id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.15643 | On the Boundary Feasibility for PDE Control with Neural Operators | [
"eess.SY",
"cs.LG",
"cs.NE",
"cs.RO",
"cs.SY"
] | The physical world dynamics are generally governed by underlying partial differential equations (PDEs) with unknown analytical forms in science and engineering problems. Neural network based data-driven approaches have been heavily studied in simulating and solving PDE problems in recent years, but it is still challeng... | {
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2411.15645 | MC-NEST -- Enhancing Mathematical Reasoning in Large Language Models
with a Monte Carlo Nash Equilibrium Self-Refine Tree | [
"cs.LG"
] | Mathematical reasoning has proven to be a critical yet challenging task for large language models (LLMs), as they often struggle with complex multi-step problems. To address these limitations, we introduce the Monte Carlo Nash Equilibrium Self-Refine Tree (MC-NEST) algorithm, an enhancement of the Monte Carlo Tree Self... | {
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2411.15647 | Circuit design in biology and machine learning. II. Anomaly detection | [
"q-bio.PE",
"cs.LG"
] | Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological systems recognize and respond to atypical environmental inputs. However, this approach has received limite... | {
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2411.15648 | Sample- and Parameter-Efficient Auto-Regressive Image Models | [
"cs.CV"
] | We introduce XTRA, a vision model pre-trained with a novel auto-regressive objective that significantly enhances both sample and parameter efficiency compared to previous auto-regressive image models. Unlike contrastive or masked image modeling methods, which have not been demonstrated as having consistent scaling beha... | {
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2411.15651 | Model Predictive Trees: Sample-Efficient Receding Horizon Planning with
Reusable Tree Search | [
"cs.RO",
"cs.SY",
"eess.SY"
] | We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse only the highest-quality trajectory from the previous iteration as a "hotstart", our method reuses the entire optimal subtree, enabling the s... | {
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2411.15653 | OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction
on Edge Devices with NPUs | [
"cs.CV"
] | Real-time object localization on edge devices is fundamental for numerous applications, ranging from surveillance to industrial automation. Traditional frameworks, such as object detection, segmentation, and keypoint detection, struggle in resource-constrained environments, often resulting in substantial target omissio... | {
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2411.15655 | Machine Learning-based sEMG Signal Classification for Hand Gesture
Recognition | [
"cs.LG",
"cs.CV"
] | EMG-based hand gesture recognition uses electromyographic~(EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin... | {
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2411.15656 | Machine-agnostic Automated Lumbar MRI Segmentation using a Cascaded
Model Based on Generative Neurons | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Automated lumbar spine segmentation is very crucial for modern diagnosis systems. In this study, we introduce a novel machine-agnostic approach for segmenting lumbar vertebrae and intervertebral discs from MRI images, employing a cascaded model that synergizes an ROI detection and a Self-organized Operational Neural Ne... | {
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2411.15657 | Training an Open-Vocabulary Monocular 3D Object Detection Model without
3D Data | [
"cs.CV"
] | Open-vocabulary 3D object detection has recently attracted considerable attention due to its broad applications in autonomous driving and robotics, which aims to effectively recognize novel classes in previously unseen domains. However, existing point cloud-based open-vocabulary 3D detection models are limited by their... | {
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2411.15659 | SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated
Convolution | [
"cs.CV"
] | We present a novel approach for accelerating convolutions during inference for CPU-based architectures. The most common method of computation involves packing the image into the columns of a matrix (im2col) and performing general matrix multiplication (GEMM) with a matrix of weights. This results in two main drawbacks:... | {
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2411.15660 | Federated PCA and Estimation for Spiked Covariance Matrices: Optimal
Rates and Efficient Algorithm | [
"math.ST",
"cs.IT",
"math.IT",
"stat.ML",
"stat.TH"
] | Federated Learning (FL) has gained significant recent attention in machine learning for its enhanced privacy and data security, making it indispensable in fields such as healthcare, finance, and personalized services. This paper investigates federated PCA and estimation for spiked covariance matrices under distributed ... | {
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2411.15661 | Improving Next Tokens via Second-to-Last Predictions with Generate and
Refine | [
"cs.CL",
"cs.LG"
] | Autoregressive language models like GPT aim to predict next tokens, while autoencoding models such as BERT are trained on tasks such as predicting masked tokens. We train a decoder-only architecture for predicting the second to last token for a sequence of tokens. Our approach yields higher computational training effic... | {
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2411.15664 | Enabling Efficient Serverless Inference Serving for LLM (Large Language
Model) in the Cloud | [
"cs.DC",
"cs.LG"
] | This review report discusses the cold start latency in serverless inference and existing solutions. It particularly reviews the ServerlessLLM method, a system designed to address the cold start problem in serverless inference for large language models. Traditional serverless approaches struggle with high latency due to... | {
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2411.15666 | Ontology-Constrained Generation of Domain-Specific Clinical Summaries | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) offer promising solutions for text summarization. However, some domains require specific information to be available in the summaries. Generating these domain-adapted summaries is still an open challenge. Similarly, hallucinations in generated content is a major drawback of current approach... | {
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2411.15669 | Implicit High-Order Moment Tensor Estimation and Learning Latent
Variable Models | [
"cs.DS",
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | We study the task of learning latent-variable models. An obstacle towards designing efficient algorithms for such models is the necessity of approximating moment tensors of super-constant degree. Motivated by such applications, we develop a general efficient algorithm for implicit moment tensor computation. Our algorit... | {
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2411.15671 | Best of Both Worlds: Advantages of Hybrid Graph Sequence Models | [
"cs.LG",
"cs.SI"
] | Modern sequence models (e.g., Transformers, linear RNNs, etc.) emerged as dominant backbones of recent deep learning frameworks, mainly due to their efficiency, representational power, and/or ability to capture long-range dependencies. Adopting these sequence models for graph-structured data has recently gained popular... | {
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2411.15672 | IRSKG: Unified Intrusion Response System Knowledge Graph Ontology for
Cyber Defense | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Cyberattacks are becoming increasingly difficult to detect and prevent due to their sophistication. In response, Autonomous Intelligent Cyber-defense Agents (AICAs) are emerging as crucial solutions. One prominent AICA agent is the Intrusion Response System (IRS), which is critical for mitigating threats after detectio... | {
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2411.15673 | Semantic Shield: Defending Vision-Language Models Against Backdooring
and Poisoning via Fine-grained Knowledge Alignment | [
"cs.CV"
] | In recent years there has been enormous interest in vision-language models trained using self-supervised objectives. However, the use of large-scale datasets scraped from the web for training also makes these models vulnerable to potential security threats, such as backdooring and poisoning attacks. In this paper, we p... | {
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2411.15674 | Quantile deep learning models for multi-step ahead time series
prediction | [
"cs.LG",
"cs.AI",
"q-fin.ST",
"stat.ME"
] | Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme value forecasting. Although deep learning models have been prominent in multi-step ahead prediction, the development and evaluation of quantile ... | {
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2411.15675 | Can a Large Language Model Learn Matrix Functions In Context? | [
"cs.LG"
] | Large Language Models (LLMs) have demonstrated the ability to solve complex tasks through In-Context Learning (ICL), where models learn from a few input-output pairs without explicit fine-tuning. In this paper, we explore the capacity of LLMs to solve non-linear numerical computations, with specific emphasis on functio... | {
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2411.15677 | How Media Competition Fuels the Spread of Misinformation | [
"cs.SI"
] | Competition among news sources may encourage some sources to share fake news and misinformation to influence the public. While sharing misinformation may lead to a short-term gain in audience engagement, it may damage the reputation of these sources, resulting in a loss of audience. To understand the rationale behind s... | {
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2411.15678 | Towards RAW Object Detection in Diverse Conditions | [
"cs.CV"
] | Existing object detection methods often consider sRGB input, which was compressed from RAW data using ISP originally designed for visualization. However, such compression might lose crucial information for detection, especially under complex light and weather conditions. We introduce the AODRaw dataset, which offers 7,... | {
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2411.15684 | Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide
Sequencing | [
"q-bio.BM",
"cs.LG"
] | Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marre... | {
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2411.15685 | State-Space Large Audio Language Models | [
"eess.AS",
"cs.AI"
] | Large Audio Language Models (LALM) combine the audio perception models and the Large Language Models (LLM) and show a remarkable ability to reason about the input audio, infer the meaning, and understand the intent. However, these systems rely on Transformers which scale quadratically with the input sequence lengths wh... | {
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2411.15692 | DrugAgent: Automating AI-aided Drug Discovery Programming through LLM
Multi-Agent Collaboration | [
"cs.LG"
] | Recent advancements in Large Language Models (LLMs) have opened new avenues for accelerating drug discovery processes. Despite their potential, several critical challenges remain unsolved, particularly in translating theoretical ideas into practical applications within the highly specialized field of pharmaceutical res... | {
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2411.15694 | Deep Sparse Latent Feature Models for Knowledge Graph Completion | [
"cs.CL"
] | Recent progress in knowledge graph completion (KGC) has focused on text-based approaches to address the challenges of large-scale knowledge graphs (KGs). Despite their achievements, these methods often overlook the intricate interconnections between entities, a key aspect of the underlying topological structure of a KG... | {
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2411.15696 | RIS with Coupled Phase Shift and Amplitude: Capacity Maximization and
Configuration Set Selection | [
"cs.IT",
"math.IT"
] | A reconfigurable intelligent surface (RIS) is a planar surface that can enhance the quality of communication by providing control over the communication environment. Reflection optimization is one of the pivotal challenges in RIS setups. While there has been lots of research regarding the reflection optimization of RIS... | {
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2411.15700 | RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large
Language Models on Dietary Supplements | [
"cs.CL",
"cs.AI",
"cs.CE"
] | \textbf{Objective:} We aimed to develop an advanced multi-task large language model (LLM) framework to extract multiple types of information about dietary supplements (DS) from clinical records. \textbf{Methods:} We used four core DS information extraction tasks - namely, named entity recognition (NER: 2,949 clinical... | {
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2411.15702 | Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial
Editing | [
"cs.IT",
"cs.CV",
"cs.NI",
"math.IT"
] | Real-time computer vision (CV) plays a crucial role in various real-world applications, whose performance is highly dependent on communication networks. Nonetheless, the data-oriented characteristics of conventional communications often do not align with the special needs of real-time CV tasks. To alleviate this issue,... | {
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2411.15703 | Analysis of Hierarchical AoII over unreliable channel: A Stochastic
Hybrid System Approach | [
"eess.SY",
"cs.SY"
] | In this work, we generalize the Stochastic Hybrid Systems (SHSs) analysis of traditional AoI to the AoII metric. Hierarchical ageing processes are adopted using the continuous AoII for the first time, where two different hierarchy schemes, i.e., a hybrid of linear ageing processes with different slopes and a hybrid of ... | {
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2411.15706 | Fixing the Perspective: A Critical Examination of Zero-1-to-3 | [
"cs.CV",
"cs.LG"
] | Novel view synthesis is a fundamental challenge in image-to-3D generation, requiring the generation of target view images from a set of conditioning images and their relative poses. While recent approaches like Zero-1-to-3 have demonstrated promising results using conditional latent diffusion models, they face signific... | {
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2411.15707 | Nimbus: Secure and Efficient Two-Party Inference for Transformers | [
"cs.CR",
"cs.AI"
] | Transformer models have gained significant attention due to their power in machine learning tasks. Their extensive deployment has raised concerns about the potential leakage of sensitive information during inference. However, when being applied to Transformers, existing approaches based on secure two-party computation ... | {
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2411.15708 | LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of
Mixture-of-Experts with Post-Training | [
"cs.CL"
] | Recently, inspired by the concept of sparsity, Mixture-of-Experts (MoE) models have gained increasing popularity for scaling model size while keeping the number of activated parameters constant. In this study, we thoroughly investigate the sparsity of the dense LLaMA model by constructing MoE for both the attention (i.... | {
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2411.15710 | Understanding Student Acceptance, Trust, and Attitudes Toward
AI-Generated Images for Educational Purposes | [
"cs.CY",
"cs.AI"
] | Recent advancements in artificial intelligence (AI) have broadened the applicability of AI-generated images across various sectors, including the creative industry and design. However, their utilization in educational contexts, particularly among undergraduate students in computer science and software engineering, rema... | {
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2411.15711 | Robustifying Long-term Human-Robot Collaboration through a Multimodal
and Hierarchical Framework | [
"cs.RO"
] | Long-term Human-Robot Collaboration (HRC) is crucial for enabling flexible manufacturing systems and integrating companion robots into daily human environments over extended periods. This paper identifies several key challenges for such collaborations, such as accurate recognition of human plan, robustness to disturban... | {
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2411.15714 | ROOT: VLM based System for Indoor Scene Understanding and Beyond | [
"cs.CV"
] | Recently, Vision Language Models (VLMs) have experienced significant advancements, yet these models still face challenges in spatial hierarchical reasoning within indoor scenes. In this study, we introduce ROOT, a VLM-based system designed to enhance the analysis of indoor scenes. Specifically, we first develop an iter... | {
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2411.15715 | Task Scheduling for Efficient Inference of Large Language Models on
Single Moderate GPU Systems | [
"cs.CE"
] | Large language models~(LLMs) are known for their high demand on computing resources and memory due to their substantial model size, which leads to inefficient inference on moderate GPU systems. Techniques like quantization or pruning can shrink model sizes but often impair accuracy, making them unsuitable for practical... | {
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2411.15716 | Tackling Data Heterogeneity in Federated Time Series Forecasting | [
"cs.LG",
"cs.CR",
"cs.IR"
] | Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Although substantial progress has been made in time series forecasting, most existing methods rely on a centralized training paradigm, wher... | {
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2411.15717 | Learning Algorithm Hyperparameters for Fast Parametric Convex
Optimization | [
"math.OC",
"cs.LG"
] | We introduce a machine-learning framework to learn the hyperparameter sequence of first-order methods (e.g., the step sizes in gradient descent) to quickly solve parametric convex optimization problems. Our computational architecture amounts to running fixed-point iterations where the hyperparameters are the same acros... | {
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2411.15719 | Comparative Analysis of Diffusion Generative Models in Computational
Pathology | [
"eess.IV",
"cs.CV"
] | Diffusion Generative Models (DGM) have rapidly surfaced as emerging topics in the field of computer vision, garnering significant interest across a wide array of deep learning applications. Despite their high computational demand, these models are extensively utilized for their superior sample quality and robust mode c... | {
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2411.15720 | Chain of Attack: On the Robustness of Vision-Language Models Against
Transfer-Based Adversarial Attacks | [
"cs.CV"
] | Pre-trained vision-language models (VLMs) have showcased remarkable performance in image and natural language understanding, such as image captioning and response generation. As the practical applications of vision-language models become increasingly widespread, their potential safety and robustness issues raise concer... | {
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2411.15721 | Research on Effectiveness Evaluation and Optimization of Baseball
Teaching Method Based on Machine Learning | [
"cs.LG"
] | In modern physical education, data-driven evaluation methods have gradually attracted attention, especially the quantitative prediction of students' sports performance through machine learning model. The purpose of this study is to use a variety of machine learning models to regress and predict students' comprehensive ... | {
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2411.15723 | GSurf: 3D Reconstruction via Signed Distance Fields with Direct Gaussian
Supervision | [
"cs.CV"
] | Surface reconstruction from multi-view images is a core challenge in 3D vision. Recent studies have explored signed distance fields (SDF) within Neural Radiance Fields (NeRF) to achieve high-fidelity surface reconstructions. However, these approaches often suffer from slow training and rendering speeds compared to 3D G... | {
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2411.15729 | OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action
Recognition under Occlusions | [
"cs.CV"
] | The lack of occlusion data in commonly used action recognition video datasets limits model robustness and impedes sustained performance improvements. We construct OccludeNet, a large-scale occluded video dataset that includes both real-world and synthetic occlusion scene videos under various natural environments. Occlu... | {
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2411.15731 | Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction
Models | [
"cs.IR",
"cs.AI"
] | The evolution of previous Click-Through Rate (CTR) models has mainly been driven by proposing complex components, whether shallow or deep, that are adept at modeling feature interactions. However, there has been less focus on improving fusion design. Instead, two naive solutions, stacked and parallel fusion, are common... | {
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2411.15732 | DynamicAvatars: Accurate Dynamic Facial Avatars Reconstruction and
Precise Editing with Diffusion Models | [
"cs.GR",
"cs.CV"
] | Generating and editing dynamic 3D head avatars are crucial tasks in virtual reality and film production. However, existing methods often suffer from facial distortions, inaccurate head movements, and limited fine-grained editing capabilities. To address these challenges, we present DynamicAvatars, a dynamic model that ... | {
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2411.15734 | Development of Pre-Trained Transformer-based Models for the Nepali
Language | [
"cs.CL",
"cs.LG"
] | Transformer-based pre-trained language models have dominated the field of Natural Language Processing (NLP) for quite some time now. However, the Nepali language, spoken by approximately 32 million people worldwide, remains significantly underrepresented in this domain. This underrepresentation is primarily attributed ... | {
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2411.15735 | Test-time Alignment-Enhanced Adapter for Vision-Language Models | [
"cs.CV"
] | Test-time adaptation with pre-trained vision-language models (VLMs) has attracted increasing attention for tackling the issue of distribution shift during the test phase. While prior methods have shown effectiveness in addressing distribution shift by adjusting classification logits, they are not optimal due to keeping... | {
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2411.15736 | Enhancing Few-Shot Out-of-Distribution Detection with Gradient Aligned
Context Optimization | [
"cs.CV"
] | Few-shot out-of-distribution (OOD) detection aims to detect OOD images from unseen classes with only a few labeled in-distribution (ID) images. To detect OOD images and classify ID samples, prior methods have been proposed by regarding the background regions of ID samples as the OOD knowledge and performing OOD regular... | {
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2411.15737 | TableTime: Reformulating Time Series Classification as Training-Free
Table Understanding with Large Language Models | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have demonstrated their effectiveness in multivariate time series classification (MTSC). Effective adaptation of LLMs for MTSC necessitates informative data representations. Existing LLM-based methods directly encode embeddings for time series within the latent space of LLMs from scratch to... | {
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2411.15738 | AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea | [
"cs.CV"
] | Instruction-based image editing aims to modify specific image elements with natural language instructions. However, current models in this domain often struggle to accurately execute complex user instructions, as they are trained on low-quality data with limited editing types. We present AnyEdit, a comprehensive multi-... | {
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2411.15740 | LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for
Low-Light Image Restoration | [
"cs.CV",
"cs.AI"
] | We introduce LTCF-Net, a novel network architecture designed for enhancing low-light images. Unlike Retinex-based methods, our approach utilizes two color spaces - LAB and YUV - to efficiently separate and process color information, by leveraging the separation of luminance from chromatic components in color images. In... | {
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2411.15741 | Proceedings of the 6th International Workshop on Reading Music Systems | [
"cs.CV",
"cs.IR",
"cs.LG"
] | The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relev... | {
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2411.15742 | PEnG: Pose-Enhanced Geo-Localisation | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Cross-view Geo-localisation is typically performed at a coarse granularity, because densely sampled satellite image patches overlap heavily. This heavy overlap would make disambiguating patches very challenging. However, by opting for sparsely sampled patches, prior work has placed an artificial upper bound on the loca... | {
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2411.15743 | Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot
Forecasting | [
"cs.LG",
"cs.AI"
] | Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns. While traditional forecasting techniques work well in in-domain scenarios with ample data, they struggle when data is scarce or not available at all, motivating the emerge... | {
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2411.15746 | PR-MIM: Delving Deeper into Partial Reconstruction in Masked Image
Modeling | [
"cs.CV"
] | Masked image modeling has achieved great success in learning representations but is limited by the huge computational costs. One cost-saving strategy makes the decoder reconstruct only a subset of masked tokens and throw the others, and we refer to this method as partial reconstruction. However, it also degrades the re... | {
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2411.15753 | FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation | [
"cs.RO"
] | Contact-rich tasks present significant challenges for robotic manipulation policies due to the complex dynamics of contact and the need for precise control. Vision-based policies often struggle with the skill required for such tasks, as they typically lack critical contact feedback modalities like force/torque informat... | {
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2411.15758 | Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven
Insights via IndustryScopeGPT | [
"cs.AI",
"cs.CY",
"cs.SI"
] | Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal... | {
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2411.15759 | Advanced Learning-Based Inter Prediction for Future Video Coding | [
"cs.MM",
"cs.CV"
] | In the fourth generation Audio Video coding Standard (AVS4), the Inter Prediction Filter (INTERPF) reduces discontinuities between prediction and adjacent reconstructed pixels in inter prediction. The paper proposes a low complexity learning-based inter prediction (LLIP) method to replace the traditional INTERPF. LLIP ... | {
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2411.15761 | MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking | [
"cs.CV"
] | Night unmanned aerial vehicle (UAV) tracking is impeded by the challenges of poor illumination, with previous daylight-optimized methods demonstrating suboptimal performance in low-light conditions, limiting the utility of UAV applications. To this end, we propose an efficient mamba-based tracker, leveraging dual enhan... | {
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2411.15762 | Robust Hybrid Precoding for Millimeter Wave MU-MISO System Via
Meta-Learning | [
"cs.IT",
"eess.SP",
"math.IT"
] | Thanks to the low cost and power consumption, hybrid analog-digital architectures are considered as a promising energy-efficient solution for massive multiple-input multiple-output (MIMO) systems. The key idea is to connect one RF chain to multiple antennas through low-cost phase shifters. However, due to the non-conve... | {
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2411.15763 | Integrating Deep Metric Learning with Coreset for Active Learning in 3D
Segmentation | [
"cs.CV",
"cs.LG"
] | Deep learning has seen remarkable advancements in machine learning, yet it often demands extensive annotated data. Tasks like 3D semantic segmentation impose a substantial annotation burden, especially in domains like medicine, where expert annotations drive up the cost. Active learning (AL) holds great potential to al... | {
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2411.15764 | LLM Online Spatial-temporal Signal Reconstruction Under Noise | [
"cs.LG",
"eess.SP"
] | This work introduces the LLM Online Spatial-temporal Reconstruction (LLM-OSR) framework, which integrates Graph Signal Processing (GSP) and Large Language Models (LLMs) for online spatial-temporal signal reconstruction. The LLM-OSR utilizes a GSP-based spatial-temporal signal handler to enhance graph signals and employ... | {
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2411.15766 | ScalingNote: Scaling up Retrievers with Large Language Models for
Real-World Dense Retrieval | [
"cs.IR"
] | Dense retrieval in most industries employs dual-tower architectures to retrieve query-relevant documents. Due to online deployment requirements, existing real-world dense retrieval systems mainly enhance performance by designing negative sampling strategies, overlooking the advantages of scaling up. Recently, Large Lan... | {
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2411.15768 | Detecting Turkish Synonyms Used in Different Time Periods | [
"cs.CL"
] | Dynamic structure of languages poses significant challenges in applying natural language processing models on historical texts, causing decreased performance in various downstream tasks. Turkish is a prominent example of rapid linguistic transformation due to the language reform in the 20th century. In this paper, we p... | {
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2411.15769 | Gradient Norm Regularization Second-Order Algorithms for Solving
Nonconvex-Strongly Concave Minimax Problems | [
"math.OC",
"cs.LG",
"stat.ML"
] | In this paper, we study second-order algorithms for solving nonconvex-strongly concave minimax problems, which have attracted much attention in recent years in many fields, especially in machine learning. We propose a gradient norm regularized trust region (GRTR) algorithm to solve nonconvex-strongly concave minimax pr... | {
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2411.15770 | Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing
Visual Question Answering | [
"cs.CV"
] | Remote Sensing Visual Question Answering (RSVQA) has gained significant research interest. However, current RSVQA methods are limited by the imaging mechanisms of optical sensors, particularly under challenging conditions such as cloud-covered and low-light scenarios. Given the all-time and all-weather imaging capabili... | {
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2411.15772 | Corner2Net: Detecting Objects as Cascade Corners | [
"cs.CV"
] | The corner-based detection paradigm enjoys the potential to produce high-quality boxes. But the development is constrained by three factors: 1) Hard to match corners. Heuristic corner matching algorithms can lead to incorrect boxes, especially when similar-looking objects co-occur. 2) Poor instance context. Two separat... | {
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2411.15773 | Context-Aware Detection of Mixed Critical Events using Video
Classification | [
"cs.CV"
] | Detecting mixed-critical events through computer vision is challenging due to the need for contextual understanding to assess event criticality accurately. Mixed critical events, such as fires of varying severity or traffic incidents, demand adaptable systems that can interpret context to trigger appropriate responses.... | {
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2411.15778 | Enhancing the automatic segmentation and analysis of 3D liver
vasculature models | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Surgical assessment of liver cancer patients requires identification of the vessel trees from medical images. Specifically, the venous trees - the portal (perfusing) and the hepatic (draining) trees are important for understanding the liver anatomy and disease state, and perform surgery planning. This research aims to ... | {
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2411.15779 | ZeroGS: Training 3D Gaussian Splatting from Unposed Images | [
"cs.CV"
] | Neural radiance fields (NeRF) and 3D Gaussian Splatting (3DGS) are popular techniques to reconstruct and render photo-realistic images. However, the pre-requisite of running Structure-from-Motion (SfM) to get camera poses limits their completeness. While previous methods can reconstruct from a few unposed images, they ... | {
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2411.15785 | A Method for Building Large Language Models with Predefined KV Cache
Capacity | [
"cs.CL"
] | This paper introduces a novel approach, the Bounded-Cache Transformer (BCT), for building large language models with a predefined Key-Value (KV) cache capacity. The BCT addresses the excessive memory consumption issue in traditional KV caches by implementing a bounded-length KV cache, which is particularly suitable for... | {
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2411.15787 | Multi-Token Enhancing for Vision Representation Learning | [
"cs.CV"
] | Vision representation learning, especially self-supervised learning, is pivotal for various vision applications. Ensemble learning has also succeeded in enhancing the performance and robustness of the vision models. However, traditional ensemble strategies are impractical for representation learning, especially self-su... | {
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2411.15795 | Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for
large-scale optimization | [
"cs.LG",
"math.OC"
] | Adaptive gradient methods have been increasingly adopted by deep learning community due to their fast convergence and reduced sensitivity to hyper-parameters. However, these methods come with limitations, such as increased memory requirements for elements like moving averages and a poorly understood convergence theory.... | {
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2411.15796 | Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset
Pruning | [
"cs.CR",
"cs.AI"
] | In this work, we systematically explore the data privacy issues of dataset pruning in machine learning systems. Our findings reveal, for the first time, that even if data in the redundant set is solely used before model training, its pruning-phase membership status can still be detected through attacks. Since this is a... | {
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2411.15798 | M3-CVC: Controllable Video Compression with Multimodal Generative Models | [
"eess.IV",
"cs.CV"
] | Traditional and neural video codecs commonly encounter limitations in controllability and generality under ultra-low-bitrate coding scenarios. To overcome these challenges, we propose M3-CVC, a controllable video compression framework incorporating multimodal generative models. The framework utilizes a semantic-motion ... | {
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2411.15799 | Symmetric Perception and Ordinal Regression for Detecting Scoliosis
Natural Image | [
"cs.CV"
] | Scoliosis is one of the most common diseases in adolescents. Traditional screening methods for the scoliosis usually use radiographic examination, which requires certified experts with medical instruments and brings the radiation risk. Considering such requirement and inconvenience, we propose to use natural images of ... | {
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2411.15800 | PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic
Environments | [
"cs.RO",
"cs.CV"
] | Simultaneous localization and mapping (SLAM) has achieved impressive performance in static environments. However, SLAM in dynamic environments remains an open question. Many methods directly filter out dynamic objects, resulting in incomplete scene reconstruction and limited accuracy of camera localization. The other w... | {
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2411.15801 | A review on Machine Learning based User-Centric Multimedia Streaming
Techniques | [
"cs.MM",
"cs.AI",
"cs.LG"
] | The multimedia content and streaming are a major means of information exchange in the modern era and there is an increasing demand for such services. This coupled with the advancement of future wireless networks B5G/6G and the proliferation of intelligent handheld mobile devices, has facilitated the availability of mul... | {
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2411.15802 | Medical Slice Transformer: Improved Diagnosis and Explainability on 3D
Medical Images with DINOv2 | [
"eess.IV",
"cs.AI",
"cs.CV"
] | MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DINOv2 are encouraging for 2D image analysis, these methods have not been applied to 3D medical images. Furthermore, deep l... | {
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2411.15804 | LoRA-Mini : Adaptation Matrices Decomposition and Selective Training | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The rapid advancements in large language models (LLMs) have revolutionized natural language processing, creating an increased need for efficient, task-specific fine-tuning methods. Traditional fine-tuning of LLMs involves updating a large number of parameters, which is computationally expensive and memory-intensive. Lo... | {
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2411.15805 | Benchmarking Active Learning for NILM | [
"cs.LG",
"cs.AI"
] | Non-intrusive load monitoring (NILM) focuses on disaggregating total household power consumption into appliance-specific usage. Many advanced NILM methods are based on neural networks that typically require substantial amounts of labeled appliance data, which can be challenging and costly to collect in real-world setti... | {
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2411.15806 | Broad Critic Deep Actor Reinforcement Learning for Continuous Control | [
"cs.LG",
"cs.AI"
] | In the domain of continuous control, deep reinforcement learning (DRL) demonstrates promising results. However, the dependence of DRL on deep neural networks (DNNs) results in the demand for extensive data and increased computational complexity. To address this issue, a novel hybrid architecture for actor-critic reinfo... | {
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2411.15808 | LRSAA: Large-scale Remote Sensing Image Target Recognition and Automatic
Annotation | [
"cs.CV"
] | This paper presents a method for object recognition and automatic labeling in large-area remote sensing images called LRSAA. The method integrates YOLOv11 and MobileNetV3-SSD object detection algorithms through ensemble learning to enhance model performance. Furthermore, it employs Poisson disk sampling segmentation te... | {
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2411.15809 | A Novel Data Augmentation Tool for Enhancing Machine Learning
Classification: A New Application of the Higher Order Dynamic Mode
Decomposition for Improved Cardiac Disease Identification | [
"eess.IV",
"cs.CV"
] | In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to improve the classification accuracy of several cardiac diseases using echocardiography images. The HODMD algorithm is used first as feature extr... | {
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2411.15811 | FastTrackTr:Towards Fast Multi-Object Tracking with Transformers | [
"cs.CV",
"cs.AI"
] | Transformer-based multi-object tracking (MOT) methods have captured the attention of many researchers in recent years. However, these models often suffer from slow inference speeds due to their structure or other issues. To address this problem, we revisited the Joint Detection and Tracking (JDT) method by looking back... | {
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2411.15813 | Lattice $\phi^{4}$ field theory as a multi-agent system of financial
markets | [
"cond-mat.dis-nn",
"cs.CE",
"cs.LG",
"cs.MA",
"hep-lat"
] | We introduce a $\phi^{4}$ lattice field theory with frustrated dynamics as a multi-agent system to reproduce stylized facts of financial markets such as fat-tailed distributions of returns and clustered volatility. Each lattice site, represented by a continuous degree of freedom, corresponds to an agent experiencing a ... | {
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2411.15817 | Properties of the Shannon, R\'{e}nyi and other entropies: dependence in
parameters, robustness in distributions and extremes | [
"cs.IT",
"math.IT",
"math.PR"
] | We calculate and analyze various entropy measures and their properties for selected probability distributions. The entropies considered include Shannon, R\'enyi, generalized R\'enyi, Tsallis, Sharma-Mittal, and modified Shannon entropy, along with the Kullback-Leibler divergence. These measures are examined for several... | {
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2411.15821 | Is Training Data Quality or Quantity More Impactful to Small Language
Model Performance? | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This study investigates the relative impact of training data quality versus quantity on the performance of small language models (SLMs), utilizing the TinyStories dataset for empirical analysis. Analysis of dataset variations with respect to size (25% and 50% of the original size) and duplication (controlled rates of 2... | {
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2411.15823 | A Human-optimized Model Predictive Control Scheme and Extremum Seeking
Parameter Estimator for Slip Control of Electric Race Cars | [
"eess.SY",
"cs.SY"
] | This paper presents a longitudinal slip control system for a rear-wheel-driven electric endurance race car. The control system integrates Model Predictive Control (MPC) with Extremum Seeking Control (ESC) to optimize the traction and regenerative braking performance of the powertrain. The MPC contains an analytical sol... | {
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2411.15824 | Variable-size Symmetry-based Graph Fourier Transforms for image
compression | [
"eess.IV",
"cs.CV"
] | Modern compression systems use linear transformations in their encoding and decoding processes, with transforms providing compact signal representations. While multiple data-dependent transforms for image/video coding can adapt to diverse statistical characteristics, assembling large datasets to learn each transform is... | {
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2411.15827 | Runtime-optimized Multi-way Stream Join Operator for Large-scale
Streaming data | [
"cs.DB",
"cs.DC"
] | Streaming computing enables the real-time processing of large volumes of data and offers significant advantages for various applications, including real-time recommendations, anomaly detection, and monitoring. The multi-way stream join operator facilitates the integration of multiple data streams into a single operator... | {
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2411.15831 | Efficient and Private: Memorisation under differentially private
parameter-efficient fine-tuning in language models | [
"cs.LG",
"cs.AI"
] | Fine-tuning large language models (LLMs) for specific tasks introduces privacy risks, as models may inadvertently memorise and leak sensitive training data. While Differential Privacy (DP) offers a solution to mitigate these risks, it introduces significant computational and performance trade-offs, particularly with st... | {
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2411.15832 | Creating Scalable AGI: the Open General Intelligence Framework | [
"cs.AI"
] | Recent advancements in Artificial Intelligence (AI), particularly with Large Language Models (LLMs), have led to significant progress in narrow tasks such as image classification, language translation, coding, and writing. However, these models face limitations in reliability and scalability due to their siloed archite... | {
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2411.15835 | Streaming SQL Multi-Way Join Method for Long State Streams | [
"cs.DB",
"cs.DC"
] | Streaming computing effectively manages large-scale streaming data in real-time, making it ideal for applications such as real-time recommendations, anomaly detection, and monitoring, all of which require immediate processing. In this context, the multi-way stream join operator is crucial, as it combines multiple data ... | {
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2411.15837 | Modality Alignment Meets Federated Broadcasting | [
"cs.CV"
] | Federated learning (FL) has emerged as a powerful approach to safeguard data privacy by training models across distributed edge devices without centralizing local data. Despite advancements in homogeneous data scenarios, maintaining performance between the global and local clients in FL over heterogeneous data remains ... | {
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2411.15839 | VaLiD: Mitigating the Hallucination of Large Vision Language Models by
Visual Layer Fusion Contrastive Decoding | [
"cs.CV"
] | Large Vision-Language Models (LVLMs) have demonstrated outstanding performance in multimodal task reasoning. However, they often generate responses that appear plausible yet do not accurately reflect the visual content, a phenomenon known as hallucination. Recent approaches have introduced training-free methods that mi... | {
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2411.15843 | Unveil Inversion and Invariance in Flow Transformer for Versatile Image
Editing | [
"cs.CV",
"cs.LG"
] | Leveraging the large generative prior of the flow transformer for tuning-free image editing requires authentic inversion to project the image into the model's domain and a flexible invariance control mechanism to preserve non-target contents. However, the prevailing diffusion inversion performs deficiently in flow-base... | {
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2411.15844 | Unveiling the Superior Paradigm: A Comparative Study of Source-Free
Domain Adaptation and Unsupervised Domain Adaptation | [
"cs.LG",
"cs.AI",
"cs.CV"
] | In domain adaptation, there are two popular paradigms: Unsupervised Domain Adaptation (UDA), which aligns distributions using source data, and Source-Free Domain Adaptation (SFDA), which leverages pre-trained source models without accessing source data. Evaluating the superiority of UDA versus SFDA is an open and timel... | {
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2411.15847 | FedQP: Towards Accurate Federated Learning using Quadratic Programming
Guided Mutation | [
"cs.LG"
] | Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference performance caused by data heterogeneity. Specifically, due to heterogeneous data, the optimization directions of different local models var... | {
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