id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.06181 | Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with
Epipolar Consistency Conditions | [
"eess.IV",
"cs.CV"
] | Neural field methods, initially successful in the inverse rendering domain, have recently been extended to CT reconstruction, marking a paradigm shift from traditional techniques. While these approaches deliver state-of-the-art results in sparse-view CT reconstruction, they struggle in limited-angle settings, where inp... | {
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2411.06182 | IDF-MFL: Infrastructure-free and Drift-free Magnetic Field Localization
for Mobile Robot | [
"cs.RO"
] | In recent years, infrastructure-based localization methods have achieved significant progress thanks to their reliable and drift-free localization capability. However, the pre-installed infrastructures suffer from inflexibilities and high maintenance costs. This poses an interesting problem of how to develop a drift-fr... | {
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2411.06183 | Sampling-Based Model Predictive Control for Dexterous Manipulation on a
Biomimetic Tendon-Driven Hand | [
"cs.RO"
] | Biomimetic and compliant robotic hands offer the potential for human-like dexterity, but controlling them is challenging due to high dimensionality, complex contact interactions, and uncertainties in state estimation. Sampling-based model predictive control (MPC), using a physics simulator as the dynamics model, is a p... | {
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2411.06184 | Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for
Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization | [
"eess.IV",
"cs.CV",
"cs.LG",
"stat.ML"
] | In the field of non-invasive medical imaging, radiomic features are utilized to measure tumor characteristics. However, these features can be affected by the techniques used to discretize the images, ultimately impacting the accuracy of diagnosis. To investigate the influence of various image discretization methods on ... | {
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2411.06191 | Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph
Modeling | [
"cs.AI",
"cs.LG"
] | By representing knowledge in a primary triple associated with additional attribute-value qualifiers, hyper-relational knowledge graph (HKG) that generalizes triple-based knowledge graph (KG) has been attracting research attention recently. Compared with KG, HKG is enriched with the semantic qualifiers as well as the hy... | {
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2411.06193 | Large Language Models and Artificial Intelligence Generated Content
Technologies Meet Communication Networks | [
"cs.IT",
"eess.SP",
"math.IT"
] | Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted great interests from both academia and industry. Although some noteworthy advancements have been made in... | {
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2411.06194 | WMT24 Test Suite: Gender Resolution in Speaker-Listener Dialogue Roles | [
"cs.CL"
] | We assess the difficulty of gender resolution in literary-style dialogue settings and the influence of gender stereotypes. Instances of the test suite contain spoken dialogue interleaved with external meta-context about the characters and the manner of speaking. We find that character and manner stereotypes outside of ... | {
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2411.06197 | Multi-object Tracking by Detection and Query: an efficient end-to-end
manner | [
"cs.CV"
] | Multi-object tracking is advancing through two dominant paradigms: traditional tracking by detection and newly emerging tracking by query. In this work, we fuse them together and propose the tracking-by-detection-and-query paradigm, which is achieved by a Learnable Associator. Specifically, the basic information intera... | {
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2411.06198 | OpenAI-o1 AB Testing: Does the o1 model really do good reasoning in math
problem solving? | [
"cs.AI"
] | The Orion-1 model by OpenAI is claimed to have more robust logical reasoning capabilities than previous large language models. However, some suggest the excellence might be partially due to the model "memorizing" solutions, resulting in less satisfactory performance when prompted with problems not in the training data.... | {
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2411.06200 | Weak to Strong Learning from Aggregate Labels | [
"cs.LG",
"cs.DS",
"stat.ML"
] | In learning from aggregate labels, the training data consists of sets or "bags" of feature-vectors (instances) along with an aggregate label for each bag derived from the (usually {0,1}-valued) labels of its instances. In learning from label proportions (LLP), the aggregate label is the average of the bag's instance la... | {
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2411.06202 | Advanced Wildfire Prediction in Morocco: Developing a Deep Learning
Dataset from Multisource Observations | [
"cs.LG"
] | Wildfires pose significant threats to ecosystems, economies, and communities worldwide, necessitating advanced predictive methods for effective mitigation. This study introduces a novel and comprehensive dataset specifically designed for wildfire prediction in Morocco, addressing its unique geographical and climatic ch... | {
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2411.06204 | Why has advanced commercial HVAC control not yet achieved its promise? | [
"eess.SY",
"cs.SY"
] | Over the last two decades, research and development efforts have shown that advanced control of heating, ventilation, and air conditioning (HVAC) equipment in commercial buildings can improve energy efficiency, reduce emissions, and turn buildings into active participants in the power grid. Despite these efforts, advan... | {
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2411.06206 | Text2CAD: Text to 3D CAD Generation via Technical Drawings | [
"cs.CV"
] | The generation of industrial Computer-Aided Design (CAD) models from user requests and specifications is crucial to enhancing efficiency in modern manufacturing. Traditional methods of CAD generation rely heavily on manual inputs and struggle with complex or non-standard designs, making them less suited for dynamic ind... | {
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2411.06207 | Exploring Knowledge Boundaries in Large Language Models for Retrieval
Judgment | [
"cs.CL"
] | Large Language Models (LLMs) are increasingly recognized for their practical applications. However, these models often encounter challenges in dynamically changing knowledge, as well as in managing unknown static knowledge. Retrieval-Augmented Generation (RAG) tackles this challenge and has shown a significant impact o... | {
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2411.06208 | IOPO: Empowering LLMs with Complex Instruction Following via
Input-Output Preference Optimization | [
"cs.CL",
"cs.AI"
] | In the realm of large language models (LLMs), the ability of models to accurately follow instructions is paramount as more agents and applications leverage LLMs for construction, where the complexity of instructions are rapidly increasing. However, on the one hand, there is only a certain amount of complex instruction ... | {
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2411.06211 | Artificial Intelligence for Collective Intelligence: A National-Scale
Research Strategy | [
"cs.AI",
"cs.CY"
] | Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by levera... | {
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2411.06212 | Multistage non-deterministic classification using secondary concept
graphs and graph convolutional networks for high-level feature extraction | [
"cs.LG",
"cs.AI"
] | Graphs, comprising nodes and edges, visually depict relationships and structures, posing challenges in extracting high-level features due to their intricate connections. Multiple connections introduce complexities in discovering patterns, where node weights may affect some features more than others. In domains with div... | {
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2411.06213 | Incorporating Human Explanations for Robust Hate Speech Detection | [
"cs.CL"
] | Given the black-box nature and complexity of large transformer language models (LM), concerns about generalizability and robustness present ethical implications for domains such as hate speech (HS) detection. Using the content rich Social Bias Frames dataset, containing human-annotated stereotypes, intent, and targeted... | {
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2411.06214 | Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model | [
"cs.LG",
"eess.SP"
] | Natural gas pipeline leaks pose severe risks, leading to substantial economic losses and potential hazards to human safety. In this study, we develop an accurate model for the early prediction of pipeline leaks. To the best of our knowledge, unlike previous anomaly detection, this is the first application to use intern... | {
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2411.06219 | RRT* Based Optimal Trajectory Generation with Linear Temporal Logic
Specifications under Kinodynamic Constraints | [
"eess.SY",
"cs.RO",
"cs.SY"
] | In this paper, we present a novel RRT*-based strategy for generating kinodynamically feasible paths that satisfy temporal logic specifications. Our approach integrates a robustness metric for Linear Temporal Logics (LTL) with the system's motion constraints, ensuring that the resulting trajectories are both optimal and... | {
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2411.06221 | Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart
Contract Vulnerability Detection and Explanation | [
"cs.CR",
"cs.AI",
"cs.SE"
] | With the rapid development of blockchain technology, smart contract security has become a critical challenge. Existing smart contract vulnerability detection methods face three main issues: (1) Insufficient quality of datasets, lacking detailed explanations and precise vulnerability locations. (2) Limited adaptability ... | {
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2411.06223 | Predictability Awareness for Efficient and Robust Multi-Agent
Coordination | [
"cs.RO"
] | To safely and efficiently solve motion planning problems in multi-agent settings, most approaches attempt to solve a joint optimization that explicitly accounts for the responses triggered in other agents. This often results in solutions with an exponential computational complexity, making these methods intractable for... | {
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2411.06228 | An $\mathbf{L^*}$ Algorithm for Deterministic Weighted Regular Languages | [
"cs.CL"
] | Extracting finite state automata (FSAs) from black-box models offers a powerful approach to gaining interpretable insights into complex model behaviors. To support this pursuit, we present a weighted variant of Angluin's (1987) $\mathbf{L^*}$ algorithm for learning FSAs. We stay faithful to the original algorithm, devi... | {
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2411.06229 | Multimodal Contrastive Learning of Urban Space Representations from POI
Data | [
"cs.AI"
] | Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spatial information modelling, underutilisation of POI semantic attributes, and computational inefficiencies. To address these issues, we propo... | {
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2411.06232 | Crowd3D++: Robust Monocular Crowd Reconstruction with Upright Space | [
"cs.CV"
] | This paper aims to reconstruct hundreds of people's 3D poses, shapes, and locations from a single image with unknown camera parameters. Due to the small and highly varying 2D human scales, depth ambiguity, and perspective distortion, no existing methods can achieve globally consistent reconstruction and accurate reproj... | {
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2411.06236 | Zero-Shot NAS via the Suppression of Local Entropy Decrease | [
"cs.LG",
"cs.CV",
"cs.NE"
] | Architecture performance evaluation is the most time-consuming part of neural architecture search (NAS). Zero-Shot NAS accelerates the evaluation by utilizing zero-cost proxies instead of training. Though effective, existing zero-cost proxies require invoking backpropagations or running networks on input data, making i... | {
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2411.06237 | Leveraging Retrieval-Augmented Generation for Persian University
Knowledge Retrieval | [
"cs.IR",
"cs.LG"
] | This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) to enhance information retrieval and query response systems for university-related question answering. By systematically extracting data from the university official webpage and employing ... | {
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2411.06239 | Web Scale Graph Mining for Cyber Threat Intelligence | [
"cs.CR",
"cs.LG",
"cs.SI"
] | Defending against today's increasingly sophisticated and large-scale cyberattacks demands accurate, real-time threat intelligence. Traditional approaches struggle to scale, integrate diverse telemetry, and adapt to a constantly evolving security landscape. We introduce Threat Intelligence Tracking via Adaptive Networks... | {
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2411.06241 | Theoretical Analysis of Learned Database Operations under Distribution
Shift through Distribution Learnability | [
"cs.LG",
"cs.DB"
] | Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits. However, when datasets change and data distribution shifts, empirical results also show performance degradation for learned models, possibly to worse than ... | {
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2411.06243 | Towards Establishing Guaranteed Error for Learned Database Operations | [
"cs.DB",
"cs.LG"
] | Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (locating items in an array), cardinality estimation (estimating the number of matching records in a database), and range-sum estimation (est... | {
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2411.06244 | Grasping Object: Challenges and Innovations in Robotics and Virtual
Reality | [
"cs.HC",
"cs.GR",
"cs.RO"
] | In real life, grasping is one of the fundamental and effective forms of interaction when manipulating objects. This holds true in the physical and virtual world; however, unlike the physical world, virtual reality (VR) is grasped in a complex formulation that includes graphics, physics, and perception. In virtual reali... | {
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2411.06248 | Robust Detection of LLM-Generated Text: A Comparative Analysis | [
"cs.CL"
] | The ability of large language models to generate complex texts allows them to be widely integrated into many aspects of life, and their output can quickly fill all network resources. As the impact of LLMs grows, it becomes increasingly important to develop powerful detectors for the generated text. This detector is ess... | {
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2411.06251 | Quasi-random Multi-Sample Inference for Large Language Models | [
"cs.AI"
] | Large language models (LLMs) are often equipped with multi-sample decoding strategies. An LLM implicitly defines an arithmetic code book, facilitating efficient and embarrassingly parallelizable \textbf{arithmetic sampling} to produce multiple samples using quasi-random codes. Traditional text generation methods, such ... | {
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2411.06253 | Knowledge Authoring with Factual English, Rules, and Actions | [
"cs.AI"
] | Knowledge representation and reasoning systems represent knowledge as collections of facts and rules. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite know... | {
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2411.06254 | KeyB2: Selecting Key Blocks is Also Important for Long Document Ranking
with Large Language Models | [
"cs.IR"
] | The rapid development of large language models (LLMs) like Llama has significantly advanced information retrieval (IR) systems. However, using LLMs for long documents, as in RankLLaMA, remains challenging due to computational complexity, especially concerning input token length. Furthermore, the internal mechanisms of ... | {
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2411.06256 | Annotative Indexing | [
"cs.IR"
] | This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the underlying indexing framework for databases that support knowledge graphs, entity retrieval, semi-st... | {
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2411.06263 | Federated Split Learning for Human Activity Recognition with
Differential Privacy | [
"cs.LG",
"cs.AI",
"cs.CR"
] | This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split Learning (FSL) with Differential Privacy (DP) over edge networks. Our FSL-DP framework leverages both accelerometer and gyroscope data, achieving significant improvements in HAR accuracy. The eval... | {
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2411.06264 | GuidelineGuard: An Agentic Framework for Medical Note Evaluation with
Guideline Adherence | [
"cs.AI",
"cs.IR"
] | Although rapid advancements in Large Language Models (LLMs) are facilitating the integration of artificial intelligence-based applications and services in healthcare, limited research has focused on the systematic evaluation of medical notes for guideline adherence. This paper introduces GuidelineGuard, an agentic fram... | {
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2411.06268 | Constraints and Variables Reduction for Optimal Power Flow Using
Hierarchical Graph Neural Networks with Virtual Node-Splitting | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Power system networks are often modeled as homogeneous graphs, which limits the ability of graph neural network (GNN) to capture individual generator features at the same nodes. By introducing the proposed virtual node-splitting strategy, generator-level attributes like costs, limits, and ramp rates can be fully captur... | {
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2411.06269 | AI's Spatial Intelligence: Evaluating AI's Understanding of Spatial
Transformations in PSVT:R and Augmented Reality | [
"cs.AI"
] | Spatial intelligence is important in Architecture, Construction, Science, Technology, Engineering, and Mathematics (STEM), and Medicine. Understanding three-dimensional (3D) spatial rotations can involve verbal descriptions and visual or interactive examples, illustrating how objects change orientation in 3D space. Rec... | {
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2411.06272 | Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating
Financial Large Language Models | [
"cs.CL",
"cs.CE"
] | As large language models become increasingly prevalent in the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. However, existing finance benchmarks often suffer from limited language and task coverage, as well as challenges such as low-quality datasets an... | {
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2411.06276 | Multi-View Majority Vote Learning Algorithms: Direct Minimization of
PAC-Bayesian Bounds | [
"cs.LG",
"cs.AI",
"stat.ML"
] | The PAC-Bayesian framework has significantly advanced the understanding of statistical learning, particularly for majority voting methods. Despite its successes, its application to multi-view learning -- a setting with multiple complementary data representations -- remains underexplored. In this work, we extend PAC-Bay... | {
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2411.06278 | A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural
Network Training on Solving Partial Differential Equations | [
"math.NA",
"cs.LG",
"cs.NA",
"math.OC"
] | We propose a scalable preconditioned primal-dual hybrid gradient algorithm for solving partial differential equations (PDEs). We multiply the PDE with a dual test function to obtain an inf-sup problem whose loss functional involves lower-order differential operators. The Primal-Dual Hybrid Gradient (PDHG) algorithm is ... | {
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2411.06284 | A Comprehensive Survey and Guide to Multimodal Large Language Models in
Vision-Language Tasks | [
"cs.AI"
] | This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, ima... | {
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2411.06286 | SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks | [
"cs.LG",
"cs.NA",
"math.NA"
] | Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use multilayer perceptrons (MLPs) as their underlying architecture, recent advancements have explored alternative neural network structures. One su... | {
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2411.06287 | Hidden in Plain Sight: Evaluating Abstract Shape Recognition in
Vision-Language Models | [
"cs.CV"
] | Despite the importance of shape perception in human vision, early neural image classifiers relied less on shape information for object recognition than other (often spurious) features. While recent research suggests that current large Vision-Language Models (VLMs) exhibit more reliance on shape, we find them to still b... | {
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2411.06288 | Smooth Zone Barrier Lyapunov Functions for Nonlinear Constrained Control
Systems | [
"eess.SY",
"cs.SY"
] | This paper introduces the Smooth Zone Barrier Lyapunov Function (s-ZBLF) for output and full-state constrained nonlinear control systems. Unlike traditional BLF methods, where control effort continuously increases as the state moves toward the constraint boundaries, the s-ZBLF method keeps the control effort nearly zer... | {
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2411.06291 | TinyML NLP Approach for Semantic Wireless Sentiment Classification | [
"cs.LG",
"cs.CR",
"cs.IT",
"math.IT"
] | Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, may often impair users' privacy and demand significant on device computational resources. Centralized learning (CL) on the edge offers an alternative energy-efficient approach, yet requires the collection of raw inform... | {
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2411.06294 | Hierarchical Performance-Based Design Optimization Framework for Soft
Grippers | [
"cs.RO"
] | This paper presents a hierarchical, performance-based framework for the design optimization of multi-fingered soft grippers. To address the need for systematically defined performance indices, the framework structures the optimization process into three integrated layers: Task Space, Motion Space, and Design Space. In ... | {
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2411.06295 | Analyzing the Evolution of Graphs and Texts | [
"cs.SI",
"cs.AI"
] | With the recent advance of representation learning algorithms on graphs (e.g., DeepWalk/GraphSage) and natural languages (e.g., Word2Vec/BERT) , the state-of-the art models can even achieve human-level performance over many downstream tasks, particularly for the task of node and sentence classification. However, most a... | {
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2411.06297 | Adaptive Aspect Ratios with Patch-Mixup-ViT-based Vehicle ReID | [
"cs.CV"
] | Vision Transformers (ViTs) have shown exceptional performance in vehicle re-identification (ReID) tasks. However, non-square aspect ratios of image or video inputs can negatively impact re-identification accuracy. To address this challenge, we propose a novel, human perception driven, and general ViT-based ReID framewo... | {
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2411.06299 | Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low
Bit-Depth Signals | [
"cs.LG",
"cs.SD",
"eess.AS",
"eess.SP"
] | This study focuses on Intelligent Fault Diagnosis (IFD) in rotating machinery utilizing a single microphone and a data-driven methodology, effectively diagnosing 42 classes of fault types and severities. The research leverages sound data from the imbalanced MaFaulDa dataset, aiming to strike a balance between high perf... | {
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2411.06303 | TiniScript: A Simplified Language for Educational Robotics | [
"cs.RO"
] | TiniScript is an intermediate programming language designed for educational robotics, aligned with STEM principles to foster integrative learning experiences. With its minimalist single-line syntax, such as F(2, 80) , TiniScript simplifies robotic programming, allowing users to bypass complex code uploading processes a... | {
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2411.06306 | Optimal Driver Warning Generation in Dynamic Driving Environment | [
"cs.RO",
"cs.AI",
"cs.HC"
] | The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning technologies, mainly the forward collision warning and unsafe lane change warning, can reduce the risk of collision caused by human errors. Howeve... | {
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2411.06308 | Exploring Out-of-distribution Detection for Sparse-view Computed
Tomography with Diffusion Models | [
"eess.IV",
"cs.CV"
] | Recent works demonstrate the effectiveness of diffusion models as unsupervised solvers for inverse imaging problems. Sparse-view computed tomography (CT) has greatly benefited from these advancements, achieving improved generalization without reliance on measurement parameters. However, this comes at the cost of potent... | {
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2411.06309 | Physics-Compliant Modeling and Scaling Laws of Multi-RIS Aided MIMO
Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | Reconfigurable intelligent surface (RIS) enables the control of wireless channels to improve coverage. To further extend coverage, multi-RIS aided systems have been explored, where multiple RISs steer the signal via a multi-hop path. However, deriving a physics-compliant channel model for multi-RIS aided systems is sti... | {
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2411.06311 | When are dynamical systems learned from time series data statistically
accurate? | [
"cs.LG",
"math-ph",
"math.DS",
"math.MP",
"math.ST",
"stat.TH"
] | Conventional notions of generalization often fail to describe the ability of learned models to capture meaningful information from dynamical data. A neural network that learns complex dynamics with a small test error may still fail to reproduce its \emph{physical} behavior, including associated statistical moments and ... | {
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2411.06315 | NeuReg: Domain-invariant 3D Image Registration on Human and Mouse Brains | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.NE",
"q-bio.QM"
] | Medical brain imaging relies heavily on image registration to accurately curate structural boundaries of brain features for various healthcare applications. Deep learning models have shown remarkable performance in image registration in recent years. Still, they often struggle to handle the diversity of 3D brain volume... | {
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2411.06316 | Prompts Matter: Comparing ML/GAI Approaches for Generating Inductive
Qualitative Coding Results | [
"cs.CL",
"cs.AI",
"cs.HC"
] | Inductive qualitative methods have been a mainstay of education research for decades, yet it takes much time and effort to conduct rigorously. Recent advances in artificial intelligence, particularly with generative AI (GAI), have led to initial success in generating inductive coding results. Like human coders, GAI too... | {
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2411.06317 | Harpocrates: A Statically Typed Privacy Conscious Programming Framework | [
"cs.CR",
"cs.SY",
"eess.SY"
] | In this paper, we introduce Harpocrates, a compiler plugin and a framework pair for Scala that binds the privacy policies to the data during data creation in form of oblivious membranes. Harpocrates eliminates raw data for a policy protected type from the application, ensuring it can only exist in protected form and ce... | {
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2411.06318 | SEM-Net: Efficient Pixel Modelling for image inpainting with Spatially
Enhanced SSM | [
"cs.CV"
] | Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. \revise{Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhibit similar patterns to the semanticly consistent regions}. Th... | {
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2411.06319 | Impact-Aware Robotic Manipulation: Quantifying the Sim-To-Real Gap for
Velocity Jumps | [
"cs.RO"
] | Impact-aware robotic manipulation benefits from an accurate map from ante-impact to post-impact velocity signals to support, e.g., motion planning and control. This work proposes an approach to generate and experimentally validate such impact maps from simulations with a physics engine, allowing to model impact scenari... | {
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2411.06320 | Self-Body Image Acquisition and Posture Generation with Redundancy using
Musculoskeletal Humanoid Shoulder Complex for Object Manipulation | [
"cs.RO"
] | We proposed a method for learning the actual body image of a musculoskeletal humanoid for posture generation and object manipulation using inverse kinematics with redundancy in the shoulder complex. The effectiveness of this method was confirmed by realizing automobile steering wheel operation. The shoulder complex has... | {
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2411.06322 | Adaptive Body Schema Learning System Considering Additional Muscles for
Musculoskeletal Humanoids | [
"cs.RO"
] | One of the important advantages of musculoskeletal humanoids is that the muscle arrangement can be easily changed and the number of muscles can be increased according to the situation. In this study, we describe an overall system of muscle addition for musculoskeletal humanoids and the adaptive body schema learning whi... | {
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2411.06323 | Motion Modification Method of Musculoskeletal Humanoids by Human
Teaching Using Muscle-Based Compensation Control | [
"cs.RO"
] | While musculoskeletal humanoids have the advantages of various biomimetic structures, it is difficult to accurately control the body, which is challenging to model. Although various learning-based control methods have been developed so far, they cannot completely absorb model errors, and recognition errors are also bou... | {
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2411.06324 | Amortized Bayesian Local Interpolation NetworK: Fast covariance
parameter estimation for Gaussian Processes | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Gaussian processes (GPs) are a ubiquitous tool for geostatistical modeling with high levels of flexibility and interpretability, and the ability to make predictions at unseen spatial locations through a process called Kriging. Estimation of Kriging weights relies on the inversion of the process' covariance matrix, crea... | {
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2411.06326 | Emotion-Aware Interaction Design in Intelligent User Interface Using
Multi-Modal Deep Learning | [
"cs.HC",
"cs.LG"
] | In an era where user interaction with technology is ubiquitous, the importance of user interface (UI) design cannot be overstated. A well-designed UI not only enhances usability but also fosters more natural, intuitive, and emotionally engaging experiences, making technology more accessible and impactful in everyday li... | {
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2411.06329 | Regret Minimization and Statistical Inference in Online Decision Making
with High-dimensional Covariates | [
"cs.LG",
"stat.ML"
] | This paper investigates regret minimization, statistical inference, and their interplay in high-dimensional online decision-making based on the sparse linear context bandit model. We integrate the $\varepsilon$-greedy bandit algorithm for decision-making with a hard thresholding algorithm for estimating sparse bandit p... | {
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2411.06333 | A Learned Proximal Alternating Minimization Algorithm and Its Induced
Network for a Class of Two-block Nonconvex and Nonsmooth Optimization | [
"math.OC",
"cs.LG"
] | This work proposes a general learned proximal alternating minimization algorithm, LPAM, for solving learnable two-block nonsmooth and nonconvex optimization problems. We tackle the nonsmoothness by an appropriate smoothing technique with automatic diminishing smoothing effect. For smoothed nonconvex problems we modify ... | {
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2411.06336 | Balancing Power and Ethics: A Framework for Addressing Human Rights
Concerns in Military AI | [
"cs.CY",
"cs.AI",
"cs.CE",
"cs.HC",
"cs.LG"
] | AI has made significant strides recently, leading to various applications in both civilian and military sectors. The military sees AI as a solution for developing more effective and faster technologies. While AI offers benefits like improved operational efficiency and precision targeting, it also raises serious ethical... | {
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2411.06338 | CRTRE: Causal Rule Generation with Target Trial Emulation Framework | [
"cs.LG"
] | Causal inference and model interpretability are gaining increasing attention, particularly in the biomedical domain. Despite recent advance, decorrelating features in nonlinear environments with human-interpretable representations remains underexplored. In this study, we introduce a novel method called causal rule gene... | {
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2411.06339 | Probabilistic Shaped Multilevel Polar Coding for Wiretap Channel | [
"cs.IT",
"math.IT"
] | A wiretap channel is served as the fundamental model of physical layer security techniques, where the secrecy capacity of the Gaussian wiretap channel is proven to be achieved by Gaussian input. However, there remains a gap between the Gaussian secrecy capacity and the secrecy rate with conventional uniformly distribut... | {
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2411.06343 | A novel algorithm for optimizing bundle adjustment in image sequence
alignment | [
"math.OC",
"cs.CV"
] | The Bundle Adjustment (BA) model is commonly optimized using a nonlinear least squares method, with the Levenberg-Marquardt (L-M) algorithm being a typical choice. However, despite the L-M algorithm's effectiveness, its sensitivity to initial conditions often results in slower convergence when applied to poorly conditi... | {
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2411.06344 | CityGuessr: City-Level Video Geo-Localization on a Global Scale | [
"cs.CV"
] | Video geolocalization is a crucial problem in current times. Given just a video, ascertaining where it was captured from can have a plethora of advantages. The problem of worldwide geolocalization has been tackled before, but only using the image modality. Its video counterpart remains relatively unexplored. Meanwhile,... | {
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2411.06346 | Activation Map Compression through Tensor Decomposition for Deep
Learning | [
"cs.LG",
"cs.CV"
] | Internet of Things and Deep Learning are synergetically and exponentially growing industrial fields with a massive call for their unification into a common framework called Edge AI. While on-device inference is a well-explored topic in recent research, backpropagation remains an open challenge due to its prohibitive co... | {
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2411.06347 | Classification in Japanese Sign Language Based on Dynamic Facial
Expressions | [
"cs.CV"
] | Sign language is a visual language expressed through hand movements and non-manual markers. Non-manual markers include facial expressions and head movements. These expressions vary across different nations. Therefore, specialized analysis methods for each sign language are necessary. However, research on Japanese Sign ... | {
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2411.06352 | Client Contribution Normalization for Enhanced Federated Learning | [
"cs.LG"
] | Mobile devices, including smartphones and laptops, generate decentralized and heterogeneous data, presenting significant challenges for traditional centralized machine learning models due to substantial communication costs and privacy risks. Federated Learning (FL) offers a promising alternative by enabling collaborati... | {
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2411.06353 | Deep Active Learning in the Open World | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Machine learning models deployed in open-world scenarios often encounter unfamiliar conditions and perform poorly in unanticipated situations. As AI systems advance and find application in safety-critical domains, effectively handling out-of-distribution (OOD) data is crucial to building open-world learning systems. In... | {
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2411.06360 | An Efficient Matrix Multiplication Algorithm for Accelerating Inference
in Binary and Ternary Neural Networks | [
"cs.LG",
"cs.DS"
] | Despite their tremendous success and versatility, Large Language Models (LLMs) suffer from inference inefficiency while relying on advanced computational infrastructure. To address these challenges and make LLMs more accessible and cost-effective, in this paper, we propose algorithms to improve the inference time and m... | {
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2411.06363 | Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot
Learning | [
"cs.CV",
"cs.AI"
] | In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity. However, in natural scenes, the spatial arrangement of key instances is often inconsistent across images. This spatial misalignment can result in mismatched semantic pixels, leading to inaccurate similari... | {
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2411.06365 | Through the Curved Cover: Synthesizing Cover Aberrated Scenes with
Refractive Field | [
"cs.CV"
] | Recent extended reality headsets and field robots have adopted covers to protect the front-facing cameras from environmental hazards and falls. The surface irregularities on the cover can lead to optical aberrations like blurring and non-parametric distortions. Novel view synthesis methods like NeRF and 3D Gaussian Spl... | {
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2411.06367 | BayesNAM: Leveraging Inconsistency for Reliable Explanations | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Neural additive model (NAM) is a recently proposed explainable artificial intelligence (XAI) method that utilizes neural network-based architectures. Given the advantages of neural networks, NAMs provide intuitive explanations for their predictions with high model performance. In this paper, we analyze a critical yet o... | {
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2411.06371 | LLM Vocabulary Compression for Low-Compute Environments | [
"cs.CL",
"cs.LG"
] | We present a method to compress the final linear layer of language models, reducing memory usage by up to 3.4x without significant performance loss. By grouping tokens based on Byte Pair Encoding (BPE) merges, we prevent materialization of the memory-intensive logits tensor. Evaluations on the TinyStories dataset show ... | {
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2411.06374 | Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold
Start Issues | [
"cs.IR",
"cs.LG"
] | With the rapid growth of digital information, personalized recommendation systems have become an indispensable part of Internet services, especially in the fields of e-commerce, social media, and online entertainment. However, traditional collaborative filtering and content-based recommendation methods have limitations... | {
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2411.06376 | Project Tracyn: Generative Artificial Intelligence based Peripherals
Trace Synthesizer | [
"cs.LG",
"cs.AI",
"cs.AR"
] | Peripheral Component Interconnect Express (PCIe) is the de facto interconnect standard for high-speed peripherals and CPUs. Prototyping and optimizing PCIe devices for emerging scenarios is an ongoing challenge. Since Transaction Layer Packets (TLPs) capture device-CPU interactions, it is crucial to analyze and generat... | {
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2411.06377 | SymmeTac: Symmetric Color LED Driven Efficient Photometric Stereo
Reconstruction Methods for Camera-based Tactile Sensors | [
"cs.RO"
] | Camera-based tactile sensors can provide high-density surface geometry and force information for robots in the interaction process with the target. However, most existing methods cannot achieve accurate reconstruction with high efficiency, impeding the applications in robots. To address these problems, we propose an ef... | {
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2411.06378 | PKF: Probabilistic Data Association Kalman Filter for Multi-Object
Tracking | [
"cs.CV"
] | In this paper, we derive a new Kalman filter with probabilistic data association between measurements and states. We formulate a variational inference problem to approximate the posterior density of the state conditioned on the measurement data. We view the unknown data association as a latent variable and apply Expect... | {
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2411.06380 | Stability Analysis of Distributed Estimators for Large-Scale
Interconnected Systems: Time-Varying and Time-Invariant Cases | [
"eess.SY",
"cs.SY"
] | This paper studies a distributed estimation problem for time-varying/time-invariant large-scale interconnected systems (LISs). A fully distributed estimator is presented by recursively solving a distributed modified Riccati equation (DMRE) with decoupling variables. By partitioning the LIS based on the transition matri... | {
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2411.06381 | SAN: Structure-Aware Network for Complex and Long-tailed Chinese Text
Recognition | [
"cs.CV"
] | In text recognition, complex glyphs and tail classes have always been factors affecting model performance. Specifically for Chinese text recognition, the lack of shape-awareness can lead to confusion among close complex characters. Since such characters are often tail classes that appear less frequently in the training... | {
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2411.06382 | Hardware-in-the-Loop for Characterization of Embedded State Estimation
for Flying Microrobots | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Autonomous flapping-wing micro-aerial vehicles (FWMAV) have a host of potential applications such as environmental monitoring, artificial pollination, and search and rescue operations. One of the challenges for achieving these applications is the implementation of an onboard sensor suite due to the small size and limit... | {
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2411.06385 | Class Granularity: How richly does your knowledge graph represent the
real world? | [
"cs.AI"
] | To effectively manage and utilize knowledge graphs, it is crucial to have metrics that can assess the quality of knowledge graphs from various perspectives. While there have been studies on knowledge graph quality metrics, there has been a lack of research on metrics that measure how richly ontologies, which form the b... | {
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} |
2411.06387 | Self-Training Meets Consistency: Improving LLMs' Reasoning with
Consistency-Driven Rationale Evaluation | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Self-training approach for large language models (LLMs) improves reasoning abilities by training the models on their self-generated rationales. Previous approaches have labeled rationales that produce correct answers for a given question as appropriate for training. However, a single measure risks misjudging rationale ... | {
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} |
2411.06389 | Optimal Execution with Reinforcement Learning | [
"q-fin.TR",
"cs.LG"
] | This study investigates the development of an optimal execution strategy through reinforcement learning, aiming to determine the most effective approach for traders to buy and sell inventory within a limited time frame. Our proposed model leverages input features derived from the current state of the limit order book. ... | {
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} |
2411.06390 | SplatFormer: Point Transformer for Robust 3D Gaussian Splatting | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has recently transformed photorealistic reconstruction, achieving high visual fidelity and real-time performance. However, rendering quality significantly deteriorates when test views deviate from the camera angles used during training, posing a major challenge for applications in immersive... | {
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} |
2411.06391 | CausalStock: Deep End-to-end Causal Discovery for News-driven Stock
Movement Prediction | [
"cs.LG",
"cs.AI",
"cs.CE",
"cs.CL"
] | There are two issues in news-driven multi-stock movement prediction tasks that are not well solved in the existing works. On the one hand, "relation discovery" is a pivotal part when leveraging the price information of other stocks to achieve accurate stock movement prediction. Given that stock relations are often unid... | {
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} |
2411.06392 | LSMGraph: A High-Performance Dynamic Graph Storage System with
Multi-Level CSR | [
"cs.DB"
] | The growing volume of graph data may exhaust the main memory. It is crucial to design a disk-based graph storage system to ingest updates and analyze graphs efficiently. However, existing dynamic graph storage systems suffer from read or write amplification and face the challenge of optimizing both read and write perfo... | {
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} |
2411.06394 | Local vs. Global Models for Hierarchical Forecasting | [
"cs.LG",
"stat.ML"
] | Hierarchical time series forecasting plays a crucial role in decision-making in various domains while presenting significant challenges for modelling as they involve multiple levels of aggregation, constraints, and availability of information. This study explores the influence of distinct information utilisation on the... | {
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} |
2411.06396 | A Variance Minimization Approach to Temporal-Difference Learning | [
"cs.LG",
"cs.AI"
] | Fast-converging algorithms are a contemporary requirement in reinforcement learning. In the context of linear function approximation, the magnitude of the smallest eigenvalue of the key matrix is a major factor reflecting the convergence speed. Traditional value-based RL algorithms focus on minimizing errors. This pape... | {
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} |
2411.06397 | A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with
Transfer Learning | [
"eess.IV",
"cs.CV"
] | COVID-19 is extremely contagious and its rapid growth has drawn attention towards its early diagnosis. Early diagnosis of COVID-19 enables healthcare professionals and government authorities to break the chain of transition and flatten the epidemic curve. With the number of cases accelerating across the developed world... | {
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} |
2411.06398 | Do you want to play a game? Learning to play Tic-Tac-Toe in Hypermedia
Environments | [
"cs.MA"
] | We demonstrate the integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style. Agents use RDF knowledge stores to reason over information and apply Reinforcement Learning techniques to learn how to interact with a Tic-Tac-Toe API. Agents form adv... | {
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} |
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