id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.08528 | Dynamic Portfolio Optimization via Augmented DDPG with Quantum Price
Levels-Based Trading Strategy | [
"cs.CE",
"cs.AI"
] | With the development of deep learning, Dynamic Portfolio Optimization (DPO) problem has received a lot of attention in recent years, not only in the field of finance but also in the field of deep learning. Some advanced research in recent years has proposed the application of Deep Reinforcement Learning (DRL) to the DP... | {
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2501.08531 | A Novel Multiple Interval Prediction Method for Electricity Prices based
on Scenarios Generation: Definition and Method | [
"eess.SY",
"cs.SY"
] | This paper presents interval prediction methodology to address limitations in existing evaluation indicators and improve prediction accuracy and reliability. First, new evaluation indicators are proposed to comprehensively assess interval prediction methods, considering both all-sample and single-sample scenarios. Seco... | {
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2501.08532 | Scenarios Generation-based Multiple Interval Prediction Method for
Electricity Prices | [
"eess.SY",
"cs.SY"
] | This paper introduces an innovative interval prediction methodology aimed at addressing the limitations of current evaluation indicators while enhancing prediction accuracy and reliability. To achieve this, new evaluation metrics are proposed, offering a comprehensive assessment of interval prediction methods across bo... | {
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2501.08537 | Complexity Control Facilitates Reasoning-Based Compositional
Generalization in Transformers | [
"cs.CL",
"cs.LG"
] | Transformers have demonstrated impressive capabilities across various tasks, yet their performance on compositional problems remains a subject of debate. In this study, we investigate the internal mechanisms underlying Transformers' behavior in compositional tasks. We find that complexity control strategies significant... | {
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2501.08538 | Homophily-aware Heterogeneous Graph Contrastive Learning | [
"cs.LG",
"cs.SI"
] | Heterogeneous graph pre-training (HGP) has demonstrated remarkable performance across various domains. However, the issue of heterophily in real-world heterogeneous graphs (HGs) has been largely overlooked. To bridge this research gap, we proposed a novel heterogeneous graph contrastive learning framework, termed HGMS,... | {
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2501.08539 | Research on stock price forecast of general electric based on mixed
CNN-LSTM model | [
"cs.CE"
] | Accurate stock price prediction is crucial for investors and financial institutions, yet the complexity of the stock market makes it highly challenging. This study aims to construct an effective model to enhance the prediction ability of General Electric's stock price trend. The CNN - LSTM model is adopted, combining t... | {
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2501.08540 | Knowledge prompt chaining for semantic modeling | [
"cs.CL",
"cs.AI",
"cs.DB"
] | The task of building semantics for structured data such as CSV, JSON, and XML files is highly relevant in the knowledge representation field. Even though we have a vast of structured data on the internet, mapping them to domain ontologies to build semantics for them is still very challenging as it requires the construc... | {
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2501.08545 | T2VEval: T2V-generated Videos Benchmark Dataset and Objective Evaluation
Method | [
"cs.CV"
] | Recent advances in text-to-video (T2V) technology, as demonstrated by models such as Runway Gen-3, Pika, Sora, and Kling, have significantly broadened the applicability and popularity of the technology. This progress has created a growing demand for accurate quality assessment metrics to evaluate the perceptual quality... | {
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2501.08547 | OMEGA: A Low-Latency GNN Serving System for Large Graphs | [
"cs.DC",
"cs.LG"
] | Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However, serving GNNs on large graphs is challenging due to the high communication, computation, and memory overheads of constructing and executing computation graphs, which represent in... | {
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2501.08549 | The Devil is in Temporal Token: High Quality Video Reasoning
Segmentation | [
"cs.CV",
"cs.AI"
] | Existing methods for Video Reasoning Segmentation rely heavily on a single special token to represent the object in the keyframe or the entire video, inadequately capturing spatial complexity and inter-frame motion. To overcome these challenges, we propose VRS-HQ, an end-to-end video reasoning segmentation approach tha... | {
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2501.08551 | A Theory of Optimistically Universal Online Learnability for General
Concept Classes | [
"stat.ML",
"cs.LG"
] | We provide a full characterization of the concept classes that are optimistically universally online learnable with $\{0, 1\}$ labels. The notion of optimistically universal online learning was defined in [Hanneke, 2021] in order to understand learnability under minimal assumptions. In this paper, following the philoso... | {
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2501.08552 | Reinforcement Learning-Enhanced Procedural Generation for Dynamic
Narrative-Driven AR Experiences | [
"cs.AI",
"cs.GR",
"cs.HC",
"cs.LG"
] | Procedural Content Generation (PCG) is widely used to create scalable and diverse environments in games. However, existing methods, such as the Wave Function Collapse (WFC) algorithm, are often limited to static scenarios and lack the adaptability required for dynamic, narrative-driven applications, particularly in aug... | {
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2501.08553 | DynamicFace: High-Quality and Consistent Video Face Swapping using
Composable 3D Facial Priors | [
"cs.CV"
] | Face swapping transfers the identity of a source face to a target face while retaining the attributes like expression, pose, hair, and background of the target face. Advanced face swapping methods have achieved attractive results. However, these methods often inadvertently transfer identity information from the target ... | {
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2501.08558 | LAMS: LLM-Driven Automatic Mode Switching for Assistive Teleoperation | [
"cs.RO",
"cs.AI",
"cs.HC",
"cs.LG"
] | Teleoperating high degrees-of-freedom (DoF) robotic manipulators via low-DoF controllers like joysticks often requires frequent switching between control modes, where each mode maps controller movements to specific robot actions. Manually performing this frequent switching can make teleoperation cumbersome and ineffici... | {
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2501.08561 | ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for
Digital Twins | [
"cs.AI",
"cs.HC",
"cs.LG",
"cs.SC"
] | In this paper, we propose an Adaptive Neuro-Symbolic Learning Framework for digital twin technology called ``ANSR-DT." Our approach combines pattern recognition algorithms with reinforcement learning and symbolic reasoning to enable real-time learning and adaptive intelligence. This integration enhances the understandi... | {
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2501.08562 | MIAFEx: An Attention-based Feature Extraction Method for Medical Image
Classification | [
"cs.CV",
"cs.LG"
] | Feature extraction techniques are crucial in medical image classification; however, classical feature extractors in addition to traditional machine learning classifiers often exhibit significant limitations in providing sufficient discriminative information for complex image sets. While Convolutional Neural Networks (C... | {
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2501.08563 | Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and
Applications | [
"cs.LG"
] | The softmax function is a cornerstone of multi-class classification, integral to a wide range of machine learning applications, from large-scale retrieval and ranking models to advanced large language models. However, its computational cost grows linearly with the number of classes, which becomes prohibitively expensiv... | {
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2501.08565 | DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale
Traveling Salesman Problem | [
"cs.AI"
] | This paper proposes a dual divide-and-optimize algorithm (DualOpt) for solving the large-scale traveling salesman problem (TSP). DualOpt combines two complementary strategies to improve both solution quality and computational efficiency. The first strategy is a grid-based divide-and-conquer procedure that partitions th... | {
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2501.08566 | Towards Lightweight and Stable Zero-shot TTS with Self-distilled
Representation Disentanglement | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Zero-shot Text-To-Speech (TTS) synthesis shows great promise for personalized voice customization through voice cloning. However, current methods for achieving zero-shot TTS heavily rely on large model scales and extensive training datasets to ensure satisfactory performance and generalizability across various speakers... | {
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2501.08569 | Evaluating SAT and SMT Solvers on Large-Scale Sudoku Puzzles | [
"cs.AI",
"cs.LO"
] | Modern SMT solvers have revolutionized the approach to constraint satisfaction problems by integrating advanced theory reasoning and encoding techniques. In this work, we evaluate the performance of modern SMT solvers in Z3, CVC5 and DPLL(T) against a standard SAT solver in DPLL. By benchmarking these solvers on novel,... | {
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2501.08570 | Information Entropy Invariance: Enhancing Length Extrapolation in
Attention Mechanisms | [
"cs.CL"
] | Since the emergence of research on improving the length extrapolation capabilities of large language models in 2021, some studies have made modifications to the scaling factor in the scaled dot-product attention mechanism as part of their proposed methods without rigorous theoretical justifications. To fill this gap, w... | {
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2501.08572 | DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe
Medication Recommendation | [
"cs.LG",
"cs.IR"
] | Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's... | {
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2501.08575 | GOTLoc: General Outdoor Text-based Localization Using Scene Graph
Retrieval with OpenStreetMap | [
"cs.RO",
"cs.CV"
] | We propose GOTLoc, a robust localization method capable of operating even in outdoor environments where GPS signals are unavailable. The method achieves this robust localization by leveraging comparisons between scene graphs generated from text descriptions and maps. Existing text-based localization studies typically r... | {
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2501.08577 | Scalable and High-Quality Neural Implicit Representation for 3D
Reconstruction | [
"cs.CV",
"cs.GR"
] | Various SDF-based neural implicit surface reconstruction methods have been proposed recently, and have demonstrated remarkable modeling capabilities. However, due to the global nature and limited representation ability of a single network, existing methods still suffer from many drawbacks, such as limited accuracy and ... | {
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2501.08579 | What Limits LLM-based Human Simulation: LLMs or Our Design? | [
"cs.CL"
] | We argue that advancing LLM-based human simulation requires addressing both LLM's inherent limitations and simulation framework design challenges. Recent studies have revealed significant gaps between LLM-based human simulations and real-world observations, highlighting these dual challenges. To address these gaps, we ... | {
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2501.08580 | Densely Connected Parameter-Efficient Tuning for Referring Image
Segmentation | [
"cs.CV"
] | In the domain of computer vision, Parameter-Efficient Tuning (PET) is increasingly replacing the traditional paradigm of pre-training followed by full fine-tuning. PET is particularly favored for its effectiveness in large foundation models, as it streamlines transfer learning costs and optimizes hardware utilization. ... | {
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2501.08581 | Normalize Then Propagate: Efficient Homophilous Regularization for
Few-shot Semi-Supervised Node Classification | [
"cs.LG"
] | Graph Neural Networks (GNNs) have demonstrated remarkable ability in semi-supervised node classification. However, most existing GNNs rely heavily on a large amount of labeled data for training, which is labor-intensive and requires extensive domain knowledge. In this paper, we first analyze the restrictions of GNNs ge... | {
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2501.08582 | LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model | [
"cs.CL"
] | Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that maintain sparsity by augmenting LoRA techniques with additional masking mechanisms. Despite these successes, such approaches suffer from an i... | {
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2501.08585 | A Systematic Review of Machine Learning Methods for Multimodal EEG Data
in Clinical Application | [
"eess.SP",
"cs.AI",
"cs.CV",
"cs.LG"
] | Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to enhance the accuracy of ML and DL models. Combining EEG with other modalities c... | {
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2501.08587 | Sound Scene Synthesis at the DCASE 2024 Challenge | [
"cs.AI",
"cs.SD",
"eess.AS"
] | This paper presents Task 7 at the DCASE 2024 Challenge: sound scene synthesis. Recent advances in sound synthesis and generative models have enabled the creation of realistic and diverse audio content. We introduce a standardized evaluation framework for comparing different sound scene synthesis systems, incorporating ... | {
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2501.08589 | Molecular Graph Contrastive Learning with Line Graph | [
"cs.LG"
] | Trapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning (GCL) came forward. Leading contrastive learning works show two kinds of view generators, that is, random or learnable data corruption and domain knowledge incorporation. While effective, the two ways also lead to... | {
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2501.08591 | OpenMLDB: A Real-Time Relational Data Feature Computation System for
Online ML | [
"cs.DB",
"cs.AI",
"cs.LG"
] | Efficient and consistent feature computation is crucial for a wide range of online ML applications. Typically, feature computation is divided into two distinct phases, i.e., offline stage for model training and online stage for model serving. These phases often rely on execution engines with different interface languag... | {
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2501.08593 | Image-to-Force Estimation for Soft Tissue Interaction in
Robotic-Assisted Surgery Using Structured Light | [
"cs.RO",
"cs.CV"
] | For Minimally Invasive Surgical (MIS) robots, accurate haptic interaction force feedback is essential for ensuring the safety of interacting with soft tissue. However, most existing MIS robotic systems cannot facilitate direct measurement of the interaction force with hardware sensors due to space limitations. This let... | {
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2501.08595 | Characterizations of voting rules based on majority margins | [
"econ.TH",
"cs.GT",
"cs.MA"
] | In the context of voting with ranked ballots, an important class of voting rules is the class of margin-based rules (also called pairwise rules). A voting rule is margin-based if whenever two elections generate the same head-to-head margins of victory or loss between candidates, then the voting rule yields the same out... | {
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2501.08597 | Dynamic Knowledge Integration for Enhanced Vision-Language Reasoning | [
"cs.CL"
] | Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in multimodal tasks, but their performance is often constrained by the lack of external knowledge integration, limiting their ability to handle knowledge-intensive tasks such as visual question answering and reasoning. To address this challe... | {
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2501.08598 | LlamaRestTest: Effective REST API Testing with Small Language Models | [
"cs.SE",
"cs.AI"
] | Modern web services rely heavily on REST APIs, typically documented using the OpenAPI specification. The widespread adoption of this standard has resulted in the development of many black-box testing tools that generate tests based on these specifications. Recent advancements in Natural Language Processing (NLP), parti... | {
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2501.08600 | AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL | [
"cs.SE",
"cs.AI"
] | As REST APIs have become widespread in modern web services, comprehensive testing of these APIs has become increasingly crucial. Due to the vast search space consisting of operations, parameters, and parameter values along with their complex dependencies and constraints, current testing tools suffer from low code cover... | {
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2501.08603 | Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based
Automatic Heuristic Design | [
"cs.AI"
] | Handcrafting heuristics for solving complex optimization tasks (e.g., route planning and task allocation) is a common practice but requires extensive domain knowledge. Recently, Large Language Model (LLM)-based automatic heuristic design (AHD) methods have shown promise in generating high-quality heuristics without man... | {
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2501.08604 | Watermarking in Diffusion Model: Gaussian Shading with Exact Diffusion
Inversion via Coupled Transformations (EDICT) | [
"cs.CV"
] | This paper introduces a novel approach to enhance the performance of Gaussian Shading, a prevalent watermarking technique, by integrating the Exact Diffusion Inversion via Coupled Transformations (EDICT) framework. While Gaussian Shading traditionally embeds watermarks in a noise latent space, followed by iterative den... | {
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2501.08605 | PACF: Prototype Augmented Compact Features for Improving Domain Adaptive
Object Detection | [
"cs.CV"
] | In recent years, there has been significant advancement in object detection. However, applying off-the-shelf detectors to a new domain leads to significant performance drop, caused by the domain gap. These detectors exhibit higher-variance class-conditional distributions in the target domain than that in the source dom... | {
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2501.08609 | Computerized Assessment of Motor Imitation for Distinguishing Autism in
Video (CAMI-2DNet) | [
"cs.CV"
] | Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used as a phenotype for addressing autism heterogeneity. Traditional methods for assessing motor imitation are subjective, labor-intensive, and require extensive human traini... | {
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2501.08612 | Neural Risk-sensitive Satisficing in Contextual Bandits | [
"cs.LG"
] | The contextual bandit problem, which is a type of reinforcement learning tasks, provides an effective framework for solving challenges in recommendation systems, such as satisfying real-time requirements, enabling personalization, addressing cold-start problems. However, contextual bandit algorithms face challenges sin... | {
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2501.08613 | Assessing the Alignment of FOL Closeness Metrics with Human Judgement | [
"cs.CL"
] | The recent successful paradigm of solving logical reasoning problems with tool-augmented large language models (LLMs) leverages translation of natural language statements into First-Order Logic~(FOL) and external theorem provers. However, the correctness of FOL statements, comprising operators and text predicates, ofte... | {
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2501.08615 | Towards Aligned Data Forgetting via Twin Machine Unlearning | [
"cs.LG"
] | Modern privacy regulations have spurred the evolution of machine unlearning, a technique enabling a trained model to efficiently forget specific training data. In prior unlearning methods, the concept of "data forgetting" is often interpreted and implemented as achieving zero classification accuracy on such data. Never... | {
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2501.08617 | RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation | [
"cs.LG",
"cs.AI",
"cs.CL"
] | While Reinforcement Learning from Human Feedback (RLHF) has shown promise in aligning generative AI, we present empirical evidence that it can also cause severe, systematic misalignment. We hypothesize that this stems from evaluator feedback depending on downstream outcome predictions (foresight) that can be influenced... | {
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2501.08618 | Disjoint Processing Mechanisms of Hierarchical and Linear Grammars in
Large Language Models | [
"cs.CL",
"cs.AI"
] | All natural languages are structured hierarchically. In humans, this structural restriction is neurologically coded: when two grammars are presented with identical vocabularies, brain areas responsible for language processing are only sensitive to hierarchical grammars. Using large language models (LLMs), we investigat... | {
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2501.08620 | CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term
Renewable Energy Forecasting | [
"cs.LG"
] | Accurately predicting renewable energy output is crucial for the efficient integration of solar and wind power into modern energy systems. This study develops and evaluates an advanced deep learning model, Channel-Time Patch Time-Series Transformer (CT-PatchTST), to forecast the power output of photovoltaic and wind en... | {
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2501.08621 | ViBidirectionMT-Eval: Machine Translation for Vietnamese-Chinese and
Vietnamese-Lao language pair | [
"cs.CL",
"cs.AI"
] | This paper presents an results of the VLSP 2022-2023 Machine Translation Shared Tasks, focusing on Vietnamese-Chinese and Vietnamese-Lao machine translation. The tasks were organized as part of the 9th, 10th annual workshop on Vietnamese Language and Speech Processing (VLSP 2022, VLSP 2023). The objective of the shared... | {
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2501.08625 | A Bioplausible Model for the Expanding Hole Illusion: Insights into
Retinal Processing and Illusory Motion | [
"q-bio.NC",
"cs.NE",
"eess.IV"
] | The Expanding Hole Illusion is a compelling visual phenomenon in which a static, concentric pattern evokes a strong perception of continuous forward motion. Despite its simplicity, this illusion challenges our understanding of how the brain processes visual information, particularly motion derived from static cues. Whi... | {
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2501.08626 | A Learning Algorithm That Attains the Human Optimum in a Repeated
Human-Machine Interaction Game | [
"cs.GT",
"cs.HC",
"cs.LG"
] | When humans interact with learning-based control systems, a common goal is to minimize a cost function known only to the human. For instance, an exoskeleton may adapt its assistance in an effort to minimize the human's metabolic cost-of-transport. Conventional approaches to synthesizing the learning algorithm solve an ... | {
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2501.08628 | Transformer-based Multivariate Time Series Anomaly Localization | [
"cs.LG"
] | With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability a... | {
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2501.08629 | Self-Organizing Edge Computing Distribution Framework for Visual SLAM | [
"cs.RO",
"cs.CV",
"cs.DC"
] | Localization within a known environment is a crucial capability for mobile robots. Simultaneous Localization and Mapping (SLAM) is a prominent solution to this problem. SLAM is a framework that consists of a diverse set of computational tasks ranging from real-time tracking to computation-intensive map optimization. Th... | {
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2501.08631 | SWSC: Shared Weight for Similar Channel in LLM | [
"cs.LG",
"cs.CL"
] | Large language models (LLMs) have spurred development in multiple industries. However, the growing number of their parameters brings substantial storage and computing burdens, making it essential to explore model compression techniques for parameter reduction and easier deployment. We propose SWSC, an LLM compression m... | {
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2501.08639 | Detecting Wildfire Flame and Smoke through Edge Computing using Transfer
Learning Enhanced Deep Learning Models | [
"cs.CV",
"eess.IV"
] | Autonomous unmanned aerial vehicles (UAVs) integrated with edge computing capabilities empower real-time data processing directly on the device, dramatically reducing latency in critical scenarios such as wildfire detection. This study underscores Transfer Learning's (TL) significance in boosting the performance of obj... | {
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2501.08640 | Quantum Reservoir Computing and Risk Bounds | [
"cs.LG",
"stat.ML"
] | We propose a way to bound the generalisation errors of several classes of quantum reservoirs using the Rademacher complexity. We give specific, parameter-dependent bounds for two particular quantum reservoir classes. We analyse how the generalisation bounds scale with growing numbers of qubits. Applying our results to ... | {
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2501.08641 | Reassessing the Role of Chain-of-Thought in Sentiment Analysis: Insights
and Limitations | [
"cs.CL",
"cs.AI"
] | The relationship between language and thought remains an unresolved philosophical issue. Existing viewpoints can be broadly categorized into two schools: one asserting their independence, and another arguing that language constrains thought. In the context of large language models, this debate raises a crucial question... | {
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2501.08643 | MonSter: Marry Monodepth to Stereo Unleashes Power | [
"cs.CV"
] | Stereo matching recovers depth from image correspondences. Existing methods struggle to handle ill-posed regions with limited matching cues, such as occlusions and textureless areas. To address this, we propose MonSter, a novel method that leverages the complementary strengths of monocular depth estimation and stereo m... | {
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2501.08648 | MAGNET: Augmenting Generative Decoders with Representation Learning and
Infilling Capabilities | [
"cs.CL",
"cs.AI"
] | While originally designed for unidirectional generative modeling, decoder-only large language models (LLMs) are increasingly being adapted for bidirectional modeling. However, unidirectional and bidirectional models are typically trained separately with distinct objectives (generation and representation learning). This... | {
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2501.08649 | Joint Learning of Depth and Appearance for Portrait Image Animation | [
"cs.CV",
"cs.LG"
] | 2D portrait animation has experienced significant advancements in recent years. Much research has utilized the prior knowledge embedded in large generative diffusion models to enhance high-quality image manipulation. However, most methods only focus on generating RGB images as output, and the co-generation of consisten... | {
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2501.08653 | Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor
Graph | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not... | {
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2501.08654 | StereoGen: High-quality Stereo Image Generation from a Single Image | [
"cs.CV"
] | State-of-the-art supervised stereo matching methods have achieved amazing results on various benchmarks. However, these data-driven methods suffer from generalization to real-world scenarios due to the lack of real-world annotated data. In this paper, we propose StereoGen, a novel pipeline for high-quality stereo image... | {
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2501.08655 | Application of Deep Reinforcement Learning to UAV Swarming for Ground
Surveillance | [
"cs.AI",
"cs.RO"
] | This paper summarizes in depth the state of the art of aerial swarms, covering both classical and new reinforcement-learning-based approaches for their management. Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailor... | {
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2501.08659 | BRIGHT-VO: Brightness-Guided Hybrid Transformer for Visual Odometry with
Multi-modality Refinement Module | [
"cs.CV"
] | Visual odometry (VO) plays a crucial role in autonomous driving, robotic navigation, and other related tasks by estimating the position and orientation of a camera based on visual input. Significant progress has been made in data-driven VO methods, particularly those leveraging deep learning techniques to extract image... | {
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2501.08662 | Product of Gaussian Mixture Diffusion Model for non-linear MRI Inversion | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Diffusion models have recently shown remarkable results in magnetic resonance imaging reconstruction. However, the employed networks typically are black-box estimators of the (smoothed) prior score with tens of millions of parameters, restricting interpretability and increasing reconstruction time. Furthermore, paralle... | {
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2501.08665 | A Survey on Facial Image Privacy Preservation in Cloud-Based Services | [
"cs.CV"
] | Facial recognition models are increasingly employed by commercial enterprises, government agencies, and cloud service providers for identity verification, consumer services, and surveillance. These models are often trained using vast amounts of facial data processed and stored in cloud-based platforms, raising signific... | {
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2501.08667 | TimeFlow: Longitudinal Brain Image Registration and Aging Progression
Analysis | [
"eess.IV",
"cs.CV"
] | Predicting future brain states is crucial for understanding healthy aging and neurodegenerative diseases. Longitudinal brain MRI registration, a cornerstone for such analyses, has long been limited by its inability to forecast future developments, reliance on extensive, dense longitudinal data, and the need to balance ... | {
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2501.08669 | SPEQ: Stabilization Phases for Efficient Q-Learning in High
Update-To-Data Ratio Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | A key challenge in Deep Reinforcement Learning is sample efficiency, especially in real-world applications where collecting environment interactions is expensive or risky. Recent off-policy algorithms improve sample efficiency by increasing the Update-To-Data (UTD) ratio and performing more gradient updates per environ... | {
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2501.08672 | GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused
Odometry with Gaussian Mapping | [
"cs.RO",
"cs.CV"
] | In recent years, 3D Gaussian splatting (3D-GS) has emerged as a novel scene representation approach. However, existing vision-only 3D-GS methods often rely on hand-crafted heuristics for point-cloud densification and face challenges in handling occlusions and high GPU memory and computation consumption. LiDAR-Inertial-... | {
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2501.08676 | FlexiClip: Locality-Preserving Free-Form Character Animation | [
"cs.CV",
"cs.GR"
] | Animating clipart images with seamless motion while maintaining visual fidelity and temporal coherence presents significant challenges. Existing methods, such as AniClipart, effectively model spatial deformations but often fail to ensure smooth temporal transitions, resulting in artifacts like abrupt motions and geomet... | {
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2501.08678 | Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric
Properties of Generated Sea Route Graphs | [
"cs.LG",
"quant-ph"
] | The demand for artificially generated data for the development, training and testing of new algorithms is omnipresent. Quantum computing (QC), does offer the hope that its inherent probabilistic functionality can be utilised in this field of generative artificial intelligence. In this study, we use quantum-classical hy... | {
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2501.08679 | Diagonal Over-parameterization in Reproducing Kernel Hilbert Spaces as
an Adaptive Feature Model: Generalization and Adaptivity | [
"cs.LG",
"stat.ML"
] | This paper introduces a diagonal adaptive kernel model that dynamically learns kernel eigenvalues and output coefficients simultaneously during training. Unlike fixed-kernel methods tied to the neural tangent kernel theory, the diagonal adaptive kernel model adapts to the structure of the truth function, significantly ... | {
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2501.08680 | Digital Twin Online Channel Modeling: Challenges,Principles, and
Applications | [
"eess.SY",
"cs.NI",
"cs.SY"
] | Different from traditional offline channel modeling, digital twin online channel modeling can sense and accurately characterize dynamic wireless channels in real time, and can therefore greatly assist 6G network optimization. This article proposes a novel promising framework and a step-by-step design procedure of digit... | {
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2501.08682 | RealVVT: Towards Photorealistic Video Virtual Try-on via Spatio-Temporal
Consistency | [
"cs.CV",
"cs.GR"
] | Virtual try-on has emerged as a pivotal task at the intersection of computer vision and fashion, aimed at digitally simulating how clothing items fit on the human body. Despite notable progress in single-image virtual try-on (VTO), current methodologies often struggle to preserve a consistent and authentic appearance o... | {
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2501.08683 | The Physics of Life: Exploring Information as a Distinctive Feature of
Living Systems | [
"cond-mat.soft",
"astro-ph.EP",
"cs.IT",
"math.IT",
"nlin.AO",
"q-bio.QM"
] | This paper explores the idea that information is an essential and distinctive feature of living systems. Unlike non-living systems, living systems actively acquire, process, and use information about their environments to respond to changing conditions, sustain themselves, and achieve other intrinsic goals. We discuss ... | {
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2501.08686 | Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching | [
"cs.DB",
"cs.CL",
"cs.IR"
] | Traditional similarity-based schema matching methods are incapable of resolving semantic ambiguities and conflicts in domain-specific complex mapping scenarios due to missing commonsense and domain-specific knowledge. The hallucination problem of large language models (LLMs) also makes it challenging for LLM-based sche... | {
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2501.08688 | Some remarks on practical stabilization via CLF-based control under
measurement noise | [
"eess.SY",
"cs.SY",
"math.OC"
] | Practical stabilization of input-affine systems in the presence of measurement errors and input constraints is considered in this brief note. Assuming that a Lyapunov function and a stabilizing control exist for an input-affine system, the required measurement accuracy at each point of the state space is computed. This... | {
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2501.08695 | Real-time Indexing for Large-scale Recommendation by Streaming Vector
Quantization Retriever | [
"cs.IR"
] | Retrievers, which form one of the most important recommendation stages, are responsible for efficiently selecting possible positive samples to the later stages under strict latency limitations. Because of this, large-scale systems always rely on approximate calculations and indexes to roughly shrink candidate scale, wi... | {
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2501.08696 | Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk
Differentiation in Chinese Psychological Support Hotlines | [
"cs.CL"
] | Mental health is a critical global public health issue, and psychological support hotlines play a pivotal role in providing mental health assistance and identifying suicide risks at an early stage. However, the emotional expressions conveyed during these calls remain underexplored in current research. This study introd... | {
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2501.08710 | Disentangled Interleaving Variational Encoding | [
"cs.LG",
"stat.ML"
] | Conflicting objectives present a considerable challenge in interleaving multi-task learning, necessitating the need for meticulous design and balance to ensure effective learning of a representative latent data space across all tasks without mutual negative impact. Drawing inspiration from the concept of marginal and c... | {
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2501.08712 | Self-supervised Transformation Learning for Equivariant Representations | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Unsupervised representation learning has significantly advanced various machine learning tasks. In the computer vision domain, state-of-the-art approaches utilize transformations like random crop and color jitter to achieve invariant representations, embedding semantically the same inputs despite transformations. Howev... | {
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2501.08716 | The Inherent Limits of Pretrained LLMs: The Unexpected Convergence of
Instruction Tuning and In-Context Learning Capabilities | [
"cs.CL"
] | Large Language Models (LLMs), trained on extensive web-scale corpora, have demonstrated remarkable abilities across diverse tasks, especially as they are scaled up. Nevertheless, even state-of-the-art models struggle in certain cases, sometimes failing at problems solvable by young children, indicating that traditional... | {
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2501.08717 | $\texttt{InfoHier}$: Hierarchical Information Extraction via Encoding
and Embedding | [
"cs.IR",
"cs.CV",
"cs.LG"
] | Analyzing large-scale datasets, especially involving complex and high-dimensional data like images, is particularly challenging. While self-supervised learning (SSL) has proven effective for learning representations from unlabelled data, it typically focuses on flat, non-hierarchical structures, missing the multi-level... | {
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2501.08726 | Task Allocation in Mobile Robot Fleets: A review | [
"cs.RO",
"cs.MA"
] | Mobile robot fleets are currently used in different scenarios such as medical environments or logistics. The management of these systems provides different challenges that vary from the control of the movement of each robot to the allocation of tasks to be performed. Task Allocation (TA) problem is a key topic for the ... | {
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2501.08727 | Transformed Low-rank Adaptation via Tensor Decomposition and Its
Applications to Text-to-image Models | [
"cs.LG"
] | Parameter-Efficient Fine-Tuning (PEFT) of text-to-image models has become an increasingly popular technique with many applications. Among the various PEFT methods, Low-Rank Adaptation (LoRA) and its variants have gained significant attention due to their effectiveness, enabling users to fine-tune models with limited co... | {
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2501.08729 | GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component
Vapor Pressures | [
"cs.LG",
"cs.CE"
] | Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we have developed GRAPPA - a hybrid graph neural network for predicting vapor pressur... | {
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2501.08737 | Resource-Constrained Federated Continual Learning: What Does Matter? | [
"cs.LG"
] | Federated Continual Learning (FCL) aims to enable sequentially privacy-preserving model training on streams of incoming data that vary in edge devices by preserving previous knowledge while adapting to new data. Current FCL literature focuses on restricted data privacy and access to previously seen data while imposing ... | {
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2501.08738 | MeshMask: Physics-Based Simulations with Masked Graph Neural Networks | [
"cs.LG",
"physics.flu-dyn"
] | We introduce a novel masked pre-training technique for graph neural networks (GNNs) applied to computational fluid dynamics (CFD) problems. By randomly masking up to 40\% of input mesh nodes during pre-training, we force the model to learn robust representations of complex fluid dynamics. We pair this masking strategy ... | {
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2501.08758 | Expanding Vietnamese SentiWordNet to Improve Performance of Vietnamese
Sentiment Analysis Models | [
"cs.CL"
] | Sentiment analysis is one of the most crucial tasks in Natural Language Processing (NLP), involving the training of machine learning models to classify text based on the polarity of opinions. Pre-trained Language Models (PLMs) can be applied to downstream tasks through fine-tuning, eliminating the need to train the mod... | {
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2501.08760 | Leveraging LLM Agents for Translating Network Configurations | [
"cs.NI",
"cs.AI",
"cs.LG",
"cs.SE"
] | Configuration translation is a critical and frequent task in network operations. When a network device is damaged or outdated, administrators need to replace it to maintain service continuity. The replacement devices may originate from different vendors, necessitating configuration translation to ensure seamless networ... | {
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2501.08763 | Few-Shot Learner Generalizes Across AI-Generated Image Detection | [
"cs.CV"
] | Current fake image detectors trained on large synthetic image datasets perform satisfactorily on limited studied generative models. However, they suffer a notable performance decline over unseen models. Besides, collecting adequate training data from online generative models is often expensive or infeasible. To overcom... | {
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2501.08769 | Enhanced Large Language Models for Effective Screening of Depression and
Anxiety | [
"cs.CL"
] | Depressive and anxiety disorders are widespread, necessitating timely identification and management. Recent advances in Large Language Models (LLMs) offer potential solutions, yet high costs and ethical concerns about training data remain challenges. This paper introduces a pipeline for synthesizing clinical interviews... | {
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} |
2501.08771 | Admitting Ignorance Helps the Video Question Answering Models to Answer | [
"cs.CV"
] | Significant progress has been made in the field of video question answering (VideoQA) thanks to deep learning and large-scale pretraining. Despite the presence of sophisticated model structures and powerful video-text foundation models, most existing methods focus solely on maximizing the correlation between answers an... | {
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} |
2501.08774 | How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in
Software Engineering | [
"cs.SE",
"cs.AI",
"cs.HC"
] | Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the... | {
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} |
2501.08778 | Networked Agents in the Dark: Team Value Learning under Partial
Observability | [
"cs.LG",
"cs.AI",
"cs.MA"
] | We propose a novel cooperative multi-agent reinforcement learning (MARL) approach for networked agents. In contrast to previous methods that rely on complete state information or joint observations, our agents must learn how to reach shared objectives under partial observability. During training, they collect individua... | {
"Other": 0,
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} |
2501.08779 | Nesterov Acceleration for Ensemble Kalman Inversion and Variants | [
"math.OC",
"cs.LG",
"stat.CO"
] | Ensemble Kalman inversion (EKI) is a derivative-free, particle-based optimization method for solving inverse problems. It can be shown that EKI approximates a gradient flow, which allows the application of methods for accelerating gradient descent. Here, we show that Nesterov acceleration is effective in speeding up th... | {
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} |
2501.08780 | Deep learning for temporal super-resolution 4D Flow MRI | [
"cs.LG"
] | 4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparent trade-offs between acquisition time, image noise, and resolution limit clinical applicability. In particular, in regions of highly transient flow, coarse temporal resol... | {
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} |
2501.08781 | Cultivating Precision: Comparative Analysis of Sensor-Based Yogurt
Fermentation Monitoring Techniques | [
"eess.SY",
"cs.SY"
] | Fermented dairy products, including yogurt, are widely consumed for their nutritional and health benefits. While numerous methods exist to monitor and understand yogurt fermentation, the literature lacks an integrated evaluation of diverse sensing approaches within a single experimental framework. To address this gap, ... | {
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} |
2501.08786 | Differentiability and overlap concentration in optimal Bayesian
inference | [
"math.PR",
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | In this short note, we consider models of optimal Bayesian inference of finite-rank tensor products. We add to the model a linear channel parametrized by $h$. We show that at every interior differentiable point $h$ of the free energy (associated with the model), the overlap concentrates at the gradient of the free ener... | {
"Other": 0,
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} |
2501.08795 | Heat transfer simulation of window frames with SPHinXsys | [
"cs.CE"
] | Maintaining a comfortable temperature inside a building requires appropriate thermal insulation of windows, which can be optimised iteratively with numerical simulation. Smoothed particle hydrodynamics(SPH) is a fully Lagrangian method widely used for simulating multi-physics applications with high computational effici... | {
"Other": 0,
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} |
2501.08799 | Exploring ChatGPT for Face Presentation Attack Detection in Zero and
Few-Shot in-Context Learning | [
"cs.CV",
"cs.CR"
] | This study highlights the potential of ChatGPT (specifically GPT-4o) as a competitive alternative for Face Presentation Attack Detection (PAD), outperforming several PAD models, including commercial solutions, in specific scenarios. Our results show that GPT-4o demonstrates high consistency, particularly in few-shot in... | {
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} |
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