id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.13742 | Benchmarking a wide range of optimisers for solving the Fermi-Hubbard
model using the variational quantum eigensolver | [
"quant-ph",
"cs.LG",
"cs.NE"
] | We numerically benchmark 30 optimisers on 372 instances of the variational quantum eigensolver for solving the Fermi-Hubbard system with the Hamiltonian variational ansatz. We rank the optimisers with respect to metrics such as final energy achieved and function calls needed to get within a certain tolerance level, and... | {
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2411.13749 | AI-Driven Agents with Prompts Designed for High Agreeableness Increase
the Likelihood of Being Mistaken for a Human in the Turing Test | [
"cs.AI"
] | Large Language Models based on transformer algorithms have revolutionized Artificial Intelligence by enabling verbal interaction with machines akin to human conversation. These AI agents have surpassed the Turing Test, achieving confusion rates up to 50%. However, challenges persist, especially with the advent of robot... | {
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2411.13751 | ScAlN-on-SiC Ku-Band Solidly-Mounted Bidimensional Mode Resonators | [
"eess.SY",
"cs.SY"
] | This letter reports on Solidly-Mounted Bidimensional Mode Resonators (S2MRs) exploiting a highly-optimized Sezawa mode in 30% Scandium-doped Aluminum Nitride (ScAlN) on Silicon Carbide (SiC) and operating near 16 GHz. Experimental results demonstrate mechanical quality factors (Qm) as high as 380, Bode quality factors ... | {
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2411.13753 | FAST-Splat: Fast, Ambiguity-Free Semantics Transfer in Gaussian
Splatting | [
"cs.CV"
] | We present FAST-Splat for fast, ambiguity-free semantic Gaussian Splatting, which seeks to address the main limitations of existing semantic Gaussian Splatting methods, namely: slow training and rendering speeds; high memory usage; and ambiguous semantic object localization. In deriving FAST-Splat , we formulate open-v... | {
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2411.13754 | Learning to Reason Iteratively and Parallelly for Complex Visual
Reasoning Scenarios | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Complex visual reasoning and question answering (VQA) is a challenging task that requires compositional multi-step processing and higher-level reasoning capabilities beyond the immediate recognition and localization of objects and events. Here, we introduce a fully neural Iterative and Parallel Reasoning Mechanism (IPR... | {
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2411.13755 | DKMGP: A Gaussian Process Approach to Multi-Task and Multi-Step Vehicle
Dynamics Modeling in Autonomous Racing | [
"cs.RO"
] | Autonomous racing is gaining attention for its potential to advance autonomous vehicle technologies. Accurate race car dynamics modeling is essential for capturing and predicting future states like position, orientation, and velocity. However, accurately modeling complex subsystems such as tires and suspension poses si... | {
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2411.13757 | GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on
LLMs | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) have revolutionized natural language processing (NLP), excelling in tasks like text generation and summarization. However, their increasing adoption in mission-critical applications raises concerns about hardware-based threats, particularly bit-flip attacks (BFAs). BFAs, enabled by fault in... | {
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2411.13760 | A Framework for Evaluating LLMs Under Task Indeterminacy | [
"cs.LG",
"cs.CL",
"cs.HC"
] | Large language model (LLM) evaluations often assume there is a single correct response -- a gold label -- for each item in the evaluation corpus. However, some tasks can be ambiguous -- i.e., they provide insufficient information to identify a unique interpretation -- or vague -- i.e., they do not clearly indicate wher... | {
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2411.13765 | Schr\"odinger Bridge Problem for Jump Diffusions | [
"math.PR",
"cs.IT",
"math.IT",
"math.OC"
] | The Schr\"odinger bridge problem (SBP) seeks to find the measure $\hat{\mathbf{P}}$ on a certain path space which interpolates between state-space distributions $\rho_0$ at time $0$ and $\rho_T$ at time $T$ while minimizing the KL divergence (relative entropy) to a reference path measure $\mathbf{R}$. In this work, we ... | {
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2411.13766 | Tiny-Align: Bridging Automatic Speech Recognition and Large Language
Model on the Edge | [
"cs.SD",
"cs.AI",
"eess.AS"
] | The combination of Large Language Models (LLM) and Automatic Speech Recognition (ASR), when deployed on edge devices (called edge ASR-LLM), can serve as a powerful personalized assistant to enable audio-based interaction for users. Compared to text-based interaction, edge ASR-LLM allows accessible and natural audio int... | {
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2411.13768 | An Evaluation-Driven Approach to Designing LLM Agents: Process and
Architecture | [
"cs.SE",
"cs.AI"
] | The advent of Large Language Models (LLMs) has enabled the development of LLM agents capable of autonomously achieving under-specified goals and continuously evolving through post-deployment improvement, sometimes without requiring code or model updates. Conventional approaches, such as pre-defined test cases and code/... | {
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2411.13770 | A Novel Passive Occupational Shoulder Exoskeleton With Adjustable Peak
Assistive Torque Angle For Overhead Tasks | [
"cs.RO"
] | Objective: Overhead tasks are a primary inducement to work-related musculoskeletal disorders. Aiming to reduce shoulder physical loads, passive shoulder exoskeletons are increasingly prevalent in the industry due to their lightweight, affordability, and effectiveness. However, they can only accommodate a specific task ... | {
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2411.13771 | Deciphering Urban Morphogenesis: A Morphospace Approach | [
"cs.CY",
"cs.SI",
"physics.soc-ph"
] | Cities emerged independently across different world regions and historical periods, raising fundamental questions: How did the first urban settlements develop? What social and spatial conditions enabled their emergence? Are these processes universal or context-dependent? Moreover, what distinguishes cities from other h... | {
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2411.13773 | FastRAG: Retrieval Augmented Generation for Semi-structured Data | [
"cs.NI",
"cs.AI"
] | Efficiently processing and interpreting network data is critical for the operation of increasingly complex networks. Recent advances in Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques have improved data processing in network management. However, existing RAG methods like VectorRAG and Gr... | {
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2411.13774 | Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via
Class Region Proposals | [
"cs.CV"
] | The Segment-Anything Model (SAM) is a vision foundation model for segmentation with a prompt-driven framework. SAM generates class-agnostic masks based on user-specified instance-referring prompts. However, adapting SAM for automated segmentation -- where manual input is absent -- of specific object classes often requi... | {
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2411.13775 | Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation
Across Languages, Domains, and Expertise Levels | [
"cs.CL",
"cs.AI"
] | This study presents a comprehensive evaluation of GPT-4's translation capabilities compared to human translators of varying expertise levels. Through systematic human evaluation using the MQM schema, we assess translations across three language pairs (Chinese$\longleftrightarrow$English, Russian$\longleftrightarrow$Eng... | {
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2411.13778 | A Survey on Adversarial Robustness of LiDAR-based Machine Learning
Perception in Autonomous Vehicles | [
"cs.LG",
"cs.AI",
"cs.CR"
] | In autonomous driving, the combination of AI and vehicular technology offers great potential. However, this amalgamation comes with vulnerabilities to adversarial attacks. This survey focuses on the intersection of Adversarial Machine Learning (AML) and autonomous systems, with a specific focus on LiDAR-based systems. ... | {
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2411.13779 | NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap
via Informational Interviews | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated impressive capabilities in generating coherent text but often struggle with grounding language and strategic dialogue. To address this gap, we focus on journalistic interviews, a domain rich in grounding communication and abundant in data. We curate a dataset of 40,000 two... | {
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2411.13785 | Throughput Maximization for Movable Antenna Systems with Movement Delay
Consideration | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we model the minimum achievable throughput within a transmission block of restricted duration and aim to maximize it in movable antenna (MA)-enabled multiuser downlink communications. Particularly, we account for the antenna moving delay caused by mechanical movement, which has not been fully considered ... | {
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2411.13786 | Adaptable Embeddings Network (AEN) | [
"cs.LG",
"cs.CL"
] | Modern day Language Models see extensive use in text classification, yet this comes at significant computational cost. Compute-effective classification models are needed for low-resource environments, most notably on edge devices. We introduce Adaptable Embeddings Networks (AEN), a novel dual-encoder architecture using... | {
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2411.13787 | Edge-Cloud Routing for Text-to-Image Model with Token-Level Multi-Metric
Prediction | [
"cs.CV",
"cs.LG"
] | Large text-to-image models demonstrate impressive generation capabilities; however, their substantial size necessitates expensive cloud servers for deployment. Conversely, light-weight models can be deployed on edge devices at lower cost but often with inferior generation quality for complex user prompts. To strike a b... | {
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2411.13789 | LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display
Advertisement Recommender System | [
"cs.IR"
] | Display advertising provides significant value to advertisers, publishers, and users. Traditional display advertising systems utilize a multi-stage architecture consisting of retrieval, coarse ranking, and final ranking. However, conventional retrieval methods rely on ID-based learning to rank mechanisms and fail to ad... | {
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2411.13794 | GalaxyEdit: Large-Scale Image Editing Dataset with Enhanced Diffusion
Adapter | [
"cs.CV"
] | Training of large-scale text-to-image and image-to-image models requires a huge amount of annotated data. While text-to-image datasets are abundant, data available for instruction-based image-to-image tasks like object addition and removal is limited. This is because of the several challenges associated with the data g... | {
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2411.13797 | Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing
Atypical Facial Expressions in Children with Autism Spectrum Disorder | [
"cs.CV"
] | Children with Autism Spectrum Disorder (ASD) often exhibit atypical facial expressions. However, the specific objective facial features that underlie this subjective perception remain unclear. In this paper, we introduce a novel dataset, Hugging Rain Man (HRM), which includes facial action units (AUs) manually annotate... | {
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2411.13800 | Explaining GPT-4's Schema of Depression Using Machine Behavior Analysis | [
"cs.CL"
] | Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema of mental disorders, that is, how it internally associates and interp... | {
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2411.13802 | SemiKong: Curating, Training, and Evaluating A Semiconductor
Industry-Specific Large Language Model | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated the potential to address some issues within the semiconductor industry. However, they are often general-purpose models that lack the specialized knowledge needed to tackle the unique challenges of this sector, such as the intricate physics and chemistry of semiconductor de... | {
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2411.13806 | Weak synchronization in heterogeneous multi-agent systems | [
"eess.SY",
"cs.SY"
] | In this paper, we propose a new framework for synchronization of heterogeneous multi agent system which we refer to as weak synchronization. This new framework of synchronization is based on achieving the network stability in the absence of any information on communication network including the connectivity. Here by ne... | {
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2411.13807 | MagicDriveDiT: High-Resolution Long Video Generation for Autonomous
Driving with Adaptive Control | [
"cs.CV"
] | The rapid advancement of diffusion models has greatly improved video synthesis, especially in controllable video generation, which is essential for applications like autonomous driving. However, existing methods are limited by scalability and how control conditions are integrated, failing to meet the needs for high-res... | {
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2411.13814 | AutoMixQ: Self-Adjusting Quantization for High Performance
Memory-Efficient Fine-Tuning | [
"cs.LG",
"cs.AI"
] | Fine-tuning large language models (LLMs) under resource constraints is a significant challenge in deep learning. Low-Rank Adaptation (LoRA), pruning, and quantization are all effective methods for improving resource efficiency. However, combining them directly often results in suboptimal performance, especially with un... | {
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2411.13815 | FLRNet: A Deep Learning Method for Regressive Reconstruction of Flow
Field From Limited Sensor Measurements | [
"physics.flu-dyn",
"cs.LG"
] | Many applications in computational and experimental fluid mechanics require effective methods for reconstructing the flow fields from limited sensor data. However, this task remains a significant challenge because the measurement operator, which provides the punctual sensor measurement for a given state of the flow fie... | {
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2411.13820 | InstCache: A Predictive Cache for LLM Serving | [
"cs.CL",
"cs.DC"
] | Large language models are revolutionizing every aspect of human life. However, the unprecedented power comes at the cost of significant computing intensity, suggesting long latency and large energy footprint. Key-Value Cache and Semantic Cache have been proposed as a solution to the above problem, but both suffer from ... | {
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2411.13821 | Heterophilic Graph Neural Networks Optimization with Causal
Message-passing | [
"cs.LG",
"cs.AI",
"stat.ML"
] | In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect analysis, we can discern heterophilic edges based on asymmetric node dependency. The learned causal structure offers more accurate relationships... | {
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2411.13826 | Interactive and Expressive Code-Augmented Planning with Large Language
Models | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making, but often struggle with complex, long-horizon planning tasks. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance.... | {
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2411.13834 | Spatiotemporal Tubes for Temporal Reach-Avoid-Stay Tasks in Unknown
Systems | [
"eess.SY",
"cs.RO",
"cs.SY"
] | The paper considers the controller synthesis problem for general MIMO systems with unknown dynamics, aiming to fulfill the temporal reach-avoid-stay task, where the unsafe regions are time-dependent, and the target must be reached within a specified time frame. The primary aim of the paper is to construct the spatiotem... | {
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2411.13836 | CLIPer: Hierarchically Improving Spatial Representation of CLIP for
Open-Vocabulary Semantic Segmentation | [
"cs.CV"
] | Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on various image-level tasks, leading to the research to adapt CLIP for pixel-level open-vocabulary semantic segmentation without additional training. The key is to improve spatial representation of image-level CLIP, such as... | {
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2411.13840 | Segment Anything in Light Fields for Real-Time Applications via
Constrained Prompting | [
"cs.CV"
] | Segmented light field images can serve as a powerful representation in many of computer vision tasks exploiting geometry and appearance of objects, such as object pose tracking. In the light field domain, segmentation presents an additional objective of recognizing the same segment through all the views. Segment Anythi... | {
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2411.13842 | Detecting Human Artifacts from Text-to-Image Models | [
"cs.CV"
] | Despite recent advancements, text-to-image generation models often produce images containing artifacts, especially in human figures. These artifacts appear as poorly generated human bodies, including distorted, missing, or extra body parts, leading to visual inconsistencies with typical human anatomy and greatly impair... | {
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2411.13846 | Exploratory Study Of Human-AI Interaction For Hindustani Music | [
"cs.HC",
"cs.AI"
] | This paper presents a study of participants interacting with and using GaMaDHaNi, a novel hierarchical generative model for Hindustani vocal contours. To explore possible use cases in human-AI interaction, we conducted a user study with three participants, each engaging with the model through three predefined interacti... | {
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2411.13847 | Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint
Segmentation | [
"cs.CV"
] | Detecting ships in synthetic aperture radar (SAR) images is challenging due to strong speckle noise, complex surroundings, and varying scales. This paper proposes MLDet, a multitask learning framework for SAR ship detection, consisting of object detection, speckle suppression, and target segmentation tasks. An angle cl... | {
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2411.13848 | Exact and approximate error bounds for physics-informed neural networks | [
"cs.LG",
"cs.NA",
"math.NA"
] | The use of neural networks to solve differential equations, as an alternative to traditional numerical solvers, has increased recently. However, error bounds for the obtained solutions have only been developed for certain equations. In this work, we report important progress in calculating error bounds of physics-infor... | {
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2411.13851 | Arm Robot: AR-Enhanced Embodied Control and Visualization for Intuitive
Robot Arm Manipulation | [
"cs.RO",
"cs.HC"
] | Embodied interaction has been introduced to human-robot interaction (HRI) as a type of teleoperation, in which users control robot arms with bodily action via handheld controllers or haptic gloves. Embodied teleoperation has made robot control intuitive to non-technical users, but differences between humans' and robots... | {
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2411.13852 | Dealing with Synthetic Data Contamination in Online Continual Learning | [
"cs.CV",
"cs.LG"
] | Image generation has shown remarkable results in generating high-fidelity realistic images, in particular with the advancement of diffusion-based models. However, the prevalence of AI-generated images may have side effects for the machine learning community that are not clearly identified. Meanwhile, the success of dee... | {
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2411.13855 | A Multimodal Approach to The Detection and Classification of Skin
Diseases | [
"eess.IV",
"cs.CV",
"cs.LG"
] | According to PBS, nearly one-third of Americans lack access to primary care services, and another forty percent delay going to avoid medical costs. As a result, many diseases are left undiagnosed and untreated, even if the disease shows many physical symptoms on the skin. With the rise of AI, self-diagnosis and improve... | {
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2411.13856 | A Data-Driven Modeling and Motion Control of Heavy-Load Hydraulic
Manipulators via Reversible Transformation | [
"cs.RO"
] | This work proposes a data-driven modeling and the corresponding hybrid motion control framework for unmanned and automated operation of industrial heavy-load hydraulic manipulator. Rather than the direct use of a neural network black box, we construct a reversible nonlinear model by using multilayer perceptron to appro... | {
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2411.13859 | Data-Driven Multi-step Nonlinear Model Predictive Control for Industrial
Heavy Load Hydraulic Robot | [
"cs.RO"
] | Automating complex industrial robots requires precise nonlinear control and efficient energy management. This paper introduces a data-driven nonlinear model predictive control (NMPC) framework to optimize control under multiple objectives. To enhance the prediction accuracy of the dynamic model, we design a single-shot... | {
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2411.13860 | Decoupled Sparse Priors Guided Diffusion Compression Model for Point
Clouds | [
"cs.CV",
"eess.IV"
] | Lossy compression methods rely on an autoencoder to transform a point cloud into latent points for storage, leaving the inherent redundancy of latent representations unexplored. To reduce redundancy in latent points, we propose a sparse priors guided method that achieves high reconstruction quality, especially at high ... | {
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2411.13862 | Image Compression Using Novel View Synthesis Priors | [
"eess.IV",
"cs.CV",
"cs.RO"
] | Real-time visual feedback is essential for tetherless control of remotely operated vehicles, particularly during inspection and manipulation tasks. Though acoustic communication is the preferred choice for medium-range communication underwater, its limited bandwidth renders it impractical to transmit images or videos i... | {
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2411.13865 | Breaking Information Cocoons: A Hyperbolic Graph-LLM Framework for
Exploration and Exploitation in Recommender Systems | [
"cs.IR",
"cs.AI",
"cs.CL",
"cs.LG"
] | Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. A key challenge lies in balancing content exploration and exploitation while allowing users to adjust their recommendation preferences. Intuitively, this balance can be modeled as a tree-structured representatio... | {
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2411.13867 | Generative Fuzzy System for Sequence Generation | [
"cs.AI",
"cs.LG"
] | Generative Models (GMs), particularly Large Language Models (LLMs), have garnered significant attention in machine learning and artificial intelligence for their ability to generate new data by learning the statistical properties of training data and creating data that resemble the original. This capability offers a wi... | {
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2411.13868 | Robust Detection of Watermarks for Large Language Models Under Human
Edits | [
"stat.ME",
"cs.CL",
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | Watermarking has offered an effective approach to distinguishing text generated by large language models (LLMs) from human-written text. However, the pervasive presence of human edits on LLM-generated text dilutes watermark signals, thereby significantly degrading detection performance of existing methods. In this pape... | {
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2411.13869 | Topology optimization of periodic lattice structures for specified
mechanical properties using machine learning considering member connectivity | [
"math.OC",
"cs.LG"
] | This study proposes a methodology to utilize machine learning (ML) for topology optimization of periodic lattice structures. In particular, we investigate data representation of lattice structures used as input data for ML models to improve the performance of the models, focusing on the filtering process and feature se... | {
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2411.13873 | Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation
Guided Correspondence Flow Network | [
"cs.CV"
] | Deep learning (DL) methods have shown remarkable successes in medical image segmentation, often using large amounts of annotated data for model training. However, acquiring a large number of diverse labeled 3D medical image datasets is highly difficult and expensive. Recently, mask propagation DL methods were developed... | {
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2411.13874 | Next-Generation Phishing: How LLM Agents Empower Cyber Attackers | [
"cs.CR",
"cs.AI"
] | The escalating threat of phishing emails has become increasingly sophisticated with the rise of Large Language Models (LLMs). As attackers exploit LLMs to craft more convincing and evasive phishing emails, it is crucial to assess the resilience of current phishing defenses. In this study we conduct a comprehensive eval... | {
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2411.13876 | Iterative decoding of short BCH codes and its post-processing | [
"cs.IT",
"math.IT"
] | Effective iterative decoding of short BCH codes faces two primary challenges: identifying an appropriate parity-check matrix and accelerating decoder convergence. To address these issues, we propose a systematic scheme to derive an optimized parity-check matrix through a heuristic approach. This involves a series of bi... | {
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2411.13878 | Sparse Zero Correlation Zone Arrays for Training Design in Spatial
Modulation Systems | [
"cs.IT",
"math.IT"
] | This paper presents a novel training matrix design for spatial modulation (SM) systems, by introducing a new class of two-dimensional (2D) arrays called sparse zero correlation zone (SZCZ) arrays. An SZCZ array is characterized by a majority of zero entries and exhibits the zero periodic auto- and cross-correlation zon... | {
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2411.13881 | Exploring applications of topological data analysis in stock index
movement prediction | [
"cs.LG",
"physics.data-an"
] | Topological Data Analysis (TDA) has recently gained significant attention in the field of financial prediction. However, the choice of point cloud construction methods, topological feature representations, and classification models has a substantial impact on prediction results. This paper addresses the classification ... | {
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2411.13883 | When Online Algorithms Influence the Environment: A Dynamical Systems
Analysis of the Unintended Consequences | [
"cs.LG",
"cs.AI",
"cs.CY"
] | We analyze the effect that online algorithms have on the environment that they are learning. As a motivation, consider recommendation systems that use online algorithms to learn optimal product recommendations based on user and product attributes. It is well known that the sequence of recommendations affects user prefe... | {
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2411.13885 | Trajectory Tracking Using Frenet Coordinates with Deep Deterministic
Policy Gradient | [
"cs.RO"
] | This paper studies the application of the DDPG algorithm in trajectory-tracking tasks and proposes a trajectorytracking control method combined with Frenet coordinate system. By converting the vehicle's position and velocity information from the Cartesian coordinate system to Frenet coordinate system, this method can m... | {
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2411.13886 | CLFace: A Scalable and Resource-Efficient Continual Learning Framework
for Lifelong Face Recognition | [
"cs.CV"
] | An important aspect of deploying face recognition (FR) algorithms in real-world applications is their ability to learn new face identities from a continuous data stream. However, the online training of existing deep neural network-based FR algorithms, which are pre-trained offline on large-scale stationary datasets, en... | {
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2411.13888 | A Hierarchical Poisson Generator for Universal Graphs under Limited
Resources | [
"cs.DM",
"cs.SI"
] | Graph generation is one of the most challenging tasks in recent years, and its core is to learn the ground truth distribution hiding in the training data. However, training data may not be available due to security concerns or unaffordable costs, which severely blows the learning models, especially the deep generative ... | {
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2411.13890 | GraCo -- A Graph Composer for Integrated Circuits | [
"cs.LG",
"cs.AR"
] | Designing integrated circuits involves substantial complexity, posing challenges in revealing its potential applications - from custom digital cells to analog circuits. Despite extensive research over the past decades in building versatile and automated frameworks, there remains open room to explore more computationall... | {
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2411.13892 | Topology-Aware Popularity Debiasing via Simplicial Complexes | [
"cs.IR"
] | Recommender systems (RS) play a critical role in delivering personalized content across various online platforms, leveraging collaborative filtering (CF) as a key technique to generate recommendations based on users' historical interaction data. Recent advancements in CF have been driven by the adoption of Graph Neural... | {
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2411.13899 | Schemato -- An LLM for Netlist-to-Schematic Conversion | [
"cs.LG",
"cs.AR"
] | Machine learning models are advancing circuit design, particularly in analog circuits. They typically generate netlists that lack human interpretability. This is a problem as human designers heavily rely on the interpretability of circuit diagrams or schematics to intuitively understand, troubleshoot, and develop desig... | {
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2411.13901 | Dressing the Imagination: A Dataset for AI-Powered Translation of Text
into Fashion Outfits and A Novel KAN Adapter for Enhanced Feature Adaptation | [
"cs.CV"
] | Specialized datasets that capture the fashion industry's rich language and styling elements can boost progress in AI-driven fashion design. We present FLORA (Fashion Language Outfit Representation for Apparel Generation), the first comprehensive dataset containing 4,330 curated pairs of fashion outfits and correspondin... | {
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2411.13902 | PIORS: Personalized Intelligent Outpatient Reception based on Large
Language Model with Multi-Agents Medical Scenario Simulation | [
"cs.CL",
"cs.AI"
] | In China, receptionist nurses face overwhelming workloads in outpatient settings, limiting their time and attention for each patient and ultimately reducing service quality. In this paper, we present the Personalized Intelligent Outpatient Reception System (PIORS). This system integrates an LLM-based reception nurse an... | {
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2411.13903 | AmpliNetECG12: A lightweight SoftMax-based relativistic amplitude
amplification architecture for 12 lead ECG classification | [
"eess.SP",
"cs.AI",
"cs.LG"
] | The urgent need to promptly detect cardiac disorders from 12-lead Electrocardiograms using limited computations is motivated by the heart's fast and complex electrical activity and restricted computational power of portable devices. Timely and precise diagnoses are crucial since delays might significantly impact patien... | {
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2411.13904 | Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel
Planning | [
"cs.CL"
] | How are LLM-based agents used in the future? While many of the existing work on agents has focused on improving the performance of a specific family of objective and challenging tasks, in this work, we take a different perspective by thinking about full delegation: agents take over humans' routine decision-making proce... | {
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2411.13907 | Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and
Optimization | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.NE"
] | Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously. However, current SL algorithms face limitations in training efficiency and suffer from prol... | {
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2411.13908 | Hybrid Physics-ML Modeling for Marine Vehicle Maneuvering Motions in the
Presence of Environmental Disturbances | [
"cs.RO"
] | A hybrid physics-machine learning modeling framework is proposed for the surface vehicles' maneuvering motions to address the modeling capability and stability in the presence of environmental disturbances. From a deep learning perspective, the framework is based on a variant version of residual networks with additiona... | {
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2411.13909 | Panther: Illuminate the Sight of Multimodal LLMs with Instruction-Guided
Visual Prompts | [
"cs.CV"
] | Multimodal large language models (MLLMs) are closing the gap to human visual perception capability rapidly, while, still lag behind on attending to subtle images details or locating small objects precisely, etc. Common schemes to tackle these issues include deploying multiple vision encoders or operating on original hi... | {
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2411.13914 | ICODE: Modeling Dynamical Systems with Extrinsic Input Information | [
"cs.LG"
] | Learning models of dynamical systems with external inputs, that may be, for example, nonsmooth or piecewise, is crucial for studying complex phenomena and predicting future state evolution, which is essential for applications such as safety guarantees and decision-making. In this work, we introduce \emph{Input Concomit... | {
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2411.13916 | Joint-repositionable Inner-wireless Planar Snake Robot | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Bio-inspired multi-joint snake robots offer the advantages of terrain adaptability due to their limbless structure and high flexibility. However, a series of dozens of motor units in typical multiple-joint snake robots results in a heavy body structure and hundreds of watts of high power consumption. This paper present... | {
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2411.13918 | Quantization without Tears | [
"cs.CV"
] | Deep neural networks, while achieving remarkable success across diverse tasks, demand significant resources, including computation, GPU memory, bandwidth, storage, and energy. Network quantization, as a standard compression and acceleration technique, reduces storage costs and enables potential inference acceleration b... | {
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2411.13919 | Predictive Maintenance Study for High-Pressure Industrial Compressors:
Hybrid Clustering Models | [
"cs.LG"
] | This study introduces a predictive maintenance strategy for high pressure industrial compressors using sensor data and features derived from unsupervised clustering integrated into classification models. The goal is to enhance model accuracy and efficiency in detecting compressor failures. After data pre processing, se... | {
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2411.13921 | NBMLSS: probabilistic forecasting of electricity prices via Neural Basis
Models for Location Scale and Shape | [
"cs.LG"
] | Forecasters using flexible neural networks (NN) in multi-horizon distributional regression setups often struggle to gain detailed insights into the underlying mechanisms that lead to the predicted feature-conditioned distribution parameters. In this work, we deploy a Neural Basis Model for Location, Scale and Shape, th... | {
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2411.13922 | Exponentially Consistent Nonparametric Clustering of Data Streams | [
"stat.ML",
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT"
] | In this paper, we consider nonparametric clustering of $M$ independent and identically distributed (i.i.d.) data streams generated from unknown distributions. The distributions of the $M$ data streams belong to $K$ underlying distribution clusters. Existing results on exponentially consistent nonparametric clustering a... | {
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2411.13924 | Robust Data-Driven Predictive Control for Mixed Platoons under Noise and
Attacks | [
"eess.SY",
"cs.SY"
] | Controlling mixed platoons, which consist of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), poses significant challenges due to the uncertain and unknown human driving behaviors. Data-driven control methods offer promising solutions by leveraging available trajectory data, but their perf... | {
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2411.13927 | Multimodal 3D Reasoning Segmentation with Complex Scenes | [
"cs.CV"
] | The recent development in multimodal learning has greatly advanced the research in 3D scene understanding in various real-world tasks such as embodied AI. However, most existing work shares two typical constraints: 1) they are short of reasoning ability for interaction and interpretation of human intension and 2) they ... | {
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2411.13929 | Transforming Engineering Diagrams: A Novel Approach for P&ID
Digitization using Transformers | [
"cs.CV"
] | The digitization of complex technical systems, such as Piping and Instrumentation Diagrams (P&IDs), is crucial for efficient maintenance and operation of complex systems in hydraulic and process engineering. Previous approaches often rely on separate modules that analyze diagram elements individually, neglecting the di... | {
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2411.13932 | XAgents: A Framework for Interpretable Rule-Based Multi-Agents
Cooperation | [
"cs.AI",
"cs.MA"
] | Extracting implicit knowledge and logical reasoning abilities from large language models (LLMs) has consistently been a significant challenge. The advancement of multi-agent systems has further en-hanced the capabilities of LLMs. Inspired by the structure of multi-polar neurons (MNs), we propose the XAgents framework, ... | {
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2411.13934 | Learning to Cooperate with Humans using Generative Agents | [
"cs.LG",
"cs.AI",
"cs.MA"
] | Training agents that can coordinate zero-shot with humans is a key mission in multi-agent reinforcement learning (MARL). Current algorithms focus on training simulated human partner policies which are then used to train a Cooperator agent. The simulated human is produced either through behavior cloning over a dataset o... | {
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2411.13935 | Fast Stochastic MPC using Affine Disturbance Feedback Gains Learned
Offline | [
"eess.SY",
"cs.SY"
] | We propose a novel Stochastic Model Predictive Control (MPC) for uncertain linear systems subject to probabilistic constraints. The proposed approach leverages offline learning to extract key features of affine disturbance feedback policies, significantly reducing the computational burden of online optimization. Specif... | {
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2411.13941 | LLMs as Continuous Learners: Improving the Reproduction of Defective
Code in Software Issues | [
"cs.SE",
"cs.AI"
] | Reproducing buggy code is the first and crucially important step in issue resolving, as it aids in identifying the underlying problems and validating that generated patches resolve the problem. While numerous approaches have been proposed for this task, they primarily address common, widespread errors and struggle to a... | {
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2411.13942 | Cooperative Grasping and Transportation using Multi-agent Reinforcement
Learning with Ternary Force Representation | [
"cs.RO"
] | Cooperative grasping and transportation require effective coordination to complete the task. This study focuses on the approach leveraging force-sensing feedback, where robots use sensors to detect forces applied by others on an object to achieve coordination. Unlike explicit communication, it avoids delays and interru... | {
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2411.13945 | Neuromorphic Attitude Estimation and Control | [
"cs.RO",
"cs.LG",
"cs.NE"
] | The real-world application of small drones is mostly hampered by energy limitations. Neuromorphic computing promises extremely energy-efficient AI for autonomous flight, but is still challenging to train and deploy on real robots. In order to reap the maximal benefits from neuromorphic computing, it is desired to perfo... | {
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2411.13949 | Separable Mixture of Low-Rank Adaptation for Continual Visual
Instruction Tuning | [
"cs.CV",
"cs.AI"
] | Visual instruction tuning (VIT) enables multimodal large language models (MLLMs) to effectively handle a wide range of vision tasks by framing them as language-based instructions. Building on this, continual visual instruction tuning (CVIT) extends the capability of MLLMs to incrementally learn new tasks, accommodating... | {
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2411.13951 | A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly
Detection Approaches for Multivariate Time Series | [
"cs.LG",
"cs.AI",
"cs.CE",
"cs.SY",
"eess.SY"
] | Benchmarking anomaly detection approaches for multivariate time series is challenging due to the lack of high-quality datasets. Current publicly available datasets are too small, not diverse and feature trivial anomalies, which hinders measurable progress in this research area. We propose a solution: a diverse, extensi... | {
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2411.13952 | Learning thin deformable object manipulation with a multi-sensory
integrated soft hand | [
"cs.RO"
] | Robotic manipulation has made significant advancements, with systems demonstrating high precision and repeatability. However, this remarkable precision often fails to translate into efficient manipulation of thin deformable objects. Current robotic systems lack imprecise dexterity, the ability to perform dexterous mani... | {
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2411.13953 | Material synthesis through simulations guided by machine learning: a
position paper | [
"cs.LG"
] | In this position paper, we propose an approach for sustainable data collection in the field of optimal mix design for marble sludge reuse. Marble sludge, a calcium-rich residual from stone-cutting processes, can be repurposed by mixing it with various ingredients. However, determining the optimal mix design is challeng... | {
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2411.13958 | Sentiment Analysis of Economic Text: A Lexicon-Based Approach | [
"cs.CE",
"cs.CL",
"cs.CY"
] | We propose an Economic Lexicon (EL) specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: 1) to have a wide coverage of terms used in documents discussing economic concepts, and 2) to provide a human-annotated sentiment score in the range [-1,1]. We... | {
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} |
2411.13961 | Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors
Guided Diffusion | [
"cs.CV"
] | Due to the singularity of real-world paired datasets and the complexity of low-light environments, this leads to supervised methods lacking a degree of scene generalisation. Meanwhile, limited by poor lighting and content guidance, existing zero-shot methods cannot handle unknown severe degradation well. To address thi... | {
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2411.13962 | Hybrid-Neuromorphic Approach for Underwater Robotics Applications: A
Conceptual Framework | [
"cs.RO"
] | This paper introduces the concept of employing neuromorphic methodologies for task-oriented underwater robotics applications. In contrast to the increasing computational demands of conventional deep learning algorithms, neuromorphic technology, leveraging spiking neural network architectures, promises sophisticated art... | {
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2411.13970 | Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor
Networks: A Deep Reinforcement Learning-based Approach | [
"eess.SP",
"cs.LG"
] | Backscatter communication (BC) becomes a promising energy-efficient solution for future wireless sensor networks (WSNs). Unmanned aerial vehicles (UAVs) enable flexible data collection from remote backscatter devices (BDs), yet conventional UAVs rely on omni-directional fixed-position antennas (FPAs), limiting channel ... | {
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2411.13975 | Transforming Static Images Using Generative Models for Video Salient
Object Detection | [
"cs.CV"
] | In many video processing tasks, leveraging large-scale image datasets is a common strategy, as image data is more abundant and facilitates comprehensive knowledge transfer. A typical approach for simulating video from static images involves applying spatial transformations, such as affine transformations and spline war... | {
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} |
2411.13978 | Breadboarding the European Moon Rover System: discussion and results of
the analogue field test campaign | [
"cs.RO",
"astro-ph.EP",
"astro-ph.IM"
] | This document compiles results obtained from the test campaign of the European Moon Rover System (EMRS) project. The test campaign, conducted at the Planetary Exploration Lab of DLR in Wessling, aimed to understand the scope of the EMRS breadboard design, its strengths, and the benefits of the modular design. The discu... | {
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} |
2411.13979 | FedRAV: Hierarchically Federated Region-Learning for Traffic Object
Classification of Autonomous Vehicles | [
"cs.DC",
"cs.AI"
] | The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scena... | {
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} |
2411.13981 | On the Fairness, Diversity and Reliability of Text-to-Image Generative
Models | [
"cs.CV",
"cs.AI"
] | The widespread availability of multimodal generative models has sparked critical discussions on their fairness, reliability, and potential for misuse. While text-to-image models can produce high-fidelity, user-guided images, they also exhibit unpredictable behavior and vulnerabilities, which can be exploited to manipul... | {
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} |
2411.13982 | Safety Without Semantic Disruptions: Editing-free Safe Image Generation
via Context-preserving Dual Latent Reconstruction | [
"cs.CV",
"cs.AI"
] | Training multimodal generative models on large, uncurated datasets can result in users being exposed to harmful, unsafe and controversial or culturally-inappropriate outputs. While model editing has been proposed to remove or filter undesirable concepts in embedding and latent spaces, it can inadvertently damage learne... | {
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} |
2411.13983 | Learning Two-agent Motion Planning Strategies from Generalized Nash
Equilibrium for Model Predictive Control | [
"cs.MA",
"cs.RO",
"cs.SY",
"eess.SY"
] | We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic interaction outcomes as the terminal cost-to-go function in a model predictive control (MPC) framework, guiding agents to implicitly account ... | {
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} |
2411.13988 | Dehazing-aided Multi-Rate Multi-Modal Pose Estimation Framework for
Mitigating Visual Disturbances in Extreme Underwater Domain | [
"cs.RO"
] | This paper delves into the potential of DU-VIO, a dehazing-aided hybrid multi-rate multi-modal Visual-Inertial Odometry (VIO) estimation framework, designed to thrive in the challenging realm of extreme underwater environments. The cutting-edge DU-VIO framework is incorporating a GAN-based pre-processing module and a h... | {
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} |
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