id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.20376 | Occlusion aware obstacle prediction using people as sensors | [
"cs.RO"
] | Navigating dynamic and unstructured environments poses significant challenges for autonomous robots, particularly due to the uncertainty introduced by occluded areas. Conventional sensing methods often fail to detect obstacles hidden behind occlusions until they are dangerously close, especially in crowded spaces where... | {
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2412.20377 | Impact of Data Distribution on Fairness Guarantees in Equitable Deep
Learning | [
"cs.LG",
"cs.CY"
] | We present a comprehensive theoretical framework analyzing the relationship between data distributions and fairness guarantees in equitable deep learning. Our work establishes novel theoretical bounds that explicitly account for data distribution heterogeneity across demographic groups, while introducing a formal analy... | {
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2412.20378 | Tri-Ergon: Fine-grained Video-to-Audio Generation with Multi-modal
Conditions and LUFS Control | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | Video-to-audio (V2A) generation utilizes visual-only video features to produce realistic sounds that correspond to the scene. However, current V2A models often lack fine-grained control over the generated audio, especially in terms of loudness variation and the incorporation of multi-modal conditions. To overcome these... | {
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2412.20381 | Prot\'eg\'e: Learn and Generate Basic Makeup Styles with Generative
Adversarial Networks (GANs) | [
"cs.CV",
"cs.MM"
] | Makeup is no longer confined to physical application; people now use mobile apps to digitally apply makeup to their photos, which they then share on social media. However, while this shift has made makeup more accessible, designing diverse makeup styles tailored to individual faces remains a challenge. This challenge c... | {
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2412.20382 | Natural Language Fine-Tuning | [
"cs.CL",
"cs.AI"
] | Large language model fine-tuning techniques typically depend on extensive labeled data, external guidance, and feedback, such as human alignment, scalar rewards, and demonstration. However, in practical application, the scarcity of specific knowledge poses unprecedented challenges to existing fine-tuning techniques. In... | {
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2412.20383 | Breaking Fine-Grained Classification Barriers with Cost-Free Data in
Few-Shot Class-Incremental Learning | [
"cs.CV"
] | Current fine-grained classification research mainly concentrates on fine-grained feature learning, but in real-world applications, the bigger issue often lies in the data. Fine-grained data annotation is challenging, and the features and semantics are highly diverse and frequently changing, making traditional methods l... | {
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2412.20385 | A Particle Algorithm for Mean-Field Variational Inference | [
"math.ST",
"cs.LG",
"math.OC",
"stat.ML",
"stat.TH"
] | Variational inference is a fast and scalable alternative to Markov chain Monte Carlo and has been widely applied to posterior inference tasks in statistics and machine learning. A traditional approach for implementing mean-field variational inference (MFVI) is coordinate ascent variational inference (CAVI), which relie... | {
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2412.20386 | PTQ4VM: Post-Training Quantization for Visual Mamba | [
"cs.CV",
"cs.LG"
] | Visual Mamba is an approach that extends the selective space state model, Mamba, to vision tasks. It processes image tokens sequentially in a fixed order, accumulating information to generate outputs. Despite its growing popularity for delivering high-quality outputs at a low computational cost across various tasks, Vi... | {
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2412.20390 | MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric
Learning | [
"cs.CV"
] | Deep metric learning aims to learn features relying on the consistency or divergence of class labels. However, in monocular depth estimation, the absence of a natural definition of class poses challenges in the leveraging of deep metric learning. Addressing this gap, this paper introduces MetricDepth, a novel method th... | {
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2412.20391 | Open-Source Heterogeneous SoCs for AI: The PULP Platform Experience | [
"cs.AR",
"cs.NE",
"eess.SP"
] | Since 2013, the PULP (Parallel Ultra-Low Power) Platform project has been one of the most active and successful initiatives in designing research IPs and releasing them as open-source. Its portfolio now ranges from processor cores to network-on-chips, peripherals, SoC templates, and full hardware accelerators. In this ... | {
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2412.20392 | Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning | [
"cs.CV"
] | Multimodal contrastive learning models (e.g., CLIP) can learn high-quality representations from large-scale image-text datasets, yet they exhibit significant vulnerabilities to backdoor attacks, raising serious safety concerns. In this paper, we disclose that CLIP's vulnerabilities primarily stem from its excessive enc... | {
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2412.20397 | Learning Policies for Dynamic Coalition Formation in Multi-Robot Task
Allocation | [
"cs.RO",
"cs.MA"
] | We propose a decentralized, learning-based framework for dynamic coalition formation in Multi-Robot Task Allocation (MRTA). Our approach extends Multi-Agent Proximal Policy Optimization (MAPPO) by incorporating spatial action maps, robot motion control, task allocation revision, and intention sharing to enable effectiv... | {
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2412.20403 | A Novel Supervisory Control Algorithm to Avoid Deadlock in a
Manufacturing System Based on Petri Net in Presence of Resource Failure | [
"eess.SY",
"cs.SY"
] | It is well established that resource failure, including robots and machines, in a manufacturing system can result in deadlocks. This issue not only hampers the system's performance but can also inflict significant damage on the manufacturing process. In this paper, we present a new algorithm developed through modeling ... | {
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2412.20404 | Open-Sora: Democratizing Efficient Video Production for All | [
"cs.CV"
] | Vision and language are the two foundational senses for humans, and they build up our cognitive ability and intelligence. While significant breakthroughs have been made in AI language ability, artificial visual intelligence, especially the ability to generate and simulate the world we see, is far lagging behind. To fac... | {
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2412.20406 | A Multidisciplinary Approach to Telegram Data Analysis | [
"cs.CR",
"cs.CL",
"cs.LG"
] | This paper presents a multidisciplinary approach to analyzing data from Telegram for early warning information regarding cyber threats. With the proliferation of hacktivist groups utilizing Telegram to disseminate information regarding future cyberattacks or to boast about successful ones, the need for effective data a... | {
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2412.20409 | Analytically Informed Inverse Kinematics Solution at Singularities | [
"cs.RO",
"cs.NA",
"math.GR",
"math.NA",
"math.RA"
] | Near kinematic singularities of a serial manipulator, the inverse kinematics (IK) problem becomes ill-conditioned, which poses computational problems for the numerical solution. Computational methods to tackle this issue are based on various forms of a pseudoinverse (PI) solution to the velocity IK problem. The damped ... | {
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2412.20412 | Multi-Objective Large Language Model Unlearning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Machine unlearning in the domain of large language models (LLMs) has attracted great attention recently, which aims to effectively eliminate undesirable behaviors from LLMs without full retraining from scratch. In this paper, we explore the Gradient Ascent (GA) approach in LLM unlearning, which is a proactive way to de... | {
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2412.20413 | EraseAnything: Enabling Concept Erasure in Rectified Flow Transformers | [
"cs.CV"
] | Removing unwanted concepts from large-scale text-to-image (T2I) diffusion models while maintaining their overall generative quality remains an open challenge. This difficulty is especially pronounced in emerging paradigms, such as Stable Diffusion (SD) v3 and Flux, which incorporate flow matching and transformer-based ... | {
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2412.20414 | Comparative Performance of Advanced NLP Models and LLMs in Multilingual
Geo-Entity Detection | [
"cs.CL",
"cs.AI",
"cs.IR"
] | The integration of advanced Natural Language Processing (NLP) methodologies and Large Language Models (LLMs) has significantly enhanced the extraction and analysis of geospatial data from multilingual texts, impacting sectors such as national and international security. This paper presents a comprehensive evaluation of... | {
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2412.20417 | Movable Antenna Array Aided Ultra Reliable Covert Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we construct a framework of the movable antenna (MA) aided covert communication shielded by the general noise uncertainty for the first time. According to the analysis performance on the derived closed-form expressions of the sum of the probabilities of the detection errors and the communication outage p... | {
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2412.20418 | Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via
Diffusion-Based Image Synthesis and Alignment | [
"eess.IV",
"cs.CV"
] | Multimodal learning has been demonstrated to enhance performance across various clinical tasks, owing to the diverse perspectives offered by different modalities of data. However, existing multimodal segmentation methods rely on well-registered multimodal data, which is unrealistic for real-world clinical images, parti... | {
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2412.20422 | Bringing Objects to Life: 4D generation from 3D objects | [
"cs.CV"
] | Recent advancements in generative modeling now enable the creation of 4D content (moving 3D objects) controlled with text prompts. 4D generation has large potential in applications like virtual worlds, media, and gaming, but existing methods provide limited control over the appearance and geometry of generated content.... | {
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2412.20423 | ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos | [
"cs.CV",
"cs.MM"
] | With the rapid development of eXtended Reality (XR), egocentric spatial shooting and display technologies have further enhanced immersion and engagement for users. Assessing the quality of experience (QoE) of egocentric spatial videos is crucial to ensure a high-quality viewing experience. However, the corresponding re... | {
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2412.20426 | Robust targeted exploration for systems with non-stochastic disturbances | [
"math.OC",
"cs.SY",
"eess.SY"
] | In this paper, we introduce a novel targeted exploration strategy designed specifically for uncertain linear time-invariant systems with energy-bounded disturbances, i.e., without making any assumptions on the distribution of the disturbances. We use classical results characterizing the set of non-falsified parameters ... | {
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2412.20427 | AmalREC: A Dataset for Relation Extraction and Classification Leveraging
Amalgamation of Large Language Models | [
"cs.IR"
] | Existing datasets for relation classification and extraction often exhibit limitations such as restricted relation types and domain-specific biases. This work presents a generic framework to generate well-structured sentences from given tuples with the help of Large Language Models (LLMs). This study has focused on the... | {
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2412.20429 | Multi-Scenario Reasoning: Unlocking Cognitive Autonomy in Humanoid
Robots for Multimodal Understanding | [
"cs.RO",
"cs.AI"
] | To improve the cognitive autonomy of humanoid robots, this research proposes a multi-scenario reasoning architecture to solve the technical shortcomings of multi-modal understanding in this field. It draws on simulation based experimental design that adopts multi-modal synthesis (visual, auditory, tactile) and builds a... | {
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2412.20430 | Unlocking adaptive digital pathology through dynamic feature learning | [
"eess.IV",
"cs.CV"
] | Foundation models have revolutionized the paradigm of digital pathology, as they leverage general-purpose features to emulate real-world pathological practices, enabling the quantitative analysis of critical histological patterns and the dissection of cancer-specific signals. However, these static general features cons... | {
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2412.20436 | Treatment Effect Estimation for Graph-Structured Targets | [
"cs.LG",
"stat.ML"
] | Treatment effect estimation, which helps understand the causality between treatment and outcome variable, is a central task in decision-making across various domains. While most studies focus on treatment effect estimation on individual targets, in specific contexts, there is a necessity to comprehend the treatment eff... | {
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2412.20438 | Integrating Natural Language Processing Techniques of Text Mining Into
Financial System: Applications and Limitations | [
"cs.CL",
"cs.AI",
"econ.GN",
"q-fin.EC"
] | The financial sector, a pivotal force in economic development, increasingly uses the intelligent technologies such as natural language processing to enhance data processing and insight extraction. This research paper through a review process of the time span of 2018-2023 explores the use of text mining as natural langu... | {
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2412.20439 | Image Augmentation Agent for Weakly Supervised Semantic Segmentation | [
"cs.CV"
] | Weakly-supervised semantic segmentation (WSSS) has achieved remarkable progress using only image-level labels. However, most existing WSSS methods focus on designing new network structures and loss functions to generate more accurate dense labels, overlooking the limitations imposed by fixed datasets, which can constra... | {
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2412.20440 | Enhancing Entertainment Translation for Indian Languages using Adaptive
Context, Style and LLMs | [
"cs.CL"
] | We address the challenging task of neural machine translation (NMT) in the entertainment domain, where the objective is to automatically translate a given dialogue from a source language content to a target language. This task has various applications, particularly in automatic dubbing, subtitling, and other content lo... | {
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2412.20446 | Explaining Black-Box Clustering Pipelines With Cluster-Explorer | [
"cs.DB"
] | Explaining the results of clustering pipelines by unraveling the characteristics of each cluster is a challenging task, often addressed manually through visualizations and queries. Existing solutions from the domain of Explainable Artificial Intelligence (XAI) are largely ineffective for cluster explanations, and inter... | {
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2412.20451 | Improving Vision-Language-Action Models via Chain-of-Affordance | [
"cs.RO"
] | Robot foundation models, particularly Vision-Language-Action (VLA) models, have garnered significant attention for their ability to enhance robot policy learning, greatly improving robot generalization and robustness. OpenAI recent model, o1, showcased impressive capabilities in solving complex problems by utilizing ex... | {
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2412.20455 | Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video
Anomaly Detection | [
"cs.CV"
] | Recently, weakly supervised video anomaly detection (WS-VAD) has emerged as a contemporary research direction to identify anomaly events like violence and nudity in videos using only video-level labels. However, this task has substantial challenges, including addressing imbalanced modality information and consistently ... | {
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2412.20466 | Single-image reflection removal via self-supervised diffusion models | [
"cs.CV",
"eess.IV"
] | Reflections often degrade the visual quality of images captured through transparent surfaces, and reflection removal methods suffers from the shortage of paired real-world samples.This paper proposes a hybrid approach that combines cycle-consistency with denoising diffusion probabilistic models (DDPM) to effectively re... | {
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2412.20467 | Utilizing Multimodal Data for Edge Case Robust Call-sign Recognition and
Understanding | [
"cs.CL"
] | Operational machine-learning based assistant systems must be robust in a wide range of scenarios. This hold especially true for the air-traffic control (ATC) domain. The robustness of an architecture is particularly evident in edge cases, such as high word error rate (WER) transcripts resulting from noisy ATC recording... | {
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2412.20468 | A Comprehensive Framework for Reliable Legal AI: Combining Specialized
Expert Systems and Adaptive Refinement | [
"cs.AI",
"cs.CY"
] | This article discusses the evolving role of artificial intelligence (AI) in the legal profession, focusing on its potential to streamline tasks such as document review, research, and contract drafting. However, challenges persist, particularly the occurrence of "hallucinations" in AI models, where they generate inaccur... | {
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2412.20470 | JADE: Joint-aware Latent Diffusion for 3D Human Generative Modeling | [
"cs.CV"
] | Generative modeling of 3D human bodies have been studied extensively in computer vision. The core is to design a compact latent representation that is both expressive and semantically interpretable, yet existing approaches struggle to achieve both requirements. In this work, we introduce JADE, a generative framework th... | {
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2412.20471 | On the Convergence of Min-Max Langevin Dynamics and Algorithm | [
"cs.GT",
"cs.LG",
"math.OC",
"stat.ML"
] | We study zero-sum games in the space of probability distributions over the Euclidean space $\mathbb{R}^d$ with entropy regularization, in the setting when the interaction function between the players is smooth and strongly convex-strongly concave. We prove an exponential convergence guarantee for the mean-field min-max... | {
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2412.20473 | Toward Scene Graph and Layout Guided Complex 3D Scene Generation | [
"cs.CV"
] | Recent advancements in object-centric text-to-3D generation have shown impressive results. However, generating complex 3D scenes remains an open challenge due to the intricate relations between objects. Moreover, existing methods are largely based on score distillation sampling (SDS), which constrains the ability to ma... | {
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2412.20476 | Cut the Deadwood Out: Post-Training Model Purification with Selective
Module Substitution | [
"cs.CL",
"cs.CR"
] | The success of DNNs often depends on training with large-scale datasets, but building such datasets is both expensive and challenging. Consequently, public datasets from open-source platforms like HuggingFace have become popular, posing significant risks of data poisoning attacks. Existing backdoor defenses in NLP prim... | {
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2412.20477 | A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network
Model for Time Variant Quadratic Programming With Application to Robot Motion
Planning | [
"cs.RO",
"cs.NE"
] | This paper develops a predefined-time convergent and noise-tolerant fractional-order zeroing neural network (PTC-NT-FOZNN) model, innovatively engineered to tackle time-variant quadratic programming (TVQP) challenges. The PTC-NT-FOZNN, stemming from a novel iteration within the variable-gain ZNN spectrum, known as FOZN... | {
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} |
2412.20480 | MR-Occ: Efficient Camera-LiDAR 3D Semantic Occupancy Prediction Using
Hierarchical Multi-Resolution Voxel Representation | [
"cs.CV"
] | Accurate 3D perception is essential for understanding the environment in autonomous driving. Recent advancements in 3D semantic occupancy prediction have leveraged camera-LiDAR fusion to improve robustness and accuracy. However, current methods allocate computational resources uniformly across all voxels, leading to in... | {
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2412.20487 | Multimodal Variational Autoencoder: a Barycentric View | [
"cs.LG",
"cs.CV",
"cs.IT",
"math.IT"
] | Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in particular variational autoencoder (VAE), to for multimodal representation learning especially in the case of missing modalities. The primary ... | {
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2412.20489 | Low-Thrust Under-Actuated Satellite Formation Guidance and Control
Strategies | [
"eess.SY",
"cs.SY"
] | This study presents autonomous guidance and control strategies for the purpose of reconfiguring close-range multi-satellite formations. The formation under consideration includes $N$ under-actuated deputy satellites and an uncontrolled virtual or physical chief spacecraft. The guidance problem is formulated as a trajec... | {
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2412.20495 | A Multiparty Homomorphic Encryption Approach to Confidential Federated
Kaplan Meier Survival Analysis | [
"cs.CR",
"cs.AI",
"cs.LG",
"stat.ML"
] | The proliferation of healthcare data has expanded opportunities for collaborative research, yet stringent privacy regulations hinder pooling sensitive patient records. We propose a \emph{multiparty homomorphic encryption-based} framework for \emph{privacy-preserving federated Kaplan--Meier survival analysis}, offering ... | {
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2412.20496 | Random Matrix Theory for Stochastic Gradient Descent | [
"hep-lat",
"cond-mat.dis-nn",
"cs.LG"
] | Investigating the dynamics of learning in machine learning algorithms is of paramount importance for understanding how and why an approach may be successful. The tools of physics and statistics provide a robust setting for such investigations. Here we apply concepts from random matrix theory to describe stochastic weig... | {
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2412.20503 | Iterative structural coarse-graining for contagion dynamics in complex
networks | [
"physics.soc-ph",
"cs.SI",
"physics.data-an"
] | Contagion dynamics in complex networks drive critical phenomena such as epidemic spread and information diffusion,but their analysis remains computationally prohibitive in large-scale, high-complexity systems. Here, we introduce the Iterative Structural Coarse-Graining (ISCG) framework, a scalable methodology that redu... | {
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2412.20504 | ReTaKe: Reducing Temporal and Knowledge Redundancy for Long Video
Understanding | [
"cs.CV",
"cs.CL",
"cs.MM"
] | Video Large Language Models (VideoLLMs) have achieved remarkable progress in video understanding. However, existing VideoLLMs often inherit the limitations of their backbone LLMs in handling long sequences, leading to challenges for long video understanding. Common solutions either simply uniformly sample videos' frame... | {
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2412.20505 | Planning, Living and Judging: A Multi-agent LLM-based Framework for
Cyclical Urban Planning | [
"cs.AI",
"cs.LG"
] | Urban regeneration presents significant challenges within the context of urbanization, requiring adaptive approaches to tackle evolving needs. Leveraging advancements in large language models (LLMs), we propose Cyclical Urban Planning (CUP), a new paradigm that continuously generates, evaluates, and refines urban plans... | {
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2412.20506 | DPBridge: Latent Diffusion Bridge for Dense Prediction | [
"cs.CV"
] | Diffusion models have demonstrated remarkable success in dense prediction problems, which aims to model per-pixel relationship between RGB images and dense signal maps, thanks to their ability to effectively capture complex data distributions. However, initiating the reverse sampling trajectory from uninformative noise... | {
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2412.20510 | Stratify: Unifying Multi-Step Forecasting Strategies | [
"cs.LG",
"cs.AI",
"stat.ML"
] | A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this process is selecting a forecasting strategy, however, with no existing frameworks to map out the space of strategies, practitioners are left with ... | {
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} |
2412.20512 | Dive into Time-Series Anomaly Detection: A Decade Review | [
"cs.LG",
"cs.AI",
"cs.DB",
"stat.ML"
] | Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financi... | {
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2412.20519 | Goal-Conditioned Data Augmentation for Offline Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Offline reinforcement learning (RL) enables policy learning from pre-collected offline datasets, relaxing the need to interact directly with the environment. However, limited by the quality of offline datasets, it generally fails to learn well-qualified policies in suboptimal datasets. To address datasets with insuffic... | {
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2412.20521 | Can Robots "Taste" Grapes? Estimating SSC with Simple RGB Sensors | [
"cs.CV",
"cs.RO"
] | In table grape cultivation, harvesting depends on accurately assessing fruit quality. While some characteristics, like color, are visible, others, such as Soluble Solid Content (SSC), or sugar content measured in degrees Brix ({\deg}Brix), require specific tools. SSC is a key quality factor that correlates with ripenes... | {
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} |
2412.20522 | MaskGaussian: Adaptive 3D Gaussian Representation from Probabilistic
Masks | [
"cs.CV"
] | While 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and real-time rendering, the high memory consumption due to the use of millions of Gaussians limits its practicality. To mitigate this issue, improvements have been made by pruning unnecessary Gaussians, either through a ... | {
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2412.20523 | Game Theory and Multi-Agent Reinforcement Learning : From Nash
Equilibria to Evolutionary Dynamics | [
"cs.MA",
"cs.AI",
"cs.GT"
] | This paper explores advanced topics in complex multi-agent systems building upon our previous work. We examine four fundamental challenges in Multi-Agent Reinforcement Learning (MARL): non-stationarity, partial observability, scalability with large agent populations, and decentralized learning. The paper provides mathe... | {
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2412.20529 | Attacks on the neural network and defense methods | [
"cs.CR",
"cs.AI"
] | This article will discuss the use of attacks on a neural network trained on audio data, as well as possible methods of protection against these attacks. FGSM, PGD and CW attacks, as well as data poisoning, will be considered. Within the framework of protection, Art-IBM and advertorch libraries will be considered. The o... | {
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2412.20530 | KVC-onGoing: Keystroke Verification Challenge | [
"cs.HC",
"cs.CV"
] | This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing), on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long se... | {
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2412.20537 | Diminishing Return of Value Expansion Methods | [
"cs.LG"
] | Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations. This paper empirically investigates potential sample efficiency gains from improved dynamics models in model-based value expansion methods. O... | {
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2412.20538 | Exploiting Aggregation and Segregation of Representations for Domain
Adaptive Human Pose Estimation | [
"cs.CV"
] | Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc. However, it suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation. To cope with the label deficiency issue, one ... | {
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2412.20541 | SAFE-MEME: Structured Reasoning Framework for Robust Hate Speech
Detection in Memes | [
"cs.CL",
"cs.CY"
] | Memes act as cryptic tools for sharing sensitive ideas, often requiring contextual knowledge to interpret. This makes moderating multimodal memes challenging, as existing works either lack high-quality datasets on nuanced hate categories or rely on low-quality social media visuals. Here, we curate two novel multimodal ... | {
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2412.20545 | The Impact of Prompt Programming on Function-Level Code Generation | [
"cs.SE",
"cs.CL",
"cs.HC",
"cs.LG"
] | Large Language Models (LLMs) are increasingly used by software engineers for code generation. However, limitations of LLMs such as irrelevant or incorrect code have highlighted the need for prompt programming (or prompt engineering) where engineers apply specific prompt techniques (e.g., chain-of-thought or input-outpu... | {
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2412.20553 | Edge of Stochastic Stability: Revisiting the Edge of Stability for SGD | [
"cs.LG",
"math.OC",
"stat.ML"
] | Recent findings by Cohen et al., 2021, demonstrate that when training neural networks with full-batch gradient descent with a step size of $\eta$, the largest eigenvalue $\lambda_{\max}$ of the full-batch Hessian consistently stabilizes at $\lambda_{\max} = 2/\eta$. These results have significant implications for conve... | {
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2412.20556 | Distributionally Robust Optimization via Iterative Algorithms in
Continuous Probability Spaces | [
"stat.ML",
"cs.LG",
"math.OC"
] | We consider a minimax problem motivated by distributionally robust optimization (DRO) when the worst-case distribution is continuous, leading to significant computational challenges due to the infinite-dimensional nature of the optimization problem. Recent research has explored learning the worst-case distribution usin... | {
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2412.20563 | Counterfactual Samples Constructing and Training for Commonsense
Statements Estimation | [
"cs.CL"
] | Plausibility Estimation (PE) plays a crucial role for enabling language models to objectively comprehend the real world. While large language models (LLMs) demonstrate remarkable capabilities in PE tasks but sometimes produce trivial commonsense errors due to the complexity of commonsense knowledge. They lack two key t... | {
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2412.20564 | Self-Disclosure to AI: The Paradox of Trust and Vulnerability in
Human-Machine Interactions | [
"cs.HC",
"cs.RO"
] | In this paper, we explore the paradox of trust and vulnerability in human-machine interactions, inspired by Alexander Reben's BlabDroid project. This project used small, unassuming robots that actively engaged with people, successfully eliciting personal thoughts or secrets from individuals, often more effectively than... | {
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2412.20565 | Enhancing autonomous vehicle safety in rain: a data-centric approach for
clear vision | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Autonomous vehicles face significant challenges in navigating adverse weather, particularly rain, due to the visual impairment of camera-based systems. In this study, we leveraged contemporary deep learning techniques to mitigate these challenges, aiming to develop a vision model that processes live vehicle camera feed... | {
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2412.20571 | Segmentation of Muscularis Propria in Colon Histopathology Images Using
Vision Transformers for Hirschsprung's Disease | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Hirschsprung's disease (HD) is a congenital birth defect diagnosed by identifying the lack of ganglion cells within the colon's muscularis propria, specifically within the myenteric plexus regions. There may be advantages for quantitative assessments of histopathology images of the colon, such as counting the ganglion ... | {
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2412.20573 | The intrinsic motivation of reinforcement and imitation learning for
sequential tasks | [
"cs.AI",
"cs.HC",
"cs.LG",
"cs.RO"
] | This work in the field of developmental cognitive robotics aims to devise a new domain bridging between reinforcement learning and imitation learning, with a model of the intrinsic motivation for learning agents to learn with guidance from tutors multiple tasks, including sequential tasks. The main contribution has bee... | {
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2412.20574 | A Survey on Time-Series Distance Measures | [
"cs.DB",
"cs.AI",
"cs.LG"
] | Distance measures have been recognized as one of the fundamental building blocks in time-series analysis tasks, e.g., querying, indexing, classification, clustering, anomaly detection, and similarity search. The vast proliferation of time-series data across a wide range of fields has increased the relevance of evaluati... | {
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2412.20581 | "The Prophet said so!": On Exploring Hadith Presence on Arabic Social
Media | [
"cs.SI"
] | Hadith, the recorded words and actions of the prophet Muhammad, is a key source of the instructions and foundations of Islam, alongside the Quran. Interpreting individual hadiths and verifying their authenticity can be difficult, even controversial, and the subject has attracted the attention of many scholars who have ... | {
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2412.20582 | Bridging the Gap: A Decade Review of Time-Series Clustering Methods | [
"cs.LG",
"cs.AI",
"cs.DB"
] | Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensio... | {
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2412.20584 | Towards Neural No-Resource Language Translation: A Comparative
Evaluation of Approaches | [
"cs.CL"
] | No-resource languages - those with minimal or no digital representation - pose unique challenges for machine translation (MT). Unlike low-resource languages, which rely on limited but existent corpora, no-resource languages often have fewer than 100 sentences available for training. This work explores the problem of no... | {
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2412.20586 | Testing and Improving the Robustness of Amortized Bayesian Inference for
Cognitive Models | [
"stat.ML",
"cs.LG",
"stat.AP",
"stat.ME"
] | Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models, which are statistical models representing cognitive processes. In this study, we test and improve the robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks. To this e... | {
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2412.20588 | Kryptonite-N: Machine Learning Strikes Back | [
"cs.LG",
"cs.AI"
] | Quinn et al propose challenge datasets in their work called ``Kryptonite-N". These datasets aim to counter the universal function approximation argument of machine learning, breaking the notation that machine learning can ``approximate any continuous function" \cite{original_paper}. Our work refutes this claim and show... | {
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2412.20595 | Controlling Out-of-Domain Gaps in LLMs for Genre Classification and
Generated Text Detection | [
"cs.CL",
"cs.AI"
] | This study demonstrates that the modern generation of Large Language Models (LLMs, such as GPT-4) suffers from the same out-of-domain (OOD) performance gap observed in prior research on pre-trained Language Models (PLMs, such as BERT). We demonstrate this across two non-topical classification tasks: 1) genre classifica... | {
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2412.20596 | Zero-Shot Image Restoration Using Few-Step Guidance of Consistency
Models (and Beyond) | [
"cs.CV"
] | In recent years, it has become popular to tackle image restoration tasks with a single pretrained diffusion model (DM) and data-fidelity guidance, instead of training a dedicated deep neural network per task. However, such "zero-shot" restoration schemes currently require many Neural Function Evaluations (NFEs) for per... | {
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2412.20597 | GliLem: Leveraging GliNER for Contextualized Lemmatization in Estonian | [
"cs.CL"
] | We present GliLem -- a novel hybrid lemmatization system for Estonian that enhances the highly accurate rule-based morphological analyzer Vabamorf with an external disambiguation module based on GliNER -- an open vocabulary NER model that is able to match text spans with text labels in natural language. We leverage the... | {
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2412.20601 | MATEY: multiscale adaptive foundation models for spatiotemporal physical
systems | [
"cs.LG",
"cs.AI",
"cs.CE"
] | Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer (ViT) architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two adaptive tokenization schemes that dynamically adjust patch sizes based on local ... | {
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} |
2412.20602 | NLP-based Regulatory Compliance -- Using GPT 4.0 to Decode Regulatory
Documents | [
"cs.CL"
] | Large Language Models (LLMs) such as GPT-4.0 have shown significant promise in addressing the semantic complexities of regulatory documents, particularly in detecting inconsistencies and contradictions. This study evaluates GPT-4.0's ability to identify conflicts within regulatory requirements by analyzing a curated co... | {
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2412.20608 | Conformable Convolution for Topologically Aware Learning of Complex
Anatomical Structures | [
"eess.IV",
"cs.CV"
] | While conventional computer vision emphasizes pixel-level and feature-based objectives, medical image analysis of intricate biological structures necessitates explicit representation of their complex topological properties. Despite their successes, deep learning models often struggle to accurately capture the connectiv... | {
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2412.20612 | Towards Explaining Uncertainty Estimates in Point Cloud Registration | [
"cs.RO",
"cs.AI"
] | Iterative Closest Point (ICP) is a commonly used algorithm to estimate transformation between two point clouds. The key idea of this work is to leverage recent advances in explainable AI for probabilistic ICP methods that provide uncertainty estimates. Concretely, we propose a method that can explain why a probabilisti... | {
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2412.20613 | Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study | [
"cs.CV"
] | With the rise of multimodal large language models, accurately extracting and understanding textual information from video content, referred to as video based optical character recognition (Video OCR), has become a crucial capability. This paper introduces a novel benchmark designed to evaluate the video OCR performance... | {
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2412.20616 | Hilbert Curve Based Molecular Sequence Analysis | [
"cs.LG",
"q-bio.OT"
] | Accurate molecular sequence analysis is a key task in the field of bioinformatics. To apply molecular sequence classification algorithms, we first need to generate the appropriate representations of the sequences. Traditional numeric sequence representation techniques are mostly based on sequence alignment that faces l... | {
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2412.20617 | Converting Time Series Data to Numeric Representations Using Alphabetic
Mapping and k-mer strategy | [
"cs.LG"
] | In the realm of data analysis and bioinformatics, representing time series data in a manner akin to biological sequences offers a novel approach to leverage sequence analysis techniques. Transforming time series signals into molecular sequence-type representations allows us to enhance pattern recognition by applying so... | {
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2412.20619 | Audiopedia: Audio QA with Knowledge | [
"cs.LG",
"cs.MM",
"cs.SD",
"eess.AS"
] | In this paper, we introduce Audiopedia, a novel task called Audio Question Answering with Knowledge, which requires both audio comprehension and external knowledge reasoning. Unlike traditional Audio Question Answering (AQA) benchmarks that focus on simple queries answerable from audio alone, Audiopedia targets knowled... | {
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2412.20620 | Matrix Concentration for Random Signed Graphs and Community Recovery in
the Signed Stochastic Block Model | [
"stat.ML",
"cs.LG",
"cs.SI"
] | We consider graphs where edges and their signs are added independently at random from among all pairs of nodes. We establish strong concentration inequalities for adjacency and Laplacian matrices obtained from this family of random graph models. Then, we apply our results to study graphs sampled from the signed stochas... | {
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2412.20621 | FreqMixFormerV2: Lightweight Frequency-aware Mixed Transformer for Human
Skeleton Action Recognition | [
"cs.CV"
] | Transformer-based human skeleton action recognition has been developed for years. However, the complexity and high parameter count demands of these models hinder their practical applications, especially in resource-constrained environments. In this work, we propose FreqMixForemrV2, which was built upon the Frequency-aw... | {
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} |
2412.20622 | HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large
Visual-Language Models | [
"cs.CV",
"cs.AI"
] | Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in performing complex multimodal tasks. However, they are still plagued by object hallucination: the misidentification or misclassification of objects present in images. To this end, we propose HALLUCINOGEN, a novel visual question answering ... | {
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2412.20631 | Slow Perception: Let's Perceive Geometric Figures Step-by-step | [
"cs.CV"
] | Recently, "visual o1" began to enter people's vision, with expectations that this slow-thinking design can solve visual reasoning tasks, especially geometric math problems. However, the reality is that current LVLMs (Large Vision Language Models) can hardly even accurately copy a geometric figure, let alone truly under... | {
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2412.20632 | EVOLVE: Emotion and Visual Output Learning via LLM Evaluation | [
"cs.RO",
"cs.HC"
] | Human acceptance of social robots is greatly effected by empathy and perceived understanding. This necessitates accurate and flexible responses to various input data from the user. While systems such as this can become increasingly complex as more states or response types are included, new research in the application o... | {
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2412.20634 | Graph Neural Networks for Next-Generation-IoT: Recent Advances and Open
Challenges | [
"cs.IT",
"math.IT"
] | Graph Neural Networks (GNNs) have emerged as a critical tool for optimizing and managing the complexities of the Internet of Things (IoT) in next-generation networks. This survey presents a comprehensive exploration of how GNNs may be harnessed in 6G IoT environments, focusing on key challenges and opportunities throug... | {
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2412.20635 | NetFlowGen: Leveraging Generative Pre-training for Network Traffic
Dynamics | [
"cs.LG",
"cs.AI",
"cs.NI"
] | Understanding the traffic dynamics in networks is a core capability for automated systems to monitor and analyze networking behaviors, reducing expensive human efforts and economic risks through tasks such as traffic classification, congestion prediction, and attack detection. However, it is still challenging to accura... | {
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2412.20637 | Knowledge Editing for Large Language Model with Knowledge Neuronal
Ensemble | [
"cs.CL"
] | As real-world knowledge is constantly evolving, ensuring the timeliness and accuracy of a model's knowledge is crucial. This has made knowledge editing in large language models increasingly important. However, existing knowledge editing methods face several challenges, including parameter localization coupling, impreci... | {
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2412.20638 | Predicting Long Term Sequential Policy Value Using Softer Surrogates | [
"cs.AI",
"cs.LG"
] | Off-policy policy evaluation (OPE) estimates the outcome of a new policy using historical data collected from a different policy. However, existing OPE methods cannot handle cases when the new policy introduces novel actions. This issue commonly occurs in real-world domains, like healthcare, as new drugs and treatments... | {
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2412.20641 | SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving
Synthetic Data Generation Using Differential Privacy | [
"cs.LG",
"cs.CR"
] | Machine learning (ML) models frequently rely on training data that may include sensitive or personal information, raising substantial privacy concerns. Legislative frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have necessitated the development of strateg... | {
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2412.20644 | Uncertainty Herding: One Active Learning Method for All Label Budgets | [
"cs.LG",
"stat.ML"
] | Most active learning research has focused on methods which perform well when many labels are available, but can be dramatically worse than random selection when label budgets are small. Other methods have focused on the low-budget regime, but do poorly as label budgets increase. As the line between "low" and "high" bud... | {
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2412.20645 | YOLO-UniOW: Efficient Universal Open-World Object Detection | [
"cs.CV"
] | Traditional object detection models are constrained by the limitations of closed-set datasets, detecting only categories encountered during training. While multimodal models have extended category recognition by aligning text and image modalities, they introduce significant inference overhead due to cross-modality fusi... | {
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2412.20646 | Enhancing Visual Representation for Text-based Person Searching | [
"cs.CV"
] | Text-based person search aims to retrieve the matched pedestrians from a large-scale image database according to the text description. The core difficulty of this task is how to extract effective details from pedestrian images and texts, and achieve cross-modal alignment in a common latent space. Prior works adopt imag... | {
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