id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.17559 | Degrees of Freedom of Cache-Aided Interference Channels Assisted by
Active Intelligent Reflecting Surfaces | [
"cs.IT",
"math.IT"
] | This paper studies cache-aided wireless networks in the presence of active intelligent reflecting surfaces (IRS) from an information-theoretic perspective. Specifically, we explore interference management in a cache-aided wireless network assisted by an active IRS, to enhance the achievable degrees of freedom (DoF). To... | {
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2411.17567 | Improving the Convergence Rates of Forward Gradient Descent with
Repeated Sampling | [
"math.ST",
"cs.LG",
"cs.NE",
"stat.TH"
] | Forward gradient descent (FGD) has been proposed as a biologically more plausible alternative of gradient descent as it can be computed without backward pass. Considering the linear model with $d$ parameters, previous work has found that the prediction error of FGD is, however, by a factor $d$ slower than the predictio... | {
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2411.17570 | Learning Explainable Treatment Policies with Clinician-Informed
Representations: A Practical Approach | [
"cs.LG",
"cs.AI",
"stat.AP",
"stat.ML"
] | Digital health interventions (DHIs) and remote patient monitoring (RPM) have shown great potential in improving chronic disease management through personalized care. However, barriers like limited efficacy and workload concerns hinder adoption of existing DHIs; while limited sample sizes and lack of interpretability li... | {
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2411.17571 | Uncertainty quantification for White Matter Hyperintensity segmentation
detects silent failures and improves automated Fazekas quantification | [
"eess.IV",
"cs.CV",
"cs.LG"
] | White Matter Hyperintensities (WMH) are key neuroradiological markers of small vessel disease present in brain MRI. Assessment of WMH is important in research and clinics. However, WMH are challenging to segment due to their high variability in shape, location, size, poorly defined borders, and similar intensity profil... | {
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2411.17576 | A Distractor-Aware Memory for Visual Object Tracking with SAM2 | [
"cs.CV"
] | Memory-based trackers are video object segmentation methods that form the target model by concatenating recently tracked frames into a memory buffer and localize the target by attending the current image to the buffered frames. While already achieving top performance on many benchmarks, it was the recent release of SAM... | {
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2411.17580 | Revisiting Point Cloud Completion: Are We Ready For The Real-World? | [
"cs.CV"
] | Point clouds acquired in constrained and challenging real-world settings are incomplete, non-uniformly sparse, or both. These obstacles present acute challenges for a vital task - point cloud completion. Using tools from Algebraic Topology and Persistent Homology ($\mathcal{PH}$), we demonstrate that current benchmark ... | {
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2411.17582 | From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in
Evolving Graphs | [
"cs.LG",
"cs.CY",
"cs.SI"
] | Professional networks provide invaluable entree to opportunity through referrals and introductions. A rich literature shows they also serve to entrench and even exacerbate a status quo of privilege and disadvantage. Hiring platforms, equipped with the ability to nudge link formation, provide a tantalizing opening for b... | {
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2411.17584 | Pre-training for Action Recognition with Automatically Generated Fractal
Datasets | [
"cs.CV"
] | In recent years, interest in synthetic data has grown, particularly in the context of pre-training the image modality to support a range of computer vision tasks, including object classification, medical imaging etc. Previous work has demonstrated that synthetic samples, automatically produced by various generative pro... | {
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2411.17592 | VideoDirector: Precise Video Editing via Text-to-Video Models | [
"cs.CV"
] | Despite the typical inversion-then-editing paradigm using text-to-image (T2I) models has demonstrated promising results, directly extending it to text-to-video (T2V) models still suffers severe artifacts such as color flickering and content distortion. Consequently, current video editing methods primarily rely on T2I m... | {
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2411.17593 | What Differentiates Educational Literature? A Multimodal Fusion Approach
of Transformers and Computational Linguistics | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The integration of new literature into the English curriculum remains a challenge since educators often lack scalable tools to rapidly evaluate readability and adapt texts for diverse classroom needs. This study proposes to address this gap through a multimodal approach that combines transformer-based text classificati... | {
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2411.17595 | Can artificial intelligence predict clinical trial outcomes? | [
"cs.LG",
"stat.AP"
] | The increasing complexity and cost of clinical trials, particularly in the context of oncology and advanced therapies, pose significant challenges for drug development. This study evaluates the predictive capabilities of large language models (LLMs) such as GPT-3.5, GPT-4, and HINT in determining clinical trial outcome... | {
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2411.17598 | Agentic AI for Improving Precision in Identifying Contributions to
Sustainable Development Goals | [
"cs.DL",
"cs.AI",
"cs.IR"
] | As research institutions increasingly commit to supporting the United Nations' Sustainable Development Goals (SDGs), there is a pressing need to accurately assess their research output against these goals. Current approaches, primarily reliant on keyword-based Boolean search queries, conflate incidental keyword matches... | {
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2411.17600 | Making History Readable | [
"cs.DL",
"cs.AI",
"cs.IR"
] | The Virginia Tech University Libraries (VTUL) Digital Library Platform (DLP) hosts digital collections that offer our users access to a wide variety of documents of historical and cultural importance. These collections are not only of academic importance but also provide our users with a glance at local historical even... | {
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2411.17603 | A Unified and Practical Approach for Generalized Deletion Propagation | [
"cs.DB"
] | Deletion Propagation problems are a family of database problems that have been studied for over 40 years. They are variants of the classical view-update problem where intended tuple deletions in the view (output of a query) are propagated back to the source (input database) in a manner that obeys certain constraints wh... | {
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2411.17605 | Distractor-free Generalizable 3D Gaussian Splatting | [
"cs.CV"
] | We present DGGS, a novel framework addressing the previously unexplored challenge of Distractor-free Generalizable 3D Gaussian Splatting (3DGS). It accomplishes two key objectives: fortifying generalizable 3DGS against distractor-laden data during both training and inference phases, while successfully extending cross-s... | {
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2411.17606 | HyperSeg: Towards Universal Visual Segmentation with Large Language
Model | [
"cs.CV"
] | This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite significant progress in current unified segmentation methods, limitations in adaptation to both image and video scenarios, as well as the complex ... | {
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2411.17607 | Scaling Speech-Text Pre-training with Synthetic Interleaved Data | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Speech language models (SpeechLMs) accept speech input and produce speech output, allowing for more natural human-computer interaction compared to text-based large language models (LLMs). Traditional approaches for developing SpeechLMs are constrained by the limited availability of unsupervised speech data and parallel... | {
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2411.17608 | Mixed-State Quantum Denoising Diffusion Probabilistic Model | [
"quant-ph",
"cs.AI",
"cs.LG"
] | Generative quantum machine learning has gained significant attention for its ability to produce quantum states with desired distributions. Among various quantum generative models, quantum denoising diffusion probabilistic models (QuDDPMs) [Phys. Rev. Lett. 132, 100602 (2024)] provide a promising approach with stepwise ... | {
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2411.17610 | Modality-Incremental Learning with Disjoint Relevance Mapping Networks
for Image-based Semantic Segmentation | [
"cs.CV"
] | In autonomous driving, environment perception has significantly advanced with the utilization of deep learning techniques for diverse sensors such as cameras, depth sensors, or infrared sensors. The diversity in the sensor stack increases the safety and contributes to robustness against adverse weather and lighting con... | {
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2411.17614 | Automating Chapter-Level Classification for Electronic Theses and
Dissertations | [
"cs.DL",
"cs.AI",
"cs.IR",
"cs.LG"
] | Traditional archival practices for describing electronic theses and dissertations (ETDs) rely on broad, high-level metadata schemes that fail to capture the depth, complexity, and interdisciplinary nature of these long scholarly works. The lack of detailed, chapter-level content descriptions impedes researchers' abilit... | {
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2411.17616 | Accelerating Vision Diffusion Transformers with Skip Branches | [
"cs.CV"
] | Diffusion Transformers (DiT), an emerging image and video generation model architecture, has demonstrated great potential because of its high generation quality and scalability properties. Despite the impressive performance, its practical deployment is constrained by computational complexity and redundancy in the seque... | {
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2411.17617 | An Ensemble Approach for Brain Tumor Segmentation and Synthesis | [
"eess.IV",
"cs.CV"
] | The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple l... | {
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2411.17624 | Machine Learning and Multi-source Remote Sensing in Forest Carbon Stock
Estimation: A Review | [
"cs.LG",
"cs.AI"
] | Quantifying forest carbon is crucial for informing decisions and policies that will protect the planet. Machine learning (ML) and remote sensing (RS) techniques have been used to do this task more effectively, yet there lacks a systematic review on the most recent ML methods and RS combinations, especially with the con... | {
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2411.17625 | Data-driven development of cycle prediction models for lithium metal
batteries using multi modal mining | [
"cs.LG"
] | Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce a novel multi modal data-driven approach employing an Automatic Battery data Collector (ABC) that integrates a large language model (LLM) with an... | {
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2411.17626 | Semi-analytical model for the calculation of solar radiation pressure
and its effects on a LEO satellite with predicting the change in position
vectors using machine learning techniques | [
"cs.CE"
] | The rapid increase in the deployment of Low Earth Orbit (LEO) satellites, catering to diverse applications such as communication, Earth observation, environmental monitoring, and scientific research, has significantly amplified the complexity of trajectory management. The current work focuses on calculating and analyzi... | {
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2411.17629 | Learning Chemical Reaction Representation with Reactant-Product
Alignment | [
"cs.LG",
"cs.AI"
] | Organic synthesis stands as a cornerstone of the chemical industry. The development of robust machine learning models to support tasks associated with organic reactions is of significant interest. However, current methods rely on hand-crafted features or direct adaptations of model architectures from other domains, whi... | {
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2411.17636 | MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics
Manipulation | [
"cs.RO",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation. While recent efforts in robotics have leveraged LLMs both for high-level and low-level planning, these approaches often face significant challenges, such as hallucinations... | {
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2411.17637 | On Limitations of LLM as Annotator for Low Resource Languages | [
"cs.CL",
"cs.LG"
] | Low-resource languages face significant challenges due to the lack of sufficient linguistic data, resources, and tools for tasks such as supervised learning, annotation, and classification. This shortage hinders the development of accurate models and datasets, making it difficult to perform critical NLP tasks like sent... | {
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2411.17645 | Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked
EHR and Pathology Lab Dataset | [
"cs.LG",
"cs.AI"
] | The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled outcomes. In this context, we leverage a linked EHR dataset of approximately on... | {
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2411.17646 | SAMWISE: Infusing wisdom in SAM2 for Text-Driven Video Segmentation | [
"cs.CV"
] | Referring Video Object Segmentation (RVOS) relies on natural language expressions to segment an object in a video clip. Existing methods restrict reasoning either to independent short clips, losing global context, or process the entire video offline, impairing their application in a streaming fashion. In this work, we ... | {
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2411.17660 | DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting | [
"cs.CV"
] | Recent progress in scene synthesis makes standalone SLAM systems purely based on optimizing hyperprimitives with a Rendering objective possible. However, the tracking performance still lacks behind traditional and end-to-end SLAM systems. An optimal trade-off between robustness, speed and accuracy has not yet been reac... | {
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2411.17661 | Non-Contextual BERT or FastText? A Comparative Analysis | [
"cs.CL",
"cs.LG"
] | Natural Language Processing (NLP) for low-resource languages, which lack large annotated datasets, faces significant challenges due to limited high-quality data and linguistic resources. The selection of embeddings plays a critical role in achieving strong performance in NLP tasks. While contextual BERT embeddings requ... | {
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2411.17662 | RoboPEPP: Vision-Based Robot Pose and Joint Angle Estimation through
Embedding Predictive Pre-Training | [
"cs.RO",
"cs.CV"
] | Vision-based pose estimation of articulated robots with unknown joint angles has applications in collaborative robotics and human-robot interaction tasks. Current frameworks use neural network encoders to extract image features and downstream layers to predict joint angles and robot pose. While images of robots inheren... | {
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2411.17666 | How do Multimodal Foundation Models Encode Text and Speech? An Analysis
of Cross-Lingual and Cross-Modal Representations | [
"cs.CL"
] | Multimodal foundation models aim to create a unified representation space that abstracts away from surface features like language syntax or modality differences. To investigate this, we study the internal representations of three recent models, analyzing the model activations from semantically equivalent sentences acro... | {
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2411.17668 | Anytime Acceleration of Gradient Descent | [
"cs.LG",
"cs.SY",
"eess.SY",
"math.OC",
"stat.ML"
] | This work investigates stepsize-based acceleration of gradient descent with {\em anytime} convergence guarantees. For smooth (non-strongly) convex optimization, we propose a stepsize schedule that allows gradient descent to achieve convergence guarantees of $O(T^{-1.119})$ for any stopping time $T$, where the stepsize ... | {
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2411.17669 | Linguistic Laws Meet Protein Sequences: A Comparative Analysis of
Subword Tokenization Methods | [
"cs.CL",
"q-bio.QM"
] | Tokenization is a crucial step in processing protein sequences for machine learning models, as proteins are complex sequences of amino acids that require meaningful segmentation to capture their functional and structural properties. However, existing subword tokenization methods, developed primarily for human language,... | {
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2411.17672 | Synthetic Data Generation with LLM for Improved Depression Prediction | [
"cs.LG"
] | Automatic detection of depression is a rapidly growing field of research at the intersection of psychology and machine learning. However, with its exponential interest comes a growing concern for data privacy and scarcity due to the sensitivity of such a topic. In this paper, we propose a pipeline for Large Language Mo... | {
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2411.17673 | SketchAgent: Language-Driven Sequential Sketch Generation | [
"cs.CV"
] | Sketching serves as a versatile tool for externalizing ideas, enabling rapid exploration and visual communication that spans various disciplines. While artificial systems have driven substantial advances in content creation and human-computer interaction, capturing the dynamic and abstract nature of human sketching rem... | {
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2411.17674 | Push the Limit of Multi-modal Emotion Recognition by Prompting LLMs with
Receptive-Field-Aware Attention Weighting | [
"cs.CL"
] | Understanding the emotions in a dialogue usually requires external knowledge to accurately understand the contents. As the LLMs become more and more powerful, we do not want to settle on the limited ability of the pre-trained language model. However, the LLMs either can only process text modality or are too expensive t... | {
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2411.17676 | Instance-Aware Graph Prompt Learning | [
"cs.LG"
] | Graph neural networks stand as the predominant technique for graph representation learning owing to their strong expressive power, yet the performance highly depends on the availability of high-quality labels in an end-to-end manner. Thus the pretraining and fine-tuning paradigm has been proposed to mitigate the label ... | {
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2411.17679 | Enhancing Character-Level Understanding in LLMs through Token Internal
Structure Learning | [
"cs.CL"
] | Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. This limitation hinders LLMs' ability to predict precise character positions, which is crucial in tasks like Chinese Spelling Correction (CSC... | {
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2411.17684 | RealSeal: Revolutionizing Media Authentication with Real-Time Realism
Scoring | [
"cs.CR",
"cs.AI"
] | The growing threat of deepfakes and manipulated media necessitates a radical rethinking of media authentication. Existing methods for watermarking synthetic data fall short, as they can be easily removed or altered, and current deepfake detection algorithms do not achieve perfect accuracy. Provenance techniques, which ... | {
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2411.17685 | Attamba: Attending To Multi-Token States | [
"cs.LG",
"cs.CL"
] | When predicting the next token in a sequence, vanilla transformers compute attention over all previous tokens, resulting in quadratic scaling of compute with sequence length. State-space models compress the entire sequence of tokens into a fixed-dimensional representation to improve efficiency, while other architecture... | {
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2411.17686 | Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for
Training-Free Acceleration | [
"cs.CV"
] | To accelerate the inference of heavy Multimodal Large Language Models (MLLMs), this study rethinks the current landscape of training-free token reduction research. We regret to find that the critical components of existing methods are tightly intertwined, with their interconnections and effects remaining unclear for co... | {
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2411.17687 | GenDeg: Diffusion-Based Degradation Synthesis for Generalizable
All-in-One Image Restoration | [
"cs.CV"
] | Deep learning-based models for All-In-One Image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor generalization to samples outside the training distribution. This limitation arises primarily from insufficient diversity in degradation va... | {
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2411.17690 | Visatronic: A Multimodal Decoder-Only Model for Speech Synthesis | [
"cs.MM",
"cs.CV",
"cs.SD",
"eess.AS"
] | In this paper, we propose a new task -- generating speech from videos of people and their transcripts (VTTS) -- to motivate new techniques for multimodal speech generation. This task generalizes the task of generating speech from cropped lip videos, and is also more complicated than the task of generating generic audio... | {
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2411.17691 | Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for
Quantized LLMs with 100T Training Tokens | [
"cs.LG",
"cs.CL"
] | We reveal that low-bit quantization favors undertrained large language models (LLMs) by observing that models with larger sizes or fewer training tokens experience less quantization-induced degradation (QiD) when applying low-bit quantization, whereas smaller models with extensive training tokens suffer significant QiD... | {
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2411.17692 | Quantifying information stored in synaptic connections rather than in
firing patterns of neural networks | [
"q-bio.NC",
"cs.IT",
"math.IT",
"physics.bio-ph"
] | A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of connections among the constituent neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing patterns of neural networks, little is ... | {
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2411.17693 | Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats | [
"cs.CL"
] | As large language models (LLMs) become increasingly capable, it is prudent to assess whether safety measures remain effective even if LLMs intentionally try to bypass them. Previous work introduced control evaluations, an adversarial framework for testing deployment strategies of untrusted models (i.e., models which mi... | {
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2411.17696 | ScribbleLight: Single Image Indoor Relighting with Scribbles | [
"cs.CV"
] | Image-based relighting of indoor rooms creates an immersive virtual understanding of the space, which is useful for interior design, virtual staging, and real estate. Relighting indoor rooms from a single image is especially challenging due to complex illumination interactions between multiple lights and cluttered obje... | {
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2411.17697 | StableAnimator: High-Quality Identity-Preserving Human Image Animation | [
"cs.CV",
"cs.AI"
] | Current diffusion models for human image animation struggle to ensure identity (ID) consistency. This paper presents StableAnimator, the first end-to-end ID-preserving video diffusion framework, which synthesizes high-quality videos without any post-processing, conditioned on a reference image and a sequence of poses. ... | {
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2411.17698 | Video-Guided Foley Sound Generation with Multimodal Controls | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To address this problem, we introduce MultiFoley, a model designed for video-guided sound generation that supports multimodal conditioning throug... | {
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2411.17702 | Finding "Good Views" of Electrocardiogram Signals for Inferring
Abnormalities in Cardiac Condition | [
"eess.SP",
"cs.LG"
] | Electrocardiograms (ECGs) are an established technique to screen for abnormal cardiac signals. Recent work has established that it is possible to detect arrhythmia directly from the ECG signal using deep learning algorithms. While a few prior approaches with contrastive learning have been successful, the best way to de... | {
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2411.17703 | Probabilistic Forecasting of Radiation Exposure for Spaceflight | [
"physics.space-ph",
"cs.LG"
] | Extended human presence beyond low-Earth orbit (BLEO) during missions to the Moon and Mars will pose significant challenges in the near future. A primary health risk associated with these missions is radiation exposure, primarily from galatic cosmic rays (GCRs) and solar proton events (SPEs). While GCRs present a more ... | {
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2411.17705 | EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method | [
"eess.SP",
"cs.AI",
"cs.LG"
] | The electroencephalography (EEG)-based motor imagery (MI) classification is a critical and challenging task in brain-computer interface (BCI) technology, which plays a significant role in assisting patients with functional impairments to regain mobility. We present a novel multi-scale atrous convolutional neural networ... | {
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2411.17706 | Optimization and Dynamic Analysis of a Vibro-Impact Nonlinear Energy
Sink with Electromagnetic Coil for Vibration Suppression and Energy
Harvesting | [
"eess.SY",
"cs.SY",
"nlin.CD"
] | This study investigates a system comprising a linear oscillator (LO) equipped with a Vibro-Impact Nonlinear Energy Sink (VI-NES) and a coil. The LO's damping and stiffness coefficients are represented by ( C ) and ( k ), respectively, while a ball inside the LO moves within a cavity, colliding with the walls at both en... | {
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2411.17707 | A Composite Fault Diagnosis Model for NPPs Based on
Bayesian-EfficientNet Module | [
"eess.SP",
"cs.AI",
"cs.SY",
"eess.SY"
] | This article focuses on the faults of important mechanical components such as pumps, valves, and pipelines in the reactor coolant system, main steam system, condensate system, and main feedwater system of nuclear power plants (NPPs). It proposes a composite multi-fault diagnosis model based on Bayesian algorithm and Ef... | {
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2411.17708 | Towards Efficient Neurally-Guided Program Induction for ARC-AGI | [
"cs.AI",
"cs.CL",
"cs.LG"
] | ARC-AGI is an open-world problem domain in which the ability to generalize out-of-distribution is a crucial quality. Under the program induction paradigm, we present a series of experiments that reveal the efficiency and generalization characteristics of various neurally-guided program induction approaches. The three p... | {
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2411.17709 | Quantity versus Diversity: Influence of Data on Detecting EEG Pathology
with Advanced ML Models | [
"eess.SP",
"cs.LG"
] | This study investigates the impact of quantity and diversity of data on the performance of various machine-learning models for detecting general EEG pathology. We utilized an EEG dataset of 2,993 recordings from Temple University Hospital and a dataset of 55,787 recordings from Elmiko Biosignals sp. z o.o. The latter c... | {
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2411.17711 | AnyECG: Foundational Models for Electrocardiogram Analysis | [
"eess.SP",
"cs.AI",
"cs.LG"
] | Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG recordings, numerous machine learning methods have been developed for automated heart disease detection to reduce human workload. Despite th... | {
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2411.17712 | Generative AI on the Edge: Architecture and Performance Evaluation | [
"cs.DC",
"cs.AI",
"cs.NI",
"cs.PF"
] | 6G's AI native vision of embedding advance intelligence in the network while bringing it closer to the user requires a systematic evaluation of Generative AI (GenAI) models on edge devices. Rapidly emerging solutions based on Open RAN (ORAN) and Network-in-a-Box strongly advocate the use of low-cost, off-the-shelf comp... | {
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2411.17713 | Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI
Conversations | [
"cs.DC",
"cs.AI"
] | This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on resource-constrained devices, achieving a throughput of at least 30 tokens per second and a time-to-fi... | {
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2411.17715 | Hybrid Quantum Deep Learning Model for Emotion Detection using raw EEG
Signal Analysis | [
"eess.SP",
"cs.AI",
"cs.LG"
] | Applications in behavioural research, human-computer interaction, and mental health depend on the ability to recognize emotions. In order to improve the accuracy of emotion recognition using electroencephalography (EEG) data, this work presents a hybrid quantum deep learning technique. Conventional EEG-based emotion re... | {
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2411.17716 | Generating CKM Using Others' Data: Cross-AP CKM Inference with Deep
Learning | [
"eess.SP",
"cs.SY",
"eess.IV",
"eess.SY"
] | Channel knowledge map (CKM) is a promising paradigm shift towards environment-aware communication and sensing by providing location-specific prior channel knowledge before real-time communication. Although CKM is particularly appealing for dense networks such as cell-free networks, it remains a challenge to efficiently... | {
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2411.17717 | Comprehensive Methodology for Sample Augmentation in EEG Biomarker
Studies for Alzheimers Risk Classification | [
"eess.SP",
"cs.LG"
] | Background: Dementia, marked by cognitive decline, is a global health challenge. Alzheimer's disease (AD), the leading type, accounts for ~70% of cases. Electroencephalography (EEG) measures show promise in identifying AD risk, but obtaining large samples for reliable comparisons is challenging. Objective: This study i... | {
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2411.17719 | SlideSpawn: An Automatic Slides Generation System for Research
Publications | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | Research papers are well structured documents. They have text, figures, equations, tables etc., to covey their ideas and findings. They are divided into sections like Introduction, Model, Experiments etc., which deal with different aspects of research. Characteristics like these set research papers apart from ordinary ... | {
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2411.17720 | MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration
on Resource-Constrained Edge Devices | [
"cs.DC",
"cs.AI",
"cs.PF"
] | The advent of foundation models have revolutionized various fields, enabling unprecedented task accuracy and flexibility in computational linguistics, computer vision and other domains. Attention mechanism has become an essential component of foundation models, due to their superb capability of capturing correlations i... | {
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2411.17721 | Automatic EEG Independent Component Classification Using ICLabel in
Python | [
"eess.SP",
"cs.LG",
"q-bio.NC"
] | ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EE... | {
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2411.17722 | When IoT Meet LLMs: Applications and Challenges | [
"cs.DC",
"cs.AI",
"cs.NI"
] | Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In t... | {
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2411.17724 | Incentives to Build Houses, Trade Houses, or Trade House Building Skills
in Simulated Worlds under Various Governing Systems or Institutions:
Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model | [
"cs.MA"
] | It has been shown that social institutions impact human motivations to produce different behaviours, such as amount of working or specialisation in labor. With advancement in artificial intelligence (AI), specifically large language models (LLMs), now it is possible to perform in-silico simulations to test various hypo... | {
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2411.17726 | EQNN: Enhanced Quantum Neural Network | [
"quant-ph",
"cs.IT",
"cs.LG",
"cs.NE",
"math.IT"
] | With the maturation of quantum computing technology, research has gradually shifted towards exploring its applications. Alongside the rise of artificial intelligence, various machine learning methods have been developed into quantum circuits and algorithms. Among them, Quantum Neural Networks (QNNs) can map inputs to q... | {
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2411.17727 | Enhanced Capture Point Control Using Thruster Dynamics and QP-Based
Optimization for Harpy | [
"cs.RO"
] | Our work aims to make significant strides in understanding unexplored locomotion control paradigms based on the integration of posture manipulation and thrust vectoring. These techniques are commonly seen in nature, such as Chukar birds using their wings to run on a nearly vertical wall. In this work, we developed a ca... | {
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2411.17728 | Analytic Continuation by Feature Learning | [
"cond-mat.str-el",
"cs.LG",
"eess.SP",
"physics.comp-ph",
"stat.ML"
] | Analytic continuation aims to reconstruct real-time spectral functions from imaginary-time Green's functions; however, this process is notoriously ill-posed and challenging to solve. We propose a novel neural network architecture, named the Feature Learning Network (FL-net), to enhance the prediction accuracy of spectr... | {
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2411.17729 | Fast convolution algorithm for state space models | [
"math.NA",
"cs.AI",
"cs.NA"
] | We present a fast, robust algorithm for applying a matrix transfer function of a linear time invariant system (LTI) in time domain. Computing $L$ states of a multiple-input multiple-output (MIMO) LTI appears to require $L$ matrix-vector multiplications. We demonstrate that, for any finite user-selected accuracy, the nu... | {
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2411.17731 | Soil Characterization of Watermelon Field through Internet of Things: A
New Approach to Soil Salinity Measurement | [
"eess.SP",
"cs.AI",
"cs.LG"
] | In the modern agricultural industry, technology plays a crucial role in the advancement of cultivation. To increase crop productivity, soil require some specific characteristics. For watermelon cultivation, soil needs to be sandy and of high temperature with proper irrigation. This research aims to design and implement... | {
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2411.17733 | Comparison of Tiny Machine Learning Techniques for Embedded Acoustic
Emission Analysis | [
"eess.SP",
"cs.LG",
"eess.AS"
] | This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, clas... | {
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2411.17735 | 3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning | [
"cs.CV",
"cs.RO"
] | Constructing compact and informative 3D scene representations is essential for effective embodied exploration and reasoning, especially in complex environments over extended periods. Existing representations, such as object-centric 3D scene graphs, oversimplify spatial relationships by modeling scenes as isolated objec... | {
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2411.17738 | An Improved Dung Beetle Optimizer for Random Forest Optimization | [
"math.OC",
"cs.NE"
] | To improve the convergence speed and optimization accuracy of the Dung Beetle Optimizer (DBO), this paper proposes an improved algorithm based on circle mapping and longitudinal-horizontal crossover strategy (CICRDBO). First, the Circle method is used to map the initial population to increase diversity. Second, the lon... | {
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2411.17745 | A Parameter Adaptive Trajectory Tracking and Motion Control Framework
for Autonomous Vehicle | [
"eess.SY",
"cs.RO",
"cs.SY"
] | This paper studies the trajectory tracking and motion control problems for autonomous vehicles (AVs). A parameter adaptive control framework for AVs is proposed to enhance tracking accuracy and yaw stability. While establishing linear quadratic regulator (LQR) and three robust controllers, the control framework address... | {
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2411.17746 | UVCG: Leveraging Temporal Consistency for Universal Video Protection | [
"cs.CV",
"cs.AI"
] | The security risks of AI-driven video editing have garnered significant attention. Although recent studies indicate that adding perturbations to images can protect them from malicious edits, directly applying image-based methods to perturb each frame in a video becomes ineffective, as video editing techniques leverage ... | {
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2411.17748 | Deployment of ARX Models for Thermal Forecasting in Power Electronics
Boards Using WBG Semiconductors | [
"eess.SP",
"cs.LG",
"stat.ML"
] | Facing the thermal management challenges of Wide Bandgap (WBG) semiconductors, this study highlights the use of ARX parametric models, which provide accurate temperature predictions without requiring detailed understanding of component thickness disparities or material physical properties, relying solely on experimenta... | {
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2411.17749 | The Partially Observable Off-Switch Game | [
"cs.GT",
"cs.AI",
"cs.MA"
] | A wide variety of goals could cause an AI to disable its off switch because "you can't fetch the coffee if you're dead" (Russell 2019). Prior theoretical work on this shutdown problem assumes that humans know everything that AIs do. In practice, however, humans have only limited information. Moreover, in many of the se... | {
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2411.17752 | Path Loss Prediction Using Deep Learning | [
"eess.SP",
"cs.LG"
] | Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that use... | {
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2411.17755 | Deciphering Acoustic Emission with Machine Learning | [
"eess.SP",
"cond-mat.mtrl-sci",
"cs.LG"
] | Acoustic emission signals have been shown to accompany avalanche-like events in materials, such as dislocation avalanches in crystalline solids, collapse of voids in porous matter or domain wall movement in ferroics. The data provided by acoustic emission measurements is tremendously rich, but it is rather challenging ... | {
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2411.17759 | Modeling and Analysis of Phase-locked loops: a non reductionist approach | [
"eess.SY",
"cs.SY"
] | Phase-locked loop (PLL), conceived in 1932 by H. Bellescize, has been the basic electronic component in the development of communication technology from the early analog radio receptors to modern digital civil and military facilities. Traditionally, the analysis is conducted by modeling the dynamical behavior of phase ... | {
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2411.17760 | Efficient Self-Improvement in Multimodal Large Language Models: A
Model-Level Judge-Free Approach | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | Self-improvement in multimodal large language models (MLLMs) is crucial for enhancing their reliability and robustness. However, current methods often rely heavily on MLLMs themselves as judges, leading to high computational costs and potential pitfalls like reward hacking and model collapse. This paper introduces a no... | {
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2411.17761 | OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection | [
"cs.CV"
] | Open-world autonomous driving encompasses domain generalization and open-vocabulary. Domain generalization refers to the capabilities of autonomous driving systems across different scenarios and sensor parameter configurations. Open vocabulary pertains to the ability to recognize various semantic categories not encount... | {
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2411.17762 | MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding | [
"cs.CV"
] | We introduce MUSE-VL, a Unified Vision-Language Model through Semantic discrete Encoding for multimodal understanding and generation. Recently, the research community has begun exploring unified models for visual generation and understanding. However, existing vision tokenizers (e.g., VQGAN) only consider low-level inf... | {
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2411.17763 | Symmetry Strikes Back: From Single-Image Symmetry Detection to 3D
Generation | [
"cs.CV"
] | Symmetry is a ubiquitous and fundamental property in the visual world, serving as a critical cue for perception and structure interpretation. This paper investigates the detection of 3D reflection symmetry from a single RGB image, and reveals its significant benefit on single-image 3D generation. We introduce Reflect3D... | {
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2411.17764 | PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised
Online Refinement | [
"cs.RO",
"cs.AI"
] | We present PROGRESSOR, a novel framework that learns a task-agnostic reward function from videos, enabling policy training through goal-conditioned reinforcement learning (RL) without manual supervision. Underlying this reward is an estimate of the distribution over task progress as a function of the current, initial, ... | {
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2411.17765 | I2VControl: Disentangled and Unified Video Motion Synthesis Control | [
"cs.CV"
] | Video synthesis techniques are undergoing rapid progress, with controllability being a significant aspect of practical usability for end-users. Although text condition is an effective way to guide video synthesis, capturing the correct joint distribution between text descriptions and video motion remains a substantial ... | {
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2411.17766 | Integrating Dual Prototypes for Task-Wise Adaption in Pre-Trained
Model-Based Class-Incremental Learning | [
"cs.LG",
"stat.ML"
] | Class-incremental learning (CIL) aims to acquire new classes while conserving historical knowledge incrementally. Despite existing pre-trained model (PTM) based methods performing excellently in CIL, it is better to fine-tune them on downstream incremental tasks with massive patterns unknown to PTMs. However, using tas... | {
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2411.17767 | Exploring Aleatoric Uncertainty in Object Detection via Vision
Foundation Models | [
"cs.CV",
"cs.LG"
] | Datasets collected from the open world unavoidably suffer from various forms of randomness or noiseness, leading to the ubiquity of aleatoric (data) uncertainty. Quantifying such uncertainty is particularly pivotal for object detection, where images contain multi-scale objects with occlusion, obscureness, and even nois... | {
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} |
2411.17769 | Omegance: A Single Parameter for Various Granularities in
Diffusion-Based Synthesis | [
"cs.CV"
] | In this work, we introduce a single parameter $\omega$, to effectively control granularity in diffusion-based synthesis. This parameter is incorporated during the denoising steps of the diffusion model's reverse process. Our approach does not require model retraining, architectural modifications, or additional computat... | {
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} |
2411.17770 | MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual
Unmixing | [
"cs.LG"
] | Multivariate time series data provide a robust framework for future predictions by leveraging information across multiple dimensions, ensuring broad applicability in practical scenarios. However, their high dimensionality and mixing patterns pose significant challenges in establishing an interpretable and explicit mapp... | {
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} |
2411.17771 | DiagramQG: A Dataset for Generating Concept-Focused Questions from
Diagrams | [
"cs.CV"
] | Visual Question Generation (VQG) has gained significant attention due to its potential in educational applications. However, VQG researches mainly focus on natural images, neglecting diagrams in educational materials used to assess students' conceptual understanding. To address this gap, we introduce DiagramQG, a datas... | {
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} |
2411.17772 | MVBoost: Boost 3D Reconstruction with Multi-View Refinement | [
"cs.CV",
"cs.AI"
] | Recent advancements in 3D object reconstruction have been remarkable, yet most current 3D models rely heavily on existing 3D datasets. The scarcity of diverse 3D datasets results in limited generalization capabilities of 3D reconstruction models. In this paper, we propose a novel framework for boosting 3D reconstructio... | {
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} |
2411.17773 | Efficient Multi-modal Large Language Models via Visual Token Grouping | [
"cs.CV"
] | The development of Multi-modal Large Language Models (MLLMs) enhances Large Language Models (LLMs) with the ability to perceive data formats beyond text, significantly advancing a range of downstream applications, such as visual question answering and image captioning. However, the substantial computational costs assoc... | {
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} |
2411.17774 | Leaning Time-Varying Instruments for Identifying Causal Effects in
Time-Series Data | [
"cs.LG",
"cs.AI"
] | Querying causal effects from time-series data is important across various fields, including healthcare, economics, climate science, and epidemiology. However, this task becomes complex in the existence of time-varying latent confounders, which affect both treatment and outcome variables over time and can introduce bias... | {
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} |
2411.17776 | Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person
Anomaly Search | [
"cs.CV",
"cs.MM"
] | Text-based person search aims to retrieve specific individuals across camera networks using natural language descriptions. However, current benchmarks often exhibit biases towards common actions like walking or standing, neglecting the critical need for identifying abnormal behaviors in real-world scenarios. To meet su... | {
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} |
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