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272600177 | 2409.08103 | 2024-09-12 | The Faetar Benchmark: Speech Recognition in a Very Under-Resourced Language | We introduce the Faetar Automatic Speech Recognition Benchmark, a benchmark corpus designed to push the limits of current approaches to low-resource speech recognition. Faetar, a Franco-Proven\c{c}al variety spoken primarily in Italy, has no standard orthography, has virtually no existing textual or speech resources ot... | [
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272600287 | 2409.08005 | 2024-09-12 | Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications | This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals a... | [
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272690289 | 2409.09095 | 2024-09-12 | meds_reader: A fast and efficient EHR processing library | The growing demand for machine learning in healthcare requires processing increasingly large electronic health record (EHR) datasets, but existing pipelines are not computationally efficient or scalable. In this paper, we introduce meds_reader, an optimized Python package for efficient EHR data processing that is desig... | [
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272600003 | 2409.07779 | 2024-09-12 | AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation | Medical image segmentation, a crucial task in computer vision, facilitates the automated delineation of anatomical structures and pathologies, supporting clinicians in diagnosis, treatment planning, and disease monitoring. Notably, transformers employing shifted window-based self-attention have demonstrated exceptional... | [
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272600326 | 2409.07772 | 2024-09-12 | Alignment with Preference Optimization Is All You Need for LLM Safety | We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achieve a significant boost in global safety score (from $57.64\%$ to $99.90\%$) as measured by LlamaGuard 3 8B, competing with state-of-the-art... | [
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272653675 | 2409.08349 | 2024-09-12 | Scientific and technological knowledge grows linearly over time | The past few centuries have witnessed a dramatic growth in scientific and technological knowledge. However, the nature of that growth - whether exponential or otherwise - remains controversial, perhaps partly due to the lack of quantitative characterizations. We evaluated knowledge as a collective thinking structure, u... | [
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272599934 | 2409.08202 | 2024-09-12 | What Makes a Maze Look Like a Maze? | A unique aspect of human visual understanding is the ability to flexibly interpret abstract concepts: acquiring lifted rules explaining what they symbolize, grounding them across familiar and unfamiliar contexts, and making predictions or reasoning about them. While off-the-shelf vision-language models excel at making ... | [
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272600219 | 2409.08248 | 2024-09-12 | TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder | Recent breakthroughs in text-to-image models have opened up promising research avenues in personalized image generation, enabling users to create diverse images of a specific subject using natural language prompts. However, existing methods often suffer from performance degradation when given only a single reference im... | [
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272600315 | 2409.07712 | 2024-09-12 | Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs | In the broader machine learning literature, data-generation methods demonstrate promising results by generating additional informative training examples via augmenting sparse labels. Such methods are less studied in graphs due to the intricate dependencies among nodes in complex topology structures. This paper presents... | [
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272600479 | 2409.07896 | 2024-09-12 | MambaMIC: An Efficient Baseline for Microscopic Image Classification with State Space Models | In recent years, CNN and Transformer-based methods have made significant progress in Microscopic Image Classification (MIC). However, existing approaches still face the dilemma between global modeling and efficient computation. While the Selective State Space Model (SSM) can simulate long-range dependencies with linear... | [
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272600327 | 2409.07787 | 2024-09-12 | Stable Language Model Pre-training by Reducing Embedding Variability | Stable pre-training is essential for achieving better-performing language models. However, tracking pre-training stability by calculating gradient variance at every step is impractical due to the significant computational costs. We explore Token Embedding Variability (TEV) as a simple and efficient proxy for assessing ... | [
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272599770 | 2409.07734 | 2024-09-12 | DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning | Federated Learning (FL) is a distributed machine learning scheme in which clients jointly participate in the collaborative training of a global model by sharing model information rather than their private datasets. In light of concerns associated with communication and privacy, one-shot FL with a single communication r... | [
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272600307 | 2409.07790 | 2024-09-12 | Full-text Error Correction for Chinese Speech Recognition with Large Language Model | Large Language Models (LLMs) have demonstrated substantial potential for error correction in Automatic Speech Recognition (ASR). However, most research focuses on utterances from short-duration speech recordings, which are the predominant form of speech data for supervised ASR training. This paper investigates the effe... | [
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272599859 | 2409.08036 | 2024-09-12 | Heterogeneous Sheaf Neural Networks | Heterogeneous graphs, with nodes and edges of different types, are commonly used to model relational structures in many real-world applications. Standard Graph Neural Networks (GNNs) struggle to process heterogeneous data due to oversmoothing. Instead, current approaches have focused on accounting for the heterogeneity... | [
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272599736 | 2409.08262 | 2024-09-12 | Learning incomplete factorization preconditioners for GMRES | Incomplete LU factorizations of sparse matrices are widely used as preconditioners in Krylov subspace methods to speed up solving linear systems. Unfortunately, computing the preconditioner itself can be time-consuming and sensitive to hyper-parameters. Instead, we replace the hand-engineered algorithm with a graph neu... | [
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272599959 | 2409.08066 | 2024-09-12 | Self-Supervised Learning of Iterative Solvers for Constrained Optimization | The real-time solution of parametric optimization problems is critical for applications that demand high accuracy under tight real-time constraints, such as model predictive control. To this end, this work presents a learning-based iterative solver for constrained optimization, comprising a neural network predictor tha... | [
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272600217 | 2409.08026 | 2024-09-12 | Scribble-Guided Diffusion for Training-free Text-to-Image Generation | Recent advancements in text-to-image diffusion models have demonstrated remarkable success, yet they often struggle to fully capture the user's intent. Existing approaches using textual inputs combined with bounding boxes or region masks fall short in providing precise spatial guidance, often leading to misaligned or u... | [
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272600325 | 2409.07751 | 2024-09-12 | Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption | The recently proposed Kolmogorov-Arnold Networks (KANs) offer enhanced interpretability and greater model expressiveness. However, KANs also present challenges related to privacy leakage during inference. Homomorphic encryption (HE) facilitates privacy-preserving inference for deep learning models, enabling resource-li... | [
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272600009 | 2409.07984 | 2024-09-12 | SPARK: Self-supervised Personalized Real-time Monocular Face Capture | Feedforward monocular face capture methods seek to reconstruct posed faces from a single image of a person. Current state of the art approaches have the ability to regress parametric 3D face models in real-time across a wide range of identities, lighting conditions and poses by leveraging large image datasets of human ... | [
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272600063 | 2409.07969 | 2024-09-12 | Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction | In the speech signal, acoustic landmarks identify times when the acoustic manifestations of the linguistically motivated distinctive features are most salient. Acoustic landmarks have been widely applied in various domains, including speech recognition, speech depression detection, clinical analysis of speech abnormali... | [
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272654143 | 2409.08422 | 2024-09-12 | Fitted Q-Iteration via Max-Plus-Linear Approximation | In this study, we consider the application of max-plus-linear approximators for Q-function in offline reinforcement learning of discounted Markov decision processes. In particular, we incorporate these approximators to propose novel fitted Q-iteration (FQI) algorithms with provable convergence. Exploiting the compatibi... | [
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272600034 | 2409.08260 | 2024-09-12 | Improving Text-guided Object Inpainting with Semantic Pre-inpainting | Recent years have witnessed the success of large text-to-image diffusion models and their remarkable potential to generate high-quality images. The further pursuit of enhancing the editability of images has sparked significant interest in the downstream task of inpainting a novel object described by a text prompt withi... | [
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272600346 | 2409.08185 | 2024-09-12 | Fine-tuning Large Language Models for Entity Matching | Generative large language models (LLMs) are a promising alternative to pre-trained language models for entity matching due to their high zero-shot performance and ability to generalize to unseen entities. Existing research on using LLMs for entity matching has focused on prompt engineering and in-context learning. This... | [
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272599960 | 2409.08014 | 2024-09-12 | An Evaluation Framework for Attributed Information Retrieval using Large Language Models | With the growing success of Large Language models (LLMs) in information-seeking scenarios, search engines are now adopting generative approaches to provide answers along with in-line citations as attribution. While existing work focuses mainly on attributed question answering, in this paper, we target information-seeki... | [
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272653999 | 2409.08409 | 2024-09-12 | Wasserstein Distributionally Robust Multiclass Support Vector Machine | We study the problem of multiclass classification for settings where data features $\mathbf{x}$ and their labels $\mathbf{y}$ are uncertain. We identify that distributionally robust one-vs-all (OVA) classifiers often struggle in settings with imbalanced data. To address this issue, we use Wasserstein distributionally r... | [
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272600133 | 2409.08255 | 2024-09-12 | LoRID: Low-Rank Iterative Diffusion for Adversarial Purification | This work presents an information-theoretic examination of diffusion-based purification methods, the state-of-the-art adversarial defenses that utilize diffusion models to remove malicious perturbations in adversarial examples. By theoretically characterizing the inherent purification errors associated with the Markov-... | [
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272709646 | 2409.11423 | 2024-09-12 | Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data | Large language models (LLMs) have demonstrated significant success in various domain-specific tasks, with their performance often improving substantially after fine-tuning. However, fine-tuning with real-world data introduces privacy risks. To mitigate these risks, developers increasingly rely on synthetic data generat... | [
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272653677 | 2409.08384 | 2024-09-12 | Noisy Low Rank Column-wise Sensing | This letter studies the AltGDmin algorithm for solving the noisy low rank column-wise sensing (LRCS) problem. Our sample complexity guarantee improves upon the best existing one by a factor $\max(r, \log(1/\epsilon))/r$ where $r$ is the rank of the unknown matrix and $\epsilon$ is the final desired accuracy. A second c... | [
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268807221 | 2409.08379 | 2024-09-12 | The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot | Large Language Models (LLMs) have been shown to enhance individual productivity in guided settings. Whereas LLMs are likely to also transform innovation processes in a collaborative work setting, it is unclear what trajectory this transformation will follow. Innovation in these contexts encompasses both capability inno... | [
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272600164 | 2409.08271 | 2024-09-12 | DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge Transfer | We present DreamBeast, a novel method based on score distillation sampling (SDS) for generating fantastical 3D animal assets composed of distinct parts. Existing SDS methods often struggle with this generation task due to a limited understanding of part-level semantics in text-to-image diffusion models. While recent di... | [
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272653658 | 2409.08330 | 2024-09-12 | Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue | Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have been show... | [
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272600046 | 2409.07972 | 2024-09-12 | Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction | The task of vision-based 3D occupancy prediction aims to reconstruct 3D geometry and estimate its semantic classes from 2D color images, where the 2D-to-3D view transformation is an indispensable step. Most previous methods conduct forward projection, such as BEVPooling and VoxelPooling, both of which map the 2D image ... | [
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272653657 | 2409.08389 | 2024-09-12 | Higher-Order Topological Directionality and Directed Simplicial Neural Networks | Topological Deep Learning (TDL) has emerged as a paradigm to process and learn from signals defined on higher-order combinatorial topological spaces, such as simplicial or cell complexes. Although many complex systems have an asymmetric relational structure, most TDL models forcibly symmetrize these relationships. In t... | [
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272600329 | 2409.08217 | 2024-09-12 | CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs | Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neural network models still struggle to capture higher-order information, i.e., information that goes \emph{beyond} pairwise interactions. Rece... | [
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272599863 | 2409.08039 | 2024-09-12 | Zero-Shot Sing Voice Conversion: built upon clustering-based phoneme representations | This study presents an innovative Zero-Shot any-to-any Singing Voice Conversion (SVC) method, leveraging a novel clustering-based phoneme representation to effectively separate content, timbre, and singing style. This approach enables precise voice characteristic manipulation. We discovered that datasets with fewer rec... | [
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272599853 | 2409.07830 | 2024-09-12 | ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable | Machine learning based autonomous driving systems often face challenges with safety-critical scenarios that are rare in real-world data, hindering their large-scale deployment. While increasing real-world training data coverage could address this issue, it is costly and dangerous. This work explores generating safety-c... | [
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272599888 | 2409.07715 | 2024-09-12 | FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments | Robust depth perception in visually-degraded environments is crucial for autonomous aerial systems. Thermal imaging cameras, which capture infrared radiation, are robust to visual degradation. However, due to lack of a large-scale dataset, the use of thermal cameras for unmanned aerial system (UAS) depth perception has... | [
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272600187 | 2409.07940 | 2024-09-12 | Control+Shift: Generating Controllable Distribution Shifts | We propose a new method for generating realistic datasets with distribution shifts using any decoder-based generative model. Our approach systematically creates datasets with varying intensities of distribution shifts, facilitating a comprehensive analysis of model performance degradation. We then use these generated d... | [
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272600264 | 2409.08258 | 2024-09-12 | Improving Virtual Try-On with Garment-focused Diffusion Models | Diffusion models have led to the revolutionizing of generative modeling in numerous image synthesis tasks. Nevertheless, it is not trivial to directly apply diffusion models for synthesizing an image of a target person wearing a given in-shop garment, i.e., image-based virtual try-on (VTON) task. The difficulty origina... | [
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272599955 | 2409.07869 | 2024-09-12 | Learning Rules from KGs Guided by Language Models | Advances in information extraction have enabled the automatic construction of large knowledge graphs (e.g., Yago, Wikidata or Google KG), which are widely used in many applications like semantic search or data analytics. However, due to their semi-automatic construction, KGs are often incomplete. Rule learning methods,... | [
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272653869 | 2409.08311 | 2024-09-12 | Theoretical guarantees in KL for Diffusion Flow Matching | Flow Matching (FM) (also referred to as stochastic interpolants or rectified flows) stands out as a class of generative models that aims to bridge in finite time the target distribution $\nu^\star$ with an auxiliary distribution $\mu$, leveraging a fixed coupling $\pi$ and a bridge which can either be deterministic or ... | [
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272653951 | 2409.08353 | 2024-09-12 | Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric Videos | Volumetric video represents a transformative advancement in visual media, enabling users to freely navigate immersive virtual experiences and narrowing the gap between digital and real worlds. However, the need for extensive manual intervention to stabilize mesh sequences and the generation of excessively large assets ... | [
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272599927 | 2409.07879 | 2024-09-12 | Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series | Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducing variance in ensemble methods.This paper introduces Randomized Spline Trees (RST), a novel algorithm t... | [
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272599846 | 2409.08102 | 2024-09-12 | Bayesian Self-Training for Semi-Supervised 3D Segmentation | 3D segmentation is a core problem in computer vision and, similarly to many other dense prediction tasks, it requires large amounts of annotated data for adequate training. However, densely labeling 3D point clouds to employ fully-supervised training remains too labor intensive and expensive. Semi-supervised training p... | [
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272653913 | 2409.08345 | 2024-09-12 | SIG: A Synthetic Identity Generation Pipeline for Generating Evaluation Datasets for Face Recognition | As Artificial Intelligence applications expand, the evaluation of models faces heightened scrutiny. Ensuring public readiness requires evaluation datasets, which differ from training data by being disjoint and ethically sourced in compliance with privacy regulations. The performance and fairness of face recognition sys... | [
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272600380 | 2409.07966 | 2024-09-12 | ProbTalk3D: Non-Deterministic Emotion Controllable Speech-Driven 3D Facial Animation Synthesis Using VQ-VAE | Audio-driven 3D facial animation synthesis has been an active field of research with attention from both academia and industry. While there are promising results in this area, recent approaches largely focus on lip-sync and identity control, neglecting the role of emotions and emotion control in the generative process.... | [
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273023253 | 2410.00004 | 2024-09-12 | Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization | The retrieval augmented generation (RAG) system such as Retro has been shown to improve language modeling capabilities and reduce toxicity and hallucinations by retrieving from a database of non-parametric memory containing trillions of entries. We introduce Retro-li that shows retrieval can also help using a small-sca... | [
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272600392 | 2409.07769 | 2024-09-12 | Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks | A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN r... | [
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272600050 | 2409.07841 | 2024-09-12 | TSELM: Target Speaker Extraction using Discrete Tokens and Language Models | We propose TSELM, a novel target speaker extraction network that leverages discrete tokens and language models. TSELM utilizes multiple discretized layers from WavLM as input tokens and incorporates cross-attention mechanisms to integrate target speaker information. Language models are employed to capture the sequence ... | [
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272600441 | 2409.08143 | 2024-09-12 | Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models | Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as surgery complicate the accurate delineation of regions of interest. The BraTS Post-Treatment 2024 Challenge published the first public data... | [
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272599743 | 2409.07723 | 2024-09-12 | Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy | Depth estimation is a cornerstone of 3D reconstruction and plays a vital role in minimally invasive endoscopic surgeries. However, most current depth estimation networks rely on traditional convolutional neural networks, which are limited in their ability to capture global information. Foundation models offer a promisi... | [
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272600053 | 2409.07833 | 2024-09-12 | Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example | In the present paper, it is shown how the columnar/layered CoLaNET spiking neural network (SNN) architecture can be used in supervised learning image classification tasks. Image pixel brightness is coded by the spike count during image presentation period. Image class label is indicated by activity of special SNN input... | [
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272600397 | 2409.08068 | 2024-09-12 | AutoPET Challenge: Tumour Synthesis for Data Augmentation | Accurate lesion segmentation in whole-body PET/CT scans is crucial for cancer diagnosis and treatment planning, but limited datasets often hinder the performance of automated segmentation models. In this paper, we explore the potential of leveraging the deep prior from a generative model to serve as a data augmenter fo... | [
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272599849 | 2409.08253 | 2024-09-12 | The Design of Informative Take-Over Requests for Semi-Autonomous Cyber-Physical Systems: Combining Spoken Language and Visual Icons in a Drone-Controller Setting | The question of how cyber-physical systems should interact with human partners that can take over control or exert oversight is becoming more pressing, as these systems are deployed for an ever larger range of tasks. Drawing on the literatures on handing over control during semi-autonomous driving and human-robot inter... | [
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272600252 | 2409.07914 | 2024-09-12 | InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation | Bimanual manipulation presents unique challenges compared to unimanual tasks due to the complexity of coordinating two robotic arms. In this paper, we introduce InterACT: Inter-dependency aware Action Chunking with Hierarchical Attention Transformers, a novel imitation learning framework designed specifically for biman... | [
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272628137 | 2409.12169 | 2024-09-12 | LogoRA: Local-Global Representation Alignment for Robust Time Series Classification | Unsupervised domain adaptation (UDA) of time series aims to teach models to identify consistent patterns across various temporal scenarios, disregarding domain-specific differences, which can maintain their predictive accuracy and effectively adapt to new domains. However, existing UDA methods struggle to adequately ex... | [
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272600239 | 2409.08234 | 2024-09-12 | LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems | The rapid evolution of cyber threats necessitates innovative solutions for detecting and analyzing malicious activity. Honeypots, which are decoy systems designed to lure and interact with attackers, have emerged as a critical component in cybersecurity. In this paper, we present a novel approach to creating realistic ... | [
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272653908 | 2409.08366 | 2024-09-12 | Critical Casimir levitation of colloids above a bull's-eye pattern | Critical Casimir forces emerge among particles or surfaces immersed in a near-critical fluid, with the sign of the force determined by surface properties and with its strength tunable by minute temperature changes. Here, we show how such forces can be used to trap a colloidal particle and levitate it above a substrate ... | [
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272600202 | 2409.07743 | 2024-09-12 | LOCKEY: A Novel Approach to Model Authentication and Deepfake Tracking | This paper presents a novel approach to deter unauthorized deepfakes and enable user tracking in generative models, even when the user has full access to the model parameters, by integrating key-based model authentication with watermarking techniques. Our method involves providing users with model parameters accompanie... | [
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272600099 | 2409.08206 | 2024-09-12 | ComAlign: Compositional Alignment in Vision-Language Models | Vision-language models (VLMs) like CLIP have showcased a remarkable ability to extract transferable features for downstream tasks. Nonetheless, the training process of these models is usually based on a coarse-grained contrastive loss between the global embedding of images and texts which may lose the compositional str... | [
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272600029 | 2409.07737 | 2024-09-12 | Ruri: Japanese General Text Embeddings | We report the development of Ruri, a series of Japanese general text embedding models. While the development of general-purpose text embedding models in English and multilingual contexts has been active in recent years, model development in Japanese remains insufficient. The primary reasons for this are the lack of dat... | [
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272600257 | 2409.07733 | 2024-09-12 | Self-similarity of temporal interaction networks arises from hyperbolic geometry with time-varying curvature | The self-similarity of complex systems has been studied intensely across different domains due to its potential applications in system modeling, complexity analysis, etc., as well as for deep theoretical interest. Existing studies rely on scale transformations conceptualized over either a definite geometric structure o... | [
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272653828 | 2409.08425 | 2024-09-12 | SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer | In this paper, we introduce SoloAudio, a novel diffusion-based generative model for target sound extraction (TSE). Our approach trains latent diffusion models on audio, replacing the previous U-Net backbone with a skip-connected Transformer that operates on latent features. SoloAudio supports both audio-oriented and la... | [
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272599889 | 2505.01074 | 2024-09-12 | WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks | The rapid evolution of wireless networks presents unprecedented challenges in managing complex and dynamic systems. Existing methods are increasingly facing fundamental limitations in addressing these challenges. In this paper, we introduce WirelessAgent, a novel framework that harnesses large language models (LLMs) to... | [
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272653989 | 2409.08386 | 2024-09-12 | Self-Supervised Inference of Agents in Trustless Environments | In this paper, we propose a novel approach where agents can form swarms to produce high-quality responses effectively. This is accomplished by utilizing agents capable of data inference and ranking, which can be effectively implemented using LLMs as response classifiers. We assess existing approaches for trustless agen... | [
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272599783 | 2409.08277 | 2024-09-12 | Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor | High frame rate and accurate depth estimation plays an important role in several tasks crucial to robotics and automotive perception. To date, this can be achieved through ToF and LiDAR devices for indoor and outdoor applications, respectively. However, their applicability is limited by low frame rate, energy consumpti... | [
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272599747 | 2409.07746 | 2024-09-12 | Learning Brain Tumor Representation in 3D High-Resolution MR Images via Interpretable State Space Models | Learning meaningful and interpretable representations from high-dimensional volumetric magnetic resonance (MR) images is essential for advancing personalized medicine. While Vision Transformers (ViTs) have shown promise in handling image data, their application to 3D multi-contrast MR images faces challenges due to com... | [
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272653591 | 2409.08308 | 2024-09-12 | DiReDi: Distillation and Reverse Distillation for AIoT Applications | Typically, the significant efficiency can be achieved by deploying different edge AI models in various real world scenarios while a few large models manage those edge AI models remotely from cloud servers. However, customizing edge AI models for each user's specific application or extending current models to new applic... | [
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272654001 | 2409.08369 | 2024-09-12 | E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning | Ensemble learning is a meta-learning approach that combines the predictions of multiple learners, demonstrating improved accuracy and robustness. Nevertheless, ensembling models like Convolutional Neural Networks (CNNs) result in high memory and computing overhead, preventing their deployment in embedded systems. These... | [
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