id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.17334 | An Explicit Discrete-Time Dynamic Vehicle Model with Assured Numerical
Stability | [
"eess.SY",
"cs.SY"
] | Numerical stability is of great significance for discrete-time dynamic vehicle model. Among the unstable factors, low-speed singularity stands out as one of the most challenging issues, which arises from that the denominator of tire side angle term only contains the vehicle longitudinal speed. Consequently, for the com... | {
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2411.17335 | MotionLLaMA: A Unified Framework for Motion Synthesis and Comprehension | [
"cs.CV"
] | This paper introduces MotionLLaMA, a unified framework for motion synthesis and comprehension, along with a novel full-body motion tokenizer called the HoMi Tokenizer. MotionLLaMA is developed based on three core principles. First, it establishes a powerful unified representation space through the HoMi Tokenizer. Using... | {
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2411.17337 | sbi reloaded: a toolkit for simulation-based inference workflows | [
"cs.LG"
] | Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters... | {
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2411.17338 | Different Bias Under Different Criteria: Assessing Bias in LLMs with a
Fact-Based Approach | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Large language models (LLMs) often reflect real-world biases, leading to efforts to mitigate these effects and make the models unbiased. Achieving this goal requires defining clear criteria for an unbiased state, with any deviation from these criteria considered biased. Some studies define an unbiased state as equal tr... | {
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2411.17339 | Knowledge-aware Evolutionary Graph Neural Architecture Search | [
"cs.NE",
"cs.AI",
"cs.LG"
] | Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. However, existing GNAS methods begin searching for architectures from a zero-knowledge state, ignoring the prior knowledge that may improve the search efficiency. The available... | {
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2411.17340 | TDAvec: Computing Vector Summaries of Persistence Diagrams for
Topological Data Analysis in R and Python | [
"math.AT",
"cs.CV"
] | Persistent homology is a widely-used tool in topological data analysis (TDA) for understanding the underlying shape of complex data. By constructing a filtration of simplicial complexes from data points, it captures topological features such as connected components, loops, and voids across multiple scales. These featur... | {
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2411.17342 | Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Every year, thousands of people suffer from skull damage and require personalized implants to fill the cranial cavity. Unfortunately, the waiting time for reconstruction surgery can extend to several weeks or even months, especially in less developed countries. One factor contributing to the extended waiting period is ... | {
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2411.17347 | Real-Time Multimodal Signal Processing for HRI in RoboCup: Understanding
a Human Referee | [
"cs.CV",
"cs.HC",
"cs.RO"
] | Advancing human-robot communication is crucial for autonomous systems operating in dynamic environments, where accurate real-time interpretation of human signals is essential. RoboCup provides a compelling scenario for testing these capabilities, requiring robots to understand referee gestures and whistle with minimal ... | {
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2411.17350 | Correlation-Aware Graph Convolutional Networks for Multi-Label Node
Classification | [
"cs.LG",
"cs.SI"
] | Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Although a few efforts have been made by utilizing Graph Convolution Networks (GCNs) to learn node representations and model correlations betwee... | {
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2411.17353 | Joint Combinatorial Node Selection and Resource Allocations in the
Lightning Network using Attention-based Reinforcement Learning | [
"cs.LG",
"q-fin.CP"
] | The Lightning Network (LN) has emerged as a second-layer solution to Bitcoin's scalability challenges. The rise of Payment Channel Networks (PCNs) and their specific mechanisms incentivize individuals to join the network for profit-making opportunities. According to the latest statistics, the total value locked within ... | {
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2411.17354 | DWCL: Dual-Weighted Contrastive Learning for Multi-View Clustering | [
"cs.CV",
"cs.LG"
] | Multi-view contrastive clustering (MVCC) has gained significant attention for generating consistent clustering structures from multiple views through contrastive learning. However, most existing MVCC methods create cross-views by combining any two views, leading to a high volume of unreliable pairs. Furthermore, these ... | {
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2411.17361 | Towards Robust Cross-Domain Recommendation with Joint Identifiability of
User Preference | [
"cs.IR"
] | Recent cross-domain recommendation (CDR) studies assume that disentangled domain-shared and domain-specific user representations can mitigate domain gaps and facilitate effective knowledge transfer. However, achieving perfect disentanglement is challenging in practice, because user behaviors in CDR are highly complex, ... | {
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2411.17363 | SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask
Propagation and Auto-prompting | [
"cs.CV"
] | Medical image segmentation often faces the challenge of prohibitively expensive annotation costs. While few-shot learning offers a promising solution to alleviate this burden, conventional approaches still rely heavily on pre-training with large volumes of labeled data from known categories. To address this issue, we p... | {
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2411.17367 | Efficient Deployment of Transformer Models in Analog In-Memory Computing
Hardware | [
"cs.AR",
"cs.LG"
] | Analog in-memory computing (AIMC) has emerged as a promising solution to overcome the von Neumann bottleneck, accelerating neural network computations and improving computational efficiency. While AIMC has demonstrated success with architectures such as CNNs, MLPs, and RNNs, deploying transformer-based models using AIM... | {
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2411.17372 | Epidemiology-informed Graph Neural Network for Heterogeneity-aware
Epidemic Forecasting | [
"cs.LG",
"cs.SI"
] | Among various spatio-temporal prediction tasks, epidemic forecasting plays a critical role in public health management. Recent studies have demonstrated the strong potential of spatio-temporal graph neural networks (STGNNs) in extracting heterogeneous spatio-temporal patterns for epidemic forecasting. However, most of ... | {
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2411.17374 | Fairness And Performance In Harmony: Data Debiasing Is All You Need | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Fairness in both machine learning (ML) predictions and human decisions is critical, with ML models prone to algorithmic and data bias, and human decisions affected by subjectivity and cognitive bias. This study investigates fairness using a real-world university admission dataset with 870 profiles, leveraging three ML ... | {
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2411.17375 | The Extractive-Abstractive Spectrum: Uncovering Verifiability Trade-offs
in LLM Generations | [
"cs.CL"
] | Across all fields of academic study, experts cite their sources when sharing information. While large language models (LLMs) excel at synthesizing information, they do not provide reliable citation to sources, making it difficult to trace and verify the origins of the information they present. In contrast, search engin... | {
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2411.17376 | RealTraj: Towards Real-World Pedestrian Trajectory Forecasting | [
"cs.CV"
] | This paper jointly addresses three key limitations in conventional pedestrian trajectory forecasting: pedestrian perception errors, real-world data collection costs, and person ID annotation costs. We propose a novel framework, RealTraj, that enhances the real-world applicability of trajectory forecasting. Our approach... | {
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2411.17382 | MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal
Domains for Time Series Forecasting | [
"cs.LG"
] | Time series forecasting is crucial in many fields, yet current deep learning models struggle with noise, data sparsity, and capturing complex multi-scale patterns. This paper presents MFF-FTNet, a novel framework addressing these challenges by combining contrastive learning with multi-scale feature extraction across bo... | {
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2411.17383 | AnchorCrafter: Animate CyberAnchors Saling Your Products via
Human-Object Interacting Video Generation | [
"cs.CV"
] | The automatic generation of anchor-style product promotion videos presents promising opportunities in online commerce, advertising, and consumer engagement. However, this remains a challenging task despite significant advancements in pose-guided human video generation. In addressing this challenge, we identify the inte... | {
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2411.17384 | Assessing Electricity Network Capacity Requirements for Industrial
Decarbonisation in Great Britain | [
"eess.SY",
"cs.SY"
] | Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countries and this may require upgrades to the existing electricity network or new network investment. While the infrastructure ... | {
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2411.17385 | DepthCues: Evaluating Monocular Depth Perception in Large Vision Models | [
"cs.CV"
] | Large-scale pre-trained vision models are becoming increasingly prevalent, offering expressive and generalizable visual representations that benefit various downstream tasks. Recent studies on the emergent properties of these models have revealed their high-level geometric understanding, in particular in the context of... | {
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2411.17386 | vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation | [
"eess.IV",
"cs.CV"
] | Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular patterns and scales, signal-to-noise ratios, and background tissues. These variations, along with domain gaps arising from varying imaging prot... | {
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2411.17387 | Robust Bayesian Optimization via Localized Online Conformal Prediction | [
"cs.LG",
"eess.SP"
] | Bayesian optimization (BO) is a sequential approach for optimizing black-box objective functions using zeroth-order noisy observations. In BO, Gaussian processes (GPs) are employed as probabilistic surrogate models to estimate the objective function based on past observations, guiding the selection of future queries to... | {
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2411.17388 | Can LLMs be Good Graph Judger for Knowledge Graph Construction? | [
"cs.CL",
"cs.AI"
] | In real-world scenarios, most of the data obtained from information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graphs (KGs) remains a critical challenge. The quality of constructed KGs may also impact the performance of some KG-dependent domains like GraphRAG ... | {
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2411.17390 | Dual-Representation Interaction Driven Image Quality Assessment with
Restoration Assistance | [
"eess.IV",
"cs.CV"
] | No-Reference Image Quality Assessment for distorted images has always been a challenging problem due to image content variance and distortion diversity. Previous IQA models mostly encode explicit single-quality features of synthetic images to obtain quality-aware representations for quality score prediction. However, p... | {
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2411.17392 | NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for
Surface Reconstruction from Point Clouds | [
"cs.CV"
] | Reconstructing continuous surfaces from unoriented and unordered 3D points is a fundamental challenge in computer vision and graphics. Recent advancements address this problem by training neural signed distance functions to pull 3D location queries to their closest points on a surface, following the predicted signed di... | {
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2411.17400 | A Generalized Unified Skew-Normal Process with Neural Bayes Inference | [
"stat.ML",
"cs.LG"
] | In recent decades, statisticians have been increasingly encountering spatial data that exhibit non-Gaussian behaviors such as asymmetry and heavy-tailedness. As a result, the assumptions of symmetry and fixed tail weight in Gaussian processes have become restrictive and may fail to capture the intrinsic properties of t... | {
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2411.17401 | One Mind, Many Tongues: A Deep Dive into Language-Agnostic Knowledge
Neurons in Large Language Models | [
"cs.CL"
] | Large language models (LLMs) have learned vast amounts of factual knowledge through self-supervised pre-training on large-scale corpora. Meanwhile, LLMs have also demonstrated excellent multilingual capabilities, which can express the learned knowledge in multiple languages. However, the knowledge storage mechanism in ... | {
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2411.17404 | BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical
Modeling Problem Solving | [
"cs.AI",
"cs.CL"
] | LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in operations research domain lack detailed annotations of the modeling process, such as variable definitions, focusing solely on objective values... | {
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2411.17406 | CoA: Chain-of-Action for Generative Semantic Labels | [
"cs.CV"
] | Recent advances in vision-language models (VLM) have demonstrated remarkable capability in image classification. These VLMs leverage a predefined set of categories to construct text prompts for zero-shot reasoning. However, in more open-ended domains like autonomous driving, using a predefined set of labels becomes imp... | {
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2411.17411 | Advancing Uncertain Combinatorics through Graphization, Hyperization,
and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond | [
"cs.AI"
] | To better handle real-world uncertainty, concepts such as fuzzy sets, neutrosophic sets, rough sets, and soft sets have been introduced. For example, neutrosophic sets, which simultaneously represent truth, indeterminacy, and falsehood, have proven to be valuable tools for modeling uncertainty in complex systems. These... | {
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2411.17418 | Multimodal Outer Arithmetic Block Dual Fusion of Whole Slide Images and
Omics Data for Precision Oncology | [
"cs.CV"
] | The integration of DNA methylation data with a Whole Slide Image (WSI) offers significant potential for enhancing the diagnostic precision of central nervous system (CNS) tumor classification in neuropathology. While existing approaches typically integrate encoded omic data with histology at either an early or late fus... | {
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2411.17420 | Cross-modal Medical Image Generation Based on Pyramid Convolutional
Attention Network | [
"cs.CE",
"eess.IV"
] | The integration of multimodal medical imaging can provide complementary and comprehensive information for the diagnosis of Alzheimer's disease (AD). However, in clinical practice, since positron emission tomography (PET) is often missing, multimodal images might be incomplete. To address this problem, we propose a meth... | {
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2411.17423 | DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and
Expressive Characters | [
"cs.CV"
] | Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging task, especially for complex elements like garments and hair, due to the lack of large-scale datasets and effective rigging methods. To address... | {
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2411.17425 | Self-supervised Video Instance Segmentation Can Boost Geographic Entity
Alignment in Historical Maps | [
"cs.CV"
] | Tracking geographic entities from historical maps, such as buildings, offers valuable insights into cultural heritage, urbanization patterns, environmental changes, and various historical research endeavors. However, linking these entities across diverse maps remains a persistent challenge for researchers. Traditionall... | {
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2411.17426 | CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning | [
"cs.LG",
"cs.AI"
] | Decoder-only models generate tokens autoregressively by caching key/value vectors, but as the cache grows, inference becomes memory-bound. To address this issue, we introduce CLOVER (Cross-Layer Orthogonal Vectors), a novel approach that treats pairs of attention layers as a set of low-rank decompositions. CLOVER appli... | {
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2411.17429 | Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in GNNs:
A Survey | [
"cs.LG",
"cs.AI"
] | Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their effectiveness is often constrained by two critical challenges: oversquashing, where the excessive compression of information from distant nodes results in significant information loss, and oversmoothing, where repeated me... | {
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2411.17430 | Snake-Inspired Mobile Robot Positioning with Hybrid Learning | [
"cs.RO",
"eess.SP"
] | Mobile robots are used in various fields, from deliveries to search and rescue applications. Different types of sensors are mounted on the robot to provide accurate navigation and, thus, allow successful completion of its task. In real-world scenarios, due to environmental constraints, the robot frequently relies only ... | {
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2411.17431 | Noise Adaptor: Enhancing Low-Latency Spiking Neural Networks through
Noise-Injected Low-Bit ANN Conversion | [
"cs.NE"
] | We present Noise Adaptor, a novel method for constructing competitive low-latency spiking neural networks (SNNs) by converting noise-injected, low-bit artificial neural networks (ANNs). This approach builds on existing ANN-to-SNN conversion techniques but offers several key improvements: (1) By injecting noise during q... | {
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2411.17432 | Communication-Efficient Cooperative SLAMMOT via Determining the Number
of Collaboration Vehicles | [
"cs.RO",
"cs.MA"
] | The SLAMMOT, i.e. simultaneous localization, mapping, and moving object (detection and) tracking, represents an emerging technology for autonomous vehicles in dynamic environments. Such single-vehicle systems still have inherent limitations, such as occlusion issues. Inspired by SLAMMOT and rapidly evolving cooperative... | {
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2411.17433 | LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model
for data forecasting using sparse measurements | [
"physics.flu-dyn",
"cs.AI"
] | This article introduces a novel methodology that integrates singular value decomposition (SVD) with a shallow linear neural network for forecasting high resolution fluid mechanics data. The method, termed LC-SVD-DLinear, combines a low-cost variant of singular value decomposition (LC-SVD) with the DLinear architecture,... | {
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2411.17434 | Recovering a group from few orbits | [
"math.RT",
"cs.IT",
"math.IT"
] | For an unknown finite group $G$ of automorphisms of a finite-dimensional Hilbert space, we find sharp bounds on the number of generic $G$-orbits needed to recover $G$ up to group isomorphism, as well as the number needed to recover $G$ as a concrete set of automorphisms. | {
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2411.17437 | "Stupid robot, I want to speak to a human!" User Frustration Detection
in Task-Oriented Dialog Systems | [
"cs.CL"
] | Detecting user frustration in modern-day task-oriented dialog (TOD) systems is imperative for maintaining overall user satisfaction, engagement, and retention. However, most recent research is focused on sentiment and emotion detection in academic settings, thus failing to fully encapsulate implications of real-world u... | {
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2411.17438 | Object-centric proto-symbolic behavioural reasoning from pixels | [
"cs.AI",
"cs.CV",
"cs.LG",
"cs.NE"
] | Autonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space ... | {
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2411.17439 | SpikeAtConv: An Integrated Spiking-Convolutional Attention Architecture
for Energy-Efficient Neuromorphic Vision Processing | [
"cs.NE"
] | Spiking Neural Networks (SNNs) offer a biologically inspired alternative to conventional artificial neural networks, with potential advantages in power efficiency due to their event-driven computation. Despite their promise, SNNs have yet to achieve competitive performance on complex visual tasks, such as image classif... | {
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2411.17440 | Identity-Preserving Text-to-Video Generation by Frequency Decomposition | [
"cs.CV",
"cs.MM"
] | Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an open problem for generative models. This paper pushes the technical frontier of IPT2V in two directions that have not been resolved in liter... | {
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2411.17444 | Maximally Separated Active Learning | [
"cs.LG"
] | Active Learning aims to optimize performance while minimizing annotation costs by selecting the most informative samples from an unlabelled pool. Traditional uncertainty sampling often leads to sampling bias by choosing similar uncertain samples. We propose an active learning method that utilizes fixed equiangular hype... | {
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2411.17447 | Exploring Structural Dynamics in Retracted and Non-Retracted Author's
Collaboration Networks: A Quantitative Analysis | [
"cs.IR"
] | Retractions undermine the reliability of scientific literature and the foundation of future research. Analyzing collaboration networks in retracted papers can identify risk factors, such as recurring co-authors or institutions. This study compared the network structures of retracted and non-retracted papers, using data... | {
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2411.17450 | A Graph Neural Network deep-dive into successful counterattacks | [
"cs.LG",
"cs.SI"
] | A counterattack in soccer is a high speed, high intensity direct attack that can occur when a team transitions from a defensive state to an attacking state after regaining possession of the ball. The aim is to create a goal-scoring opportunity by convering a lot of ground with minimal passes before the opposing team ca... | {
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2411.17451 | VLRewardBench: A Challenging Benchmark for Vision-Language Generative
Reward Models | [
"cs.CV",
"cs.CL"
] | Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored. Current assessment methods primarily rely on AI-annotated preference labels from traditional VL tasks, which can introduce biases and often fail to ... | {
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2411.17454 | FLEX-CLIP: Feature-Level GEneration Network Enhanced CLIP for X-shot
Cross-modal Retrieval | [
"cs.CV",
"cs.CL"
] | Given a query from one modality, few-shot cross-modal retrieval (CMR) retrieves semantically similar instances in another modality with the target domain including classes that are disjoint from the source domain. Compared with classical few-shot CMR methods, vision-language pretraining methods like CLIP have shown gre... | {
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2411.17458 | Spatially Visual Perception for End-to-End Robotic Learning | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Recent advances in imitation learning have shown significant promise for robotic control and embodied intelligence. However, achieving robust generalization across diverse mounted camera observations remains a critical challenge. In this paper, we introduce a video-based spatial perception framework that leverages 3D s... | {
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2411.17459 | WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent
Video Diffusion Model | [
"cs.CV",
"cs.AI"
] | Video Variational Autoencoder (VAE) encodes videos into a low-dimensional latent space, becoming a key component of most Latent Video Diffusion Models (LVDMs) to reduce model training costs. However, as the resolution and duration of generated videos increase, the encoding cost of Video VAEs becomes a limiting bottlene... | {
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2411.17461 | SoK: Decentralized AI (DeAI) | [
"cs.LG",
"cs.AI",
"cs.CR"
] | The centralization of Artificial Intelligence (AI) poses significant challenges, including single points of failure, inherent biases, data privacy concerns, and scalability issues. These problems are especially prevalent in closed-source large language models (LLMs), where user data is collected and used without transp... | {
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2411.17465 | ShowUI: One Vision-Language-Action Model for GUI Visual Agent | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.HC"
] | Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. While most agents are language-based, relying on closed-source API with text-rich meta-information (e.g., HTML or accessibility tree), they show limitations in perceiving UI visuals as humans do, high... | {
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2411.17467 | Learning 3D Representations from Procedural 3D Programs | [
"cs.CV"
] | Self-supervised learning has emerged as a promising approach for acquiring transferable 3D representations from unlabeled 3D point clouds. Unlike 2D images, which are widely accessible, acquiring 3D assets requires specialized expertise or professional 3D scanning equipment, making it difficult to scale and raising cop... | {
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2411.17468 | Adversarial Bounding Boxes Generation (ABBG) Attack against Visual
Object Trackers | [
"cs.CV"
] | Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which... | {
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2411.17470 | Towards Precise Scaling Laws for Video Diffusion Transformers | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Achieving optimal performance of video diffusion transformers within given data and compute budget is crucial due to their high training costs. This necessitates precisely determining the optimal model size and training hyperparameters before large-scale training. While scaling laws are employed in language models to p... | {
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2411.17471 | Learning New Concepts, Remembering the Old: A Novel Continual Learning | [
"cs.LG",
"cs.CR",
"cs.CV"
] | Concept Bottleneck Models (CBMs) enhance model interpretability by introducing human-understandable concepts within the architecture. However, existing CBMs assume static datasets, limiting their ability to adapt to real-world, continuously evolving data streams. To address this, we define a novel concept-incremental a... | {
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2411.17472 | Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian
Theory | [
"cs.CV",
"cs.LG",
"stat.ML"
] | Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images from textual prompts. Despite these advances, existing models struggle with complex prompts involving multiple objects and attributes, often misaligning modifiers with their co... | {
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2411.17473 | TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba | [
"cs.CV"
] | Mamba has shown great potential for computer vision due to its linear complexity in modeling the global context with respect to the input length. However, existing lightweight Mamba-based backbones cannot demonstrate performance that matches Convolution or Transformer-based methods. We observe that simply modifying the... | {
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2411.17474 | Probing the Mid-level Vision Capabilities of Self-Supervised Learning | [
"cs.CV"
] | Mid-level vision capabilities - such as generic object localization and 3D geometric understanding - are not only fundamental to human vision but are also crucial for many real-world applications of computer vision. These abilities emerge with minimal supervision during the early stages of human visual development. Des... | {
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2411.17475 | COBRA: A Continual Learning Approach to Vision-Brain Understanding | [
"cs.CV"
] | Vision-Brain Understanding (VBU) aims to extract visual information perceived by humans from brain activity recorded through functional Magnetic Resonance Imaging (fMRI). Despite notable advancements in recent years, existing studies in VBU continue to face the challenge of catastrophic forgetting, where models lose kn... | {
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2411.17481 | Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval
and Grounding | [
"cs.CV"
] | Video Paragraph Grounding (VPG) aims to precisely locate the most appropriate moments within a video that are relevant to a given textual paragraph query. However, existing methods typically rely on large-scale annotated temporal labels and assume that the correspondence between videos and paragraphs is known. This is ... | {
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2411.17483 | Fast and Exact Similarity Search in less than a Blink of an Eye | [
"cs.DB"
] | Similarity search is a fundamental operation for analyzing data series (DS), which are ordered sequences of real values. To enhance efficiency, summarization techniques are employed that reduce the dimensionality of DS. SAX-based approaches are the state-of-the-art for exact similarity queries, but their performance de... | {
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2411.17485 | Storing overlapping associative memories on latent manifolds in low-rank
spiking networks | [
"q-bio.NC",
"cs.LG",
"cs.NE"
] | Associative memory architectures such as the Hopfield network have long been important conceptual and theoretical models for neuroscience and artificial intelligence. However, translating these abstract models into spiking neural networks has been surprisingly difficult. Indeed, much previous work has been restricted t... | {
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2411.17488 | Structure-Guided MR-to-CT Synthesis with Spatial and Semantic Alignments
for Attenuation Correction of Whole-Body PET/MR Imaging | [
"eess.IV",
"cs.CV"
] | Deep-learning-based MR-to-CT synthesis can estimate the electron density of tissues, thereby facilitating PET attenuation correction in whole-body PET/MR imaging. However, whole-body MR-to-CT synthesis faces several challenges including the issue of spatial misalignment and the complexity of intensity mapping, primaril... | {
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2411.17489 | Puzzle Similarity: A Perceptually-guided No-Reference Metric for
Artifact Detection in 3D Scene Reconstructions | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Modern reconstruction techniques can effectively model complex 3D scenes from sparse 2D views. However, automatically assessing the quality of novel views and identifying artifacts is challenging due to the lack of ground truth images and the limitations of no-reference image metrics in predicting detailed artifact map... | {
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2411.17490 | Learning Visual Hierarchies with Hyperbolic Embeddings | [
"cs.CV"
] | Structuring latent representations in a hierarchical manner enables models to learn patterns at multiple levels of abstraction. However, most prevalent image understanding models focus on visual similarity, and learning visual hierarchies is relatively unexplored. In this work, for the first time, we introduce a learni... | {
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2411.17491 | What's in the Image? A Deep-Dive into the Vision of Vision Language
Models | [
"cs.CV",
"cs.AI"
] | Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in comprehending complex visual content. However, the mechanisms underlying how VLMs process visual information remain largely unexplored. In this paper, we conduct a thorough empirical analysis, focusing on attention modules across layers... | {
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2411.17495 | A Machine Learning-based Anomaly Detection Framework in Life Insurance
Contracts | [
"stat.AP",
"cs.LG"
] | Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide coverage in case of an accident. Thus, trust in the integrity of the data stored in databases is crucial. One method to... | {
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2411.17499 | Time-Series Forecasting in Smart Manufacturing Systems: An Experimental
Evaluation of the State-of-the-art Algorithms | [
"cs.LG"
] | TSF is growing in various domains including manufacturing. Although numerous TSF algorithms have been developed recently, the validation and evaluation of algorithms hold substantial value for researchers and practitioners and are missing. This study aims to fill this gap by evaluating the SoTA TSF algorithms on thirte... | {
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2411.17501 | Inference Scaling fLaws: The Limits of LLM Resampling with Imperfect
Verifiers | [
"cs.LG",
"cs.AI"
] | Recent research has generated hope that inference scaling could allow weaker language models to match or exceed the accuracy of stronger models, such as by repeatedly sampling solutions to a coding problem until it passes unit tests. The central thesis of this paper is that there is no free lunch for inference scaling:... | {
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2411.17502 | Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel
Service Industry | [
"cs.LG"
] | This study develops a deep learning-based approach to automate inbound load plan adjustments for a large transportation and logistics company. It addresses a critical challenge for the efficient and resilient planning of E-commerce operations in presence of increasing uncertainties. The paper introduces an innovative d... | {
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2411.17505 | Resonant Inductive Coupling Power Transfer for Mid-Sized Inspection
Robot | [
"cs.RO"
] | This paper presents a wireless power transfer (WPT) for a mid-sized inspection mobile robot. The objective is to transmit 100 W of power over 1 meter of distance, achieved through lightweight Litz wire coils weighing 320 g held together with a coil structure of 3.54 kg. The Wireless Power Transfer System (WPTS) is moun... | {
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2411.17506 | Neural network modelling of kinematic and dynamic features for signature
verification | [
"cs.LG"
] | Online signature parameters, which are based on human characteristics, broaden the applicability of an automatic signature verifier. Although kinematic and dynamic features have previously been suggested, accurately measuring features such as arm and forearm torques remains challenging. We present two approaches for es... | {
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2411.17508 | Learning-Based On-Track System Identification for Scaled Autonomous
Racing in Under a Minute | [
"cs.RO"
] | Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as state-of-the-art (SotA) model-based techniques rely on precise knowledge of the vehicle's parameters. Yet, system identification in dynamic racing conditions is challenging due to varying track and tire conditions. Traditional methods requi... | {
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2411.17511 | Training Hamiltonian neural networks without backpropagation | [
"cs.LG",
"cs.NA",
"math.NA"
] | Neural networks that synergistically integrate data and physical laws offer great promise in modeling dynamical systems. However, iterative gradient-based optimization of network parameters is often computationally expensive and suffers from slow convergence. In this work, we present a backpropagation-free algorithm to... | {
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2411.17513 | Perceptually Optimized Super Resolution | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Modern deep-learning based super-resolution techniques process images and videos independently of the underlying content and viewing conditions. However, the sensitivity of the human visual system to image details changes depending on the underlying content characteristics, such as spatial frequency, luminance, color, ... | {
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2411.17515 | SuperMat: Physically Consistent PBR Material Estimation at Interactive
Rates | [
"cs.CV"
] | Decomposing physically-based materials from images into their constituent properties remains challenging, particularly when maintaining both computational efficiency and physical consistency. While recent diffusion-based approaches have shown promise, they face substantial computational overhead due to multiple denoisi... | {
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2411.17521 | BESTAnP: Bi-Step Efficient and Statistically Optimal Estimator for
Acoustic-n-Point Problem | [
"cs.RO"
] | We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial spherical coordinates and reveal their inherent relationships to translation and orientation. Based on this, we propose a... | {
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2411.17522 | On Statistical Rates of Conditional Diffusion Transformers:
Approximation, Estimation and Minimax Optimality | [
"stat.ML",
"cs.AI",
"cs.CV",
"cs.LG"
] | We investigate the approximation and estimation rates of conditional diffusion transformers (DiTs) with classifier-free guidance. We present a comprehensive analysis for ``in-context'' conditional DiTs under four common data assumptions. We show that both conditional DiTs and their latent variants lead to the minimax o... | {
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2411.17525 | Pushing the Limits of Large Language Model Quantization via the
Linearity Theorem | [
"cs.LG"
] | Quantizing large language models has become a standard way to reduce their memory and computational costs. Typically, existing methods focus on breaking down the problem into individual layer-wise sub-problems, and minimizing per-layer error, measured via various metrics. Yet, this approach currently lacks theoretical ... | {
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2411.17528 | Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from
Data Streams | [
"cs.LG"
] | Markov chains are simple yet powerful mathematical structures to model temporally dependent processes. They generally assume stationary data, i.e., fixed transition probabilities between observations/states. However, live, real-world processes, like in the context of activity tracking, biological time series, or indust... | {
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2411.17530 | HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for
Autonomous Driving | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | We present the updated version of the HSI-Drive dataset aimed at developing automated driving systems (ADS) using hyperspectral imaging (HSI). The v2.0 version includes new annotated images from videos recorded during winter and fall in real driving scenarios. Added to the spring and summer images included in the previ... | {
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2411.17532 | FTMoMamba: Motion Generation with Frequency and Text State Space Models | [
"cs.CV"
] | Diffusion models achieve impressive performance in human motion generation. However, current approaches typically ignore the significance of frequency-domain information in capturing fine-grained motions within the latent space (e.g., low frequencies correlate with static poses, and high frequencies align with fine-gra... | {
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2411.17534 | Dynamic Trajectory Adaptation for Efficient UAV Inspections of Wind
Energy Units | [
"cs.RO",
"cs.SY",
"eess.SY"
] | The research presents an automated method for determining the trajectory of an unmanned aerial vehicle (UAV) for wind turbine inspection. The proposed method enables efficient data collection from multiple wind installations using UAV optical sensors, considering the spatial positioning of blades and other components o... | {
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2411.17535 | IMPROVE: Improving Medical Plausibility without Reliance on
HumanValidation -- An Enhanced Prototype-Guided Diffusion Framework | [
"cs.CV"
] | Generative models have proven to be very effective in generating synthetic medical images and find applications in downstream tasks such as enhancing rare disease datasets, long-tailed dataset augmentation, and scaling machine learning algorithms. For medical applications, the synthetically generated medical images by ... | {
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2411.17536 | Box for Mask and Mask for Box: weak losses for multi-task partially
supervised learning | [
"cs.CV"
] | Object detection and semantic segmentation are both scene understanding tasks yet they differ in data structure and information level. Object detection requires box coordinates for object instances while semantic segmentation requires pixel-wise class labels. Making use of one task's information to train the other woul... | {
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2411.17537 | Towards Maximum Likelihood Training for Transducer-based Streaming
Speech Recognition | [
"eess.AS",
"cs.LG"
] | Transducer neural networks have emerged as the mainstream approach for streaming automatic speech recognition (ASR), offering state-of-the-art performance in balancing accuracy and latency. In the conventional framework, streaming transducer models are trained to maximize the likelihood function based on non-streaming ... | {
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} |
2411.17538 | Isotropy Matters: Soft-ZCA Whitening of Embeddings for Semantic Code
Search | [
"cs.CL"
] | Low isotropy in an embedding space impairs performance on tasks involving semantic inference. Our study investigates the impact of isotropy on semantic code search performance and explores post-processing techniques to mitigate this issue. We analyze various code language models, examine isotropy in their embedding spa... | {
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} |
2411.17539 | AI-Augmented Ethical Hacking: A Practical Examination of Manual
Exploitation and Privilege Escalation in Linux Environments | [
"cs.CR",
"cs.AI",
"cs.NI"
] | This study explores the application of generative AI (GenAI) within manual exploitation and privilege escalation tasks in Linux-based penetration testing environments, two areas critical to comprehensive cybersecurity assessments. Building on previous research into the role of GenAI in the ethical hacking lifecycle, th... | {
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} |
2411.17543 | Rapid Deployment of Domain-specific Hyperspectral Image Processors with
Application to Autonomous Driving | [
"cs.CV",
"cs.AI",
"cs.AR",
"cs.LG",
"eess.IV"
] | The article discusses the use of low cost System-On-Module (SOM) platforms for the implementation of efficient hyperspectral imaging (HSI) processors for application in autonomous driving. The work addresses the challenges of shaping and deploying multiple layer fully convolutional networks (FCN) for low-latency, on-bo... | {
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} |
2411.17552 | Ensuring Safety in Target Pursuit Control: A CBF-Safe Reinforcement
Learning Approach | [
"eess.SY",
"cs.SY"
] | This paper addresses the target-pursuit problem, aiming to ensure each pursuer's safety regarding collision avoidance, sensing range, and input saturation. An input-constrained CBF is proposed to dynamically regulate the pursuer's control, ensuring effective target pursuit even when the target performs evasive maneuver... | {
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} |
2411.17554 | Navigating Spatial Inequities in Freight Truck Crash Severity via
Counterfactual Inference in Los Angeles | [
"cs.LG"
] | Freight truck-related crashes pose significant challenges, leading to substantial economic losses, injuries, and fatalities, with pronounced spatial disparities across different regions. This study adopts a transport geography perspective to examine spatial justice concerns by employing deep counterfactual inference mo... | {
"Other": 0,
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} |
2411.17555 | Multiscale spatiotemporal heterogeneity analysis of bike-sharing
system's self-loop phenomenon: Evidence from Shanghai | [
"cs.LG",
"cs.CY"
] | Bike-sharing is an environmentally friendly shared mobility mode, but its self-loop phenomenon, where bikes are returned to the same station after several time usage, significantly impacts equity in accessing its services. Therefore, this study conducts a multiscale analysis with a spatial autoregressive model and doub... | {
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} |
2411.17556 | TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer
Attention and Focal Modulation | [
"eess.IV",
"cs.CV"
] | Incorporating modern computer vision techniques into clinical protocols shows promise in improving skin lesion segmentation. The U-Net architecture has been a key model in this area, iteratively improved to address challenges arising from the heterogeneity of dermatologic images due to varying clinical settings, lighti... | {
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} |
2411.17557 | A Bilayer Segmentation-Recombination Network for Accurate Segmentation
of Overlapping C. elegans | [
"cs.CV",
"cs.AI"
] | Caenorhabditis elegans (C. elegans) is an excellent model organism because of its short lifespan and high degree of homology with human genes, and it has been widely used in a variety of human health and disease models. However, the segmentation of C. elegans remains challenging due to the following reasons: 1) the act... | {
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} |
2411.17558 | Natural Language Understanding and Inference with MLLM in Visual
Question Answering: A Survey | [
"cs.CL",
"cs.CV"
] | Visual Question Answering (VQA) is a challenge task that combines natural language processing and computer vision techniques and gradually becomes a benchmark test task in multimodal large language models (MLLMs). The goal of our survey is to provide an overview of the development of VQA and a detailed description of t... | {
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} |
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