and testing on large datasets, consuming significant computational time, energy, and resources. There is a need for more efficient methods that can achieve comparable or superior diagnostic... | {
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2412.09448 | DumpyOS: A Data-Adaptive Multi-ary Index for Scalable Data Series
Similarity Search | [
"cs.DB"
] | Data series indexes are necessary for managing and analyzing the increasing amounts of data series collections that are nowadays available. These indexes support both exact and approximate similarity search, with approximate search providing high-quality results within milliseconds, which makes it very attractive for c... | {
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} |
2412.09453 | Finite-PINN: A Physics-Informed Neural Network Architecture for Solving
Solid Mechanics Problems with General Geometries | [
"cs.CE",
"cs.LG",
"math.AP"
] | PINN models have demonstrated impressive capabilities in addressing fluid PDE problems, and their potential in solid mechanics is beginning to emerge. This study identifies two key challenges when using PINN to solve general solid mechanics problems. These challenges become evident when comparing the limitations of PIN... | {
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} |
2412.09460 | The Impact of Copyrighted Material on Large Language Models: A Norwegian
Perspective | [
"cs.CL"
] | The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of publisher-controlled copyrighted corpora on the performance of generative large language models (LLMs) for Norwegian. When ... | {
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} |
2412.09465 | OFTSR: One-Step Flow for Image Super-Resolution with Tunable
Fidelity-Realism Trade-offs | [
"cs.CV"
] | Recent advances in diffusion and flow-based generative models have demonstrated remarkable success in image restoration tasks, achieving superior perceptual quality compared to traditional deep learning approaches. However, these methods either require numerous sampling steps to generate high-quality images, resulting ... | {
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} |
2412.09466 | Distributional Reinforcement Learning based Integrated Decision Making
and Control for Autonomous Surface Vehicles | [
"cs.RO"
] | With the growing demands for Autonomous Surface Vehicles (ASVs) in recent years, the number of ASVs being deployed for various maritime missions is expected to increase rapidly in the near future. However, it is still challenging for ASVs to perform sensor-based autonomous navigation in obstacle-filled and congested wa... | {
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} |
2412.09467 | Audios Don't Lie: Multi-Frequency Channel Attention Mechanism for Audio
Deepfake Detection | [
"cs.SD",
"cs.CL",
"eess.AS"
] | With the rapid development of artificial intelligence technology, the application of deepfake technology in the audio field has gradually increased, resulting in a wide range of security risks. Especially in the financial and social security fields, the misuse of deepfake audios has raised serious concerns. To address ... | {
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} |
2412.09468 | STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized
Variational Autoencoders for Financial Trading | [
"cs.LG",
"cs.AI"
] | In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. Recently, we have witnessed the rise of variational autoencoder-based latent factor models, which learn latent factors self-adaptively. While these models focus on modeling overall market conditions, they oft... | {
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} |
2412.09469 | Neural Network Symmetrisation in Concrete Settings | [
"cs.LG"
] | Cornish (2024) recently gave a general theory of neural network symmetrisation in the abstract context of Markov categories. We give a high-level overview of these results, and their concrete implications for the symmetrisation of deterministic functions and of Markov kernels. | {
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} |
2412.09472 | A Novel Ensemble-Based Deep Learning Model with Explainable AI for
Accurate Kidney Disease Diagnosis | [
"cs.LG"
] | Chronic Kidney Disease (CKD) represents a significant global health challenge, characterized by the progressive decline in renal function, leading to the accumulation of waste products and disruptions in fluid balance within the body. Given its pervasive impact on public health, there is a pressing need for effective d... | {
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} |
2412.09475 | New keypoint-based approach for recognising British Sign Language (BSL)
from sequences | [
"cs.CV",
"cs.AI"
] | In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's performance is assessed using the BOBSL dataset, revealing that the keypoint-based approach surpasses its RGB-based counterpart in computational ... | {
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} |
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