id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.10573 | ExeChecker: Where Did I Go Wrong? | [
"cs.CV",
"cs.HC",
"cs.LG"
] | In this paper, we present a contrastive learning based framework, ExeChecker, for the interpretation of rehabilitation exercises. Our work builds upon state-of-the-art advances in the area of human pose estimation, graph-attention neural networks, and transformer interpretablity. The downstream task is to assist rehabi... | {
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2412.10575 | Who's the (Multi-)Fairest of Them \textsc{All}: Rethinking
Interpolation-Based Data Augmentation Through the Lens of Multicalibration | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Data augmentation methods, especially SoTA interpolation-based methods such as Fair Mixup, have been widely shown to increase model fairness. However, this fairness is evaluated on metrics that do not capture model uncertainty and on datasets with only one, relatively large, minority group. As a remedy, multicalibratio... | {
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2412.10576 | Agro-STAY : Collecte de donn\'ees et analyse des informations en
agriculture alternative issues de YouTube | [
"cs.IR"
] | To address the current crises (climatic, social, economic), the self-sufficiency -- a set of practices that combine energy sobriety, self-production of food and energy, and self-construction - arouses an increasing interest. The CNRS STAY project (Savoirs Techniques pour l'Auto-suffisance, sur YouTube) explores this to... | {
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2412.10582 | WHAT-IF: Exploring Branching Narratives by Meta-Prompting Large Language
Models | [
"cs.CL"
] | WHAT-IF -- Writing a Hero's Alternate Timeline through Interactive Fiction -- is a system that uses zero-shot meta-prompting to create branching narratives from a prewritten story. Played as an interactive fiction (IF) game, WHAT-IF lets the player choose between decisions that the large language model (LLM) GPT-4 gene... | {
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2412.10587 | Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for
Compositional Analysis from Dried Solution Drops | [
"cs.CV",
"cs.AI",
"cs.CL"
] | When microliter drops of salt solutions dry on non-porous surfaces, they form erratic yet characteristic deposit patterns influenced by complex crystallization dynamics and fluid motion. Using OpenAI's image-enabled language models, we analyzed deposits from 12 salts with 200 images per salt and per model. GPT-4o class... | {
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2412.10589 | PanSR: An Object-Centric Mask Transformer for Panoptic Segmentation | [
"cs.CV"
] | Panoptic segmentation is a fundamental task in computer vision and a crucial component for perception in autonomous vehicles. Recent mask-transformer-based methods achieve impressive performance on standard benchmarks but face significant challenges with small objects, crowded scenes and scenes exhibiting a wide range ... | {
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2412.10594 | Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics | [
"cs.CV",
"cs.LG"
] | Human perception of similarity across uni- and multimodal inputs is highly complex, making it challenging to develop automated metrics that accurately mimic it. General purpose vision-language models, such as CLIP and large multi-modal models (LMMs), can be applied as zero-shot perceptual metrics, and several recent wo... | {
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2412.10595 | Recommendation and Temptation | [
"cs.IR",
"cs.CY",
"cs.GT"
] | Traditional recommender systems based on utility maximization and revealed preferences often fail to capture users' dual-self nature, where consumption choices are driven by both long-term benefits (enrichment) and desire for instant gratification (temptation). Consequently, these systems may generate recommendations t... | {
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2412.10597 | Err on the Side of Texture: Texture Bias on Real Data | [
"cs.CV",
"cs.CR"
] | Bias significantly undermines both the accuracy and trustworthiness of machine learning models. To date, one of the strongest biases observed in image classification models is texture bias-where models overly rely on texture information rather than shape information. Yet, existing approaches for measuring and mitigatin... | {
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2412.10599 | Advances in Transformers for Robotic Applications: A Review | [
"cs.RO",
"cs.AI"
] | The introduction of Transformers architecture has brought about significant breakthroughs in Deep Learning (DL), particularly within Natural Language Processing (NLP). Since their inception, Transformers have outperformed many traditional neural network architectures due to their "self-attention" mechanism and their sc... | {
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2412.10604 | EvalGIM: A Library for Evaluating Generative Image Models | [
"cs.CV"
] | As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound, there are few unified benchmarking libraries that provide a framework for performing evaluations across many datasets and metrics. Further... | {
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2412.10605 | Client-Side Patching against Backdoor Attacks in Federated Learning | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Federated learning is a versatile framework for training models in decentralized environments. However, the trust placed in clients makes federated learning vulnerable to backdoor attacks launched by malicious participants. While many defenses have been proposed, they often fail short when facing heterogeneous data dis... | {
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2412.10609 | A systematic review of norm emergence in multi-agent systems | [
"cs.MA"
] | Multi-agent systems (MAS) have gained relevance in the field of artificial intelligence by offering tools for modelling complex environments where autonomous agents interact to achieve common or individual goals. In these systems, norms emerge as a fundamental component to regulate the behaviour of agents, promoting co... | {
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2412.10612 | Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting
Approach | [
"cs.CR",
"cs.DS",
"cs.IT",
"math.IT"
] | Data engineering often requires accuracy (utility) constraints on results, posing significant challenges in designing differentially private (DP) mechanisms, particularly under stringent privacy parameter $\epsilon$. In this paper, we propose a privacy-boosting framework that is compatible with most noise-adding DP mec... | {
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2412.10615 | Finite Sample Analysis of Tensor Decomposition for Learning Mixtures of
Linear Systems | [
"eess.SY",
"cs.SY",
"stat.ML"
] | We study the problem of learning mixtures of linear dynamical systems (MLDS) from input-output data. This mixture setting allows us to leverage observations from related dynamical systems to improve the estimation of individual models. Building on spectral methods for mixtures of linear regressions, we propose a moment... | {
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2412.10616 | Hybrid Preference Optimization for Alignment: Provably Faster
Convergence Rates by Combining Offline Preferences with Online Exploration | [
"cs.LG"
] | Reinforcement Learning from Human Feedback (RLHF) is currently the leading approach for aligning large language models with human preferences. Typically, these models rely on extensive offline preference datasets for training. However, offline algorithms impose strict concentrability requirements, which are often diffi... | {
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2412.10617 | BinarySelect to Improve Accessibility of Black-Box Attack Research | [
"cs.CR",
"cs.CL"
] | Adversarial text attack research is useful for testing the robustness of NLP models, however, the rise of transformers has greatly increased the time required to test attacks. Especially when researchers do not have access to adequate resources (e.g. GPUs). This can hinder attack research, as modifying one example for ... | {
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2412.10621 | WaveGNN: Modeling Irregular Multivariate Time Series for Accurate
Predictions | [
"cs.LG",
"cs.AI"
] | Accurately modeling and analyzing time series data is crucial for downstream applications across various fields, including healthcare, finance, astronomy, and epidemiology. However, real-world time series often exhibit irregularities such as misaligned timestamps, missing entries, and variable sampling rates, complicat... | {
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2412.10622 | A recent evaluation on the performance of LLMs on radiation oncology
physics using questions of randomly shuffled options | [
"physics.med-ph",
"cs.AI"
] | Purpose: We present an updated study evaluating the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released models. Methods: A set of 100 multiple-choice radiation oncology physics questions, previously created by a well-experienced physicist, w... | {
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2412.10623 | Ares: Approximate Representations via Efficient Sparsification -- A
Stateless Approach through Polynomial Homomorphism | [
"cs.LG"
] | The increasing prevalence of high-dimensional data demands efficient and scalable compression methods to support modern applications. However, existing techniques like PCA and Autoencoders often rely on auxiliary metadata or intricate architectures, limiting their practicality for streaming or infinite datasets. In thi... | {
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2412.10624 | CATALOG: A Camera Trap Language-guided Contrastive Learning Model | [
"cs.CV",
"cs.LG"
] | Foundation Models (FMs) have been successful in various computer vision tasks like image classification, object detection and image segmentation. However, these tasks remain challenging when these models are tested on datasets with different distributions from the training dataset, a problem known as domain shift. This... | {
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2412.10625 | Certainty-Equivalence Model Predictive Control: Stability, Performance,
and Beyond | [
"math.OC",
"cs.SY",
"eess.SY"
] | Handling model mismatch is a common challenge in model-based controller design, particularly in model predictive control (MPC). While robust MPC is effective in managing uncertainties, its inherent conservatism often makes it less desirable in practice. Certainty-equivalence MPC (CE-MPC), which relies on a nominal mode... | {
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2412.10626 | Shannon information and integrated information: message and meaning | [
"q-bio.NC",
"cs.IT",
"math.IT"
] | Information theory, introduced by Shannon, has been extremely successful and influential as a mathematical theory of communication. Shannon's notion of information does not consider the meaning of the messages being communicated but only their probability. Even so, computational approaches regularly appeal to "informat... | {
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2412.10627 | An Active Parameter Learning Approach to The Identification of Safe
Regions | [
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"cs.SY"
] | We consider the problem of identification of safe regions in the environment of an autonomous system. The environment is divided into a finite collections of Voronoi cells, with each cell having a representative, the Voronoi center. The extent to which each region is considered to be safe by an oracle is captured throu... | {
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2412.10628 | Versatile Locomotion Skills for Hexapod Robots | [
"cs.RO"
] | Hexapod robots are potentially suitable for carrying out tasks in cluttered environments since they are stable, compact, and light weight. They also have multi-joint legs and variable height bodies that make them good candidates for tasks such as stairs climbing and squeezing under objects in a typical home environment... | {
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2412.10629 | Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained
Iterative Refinement | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Abstract Purpose: High-quality 4D MRI requires an impractically long scanning time for dense k-space signal acquisition covering all respiratory phases. Accelerated sparse sampling followed by reconstruction enhancement is desired but often results in degraded image quality and long reconstruction time. We hereby propo... | {
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2412.10630 | Upstream flow geometries can be uniquely learnt from single-point
turbulence signatures | [
"physics.flu-dyn",
"cs.LG"
] | We test the hypothesis that the microscopic temporal structure of near-field turbulence downstream of a sudden contraction contains geometry-identifiable information pertaining to the shape of the upstream obstruction. We measure a set of spatially sparse velocity time-series data downstream of differently-shaped orifi... | {
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2412.10631 | ARMADA: Augmented Reality for Robot Manipulation and Robot-Free Data
Acquisition | [
"cs.RO"
] | Teleoperation for robot imitation learning is bottlenecked by hardware availability. Can high-quality robot data be collected without a physical robot? We present a system for augmenting Apple Vision Pro with real-time virtual robot feedback. By providing users with an intuitive understanding of how their actions trans... | {
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2412.10642 | Study of Iterative Detection and Decoding for Multiuser Systems and MMSE
Refinements with Active or Passive RIS | [
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"math.IT"
] | An iterative detection and decoding (IDD) scheme is proposed for multiuser multiple-antenna systems assisted by an active or a passive Reconfigurable Intelligent Surface (RIS). The proposed approach features an IDD strategy that incorporates Low-Density Parity-Check (LDPC) codes, RIS processing with refinements of soft... | {
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2412.10643 | Scientific Realism vs. Anti-Realism: Toward a Common Ground | [
"stat.OT",
"cs.LG",
"stat.ME"
] | The debate between scientific realism and anti-realism remains at a stalemate, making reconciliation seem hopeless. Yet, important work remains: exploring a common ground, even if only to uncover deeper points of disagreement and, ideally, to benefit both sides of the debate. I propose such a common ground. Specificall... | {
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2412.10644 | Model-driven deep neural network for enhanced direction finding with
commodity 5G gNodeB | [
"eess.SP",
"cs.AI"
] | Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wireless networks heavily rely on pure model-driven techniques to achieve positioning functionality, often succumbing to performance deterioratio... | {
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2412.10647 | Enhancement of text recognition for hanja handwritten documents of
Ancient Korea | [
"cs.CV"
] | We implemented a high-performance optical character recognition model for classical handwritten documents using data augmentation with highly variable cropping within the document region. Optical character recognition in handwritten documents, especially classical documents, has been a challenging topic in many countri... | {
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2412.10649 | Hidden Echoes Survive Training in Audio To Audio Generative Instrument
Models | [
"cs.SD",
"cs.AI",
"cs.MM",
"eess.AS"
] | As generative techniques pervade the audio domain, there has been increasing interest in tracing back through these complicated models to understand how they draw on their training data to synthesize new examples, both to ensure that they use properly licensed data and also to elucidate their black box behavior. In thi... | {
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2412.10650 | DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object
Re-Identification | [
"cs.CV"
] | Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by combining complementary information from multiple modalities. Existing multi-modal object ReID methods primarily focus on the fusion of heterogeneous features. However, they often overlook the dynamic quality changes in multi-modal imaging... | {
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2412.10651 | LAN: Learning to Adapt Noise for Image Denoising | [
"cs.CV",
"cs.AI"
] | Removing noise from images, a.k.a image denoising, can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image denoising with the emergence of advanced deep... | {
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2412.10652 | Centaur: Bridging the Impossible Trinity of Privacy, Efficiency, and
Performance in Privacy-Preserving Transformer Inference | [
"cs.LG",
"cs.CR"
] | As pre-trained models, like Transformers, are increasingly deployed on cloud platforms for inference services, the privacy concerns surrounding model parameters and inference data are becoming more acute. Current Privacy-Preserving Transformer Inference (PPTI) frameworks struggle with the "impossible trinity" of privac... | {
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2412.10654 | Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through
Structured Data | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, they often struggle with complex reasoning tasks and are prone to hallucination. Recent research has shown promising results in leveraging knowledge graphs (KGs) to enhance LLM performance. ... | {
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2412.10656 | Global Estimation of Subsurface Eddy Kinetic Energy of Mesoscale Eddies
Using a Multiple-input Residual Neural Network | [
"physics.ao-ph",
"cs.LG"
] | Oceanic eddy kinetic energy (EKE) is a key quantity for measuring the intensity of mesoscale eddies and for parameterizing eddy effects in ocean climate models. Three decades of satellite altimetry observations allow a global assessment of sea surface information. However, the subsurface EKE with spatial filter has not... | {
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2412.10658 | Combining Priors with Experience: Confidence Calibration Based on
Binomial Process Modeling | [
"stat.ME",
"cs.AI",
"cs.LG"
] | Confidence calibration of classification models is a technique to estimate the true posterior probability of the predicted class, which is critical for ensuring reliable decision-making in practical applications. Existing confidence calibration methods mostly use statistical techniques to estimate the calibration curve... | {
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2412.10659 | MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with
Spatial Transcriptomics | [
"cs.CV",
"cs.LG",
"q-bio.QM"
] | The detection of anomalous tissue regions (ATRs) within affected tissues is crucial in clinical diagnosis and pathological studies. Conventional automated ATR detection methods, primarily based on histology images alone, falter in cases where ATRs and normal tissues have subtle visual differences. The recent spatial tr... | {
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2412.10663 | Memory-Efficient 4-bit Preconditioned Stochastic Optimization | [
"cs.LG",
"cs.CV",
"math.OC"
] | Preconditioned stochastic optimization algorithms, exemplified by Shampoo, have demonstrated superior performance over first-order optimizers, providing both theoretical advantages in convergence rates and practical improvements in large-scale neural network training. However, they incur substantial memory overhead due... | {
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2412.10664 | Structured Sampling for Robust Euclidean Distance Geometry | [
"cs.LG",
"cs.IT",
"math.IT",
"math.OC",
"stat.ML"
] | This paper addresses the problem of estimating the positions of points from distance measurements corrupted by sparse outliers. Specifically, we consider a setting with two types of nodes: anchor nodes, for which exact distances to each other are known, and target nodes, for which complete but corrupted distance measur... | {
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2412.10665 | Pretrained Event Classification Model for High Energy Physics Analysis | [
"hep-ph",
"cs.LG"
] | We introduce a foundation model for event classification in high-energy physics, built on a Graph Neural Network architecture and trained on 120 million simulated proton-proton collision events spanning 12 distinct physics processes. The model is pretrained to learn a general and robust representation of collision data... | {
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2412.10669 | FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning | [
"cs.LG",
"stat.ML"
] | Recent studies have highlighted significant fairness issues in Graph Transformer (GT) models, particularly against subgroups defined by sensitive features. Additionally, GTs are computationally intensive and memory-demanding, limiting their application to large-scale graphs. Our experiments demonstrate that graph parti... | {
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2412.10670 | Magnisketch Drone Control | [
"cs.RO",
"cs.SY",
"eess.SY"
] | The use of Unmanned Aerial Vehicles (UAVs) for aerial tasks and environmental manipulation is increasingly desired. This can be demonstrated via art tasks. This paper presents the development of Magnasketch, capable of translating image inputs into art on a magnetic drawing board via a Bitcraze Crazyflie 2.0 quadrotor.... | {
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2412.10673 | Proposing and solving olympiad geometry with guided tree search | [
"cs.AI",
"cs.LG"
] | Mathematics olympiads are prestigious competitions, with problem proposing and solving highly honored. Building artificial intelligence that proposes and solves olympiads presents an unresolved challenge in automated theorem discovery and proving, especially in geometry for its combination of numerical and spatial elem... | {
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2412.10674 | USM: Unbiased Survey Modeling for Limiting Negative User Experiences in
Recommendation Systems | [
"cs.IR",
"cs.AI"
] | Reducing negative user experiences is essential for the success of recommendation platforms. Exposing users to inappropriate content could not only adversely affect users' psychological well-beings, but also potentially drive users away from the platform, sabotaging the platform's long-term success. However, recommenda... | {
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2412.10675 | Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End
LLM Plan Generation | [
"cs.CL",
"cs.AI"
] | The capability of Large Language Models (LLMs) to plan remains a topic of debate. Some critics argue that strategies to boost LLMs' reasoning skills are ineffective in planning tasks, while others report strong outcomes merely from training models on a planning corpus. This study reassesses recent strategies by develop... | {
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2412.10679 | U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face
Video-based Blood Pressure Measurement | [
"cs.CV",
"eess.IV"
] | Blood pressure (BP) measurement plays an essential role in assessing health on a daily basis. Remote photoplethysmography (rPPG), which extracts pulse waves from camera-captured face videos, has the potential to easily measure BP for daily health monitoring. However, there are many uncertainties in BP estimation using ... | {
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2412.10680 | UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models
for Universal Cross-Domain Retrieval | [
"cs.CV",
"cs.IR",
"cs.MM"
] | Universal Cross-Domain Retrieval (UCDR) retrieves relevant images from unseen domains and classes without semantic labels, ensuring robust generalization. Existing methods commonly employ prompt tuning with pre-trained vision-language models but are inherently limited by static prompts, reducing adaptability. We propos... | {
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2412.10681 | One Pixel is All I Need | [
"cs.CV"
] | Vision Transformers (ViTs) have achieved record-breaking performance in various visual tasks. However, concerns about their robustness against backdoor attacks have grown. Backdoor attacks involve associating a specific trigger with a target label, causing the model to predict the attacker-specified label when the trig... | {
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2412.10682 | Stochastic $k$-Submodular Bandits with Full Bandit Feedback | [
"cs.LG",
"cs.DS",
"stat.ML"
] | In this paper, we present the first sublinear $\alpha$-regret bounds for online $k$-submodular optimization problems with full-bandit feedback, where $\alpha$ is a corresponding offline approximation ratio. Specifically, we propose online algorithms for multiple $k$-submodular stochastic combinatorial multi-armed bandi... | {
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2412.10684 | Inference Scaling for Bridging Retrieval and Augmented Generation | [
"cs.CL"
] | Retrieval-augmented generation (RAG) has emerged as a popular approach to steering the output of a large language model (LLM) by incorporating retrieved contexts as inputs. However, existing work observed the generator bias, such that improving the retrieval results may negatively affect the outcome. In this work, we s... | {
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2412.10687 | Linked Adapters: Linking Past and Future to Present for Effective
Continual Learning | [
"cs.LG",
"cs.CV"
] | Continual learning allows the system to learn and adapt to new tasks while retaining the knowledge acquired from previous tasks. However, deep learning models suffer from catastrophic forgetting of knowledge learned from earlier tasks while learning a new task. Moreover, retraining large models like transformers from s... | {
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2412.10689 | Learning to Verify Summary Facts with Fine-Grained LLM Feedback | [
"cs.CL",
"cs.AI"
] | Training automatic summary fact verifiers often faces the challenge of a lack of human-labeled data. In this paper, we explore alternative way of leveraging Large Language Model (LLM) generated feedback to address the inherent limitation of using human-labeled data. We introduce FineSumFact, a large-scale dataset conta... | {
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2412.10690 | Affiliation-based Local Community Detection across Multiple Networks | [
"cs.SI"
] | Real-world networks are often constructed from different sources or domains, including various types of entities and diverse relationships between networks, thus forming multi-domain networks. A single network typically fails to capture the complete graph structure and the diverse relationships among multiple networks.... | {
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2412.10694 | Grasp What You Want: Embodied Dexterous Grasping System Driven by Your
Voice | [
"cs.RO"
] | In recent years, as robotics has advanced, human-robot collaboration has gained increasing importance. However, current robots struggle to fully and accurately interpret human intentions from voice commands alone. Traditional gripper and suction systems often fail to interact naturally with humans, lack advanced manipu... | {
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2412.10695 | $L_1$-Based Adaptive Identification with Saturated Observations | [
"eess.SY",
"cs.SY"
] | It is well-known that saturated output observations are prevalent in various practical systems and that the $\ell_1$-norm is more robust than the $\ell_2$-norm-based parameter estimation. Unfortunately, adaptive identification based on both saturated observations and the $\ell_1$-optimization turns out to be a challeng... | {
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2412.10700 | Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground
Integrated Networks | [
"cs.MA",
"cs.LG",
"cs.SY",
"eess.SY"
] | The Space-Air-Ground Integrated Network (SAGIN) framework is a crucial foundation for future networks, where satellites and aerial nodes assist in computational task offloading. The low-altitude economy, leveraging the flexibility and multifunctionality of Unmanned Aerial Vehicles (UAVs) in SAGIN, holds significant pot... | {
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2412.10701 | Beyond Quantile Methods: Improved Top-K Threshold Estimation for
Traditional and Learned Sparse Indexes | [
"cs.IR"
] | Top-k threshold estimation is the problem of estimating the score of the k-th highest ranking result of a search query. A good estimate can be used to speed up many common top-k query processing algorithms, and thus a number of researchers have recently studied the problem. Among the various approaches that have been p... | {
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2412.10702 | Memory Efficient Matting with Adaptive Token Routing | [
"cs.CV"
] | Transformer-based models have recently achieved outstanding performance in image matting. However, their application to high-resolution images remains challenging due to the quadratic complexity of global self-attention. To address this issue, we propose MEMatte, a \textbf{m}emory-\textbf{e}fficient \textbf{m}atting fr... | {
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2412.10703 | Doubly-Bounded Queue for Constrained Online Learning: Keeping Pace with
Dynamics of Both Loss and Constraint | [
"cs.LG"
] | We consider online convex optimization with time-varying constraints and conduct performance analysis using two stringent metrics: dynamic regret with respect to the online solution benchmark, and hard constraint violation that does not allow any compensated violation over time. We propose an efficient algorithm called... | {
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2412.10704 | VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal
Retrieval-Augmented Generation | [
"cs.CL"
] | Understanding information from a collection of multiple documents, particularly those with visually rich elements, is important for document-grounded question answering. This paper introduces VisDoMBench, the first comprehensive benchmark designed to evaluate QA systems in multi-document settings with rich multimodal c... | {
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2412.10705 | Efficient Adaptation of Multilingual Models for Japanese ASR | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] | This study explores fine-tuning multilingual ASR (Automatic Speech Recognition) models, specifically OpenAI's Whisper-Tiny, to improve performance in Japanese. While multilingual models like Whisper offer versatility, they often lack precision in specific languages. Conversely, monolingual models like ReazonSpeech exce... | {
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2412.10706 | SHIFT Planner: Speedy Hybrid Iterative Field and Segmented Trajectory
Optimization with IKD-tree for Uniform Lightweight Coverage | [
"cs.RO"
] | This paper introduces a comprehensive planning and navigation framework that address these limitations by integrating semantic mapping, adaptive coverage planning, dynamic obstacle avoidance and precise trajectory tracking. Our framework begins by generating panoptic occupancy local semantic maps and accurate localizat... | {
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2412.10707 | MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation
and Synergistic Prompt | [
"cs.CV",
"cs.MM"
] | Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities. Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal object ReID tasks. However, they remain unexplored... | {
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2412.10710 | Virtual Trial Room with Computer Vision and Machine Learning | [
"cs.CV"
] | Online shopping has revolutionized the retail industry, providing customers with convenience and accessibility. However, customers often hesitate to purchase wearable products such as watches, jewelry, glasses, shoes, and clothes due to the lack of certainty regarding fit and suitability. This leads to significant retu... | {
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2412.10712 | Towards Effective, Efficient and Unsupervised Social Event Detection in
the Hyperbolic Space | [
"cs.CL"
] | The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resulting in inadequately expressive message representations (ineffective) and prolonged learning durations (inefficient). In response to the ch... | {
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2412.10713 | RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted
Behaviors | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.RO"
] | Evaluating deep reinforcement learning (DRL) agents against targeted behavior attacks is critical for assessing their robustness. These attacks aim to manipulate the victim into specific behaviors that align with the attacker's objectives, often bypassing traditional reward-based defenses. Prior methods have primarily ... | {
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2412.10714 | Movie Recommendation using Web Crawling | [
"cs.IR"
] | In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system train... | {
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2412.10716 | Control of Overfitting with Physics | [
"cs.LG",
"cs.AI",
"stat.ML"
] | While there are many works on the applications of machine learning, not so many of them are trying to understand the theoretical justifications to explain their efficiency. In this work, overfitting control (or generalization property) in machine learning is explained using analogies from physics and biology. For stoch... | {
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2412.10717 | HITgram: A Platform for Experimenting with n-gram Language Models | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) are powerful but resource intensive, limiting accessibility. HITgram addresses this gap by offering a lightweight platform for n-gram model experimentation, ideal for resource-constrained environments. It supports unigrams to 4-grams and incorporates features like context sensitive weightin... | {
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2412.10718 | Grid: Omni Visual Generation | [
"cs.CV"
] | Visual generation has witnessed remarkable progress in single-image tasks, yet extending these capabilities to temporal sequences remains challenging. Current approaches either build specialized video models from scratch with enormous computational costs or add separate motion modules to image generators, both requirin... | {
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2412.10719 | Just a Few Glances: Open-Set Visual Perception with Image Prompt
Paradigm | [
"cs.CV",
"cs.AI"
] | To break through the limitations of pre-training models on fixed categories, Open-Set Object Detection (OSOD) and Open-Set Segmentation (OSS) have attracted a surge of interest from researchers. Inspired by large language models, mainstream OSOD and OSS methods generally utilize text as a prompt, achieving remarkable p... | {
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2412.10720 | Bridging Vision and Language: Modeling Causality and Temporality in
Video Narratives | [
"cs.CV"
] | Video captioning is a critical task in the field of multimodal machine learning, aiming to generate descriptive and coherent textual narratives for video content. While large vision-language models (LVLMs) have shown significant progress, they often struggle to capture the causal and temporal dynamics inherent in compl... | {
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2412.10723 | HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via
Hierarchical Edge Partitioning | [
"cs.LG",
"cs.CV"
] | One-shot methods have significantly advanced the field of neural architecture search (NAS) by adopting weight-sharing strategy to reduce search costs. However, the accuracy of performance estimation can be compromised by co-adaptation. Few-shot methods divide the entire supernet into individual sub-supernets by splitti... | {
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2412.10726 | NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | The rapid advancement of Vision-Language Models (VLMs) has significantly advanced the development of Embodied Question Answering (EQA), enhancing agents' abilities in language understanding and reasoning within complex and realistic scenarios. However, EQA in real-world scenarios remains challenging, as human-posed que... | {
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2412.10730 | MAL: Cluster-Masked and Multi-Task Pretraining for Enhanced xLSTM Vision
Performance | [
"cs.CV"
] | The Long Short-Term Memory (LSTM) networks have traditionally faced challenges in scaling and effectively capturing complex dependencies in visual tasks. The xLSTM architecture has emerged to address these limitations, incorporating exponential gating and a parallel matrix memory structure to enhance performance and sc... | {
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2412.10734 | OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous
Driving | [
"cs.CV"
] | The rapid advancement of deep learning has intensified the need for comprehensive data for use by autonomous driving algorithms. High-quality datasets are crucial for the development of effective data-driven autonomous driving solutions. Next-generation autonomous driving datasets must be multimodal, incorporating data... | {
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2412.10737 | Sentiment and Hashtag-aware Attentive Deep Neural Network for Multimodal
Post Popularity Prediction | [
"cs.IR",
"cs.SI"
] | Social media users articulate their opinions on a broad spectrum of subjects and share their experiences through posts comprising multiple modes of expression, leading to a notable surge in such multimodal content on social media platforms. Nonetheless, accurately forecasting the popularity of these posts presents a co... | {
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2412.10739 | DSRC: Learning Density-insensitive and Semantic-aware Collaborative
Representation against Corruptions | [
"cs.CV"
] | As a potential application of Vehicle-to-Everything (V2X) communication, multi-agent collaborative perception has achieved significant success in 3D object detection. While these methods have demonstrated impressive results on standard benchmarks, the robustness of such approaches in the face of complex real-world envi... | {
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2412.10741 | RegMixMatch: Optimizing Mixup Utilization in Semi-Supervised Learning | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Consistency regularization and pseudo-labeling have significantly advanced semi-supervised learning (SSL). Prior works have effectively employed Mixup for consistency regularization in SSL. However, our findings indicate that applying Mixup for consistency regularization may degrade SSL performance by compromising the ... | {
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2412.10742 | WEPO: Web Element Preference Optimization for LLM-based Web Navigation | [
"cs.CL"
] | The rapid advancement of autonomous web navigation has significantly benefited from grounding pretrained Large Language Models (LLMs) as agents. However, current research has yet to fully leverage the redundancy of HTML elements for contrastive training. This paper introduces a novel approach to LLM-based web navigatio... | {
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2412.10743 | NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with
Flow Models | [
"cs.LG",
"physics.chem-ph",
"q-bio.BM"
] | Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. ... | {
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2412.10745 | Enhancing Event Extraction from Short Stories through Contextualized
Prompts | [
"cs.IR"
] | Event extraction is an important natural language processing (NLP) task of identifying events in an unstructured text. Although a plethora of works deal with event extraction from new articles, clinical text etc., only a few works focus on event extraction from literary content. Detecting events in short stories presen... | {
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2412.10748 | A Pioneering Neural Network Method for Efficient and Robust Fluid
Simulation | [
"cs.CV",
"cs.GR",
"cs.LG",
"physics.flu-dyn"
] | Fluid simulation is an important research topic in computer graphics (CG) and animation in video games. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed... | {
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2412.10749 | Patch-level Sounding Object Tracking for Audio-Visual Question Answering | [
"cs.MM",
"cs.CV"
] | Answering questions related to audio-visual scenes, i.e., the AVQA task, is becoming increasingly popular. A critical challenge is accurately identifying and tracking sounding objects related to the question along the timeline. In this paper, we present a new Patch-level Sounding Object Tracking (PSOT) method. It begin... | {
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2412.10751 | p-Mean Regret for Stochastic Bandits | [
"cs.LG",
"cs.GT"
] | In this work, we extend the concept of the $p$-mean welfare objective from social choice theory (Moulin 2004) to study $p$-mean regret in stochastic multi-armed bandit problems. The $p$-mean regret, defined as the difference between the optimal mean among the arms and the $p$-mean of the expected rewards, offers a flex... | {
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2412.10754 | Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement
Learning for Micro-Architecture Design Space Exploration | [
"cs.LG",
"cs.AR"
] | With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex. The vast design space presents significant challenges for human designers, making design space exploration (DSE) algorithms a significant tool for $\mu$-arch design. In recent years, efforts have been made... | {
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2412.10756 | Damage Assessment after Natural Disasters with UAVs: Semantic Feature
Extraction using Deep Learning | [
"cs.CV",
"cs.LG"
] | Unmanned aerial vehicle-assisted disaster recovery missions have been promoted recently due to their reliability and flexibility. Machine learning algorithms running onboard significantly enhance the utility of UAVs by enabling real-time data processing and efficient decision-making, despite being in a resource-constra... | {
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2412.10758 | Optimizing Vision-Language Interactions Through Decoder-Only Models | [
"cs.CV"
] | Vision-Language Models (VLMs) have emerged as key enablers for multimodal tasks, but their reliance on separate visual encoders introduces challenges in efficiency, scalability, and modality alignment. To address these limitations, we propose MUDAIF (Multimodal Unified Decoder with Adaptive Input Fusion), a decoder-onl... | {
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} |
2412.10761 | Rebalanced Vision-Language Retrieval Considering Structure-Aware
Distillation | [
"cs.CV",
"cs.AI"
] | Vision-language retrieval aims to search for similar instances in one modality based on queries from another modality. The primary objective is to learn cross-modal matching representations in a latent common space. Actually, the assumption underlying cross-modal matching is modal balance, where each modality contains ... | {
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} |
2412.10763 | Impact of Trip Distance Distribution Time Dependency and Aggregation
Levels in Bathtub Models -- A Comparative Simulation Analysis | [
"eess.SY",
"cs.SY"
] | Bathtub models are used to study urban traffic within a certain area. They do not require to take into account the detailed network topology. The emergence of different bathtub models has raised the question of which model can provide more robust and accurate results under different demand scenarios and network propert... | {
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"cs.SY": 1
} |
2412.10765 | Neural Network Meta Classifier: Improving the Reliability of Anomaly
Segmentation | [
"cs.CV"
] | Deep neural networks (DNNs) are a contemporary solution for semantic segmentation and are usually trained to operate on a predefined closed set of classes. In open-set environments, it is possible to encounter semantically unknown objects or anomalies. Road driving is an example of such an environment in which, from a ... | {
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} |
2412.10768 | VinTAGe: Joint Video and Text Conditioning for Holistic Audio Generation | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | Recent advances in audio generation have focused on text-to-audio (T2A) and video-to-audio (V2A) tasks. However, T2A or V2A methods cannot generate holistic sounds (onscreen and off-screen). This is because T2A cannot generate sounds aligning with onscreen objects, while V2A cannot generate semantically complete (offsc... | {
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} |
2412.10770 | Learned Data Compression: Challenges and Opportunities for the Future | [
"cs.DB",
"cs.IR"
] | Compressing integer keys is a fundamental operation among multiple communities, such as database management (DB), information retrieval (IR), and high-performance computing (HPC). Recent advances in \emph{learned indexes} have inspired the development of \emph{learned compressors}, which leverage simple yet compact mac... | {
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} |
2412.10773 | Omni Differential Drive for Simultaneous Reconfiguration and
Omnidirectional Mobility of Wheeled Robots | [
"cs.RO"
] | Wheeled robots are highly efficient in human living environments. However, conventional wheeled designs, limited by degrees of freedom, struggle to meet varying footprint needs and achieve omnidirectional mobility. This paper proposes a novel robot drive model inspired by human movements, termed as the Omni Differentia... | {
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} |
2412.10776 | Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency
Modulation, Spatial Purification, and Scale Diversification | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The accelerated MRI reconstruction process presents a challenging ill-posed inverse problem due to the extensive under-sampling in k-space. Recently, Vision Transformers (ViTs) have become the mainstream for this task, demonstrating substantial performance improvements. However, there are still three significant issues... | {
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} |
2412.10778 | Sample-efficient Unsupervised Policy Cloning from Ensemble
Self-supervised Labeled Videos | [
"cs.CV",
"cs.AI"
] | Current advanced policy learning methodologies have demonstrated the ability to develop expert-level strategies when provided enough information. However, their requirements, including task-specific rewards, expert-labeled trajectories, and huge environmental interactions, can be expensive or even unavailable in many s... | {
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} |
2412.10780 | Continual Learning for Behavior-based Driver Identification | [
"cs.LG"
] | Behavior-based Driver Identification is an emerging technology that recognizes drivers based on their unique driving behaviors, offering important applications such as vehicle theft prevention and personalized driving experiences. However, most studies fail to account for the real-world challenges of deploying Deep Lea... | {
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} |
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