id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.02839 | Geographical Information Alignment Boosts Traffic Analysis via Transpose
Cross-attention | [
"cs.LG"
] | Traffic accident prediction is crucial for enhancing road safety and mitigating congestion, and recent Graph Neural Networks (GNNs) have shown promise in modeling the inherent graph-based traffic data. However, existing GNN- based approaches often overlook or do not explicitly exploit geographic position information, w... | {
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2412.02843 | Batch Normalization Decomposed | [
"cs.LG",
"cs.NE"
] | \emph{Batch normalization} is a successful building block of neural network architectures. Yet, it is not well understood. A neural network layer with batch normalization comprises three components that affect the representation induced by the network: \emph{recentering} the mean of the representation to zero, \emph{re... | {
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2412.02845 | Optimized IoT Intrusion Detection using Machine Learning Technique | [
"cs.CR",
"cs.LG"
] | An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic response to threats are a few of the problems that intrusion identification is used t... | {
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2412.02851 | Block MedCare: Advancing healthcare through blockchain integration with
AI and IoT | [
"cs.SE",
"cs.AI",
"cs.CR"
] | This research explores the integration of blockchain technology in healthcare, focusing on enhancing the security and efficiency of Electronic Health Record (EHR) management. We propose a novel Ethereum-based system that empowers patients with secure control over their medical data. Our approach addresses key challenge... | {
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2412.02852 | Effortless Efficiency: Low-Cost Pruning of Diffusion Models | [
"cs.CV"
] | Diffusion models have achieved impressive advancements in various vision tasks. However, these gains often rely on increasing model size, which escalates computational complexity and memory demands, complicating deployment, raising inference costs, and causing environmental impact. While some studies have explored prun... | {
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2412.02855 | Optimized CNNs for Rapid 3D Point Cloud Object Recognition | [
"cs.CV",
"cs.LG"
] | This study introduces a method for efficiently detecting objects within 3D point clouds using convolutional neural networks (CNNs). Our approach adopts a unique feature-centric voting mechanism to construct convolutional layers that capitalize on the typical sparsity observed in input data. We explore the trade-off bet... | {
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2412.02856 | Is Large-Scale Pretraining the Secret to Good Domain Generalization? | [
"cs.CV",
"cs.LG"
] | Multi-Source Domain Generalization (DG) is the task of training on multiple source domains and achieving high classification performance on unseen target domains. Recent methods combine robust features from web-scale pretrained backbones with new features learned from source data, and this has dramatically improved ben... | {
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2412.02857 | Measuring Bias of Web-filtered Text Datasets and Bias Propagation
Through Training | [
"cs.LG"
] | We investigate biases in pretraining datasets for large language models (LLMs) through dataset classification experiments. Building on prior work demonstrating the existence of biases in popular computer vision datasets, we analyze popular open-source pretraining datasets for LLMs derived from CommonCrawl including C4,... | {
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2412.02858 | Unpaired Modality Translation for Pseudo Labeling of Histology Images | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The segmentation of histological images is critical for various biomedical applications, yet the lack of annotated data presents a significant challenge. We propose a microscopy pseudo labeling pipeline utilizing unsupervised image translation to address this issue. Our method generates pseudo labels by translating bet... | {
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2412.02859 | Novel Magnetic Actuation Strategies for Precise Ferrofluid Marble
Manipulation in Magnetic Digital Microfluidics: Position Control and
Applications | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Precise manipulation of liquid marbles has significant potential in various applications such as lab-on-a-chip systems, drug delivery, and biotechnology and has been a challenge for researchers. Ferrofluid marble (FM) is a marble with a ferrofluid core that can easily be manipulated by a magnetic field. Although FMs ha... | {
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2412.02861 | An Information-Theoretic Analysis of Thompson Sampling for Logistic
Bandits | [
"stat.ML",
"cs.LG"
] | We study the performance of the Thompson Sampling algorithm for logistic bandit problems. In this setting, an agent receives binary rewards with probabilities determined by a logistic function, $\exp(\beta \langle a, \theta \rangle)/(1+\exp(\beta \langle a, \theta \rangle))$, with slope parameter $\beta>0$, and where b... | {
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2412.02863 | Proximal Control of UAVs with Federated Learning for Human-Robot
Collaborative Domains | [
"cs.RO",
"cs.AI",
"cs.LG"
] | The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a m... | {
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2412.02865 | Memory-efficient Continual Learning with Neural Collapse Contrastive | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Contrastive learning has significantly improved representation quality, enhancing knowledge transfer across tasks in continual learning (CL). However, catastrophic forgetting remains a key challenge, as contrastive based methods primarily focus on "soft relationships" or "softness" between samples, which shift with cha... | {
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2412.02868 | A Novel Compact LLM Framework for Local, High-Privacy EHR Data
Applications | [
"cs.AI"
] | Large Language Models (LLMs) have shown impressive capabilities in natural language processing, yet their use in sensitive domains like healthcare, particularly with Electronic Health Records (EHR), faces significant challenges due to privacy concerns and limited computational resources. This paper presents a compact L... | {
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2412.02869 | Constrained Identifiability of Causal Effects | [
"cs.AI",
"stat.ME"
] | We study the identification of causal effects in the presence of different types of constraints (e.g., logical constraints) in addition to the causal graph. These constraints impose restrictions on the models (parameterizations) induced by the causal graph, reducing the set of models considered by the identifiability p... | {
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2412.02871 | MAGMA: Manifold Regularization for MAEs | [
"cs.CV"
] | Masked Autoencoders (MAEs) are an important divide in self-supervised learning (SSL) due to their independence from augmentation techniques for generating positive (and/or negative) pairs as in contrastive frameworks. Their masking and reconstruction strategy also nicely aligns with SSL approaches in natural language p... | {
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2412.02874 | A Generalized Thrust Estimation and Control Approach for Multirotors
Micro Aerial Vehicles | [
"cs.RO"
] | This paper addresses the problem of thrust estimation and control for the rotors of small-sized multirotors Uncrewed Aerial Vehicles (UAVs). Accurate control of the thrust generated by each rotor during flight is one of the main challenges for robust control of quadrotors. The most common approach is to approximate the... | {
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2412.02875 | Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Autonomous agents for cyber applications take advantage of modern defense techniques by adopting intelligent agents with conventional and learning-enabled components. These intelligent agents are trained via reinforcement learning (RL) algorithms, and can learn, adapt to, reason about and deploy security rules to defen... | {
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2412.02878 | Modeling and Discovering Direct Causes for Predictive Models | [
"cs.LG",
"cs.AI",
"stat.ME"
] | We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models) by representing it using causal graphs. The framework enables us to define and identify features that directly cause the predictions, which has broad implications for data collection and... | {
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2412.02879 | Pairwise Spatiotemporal Partial Trajectory Matching for Co-movement
Analysis | [
"cs.CV"
] | Spatiotemporal pairwise movement analysis involves identifying shared geographic-based behaviors between individuals within specific time frames. Traditionally, this task relies on sequence modeling and behavior analysis techniques applied to tabular or video-based data, but these methods often lack interpretability an... | {
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2412.02881 | A Minimalistic 3D Self-Organized UAV Flocking Approach for Desert
Exploration | [
"cs.RO"
] | In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The metho... | {
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2412.02883 | TDD-Bench Verified: Can LLMs Generate Tests for Issues Before They Get
Resolved? | [
"cs.SE",
"cs.CL",
"cs.LG"
] | Test-driven development (TDD) is the practice of writing tests first and coding later, and the proponents of TDD expound its numerous benefits. For instance, given an issue on a source code repository, tests can clarify the desired behavior among stake-holders before anyone writes code for the agreed-upon fix. Although... | {
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2412.02884 | Were You Helpful -- Predicting Helpful Votes from Amazon Reviews | [
"cs.NE"
] | This project investigates factors that influence the perceived helpfulness of Amazon product reviews through machine learning techniques. After extensive feature analysis and correlation testing, we identified key metadata characteristics that serve as strong predictors of review helpfulness. While we initially explore... | {
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2412.02886 | Patchfinder: Leveraging Visual Language Models for Accurate Information
Retrieval using Model Uncertainty | [
"cs.CV"
] | For decades, corporations and governments have relied on scanned documents to record vast amounts of information. However, extracting this information is a slow and tedious process due to the sheer volume and complexity of these records. The rise of Vision Language Models (VLMs) presents a way to efficiently and accura... | {
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2412.02889 | Deep-Learning Based Docking Methods: Fair Comparisons to Conventional
Docking Workflows | [
"cs.AI",
"q-bio.BM"
] | The diffusion learning method, DiffDock, for docking small-molecule ligands into protein binding sites was recently introduced. Results included comparisons to more conventional docking approaches, with DiffDock showing superior performance. Here, we employ a fully automatic workflow using the Surflex-Dock methods to g... | {
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2412.02890 | EvRT-DETR: The Surprising Effectiveness of DETR-based Detection for
Event Cameras | [
"cs.CV"
] | Event-based cameras (EBCs) have emerged as a bio-inspired alternative to traditional cameras, offering advantages in power efficiency, temporal resolution, and high dynamic range. However, the development of image analysis methods for EBCs is challenging due to the sparse and asynchronous nature of the data. This work ... | {
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2412.02893 | Removing Spurious Correlation from Neural Network Interpretations | [
"cs.CL",
"cs.AI",
"cs.LG",
"stat.AP",
"stat.ME"
] | The existing algorithms for identification of neurons responsible for undesired and harmful behaviors do not consider the effects of confounders such as topic of the conversation. In this work, we show that confounders can create spurious correlations and propose a new causal mediation approach that controls the impact... | {
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2412.02894 | Reconstruction of dynamic systems using genetic algorithms with dynamic
search limits | [
"cs.NE",
"math.DS",
"nlin.CD"
] | Mathematical modeling is a powerful tool for describing, predicting, and understanding complex phenomena exhibited by real-world systems. However, identifying the equations that govern a system's dynamics from experimental data remains a significant challenge without a definitive solution. In this study, evolutionary c... | {
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2412.02896 | GUESS: Generative Uncertainty Ensemble for Self Supervision | [
"cs.LG",
"cs.CV"
] | Self-supervised learning (SSL) frameworks consist of pretext task, and loss function aiming to learn useful general features from unlabeled data. The basic idea of most SSL baselines revolves around enforcing the invariance to a variety of data augmentations via the loss function. However, one main issue is that, inatt... | {
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2412.02897 | MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions
and Actions | [
"cs.CL",
"cs.AI"
] | Narrative understanding and story generation are critical challenges in natural language processing (NLP), with much of the existing research focused on summarization and question-answering tasks. While previous studies have explored predicting plot endings and generating extended narratives, they often neglect the log... | {
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2412.02899 | Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile
Robots in Unmanned Farms | [
"cs.RO"
] | Unmanned and intelligent agricultural systems are crucial for enhancing agricultural efficiency and for helping mitigate the effect of labor shortage. However, unlike urban environments, agricultural fields impose distinct and unique challenges on autonomous robotic systems, such as the unstructured and dynamic nature ... | {
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2412.02900 | MACAW: A Causal Generative Model for Medical Imaging | [
"eess.IV",
"cs.LG"
] | Although deep learning techniques show promising results for many neuroimaging tasks in research settings, they have not yet found widespread use in clinical scenarios. One of the reasons for this problem is that many machine learning models only identify correlations between the input images and the outputs of interes... | {
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2412.02901 | SuperLoc: The Key to Robust LiDAR-Inertial Localization Lies in
Predicting Alignment Risks | [
"cs.RO"
] | Map-based LiDAR localization, while widely used in autonomous systems, faces significant challenges in degraded environments due to lacking distinct geometric features. This paper introduces SuperLoc, a robust LiDAR localization package that addresses key limitations in existing methods. SuperLoc features a novel predi... | {
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2412.02903 | EgoCast: Forecasting Egocentric Human Pose in the Wild | [
"cs.CV"
] | Accurately estimating and forecasting human body pose is important for enhancing the user's sense of immersion in Augmented Reality. Addressing this need, our paper introduces EgoCast, a bimodal method for 3D human pose forecasting using egocentric videos and proprioceptive data. We study the task of human pose forecas... | {
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2412.02904 | Enhancing Trust in Large Language Models with Uncertainty-Aware
Fine-Tuning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering capabilities. However, these models are sometimes prone to generating credible-sounding but incorrect information, a phenomenon known as LLM hallucinations. Reliable uncertain... | {
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2412.02906 | Does Few-Shot Learning Help LLM Performance in Code Synthesis? | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have made significant strides at code generation through improved model design, training, and chain-of-thought. However, prompt-level optimizations remain an important yet under-explored aspect of LLMs for coding. This work focuses on the few-shot examples present in most code generation pr... | {
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2412.02912 | ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | We introduce ShapeWords, an approach for synthesizing images based on 3D shape guidance and text prompts. ShapeWords incorporates target 3D shape information within specialized tokens embedded together with the input text, effectively blending 3D shape awareness with textual context to guide the image synthesis process... | {
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2412.02915 | Single-Cell Omics Arena: A Benchmark Study for Large Language Models on
Cell Type Annotation Using Single-Cell Data | [
"cs.CL",
"q-bio.GN"
] | Over the past decade, the revolution in single-cell sequencing has enabled the simultaneous molecular profiling of various modalities across thousands of individual cells, allowing scientists to investigate the diverse functions of complex tissues and uncover underlying disease mechanisms. Among all the analytical step... | {
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2412.02919 | Higher Order Transformers: Efficient Attention Mechanism for Tensor
Structured Data | [
"cs.LG",
"cs.AI"
] | Transformers are now ubiquitous for sequence modeling tasks, but their extension to multi-dimensional data remains a challenge due to the quadratic cost of the attention mechanism. In this paper, we propose Higher-Order Transformers (HOT), a novel architecture designed to efficiently process data with more than two axe... | {
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2412.02920 | Assessing the performance of CT image denoisers using Laguerre-Gauss
Channelized Hotelling Observer for lesion detection | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | The remarkable success of deep learning methods in solving computer vision problems, such as image classification, object detection, scene understanding, image segmentation, etc., has paved the way for their application in biomedical imaging. One such application is in the field of CT image denoising, whereby deep lear... | {
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2412.02924 | Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep
Neural Networks | [
"cs.LG"
] | Accurate prediction over long time horizons is crucial for modeling complex physical processes such as wave propagation. Although deep neural networks show promise for real-time forecasting, they often struggle with accumulating phase and amplitude errors as predictions extend over a long period. To address this issue,... | {
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2412.02929 | Panoptic Diffusion Models: co-generation of images and segmentation maps | [
"cs.CV",
"cs.AI"
] | Recently, diffusion models have demonstrated impressive capabilities in text-guided and image-conditioned image generation. However, existing diffusion models cannot simultaneously generate a segmentation map of objects and a corresponding image from the prompt. Previous attempts either generate segmentation maps based... | {
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2412.02930 | Video LLMs for Temporal Reasoning in Long Videos | [
"cs.CV"
] | This paper introduces TemporalVLM, a video large language model capable of effective temporal reasoning and fine-grained understanding in long videos. At the core, our approach includes a visual encoder for mapping a long-term input video into features which are time-aware and contain both local and global cues. In par... | {
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2412.02931 | Inverse Delayed Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY"
] | Inverse Reinforcement Learning (IRL) has demonstrated effectiveness in a variety of imitation tasks. In this paper, we introduce an IRL framework designed to extract rewarding features from expert trajectories affected by delayed disturbances. Instead of relying on direct observations, our approach employs an efficient... | {
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2412.02934 | BGTplanner: Maximizing Training Accuracy for Differentially Private
Federated Recommenders via Strategic Privacy Budget Allocation | [
"cs.LG",
"cs.DC"
] | To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentiall... | {
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2412.02935 | Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in
Conversation | [
"cs.CL"
] | Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying human emotional states by combining data from multiple different modalities (e.g., audio, images, text, video, etc.). Most existing multimodal emotion recognition methods use GCN to improve performance, but existing GCN methods ... | {
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2412.02940 | SAVER: A Toolbox for Sampling-Based, Probabilistic Verification of
Neural Networks | [
"cs.LG",
"cs.AI"
] | We present a neural network verification toolbox to 1) assess the probability of satisfaction of a constraint, and 2) synthesize a set expansion factor to achieve the probability of satisfaction. Specifically, the tool box establishes with a user-specified level of confidence whether the output of the neural network fo... | {
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2412.02942 | STDCformer: A Transformer-Based Model with a Spatial-Temporal Causal
De-Confounding Strategy for Crowd Flow Prediction | [
"cs.AI"
] | Existing works typically treat spatial-temporal prediction as the task of learning a function $F$ to transform historical observations to future observations. We further decompose this cross-time transformation into three processes: (1) Encoding ($E$): learning the intrinsic representation of observations, (2) Cross-Ti... | {
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2412.02943 | Modularized Neural Network Incorporating Physical Priors for Smart
Building Control, Accuracy or Consistency? | [
"eess.SY",
"cs.SY"
] | Model predictive control can achieve significant energy savings, offer grid flexibility, and mitigate carbon emissions. However, the challenge of identifying individual control-oriented building dynamic models limits large-scale real-world applications. To address this issue, this study proposed a Modularized Neural Ne... | {
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2412.02946 | Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large
Vision-Language Model via Causality Analysis | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] | Recent advancements in large vision-language models (LVLM) have significantly enhanced their ability to comprehend visual inputs alongside natural language. However, a major challenge in their real-world application is hallucination, where LVLMs generate non-existent visual elements, eroding user trust. The underlying ... | {
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2412.02950 | An indoor DSO-based ceiling-vision odometry system for indoor industrial
environments | [
"cs.RO",
"cs.CV"
] | Autonomous Mobile Robots operating in indoor industrial environments require a localization system that is reliable and robust. While Visual Odometry (VO) can offer a reasonable estimation of the robot's state, traditional VO methods encounter challenges when confronted with dynamic objects in the scene. Alternatively,... | {
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2412.02951 | Incorporating System-level Safety Requirements in Perception Models via
Reinforcement Learning | [
"cs.RO",
"cs.LG"
] | Perception components in autonomous systems are often developed and optimized independently of downstream decision-making and control components, relying on established performance metrics like accuracy, precision, and recall. Traditional loss functions, such as cross-entropy loss and negative log-likelihood, focus on ... | {
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2412.02953 | The effects of four-wheel steering on the path-tracking control of
automated vehicles | [
"eess.SY",
"cs.SY"
] | In this study, we analyze the stability of a path-tracking controller designed for a four-wheel steering vehicle, incorporating the effects of the reference path curvature. By employing a simplified kinematic model of the vehicle with steerable front and rear wheels, we derive analytical expressions for the stability r... | {
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2412.02956 | Curriculum-style Data Augmentation for LLM-based Metaphor Detection | [
"cs.CL"
] | Recently, utilizing large language models (LLMs) for metaphor detection has achieved promising results. However, these methods heavily rely on the capabilities of closed-source LLMs, which come with relatively high inference costs and latency. To address this, we propose a method for metaphor detection by fine-tuning o... | {
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2412.02957 | 3D Interaction Geometric Pre-training for Molecular Relational Learning | [
"cs.LG",
"cs.AI"
] | Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery. Despite recent progress, earlier MRL approaches are limited to using only the 2D topological str... | {
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2412.02960 | Semantic Segmentation Prior for Diffusion-Based Real-World
Super-Resolution | [
"cs.CV"
] | Real-world image super-resolution (Real-ISR) has achieved a remarkable leap by leveraging large-scale text-to-image models, enabling realistic image restoration from given recognition textual prompts. However, these methods sometimes fail to recognize some salient objects, resulting in inaccurate semantic restoration i... | {
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2412.02962 | Partially Conditioned Patch Parallelism for Accelerated Diffusion Model
Inference | [
"cs.CV",
"cs.DC"
] | Diffusion models have exhibited exciting capabilities in generating images and are also very promising for video creation. However, the inference speed of diffusion models is limited by the slow sampling process, restricting its use cases. The sequential denoising steps required for generating a single sample could tak... | {
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2412.02968 | How Many Ratings per Item are Necessary for Reliable Significance
Testing? | [
"cs.LG"
] | Most approaches to machine learning evaluation assume that machine and human responses are repeatable enough to be measured against data with unitary, authoritative, "gold standard" responses, via simple metrics such as accuracy, precision, and recall that assume scores are independent given the test item. However, AI ... | {
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2412.02969 | Unified Inductive Logic: From Formal Learning to Statistical Inference
to Supervised Learning | [
"stat.OT",
"cs.LG"
] | While the traditional conception of inductive logic is Carnapian, I develop a Peircean alternative and use it to unify formal learning theory, statistics, and a significant part of machine learning: supervised learning. Some crucial standards for evaluating non-deductive inferences have been assumed separately in those... | {
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2412.02971 | MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting | [
"cs.CV"
] | In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-conditioned autocorrection of inaccuracies within these reports. Using the MIMIC-CXR dataset, we first intentionally introduce a diverse range of... | {
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2412.02975 | Theoretical limitations of multi-layer Transformer | [
"cs.LG",
"cs.AI",
"cs.CC",
"cs.DS"
] | Transformers, especially the decoder-only variants, are the backbone of most modern large language models; yet we do not have much understanding of their expressive power except for the simple $1$-layer case. Due to the difficulty of analyzing multi-layer models, all previous work relies on unproven complexity conjec... | {
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2412.02976 | Stain-aware Domain Alignment for Imbalance Blood Cell Classification | [
"cs.CV"
] | Blood cell identification is critical for hematological analysis as it aids physicians in diagnosing various blood-related diseases. In real-world scenarios, blood cell image datasets often present the issues of domain shift and data imbalance, posing challenges for accurate blood cell identification. To address these ... | {
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2412.02978 | Progressive Vision-Language Prompt for Multi-Organ Multi-Class Cell
Semantic Segmentation with Single Branch | [
"cs.CV"
] | Pathological cell semantic segmentation is a fundamental technology in computational pathology, essential for applications like cancer diagnosis and effective treatment. Given that multiple cell types exist across various organs, with subtle differences in cell size and shape, multi-organ, multi-class cell segmentation... | {
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2412.02980 | Surveying the Effects of Quality, Diversity, and Complexity in Synthetic
Data From Large Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it difficult to understand where improvement comes from and what bottleneck... | {
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2412.02983 | Is Foreground Prototype Sufficient? Few-Shot Medical Image Segmentation
with Background-Fused Prototype | [
"cs.CV"
] | Few-shot Semantic Segmentation(FSS)aim to adapt a pre-trained model to new classes with as few as a single labeled training sample per class. The existing prototypical work used in natural image scenarios biasedly focus on capturing foreground's discrimination while employing a simplistic representation for background,... | {
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2412.02985 | Robust Model Predictive Control for Constrained Uncertain Systems Based
on Concentric Container and Varying Tube | [
"eess.SY",
"cs.SY"
] | This paper proposes a novel robust model predictive control (RMPC) method for the stabilization of constrained systems subject to additive disturbance (AD) and multiplicative disturbance (MD). Concentric containers are introduced to facilitate the characterization of MD, and varying tubes are constructed to bound reach... | {
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2412.02987 | Advancing Conversational Psychotherapy: Integrating Privacy,
Dual-Memory, and Domain Expertise with Large Language Models | [
"cs.CL",
"cs.CY"
] | Mental health has increasingly become a global issue that reveals the limitations of traditional conversational psychotherapy, constrained by location, time, expense, and privacy concerns. In response to these challenges, we introduce SoulSpeak, a Large Language Model (LLM)-enabled chatbot designed to democratize acces... | {
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2412.02988 | Preference-based Pure Exploration | [
"stat.ML",
"cs.LG"
] | We study the preference-based pure exploration problem for bandits with vector-valued rewards. The rewards are ordered using a (given) preference cone $\mathcal{C}$ and our goal is to identify the set of Pareto optimal arms. First, to quantify the impact of preferences, we derive a novel lower bound on sample complexit... | {
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2412.02993 | EchoONE: Segmenting Multiple echocardiography Planes in One Model | [
"cs.CV"
] | In clinical practice of echocardiography examinations, multiple planes containing the heart structures of different view are usually required in screening, diagnosis and treatment of cardiac disease. AI models for echocardiography have to be tailored for each specific plane due to the dramatic structure differences, th... | {
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2412.02996 | CLAS: A Machine Learning Enhanced Framework for Exploring Large 3D
Design Datasets | [
"cs.CV",
"cs.HC",
"cs.IR"
] | Three-dimensional (3D) objects have wide applications. Despite the growing interest in 3D modeling in academia and industries, designing and/or creating 3D objects from scratch remains time-consuming and challenging. With the development of generative artificial intelligence (AI), designers discover a new way to create... | {
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2412.02998 | QuadricsReg: Large-Scale Point Cloud Registration using Quadric
Primitives | [
"cs.RO",
"cs.CV",
"cs.GR"
] | In the realm of large-scale point cloud registration, designing a compact symbolic representation is crucial for efficiently processing vast amounts of data, ensuring registration robustness against significant viewpoint variations and occlusions. This paper introduces a novel point cloud registration method, i.e., Qua... | {
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2412.03002 | AdvDreamer Unveils: Are Vision-Language Models Truly Ready for
Real-World 3D Variations? | [
"cs.CV"
] | Vision Language Models (VLMs) have exhibited remarkable generalization capabilities, yet their robustness in dynamic real-world scenarios remains largely unexplored. To systematically evaluate VLMs' robustness to real-world 3D variations, we propose AdvDreamer, the first framework that generates physically reproducible... | {
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2412.03008 | Provably Extending PageRank-based Local Clustering Algorithm to Weighted
Directed Graphs with Self-Loops and to Hypergraphs | [
"cs.SI",
"cs.DS",
"cs.LG"
] | Local clustering aims to find a compact cluster near the given starting instances. This work focuses on graph local clustering, which has broad applications beyond graphs because of the internal connectivities within various modalities. While most existing studies on local graph clustering adopt the discrete graph sett... | {
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2412.03009 | Data Acquisition for Improving Model Fairness using Reinforcement
Learning | [
"cs.LG",
"cs.CY"
] | Machine learning systems are increasingly being used in critical decision making such as healthcare, finance, and criminal justice. Concerns around their fairness have resulted in several bias mitigation techniques that emphasize the need for high-quality data to ensure fairer decisions. However, the role of earlier st... | {
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2412.03011 | Human Multi-View Synthesis from a Single-View Model:Transferred Body and
Face Representations | [
"cs.CV",
"cs.AI"
] | Generating multi-view human images from a single view is a complex and significant challenge. Although recent advancements in multi-view object generation have shown impressive results with diffusion models, novel view synthesis for humans remains constrained by the limited availability of 3D human datasets. Consequent... | {
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2412.03012 | Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space
Pose Tracking with a Wheeled-Quadrupedal-Manipulator | [
"cs.RO",
"cs.LG"
] | In this paper, we study the whole-body loco-manipulation problem using reinforcement learning (RL). Specifically, we focus on the problem of how to coordinate the floating base and the robotic arm of a wheeled-quadrupedal manipulator robot to achieve direct six-dimensional (6D) end-effector (EE) pose tracking in task s... | {
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2412.03013 | A Performance Investigation of Multimodal Multiobjective Optimization
Algorithms in Solving Two Types of Real-World Problems | [
"cs.NE"
] | In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on benchmark function sets such as the 2019 IEEE Congress on Evolutionary Computation test suite (CEC 2019), and their performance on real-worl... | {
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2412.03015 | Benchmarking Attention Mechanisms and Consistency Regularization
Semi-Supervised Learning for Post-Flood Building Damage Assessment in
Satellite Images | [
"cs.CV"
] | Post-flood building damage assessment is critical for rapid response and post-disaster reconstruction planning. Current research fails to consider the distinct requirements of disaster assessment (DA) from change detection (CD) in neural network design. This paper focuses on two key differences: 1) building change feat... | {
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2412.03017 | Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA
Approach | [
"cs.CV"
] | Diffusion prior-based methods have shown impressive results in real-world image super-resolution (SR). However, most existing methods entangle pixel-level and semantic-level SR objectives in the training process, struggling to balance pixel-wise fidelity and perceptual quality. Meanwhile, users have varying preferences... | {
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2412.03018 | Hamiltonian-based neural networks for systems under nonholonomic
constraints | [
"physics.class-ph",
"cs.LG"
] | There has been increasing interest in methodologies that incorporate physics priors into neural network architectures to enhance their modeling capabilities. A family of these methodologies that has gained traction are Hamiltonian neural networks (HNN) and their variations. These architectures explicitly encode Hamilto... | {
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2412.03019 | Unsupervised Network for Single Image Raindrop Removal | [
"cs.CV"
] | Image quality degradation caused by raindrops is one of the most important but challenging problems that reduce the performance of vision systems. Most existing raindrop removal algorithms are based on a supervised learning method using pairwise images, which are hard to obtain in real-world applications. This study pr... | {
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2412.03021 | PEMF-VVTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm | [
"cs.CV",
"cs.AI"
] | Video Virtual Try-on aims to fluently transfer the garment image to a semantically aligned try-on area in the source person video. Previous methods leveraged the inpainting mask to remove the original garment in the source video, thus achieving accurate garment transfer on simple model videos. However, when these metho... | {
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2412.03025 | Human Variability vs. Machine Consistency: A Linguistic Analysis of
Texts Generated by Humans and Large Language Models | [
"cs.CL"
] | The rapid advancements in large language models (LLMs) have significantly improved their ability to generate natural language, making texts generated by LLMs increasingly indistinguishable from human-written texts. Recent research has predominantly focused on using LLMs to classify text as either human-written or machi... | {
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2412.03026 | ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D
Spatial Transcriptomics | [
"cs.CV"
] | Spatial transcriptomics (ST) is an emerging technology that enables medical computer vision scientists to automatically interpret the molecular profiles underlying morphological features. Currently, however, most deep learning-based ST analyses are limited to two-dimensional (2D) sections, which can introduce diagnosti... | {
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2412.03028 | Specification Generation for Neural Networks in Systems | [
"cs.AI",
"cs.SY",
"eess.SY"
] | Specifications - precise mathematical representations of correct domain-specific behaviors - are crucial to guarantee the trustworthiness of computer systems. With the increasing development of neural networks as computer system components, specifications gain more importance as they can be used to regulate the behavio... | {
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2412.03035 | A Granger-Causal Perspective on Gradient Descent with Application to
Pruning | [
"cs.LG"
] | Stochastic Gradient Descent (SGD) is the main approach to optimizing neural networks. Several generalization properties of deep networks, such as convergence to a flatter minima, are believed to arise from SGD. This article explores the causality aspect of gradient descent. Specifically, we show that the gradient desce... | {
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2412.03036 | Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage
Dataset | [
"eess.IV",
"cs.CV"
] | Computed Tomography (CT) is a technology that reconstructs cross-sectional images using X-ray images taken from multiple directions. In CT, hundreds of X-ray images acquired as the X-ray source and detector rotate around a central axis, are used for precise reconstruction. In security baggage inspection, X-ray imaging ... | {
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2412.03038 | MILLION: A General Multi-Objective Framework with Controllable Risk for
Portfolio Management | [
"q-fin.PM",
"cs.AI",
"cs.LG"
] | Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which co... | {
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2412.03039 | MRNet: Multifaceted Resilient Networks for Medical Image-to-Image
Translation | [
"eess.IV",
"cs.AI"
] | We propose a Multifaceted Resilient Network(MRNet), a novel architecture developed for medical image-to-image translation that outperforms state-of-the-art methods in MRI-to-CT and MRI-to-MRI conversion. MRNet leverages the Segment Anything Model (SAM) to exploit frequency-based features to build a powerful method for ... | {
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2412.03044 | Frequency-Guided Diffusion Model with Perturbation Training for
Skeleton-Based Video Anomaly Detection | [
"cs.CV"
] | Video anomaly detection is an essential yet challenging open-set task in computer vision, often addressed by leveraging reconstruction as a proxy task. However, existing reconstruction-based methods encounter challenges in two main aspects: (1) limited model robustness for open-set scenarios, (2) and an overemphasis on... | {
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2412.03046 | Real-time Dynamics of Soft Manipulators with Cross-section Inflation:
Application to the Octopus Muscular Hydrostat | [
"cs.RO"
] | Inspired by the embodied intelligence of biological creatures like the octopus, the soft robotic arm utilizes its highly flexible structure to perform various tasks in the complex environment. While the classic Cosserat rod theory investigates the bending, twisting, shearing, and stretching of the soft arm, it fails to... | {
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} |
2412.03050 | Topology Reconstruction of a Class of Electrical Networks with Limited
Boundary Measurements | [
"eess.SY",
"cs.SY"
] | We consider the problem of recovering the topology and the edge conductance value, as well as characterizing a set of electrical networks that satisfy the limitedly available Thevenin impedance measurements. The measurements are obtained from an unknown electrical network, which is assumed to belong to a class of circu... | {
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} |
2412.03051 | Less is More: A Stealthy and Efficient Adversarial Attack Method for
DRL-based Autonomous Driving Policies | [
"cs.LG",
"cs.AI"
] | Despite significant advancements in deep reinforcement learning (DRL)-based autonomous driving policies, these policies still exhibit vulnerability to adversarial attacks. This vulnerability poses a formidable challenge to the practical deployment of these policies in autonomous driving. Designing effective adversarial... | {
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} |
2412.03052 | Point-GR: Graph Residual Point Cloud Network for 3D Object
Classification and Segmentation | [
"cs.CV",
"eess.IV"
] | In recent years, the challenge of 3D shape analysis within point cloud data has gathered significant attention in computer vision. Addressing the complexities of effective 3D information representation and meaningful feature extraction for classification tasks remains crucial. This paper presents Point-GR, a novel deep... | {
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} |
2412.03054 | TREND: Unsupervised 3D Representation Learning via Temporal Forecasting
for LiDAR Perception | [
"cs.CV"
] | Labeling LiDAR point clouds is notoriously time-and-energy-consuming, which spurs recent unsupervised 3D representation learning methods to alleviate the labeling burden in LiDAR perception via pretrained weights. Almost all existing work focus on a single frame of LiDAR point cloud and neglect the temporal LiDAR seque... | {
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} |
2412.03055 | Real-Time AIoT for UAV Antenna Interference Detection via Edge-Cloud
Collaboration | [
"eess.SP",
"cs.CV"
] | In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often originates from unauthorized or malfunctioning antennas, and radio monitoring agencies must address numerous sources of such antennas annually. Unmanned aerial vehicles (UA... | {
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} |
2412.03056 | Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding
for Point Cloud Classification | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | This paper introduces Point-GN, a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike conventional deep learning models that rely on a large number of trainable parameters, Point-GN leverages non-learnable components-specifically, Farthest Point Sampling (FPS), k-Nearest Neighb... | {
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} |
2412.03058 | Revisiting Energy-Based Model for Out-of-Distribution Detection | [
"cs.CV"
] | Out-of-distribution (OOD) detection is an essential approach to robustifying deep learning models, enabling them to identify inputs that fall outside of their trained distribution. Existing OOD detection methods usually depend on crafted data, such as specific outlier datasets or elaborate data augmentations. While thi... | {
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} |
2412.03059 | CLAP: Unsupervised 3D Representation Learning for Fusion 3D Perception
via Curvature Sampling and Prototype Learning | [
"cs.CV"
] | Unsupervised 3D representation learning via masked-and-reconstruction with differentiable rendering is promising to reduce the labeling burden for fusion 3D perception. However, previous literature conduct pre-training for different modalities separately because of the hight GPU memory consumption. Consequently, the in... | {
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} |
2412.03061 | Lightweight Stochastic Video Prediction via Hybrid Warping | [
"cs.CV"
] | Accurate video prediction by deep neural networks, especially for dynamic regions, is a challenging task in computer vision for critical applications such as autonomous driving, remote working, and telemedicine. Due to inherent uncertainties, existing prediction models often struggle with the complexity of motion dynam... | {
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} |
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