id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.12220 | Relieving Universal Label Noise for Unsupervised Visible-Infrared Person
Re-Identification by Inferring from Neighbors | [
"cs.CV",
"cs.AI"
] | Unsupervised visible-infrared person re-identification (USL-VI-ReID) is of great research and practical significance yet remains challenging due to the absence of annotations. Existing approaches aim to learn modality-invariant representations in an unsupervised setting. However, these methods often encounter label noi... | {
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2412.12221 | Parallel Greedy Best-First Search with a Bound on the Number of
Expansions Relative to Sequential Search | [
"cs.DS",
"cs.AI"
] | Parallelization of non-admissible search algorithms such as GBFS poses a challenge because straightforward parallelization can result in search behavior which significantly deviates from sequential search. Previous work proposed PUHF, a parallel search algorithm which is constrained to only expand states that can be ex... | {
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2412.12222 | Endangered Alert: A Field-Validated Self-Training Scheme for Detecting
and Protecting Threatened Wildlife on Roads and Roadsides | [
"cs.CV"
] | Traffic accidents are a global safety concern, resulting in numerous fatalities each year. A considerable number of these deaths are caused by animal-vehicle collisions (AVCs), which not only endanger human lives but also present serious risks to animal populations. This paper presents an innovative self-training metho... | {
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2412.12223 | Can video generation replace cinematographers? Research on the cinematic
language of generated video | [
"cs.CV",
"cs.AI"
] | Recent advancements in text-to-video (T2V) generation have leveraged diffusion models to enhance the visual coherence of videos generated from textual descriptions. However, most research has primarily focused on object motion, with limited attention given to cinematic language in videos, which is crucial for cinematog... | {
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2412.12224 | Application of machine learning in grain-related clustering of Laue
spots in a polycrystalline energy dispersive Laue pattern | [
"cond-mat.mtrl-sci",
"cs.LG",
"physics.app-ph",
"physics.data-an"
] | We address the identification of grain-corresponding Laue reflections in energy dispersive Laue diffraction (EDLD) experiments by formulating it as a clustering problem solvable through unsupervised machine learning (ML). To achieve reliable and efficient identification of grains in a Laue pattern, we employ a combinat... | {
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2412.12225 | DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.MM"
] | Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models often focus on extracting shared information across modalities or directly fusing heterogeneous modalities, such approaches can introduce re... | {
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2412.12226 | Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in
Language Models for Time Series Forecasting | [
"cs.LG",
"cs.AI"
] | Encoding time series into tokens and using language models for processing has been shown to substantially augment the models' ability to generalize to unseen tasks. However, existing language models for time series forecasting encounter several obstacles, including aliasing distortion and prolonged inference times, pri... | {
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2412.12227 | EDformer: Embedded Decomposition Transformer for Interpretable
Multivariate Time Series Predictions | [
"cs.LG",
"cs.AI"
] | Time series forecasting is a crucial challenge with significant applications in areas such as weather prediction, stock market analysis, and scientific simulations. This paper introduces an embedded decomposed transformer, 'EDformer', for multivariate time series forecasting tasks. Without altering the fundamental elem... | {
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2412.12228 | Linear Equations with Min and Max Operators: Computational Complexity | [
"cs.CC",
"cs.AI",
"math.OC"
] | We consider a class of optimization problems defined by a system of linear equations with min and max operators. This class of optimization problems has been studied under restrictive conditions, such as, (C1) the halting or stability condition; (C2) the non-negative coefficients condition; (C3) the sum up to 1 conditi... | {
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2412.12230 | The impact of AI on engineering design procedures for dynamical systems | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniq... | {
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2412.12231 | Demonstrating Data-to-Knowledge Pipelines for Connecting Production
Sites in the World Wide Lab | [
"cs.RO",
"cs.LG"
] | The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for pro... | {
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2412.12232 | You Only Submit One Image to Find the Most Suitable Generative Model | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Deep generative models have achieved promising results in image generation, and various generative model hubs, e.g., Hugging Face and Civitai, have been developed that enable model developers to upload models and users to download models. However, these model hubs lack advanced model management and identification mecha... | {
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2412.12234 | Deep Learning for Hydroelectric Optimization: Generating Long-Term River
Discharge Scenarios with Ensemble Forecasts from Global Circulation Models | [
"cs.LG",
"eess.SP",
"stat.ML"
] | Hydroelectric power generation is a critical component of the global energy matrix, particularly in countries like Brazil, where it represents the majority of the energy supply. However, its strong dependence on river discharges, which are inherently uncertain due to climate variability, poses significant challenges. R... | {
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2412.12237 | Equivariant Action Sampling for Reinforcement Learning and Planning | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Reinforcement learning (RL) algorithms for continuous control tasks require accurate sampling-based action selection. Many tasks, such as robotic manipulation, contain inherent problem symmetries. However, correctly incorporating symmetry into sampling-based approaches remains a challenge. This work addresses the chall... | {
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2412.12238 | Expanded Comprehensive Robotic Cholecystectomy Dataset (CRCD) | [
"cs.RO"
] | In recent years, the application of machine learning to minimally invasive surgery (MIS) has attracted considerable interest. Datasets are critical to the use of such techniques. This paper presents a unique dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers using the da Vinci Research Kit ... | {
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2412.12242 | OmniPrism: Learning Disentangled Visual Concept for Image Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However, existing methods are typically constrained to single-aspect concept generation or are easily disrupted by irrelevant concepts in multi-aspect concept scenarios, leading to concep... | {
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2412.12244 | ChronoFlow: A Data-Driven Model for Gyrochronology | [
"astro-ph.SR",
"astro-ph.GA",
"astro-ph.IM",
"cs.LG"
] | Gyrochronology is a technique for constraining stellar ages using rotation periods, which change over a star's main sequence lifetime due to magnetic braking. This technique shows promise for main sequence FGKM stars, where other methods are imprecise. However, models have historically struggled to capture the observed... | {
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2412.12276 | Emergence of Abstractions: Concept Encoding and Decoding Mechanism for
In-Context Learning in Transformers | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Humans distill complex experiences into fundamental abstractions that enable rapid learning and adaptation. Similarly, autoregressive transformers exhibit adaptive learning through in-context learning (ICL), which begs the question of how. In this paper, we propose concept encoding-decoding mechanism to explain ICL by ... | {
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2412.12278 | Towards a Universal Synthetic Video Detector: From Face or Background
Manipulations to Fully AI-Generated Content | [
"cs.CV"
] | Existing DeepFake detection techniques primarily focus on facial manipulations, such as face-swapping or lip-syncing. However, advancements in text-to-video (T2V) and image-to-video (I2V) generative models now allow fully AI-generated synthetic content and seamless background alterations, challenging face-centric detec... | {
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2412.12300 | Unanswerability Evaluation for Retrieval Augmented Generation | [
"cs.CL"
] | Existing evaluation frameworks for retrieval-augmented generation (RAG) systems focus on answerable queries, but they overlook the importance of appropriately rejecting unanswerable requests. In this paper, we introduce UAEval4RAG, a framework designed to evaluate whether RAG systems can handle unanswerable queries eff... | {
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2412.12306 | Ultra-wideband Double-Directionally Resolved Channel Measurements of
Line-of-Sight Microcellular Scenarios in the Upper Mid-band | [
"eess.SY",
"cs.SY"
] | The growing demand for higher data rates and expanded bandwidth is driving the exploration of new frequency ranges, including the upper mid-band spectrum (6-24 GHz), which is a promising candidate for future Frequency Range 3 (FR3) applications. This paper presents ultra-wideband double-directional channel measurements... | {
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2412.12310 | Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary
Expansion | [
"cs.CL"
] | This paper addresses the critical need for democratizing large language models (LLM) in the Arab world, a region that has seen slower progress in developing models comparable to state-of-the-art offerings like GPT-4 or ChatGPT 3.5, due to a predominant focus on mainstream languages (e.g., English and Chinese). One prac... | {
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2412.12314 | A Feasible Workflow for Retinal Vein Cannulation in Ex Vivo Porcine Eyes
with Robotic Assistance | [
"cs.RO"
] | A potential Retinal Vein Occlusion (RVO) treatment involves Retinal Vein Cannulation (RVC), which requires the surgeon to insert a microneedle into the affected retinal vein and administer a clot-dissolving drug. This procedure presents significant challenges due to human physiological limitations, such as hand tremors... | {
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2412.12318 | Graph-Guided Textual Explanation Generation Framework | [
"cs.CL"
] | Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model's reasoning about its predictions. However, recent work has questioned their faithfulness, as they may not accurately reflect the model's internal reasoning process regarding its predicted answer. In contrast, ... | {
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2412.12322 | RAG Playground: A Framework for Systematic Evaluation of Retrieval
Strategies and Prompt Engineering in RAG Systems | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.IR"
] | We present RAG Playground, an open-source framework for systematic evaluation of Retrieval-Augmented Generation (RAG) systems. The framework implements and compares three retrieval approaches: naive vector search, reranking, and hybrid vector-keyword search, combined with ReAct agents using different prompting strategi... | {
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2412.12324 | F-RBA: A Federated Learning-based Framework for Risk-based
Authentication | [
"cs.CR",
"cs.LG"
] | The proliferation of Internet services has led to an increasing need to protect private data. User authentication serves as a crucial mechanism to ensure data security. Although robust authentication forms the cornerstone of remote service security, it can still leave users vulnerable to credential disclosure, device-t... | {
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2412.12326 | Achieving Collective Welfare in Multi-Agent Reinforcement Learning via
Suggestion Sharing | [
"cs.MA",
"cs.AI",
"cs.LG"
] | In human society, the conflict between self-interest and collective well-being often obstructs efforts to achieve shared welfare. Related concepts like the Tragedy of the Commons and Social Dilemmas frequently manifest in our daily lives. As artificial agents increasingly serve as autonomous proxies for humans, we prop... | {
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2412.12327 | Leveraging Group Classification with Descending Soft Labeling for Deep
Imbalanced Regression | [
"cs.LG"
] | Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning. While recent works have already shown that incorporating various classification-based regularizers can produce enhanced outcomes, the ro... | {
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2412.12330 | Searching Personal Collections | [
"cs.IR"
] | This article describes the history of information retrieval on personal document collections. | {
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2412.12331 | Efficient Object-centric Representation Learning with Pre-trained
Geometric Prior | [
"cs.CV",
"cs.MM"
] | This paper addresses key challenges in object-centric representation learning of video. While existing approaches struggle with complex scenes, we propose a novel weakly-supervised framework that emphasises geometric understanding and leverages pre-trained vision models to enhance object discovery. Our method introduce... | {
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2412.12347 | AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific
Discovery | [
"cs.LG",
"cond-mat.mtrl-sci",
"physics.optics"
] | Advances in robotic control and sensing have propelled the rise of automated scientific laboratories capable of high-throughput experiments. However, automated scientific laboratories are currently limited by human intuition in their ability to efficiently design and interpret experiments in high-dimensional spaces, th... | {
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2412.12349 | Domain Generalization in Autonomous Driving: Evaluating YOLOv8s,
RT-DETR, and YOLO-NAS with the ROAD-Almaty Dataset | [
"cs.CV"
] | This study investigates the domain generalization capabilities of three state-of-the-art object detection models - YOLOv8s, RT-DETR, and YOLO-NAS - within the unique driving environment of Kazakhstan. Utilizing the newly constructed ROAD-Almaty dataset, which encompasses diverse weather, lighting, and traffic condition... | {
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2412.12351 | Krony-PT: GPT2 compressed with Kronecker Products | [
"cs.LG",
"cs.CL"
] | We introduce Krony-PT, a compression technique of GPT2 \citep{radford2019language} based on Kronecker Products. We specifically target the MLP layers of each transformer layer, and systematically compress the feed forward layer matrices to various degrees. We introduce a modified Van Loan decomposition to initialize th... | {
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2412.12358 | BioRAGent: A Retrieval-Augmented Generation System for Showcasing
Generative Query Expansion and Domain-Specific Search for Scientific Q&A | [
"cs.CL",
"cs.AI"
] | We present BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system for biomedical question answering. The system uses large language models (LLMs) for query expansion, snippet extraction, and answer generation while maintaining transparency through citation links to the source documents and disp... | {
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2412.12359 | LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters
through Modality Linear Representation-Steering | [
"cs.CV",
"cs.CL"
] | Multimodal Large Language Models (MLLMs) have significantly advanced visual tasks by integrating visual representations into large language models (LLMs). The textual modality, inherited from LLMs, equips MLLMs with abilities like instruction following and in-context learning. In contrast, the visual modality enhances ... | {
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2412.12361 | The Ramanujan Library -- Automated Discovery on the Hypergraph of
Integer Relations | [
"cs.AI",
"cs.MS",
"math.NT"
] | Fundamental mathematical constants appear in nearly every field of science, from physics to biology. Formulas that connect different constants often bring great insight by hinting at connections between previously disparate fields. Discoveries of such relations, however, have remained scarce events, relying on sporadic... | {
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2412.12362 | How Different AI Chatbots Behave? Benchmarking Large Language Models in
Behavioral Economics Games | [
"cs.AI",
"cs.CL"
] | The deployment of large language models (LLMs) in diverse applications requires a thorough understanding of their decision-making strategies and behavioral patterns. As a supplement to a recent study on the behavioral Turing test, this paper presents a comprehensive analysis of five leading LLM-based chatbot families a... | {
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2412.12364 | LogBabylon: A Unified Framework for Cross-Log File Integration and
Analysis | [
"cs.SE",
"cs.AI"
] | Logs are critical resources that record events, activities, or messages produced by software applications, operating systems, servers, and network devices. However, consolidating the heterogeneous logs and cross-referencing them is challenging and complicated. Manually analyzing the log data is time-consuming and prone... | {
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2412.12370 | Scam Detection for Ethereum Smart Contracts: Leveraging Graph
Representation Learning for Secure Blockchain | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.DC",
"cs.SI"
] | Due to the increasing abuse of fraudulent activities that result in significant financial and reputational harm, Ethereum smart contracts face a significant problem in detecting fraud. Existing monitoring methods typically rely on lease code analysis or physically extracted features, which suffer from scalability and a... | {
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2412.12371 | Priority-Aware Model-Distributed Inference at Edge Networks | [
"cs.DC",
"cs.LG"
] | Distributed inference techniques can be broadly classified into data-distributed and model-distributed schemes. In data-distributed inference (DDI), each worker carries the entire Machine Learning (ML) model but processes only a subset of the data. However, feeding the data to workers results in high communication cost... | {
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2412.12373 | Quantum Adversarial Machine Learning and Defense Strategies: Challenges
and Opportunities | [
"quant-ph",
"cs.CR",
"cs.LG"
] | As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles: (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring transpar... | {
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2412.12374 | Privacy in Metalearning and Multitask Learning: Modeling and Separations | [
"cs.LG",
"cs.CR"
] | Model personalization allows a set of individuals, each facing a different learning task, to train models that are more accurate for each person than those they could develop individually. The goals of personalization are captured in a variety of formal frameworks, such as multitask learning and metalearning. Combining... | {
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2412.12382 | Parallel Motif-Based Community Detection | [
"cs.SI"
] | Community detection is a central task in graph analytics. Given the substantial growth in graph size, scalability in community detection continues to be an unresolved challenge. Recently, alongside established methods like Louvain and Infomap, motif-based community detection has emerged. Techniques like Tectonic are no... | {
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2412.12385 | Enhancing Temporal Link Prediction with HierTKG: A Hierarchical Temporal
Knowledge Graph Framework | [
"cs.SI",
"cs.AI"
] | The rapid spread of misinformation on social media, especially during crises, challenges public decision-making. To address this, we propose HierTKG, a framework combining Temporal Graph Networks (TGN) and hierarchical pooling (DiffPool) to model rumor dynamics across temporal and structural scales. HierTKG captures ke... | {
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2412.12386 | Interpretable LLM-based Table Question Answering | [
"cs.CL",
"cs.LG"
] | Interpretability for Table Question Answering (Table QA) is critical, particularly in high-stakes industries like finance or healthcare. Although recent approaches using Large Language Models (LLMs) have significantly improved Table QA performance, their explanations for how the answers are generated are ambiguous. To ... | {
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2412.12390 | Development of an End-to-end Machine Learning System with Application to
In-app Purchases | [
"cs.LG"
] | Machine learning (ML) systems have become vital in the mobile gaming industry. Companies like King have been using them in production to optimize various parts of the gaming experience. One important area is in-app purchases: purchases made in the game by players in order to enhance and customize their gameplay experie... | {
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2412.12391 | Efficient Scaling of Diffusion Transformers for Text-to-Image Generation | [
"cs.CV",
"cs.CL",
"cs.LG"
] | We empirically study the scaling properties of various Diffusion Transformers (DiTs) for text-to-image generation by performing extensive and rigorous ablations, including training scaled DiTs ranging from 0.3B upto 8B parameters on datasets up to 600M images. We find that U-ViT, a pure self-attention based DiT model p... | {
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2412.12392 | MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors | [
"cs.CV",
"cs.RO"
] | We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system is robust on in-the-wild video sequences despite making no assumption on a fixed or parametric camera model beyond a unique camera centre. We in... | {
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2412.12395 | Sound Classification of Four Insect Classes | [
"cs.SD",
"cs.AI",
"eess.AS"
] | The goal of this project is to classify four different insect sounds: cicada, beetle, termite, and cricket. One application of this project is for pest control to monitor and protect our ecosystem. Our project leverages data augmentation, including pitch shifting and speed changing, to improve model generalization. Thi... | {
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2412.12397 | Optimizing Hyperparameters for Quantum Data Re-Uploaders in Calorimetric
Particle Identification | [
"quant-ph",
"cs.LG"
] | We present an application of a single-qubit Data Re-Uploading (QRU) quantum model for particle classification in calorimetric experiments. Optimized for Noisy Intermediate-Scale Quantum (NISQ) devices, this model requires minimal qubits while delivering strong classification performance. Evaluated on a novel simulated ... | {
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2412.12400 | Using machine learning to inform harvest control rule design in complex
fishery settings | [
"q-bio.PE",
"cs.LG",
"q-bio.QM"
] | In fishery science, harvest management of size-structured stochastic populations is a long-standing and difficult problem. Rectilinear precautionary policies based on biomass and harvesting reference points have now become a standard approach to this problem. While these standard feedback policies are adapted from anal... | {
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2412.12401 | Causally Consistent Normalizing Flow | [
"cs.LG"
] | Causal inconsistency arises when the underlying causal graphs captured by generative models like \textit{Normalizing Flows} (NFs) are inconsistent with those specified in causal models like \textit{Struct Causal Models} (SCMs). This inconsistency can cause unwanted issues including the unfairness problem. Prior works t... | {
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2412.12406 | Global SLAM in Visual-Inertial Systems with 5G Time-of-Arrival
Integration | [
"cs.RO"
] | This paper presents a novel approach that integrates 5G Time of Arrival (ToA) measurements into ORB-SLAM3 to enable global localization and enhance mapping capabilities for indoor drone navigation. We extend ORB-SLAM3's optimization pipeline to jointly process ToA data from 5G base stations alongside visual and inertia... | {
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2412.12408 | Automated Generation of Massive Reasonable Empirical Theorems by Forward
Reasoning Based on Strong Relevant Logics -- A Solution to the Problem of LLM
Pre-training Data Exhaustion | [
"cs.AI"
] | Recently, it is often said that the data used for the pre-training of large language models (LLMs) have been exhausted. This paper proposes a solution to the problem: Automated generation of massive reasonable empirical theorems by forward reasoning based on strong relevant logics. In fact, this can be regarded as a pa... | {
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2412.12409 | Improving Cooperation in Language Games with Bayesian Inference and the
Cognitive Hierarchy | [
"cs.AI",
"cs.GT"
] | In two-player cooperative games, agents can play together effectively when they have accurate assumptions about how their teammate will behave, but may perform poorly when these assumptions are inaccurate. In language games, failure may be due to disagreement in the understanding of either the semantics or pragmatics o... | {
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2412.12416 | DeepSN: A Sheaf Neural Framework for Influence Maximization | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Influence maximization is key topic in data mining, with broad applications in social network analysis and viral marketing. In recent years, researchers have increasingly turned to machine learning techniques to address this problem. They have developed methods to learn the underlying diffusion processes in a data-driv... | {
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2412.12417 | Bridging the Gap: Enhancing LLM Performance for Low-Resource African
Languages with New Benchmarks, Fine-Tuning, and Cultural Adjustments | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) have shown remarkable performance across various tasks, yet significant disparities remain for non-English languages, and especially native African languages. This paper addresses these disparities by creating approximately 1 million human-translated words of new benchmark data in 8 low-res... | {
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2412.12422 | Assessing the Limitations of Large Language Models in Clinical Fact
Decomposition | [
"cs.CL"
] | Verifying factual claims is critical for using large language models (LLMs) in healthcare. Recent work has proposed fact decomposition, which uses LLMs to rewrite source text into concise sentences conveying a single piece of information, as an approach for fine-grained fact verification. Clinical documentation poses u... | {
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2412.12423 | GG-SSMs: Graph-Generating State Space Models | [
"cs.LG"
] | State Space Models (SSMs) are powerful tools for modeling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, one-dimensional sequential processing, which restricts their ability to model non-local interactions in high-dimensional data. While methods like... | {
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2412.12427 | Ultra-wideband Time Difference of Arrival Indoor Localization: From
Sensor Placement to System Evaluation | [
"cs.RO"
] | Wireless indoor localization has attracted significant research interest due to its high accuracy, low cost, lightweight design, and low power consumption. Specifically, ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has emerged as a scalable positioning solution for mobile robots, consumer e... | {
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2412.12432 | Three Things to Know about Deep Metric Learning | [
"cs.CV",
"cs.AI"
] | This paper addresses supervised deep metric learning for open-set image retrieval, focusing on three key aspects: the loss function, mixup regularization, and model initialization. In deep metric learning, optimizing the retrieval evaluation metric, recall@k, via gradient descent is desirable but challenging due to its... | {
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2412.12433 | Refining Dimensions for Improving Clustering-based Cross-lingual Topic
Models | [
"cs.CL",
"cs.IR",
"cs.LG"
] | Recent works in clustering-based topic models perform well in monolingual topic identification by introducing a pipeline to cluster the contextualized representations. However, the pipeline is suboptimal in identifying topics across languages due to the presence of language-dependent dimensions (LDDs) generated by mult... | {
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2412.12434 | Circuit-Theoretic Joint Parameter-State Estimation of Utility-Scale
Photovoltaic, Battery, and Grid Systems | [
"eess.SY",
"cs.SY"
] | Solar PV and battery storage systems have become integral to modern power grids. Therefore, bulk grid models in real-time operation must include their physical behavior accurately for analysis and optimization. AC state estimation is critical to building real-time bulk power systems models. However, current ACSE techni... | {
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2412.12437 | Swarm Intelligence in Collision-free Formation Control for Multi-UAV
Systems with 3D Obstacle Avoidance Maneuvers | [
"cs.RO"
] | Recent advances in multi-agent systems manipulation have demonstrated a rising demand for the implementation of multi-UAV systems in urban areas which are always subjected to the presence of static and dynamic obstacles. The focus of the presented research is on the introduction of a nature-inspired collision-free cont... | {
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2412.12441 | Numerical Pruning for Efficient Autoregressive Models | [
"cs.LG",
"cs.AI"
] | Transformers have emerged as the leading architecture in deep learning, proving to be versatile and highly effective across diverse domains beyond language and image processing. However, their impressive performance often incurs high computational costs due to their substantial model size. This paper focuses on compres... | {
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2412.12442 | Multi-Task Reinforcement Learning for Quadrotors | [
"cs.RO",
"cs.LG"
] | Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenarios. However, these specialized policies often struggle with novel tasks, requiring a complete retraining of the policy from scratch. To add... | {
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2412.12444 | LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers | [
"cs.LG",
"cs.AI"
] | Diffusion Transformers have emerged as the preeminent models for a wide array of generative tasks, demonstrating superior performance and efficacy across various applications. The promising results come at the cost of slow inference, as each denoising step requires running the whole transformer model with a large amoun... | {
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2412.12445 | Persona-SQ: A Personalized Suggested Question Generation Framework For
Real-world Documents | [
"cs.CL"
] | Suggested questions (SQs) provide an effective initial interface for users to engage with their documents in AI-powered reading applications. In practical reading sessions, users have diverse backgrounds and reading goals, yet current SQ features typically ignore such user information, resulting in homogeneous or ineff... | {
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2412.12447 | PERC: Plan-As-Query Example Retrieval for Underrepresented Code
Generation | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Code generation with large language models has shown significant promise, especially when employing retrieval-augmented generation (RAG) with few-shot examples. However, selecting effective examples that enhance generation quality remains a challenging task, particularly when the target programming language (PL) is und... | {
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2412.12448 | Task-Parameter Nexus: Task-Specific Parameter Learning for Model-Based
Control | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper presents the Task-Parameter Nexus (TPN), a learning-based approach for online determination of the (near-)optimal control parameters of model-based controllers (MBCs) for tracking tasks. In TPN, a deep neural network is introduced to predict the control parameters for any given tracking task at runtime, espe... | {
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2412.12449 | Adversarially robust generalization theory via Jacobian regularization
for deep neural networks | [
"stat.ML",
"cs.LG"
] | Powerful deep neural networks are vulnerable to adversarial attacks. To obtain adversarially robust models, researchers have separately developed adversarial training and Jacobian regularization techniques. There are abundant theoretical and empirical studies for adversarial training, but theoretical foundations for Ja... | {
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2412.12456 | Graph Learning in the Era of LLMs: A Survey from the Perspective of
Data, Models, and Tasks | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.DB"
] | With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) into a unified Model architecture (e.g., LLM as enhancer, LLM as collaborat... | {
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2412.12459 | LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Topic modeling is widely used for uncovering thematic structures within text corpora, yet traditional models often struggle with specificity and coherence in domain-focused applications. Guided approaches, such as SeededLDA and CorEx, incorporate user-provided seed words to improve relevance but remain labor-intensive ... | {
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2412.12460 | PromptDet: A Lightweight 3D Object Detection Framework with LiDAR
Prompts | [
"cs.CV"
] | Multi-camera 3D object detection aims to detect and localize objects in 3D space using multiple cameras, which has attracted more attention due to its cost-effectiveness trade-off. However, these methods often struggle with the lack of accurate depth estimation caused by the natural weakness of the camera in ranging. R... | {
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2412.12463 | Pattern Analogies: Learning to Perform Programmatic Image Edits by
Analogy | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.HC"
] | Pattern images are everywhere in the digital and physical worlds, and tools to edit them are valuable. But editing pattern images is tricky: desired edits are often programmatic: structure-aware edits that alter the underlying program which generates the pattern. One could attempt to infer this underlying program, but ... | {
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2412.12464 | LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph
Reasoning for Cold-start Sequential Recommendation | [
"cs.IR"
] | Knowledge Graphs (KGs) represent relationships between entities in a graph structure and have been widely studied as promising tools for realizing recommendations that consider the accurate content information of items. However, traditional KG-based recommendation methods face fundamental challenges: insufficient consi... | {
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2412.12465 | Core Context Aware Attention for Long Context Language Modeling | [
"cs.CL",
"cs.LG"
] | Transformer-based Large Language Models (LLMs) have exhibited remarkable success in various natural language processing tasks primarily attributed to self-attention mechanism, which requires a token to consider all preceding tokens as its context to compute the attention score. However, when the context length L become... | {
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2412.12468 | Transferable and Forecastable User Targeting Foundation Model | [
"cs.LG",
"cs.AI"
] | User targeting, the process of selecting targeted users from a pool of candidates for non-expert marketers, has garnered substantial attention with the advancements in digital marketing. However, existing user targeting methods encounter two significant challenges: (i) Poor cross-domain and cross-scenario transferabili... | {
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2412.12469 | Optimal Control Operator Perspective and a Neural Adaptive Spectral
Method | [
"eess.SY",
"cs.AI",
"cs.LG",
"cs.SY",
"math.OC"
] | Optimal control problems (OCPs) involve finding a control function for a dynamical system such that a cost functional is optimized. It is central to physical systems in both academia and industry. In this paper, we propose a novel instance-solution control operator perspective, which solves OCPs in a one-shot manner wi... | {
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2412.12472 | Knowledge Boundary of Large Language Models: A Survey | [
"cs.CL"
] | Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowl... | {
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2412.12473 | A Method for Enhancing Generalization of Adam by Multiple Integrations | [
"cs.LG"
] | The insufficient generalization of adaptive moment estimation (Adam) has hindered its broader application. Recent studies have shown that flat minima in loss landscapes are highly associated with improved generalization. Inspired by the filtering effect of integration operations on high-frequency signals, we propose mu... | {
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2412.12475 | RareAgents: Advancing Rare Disease Care through LLM-Empowered
Multi-disciplinary Team | [
"cs.CL",
"cs.AI"
] | Rare diseases, despite their low individual incidence, collectively impact around 300 million people worldwide due to the vast number of diseases. The involvement of multiple organs and systems, and the shortage of specialized doctors with relevant experience make diagnosing and treating rare diseases more challenging ... | {
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2412.12478 | Human-in-the-Loop Generation of Adversarial Texts: A Case Study on
Tibetan Script | [
"cs.CL",
"cs.CR",
"cs.HC"
] | DNN-based language models perform excellently on various tasks, but even SOTA LLMs are susceptible to textual adversarial attacks. Adversarial texts play crucial roles in multiple subfields of NLP. However, current research has the following issues. (1) Most textual adversarial attack methods target rich-resourced lang... | {
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2412.12480 | Subversion Strategy Eval: Evaluating AI's stateless strategic
capabilities against control protocols | [
"cs.LG",
"cs.AI"
] | AI control protocols are plans for usefully deploying AI systems in a way that is safe, even if the AI intends to subvert the protocol. Previous work evaluated protocols by subverting them with a human-AI red team, where an AI follows the human-written strategy. This paper investigates how well AI systems can generate ... | {
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2412.12483 | AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph
Neural Networks | [
"cs.LG"
] | In real-world applications, spectral Graph Neural Networks (GNNs) are powerful tools for processing diverse types of graphs. However, a single GNN often struggles to handle different graph types-such as homogeneous and heterogeneous graphs-simultaneously. This challenge has led to the manual design of GNNs tailored to ... | {
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2412.12484 | Evolutionary Optimization for Designing Variational Quantum Circuits
with High Model Capacity | [
"quant-ph",
"cs.AI",
"cs.ET",
"cs.LG",
"cs.NE"
] | Recent advancements in quantum computing (QC) and machine learning (ML) have garnered significant attention, leading to substantial efforts toward the development of quantum machine learning (QML) algorithms to address a variety of complex challenges. The design of high-performance QML models, however, requires expert-... | {
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2412.12486 | Boosting Long-Context Management via Query-Guided Activation Refilling | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Processing long contexts poses a significant challenge for large language models (LLMs) due to their inherent context-window limitations and the computational burden of extensive key-value (KV) activations, which severely impact efficiency. For information-seeking tasks, full context perception is often unnecessary, as... | {
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2412.12487 | Echo: Simulating Distributed Training At Scale | [
"cs.LG",
"cs.DC"
] | Simulation offers unique values for both enumeration and extrapolation purposes, and is becoming increasingly important for managing the massive machine learning (ML) clusters and large-scale distributed training jobs. In this paper, we build Echo to tackle three key challenges in large-scale training simulation: (1) t... | {
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2412.12492 | DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for
Semi-Supervised Medical Image Segmentation | [
"cs.CV"
] | Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. However, it often suffers from error supervision from low-quality pseudo labels. Vision-Language Model (VLM) has great potential to enhance pseudo labe... | {
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2412.12493 | A Simple and Fast Way to Handle Semantic Errors in Transactions | [
"cs.DB",
"cs.AI"
] | Many computer systems are now being redesigned to incorporate LLM-powered agents, enabling natural language input and more flexible operations. This paper focuses on handling database transactions created by large language models (LLMs). Transactions generated by LLMs may include semantic errors, requiring systems to t... | {
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} |
2412.12494 | Multi-UAV Collaborative Trajectory Planning for Seamless Data Collection
and Transmission | [
"eess.SY",
"cs.SY"
] | Unmanned aerial vehicles (UAVs) have attracted plenty of attention due to their high flexibility and enhanced communication ability. However, the limited coverage and energy of UAVs make it difficult to provide timely wireless service for large-scale sensor networks, which also exist in multiple UAVs. To this end, the ... | {
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"cs.SY": 1
} |
2412.12496 | Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training | [
"cs.CV",
"cs.AI"
] | Vision Mamba (e.g., Vim) has successfully been integrated into computer vision, and token reduction has yielded promising outcomes in Vision Transformers (ViTs). However, token reduction performs less effectively on Vision Mamba compared to ViTs. Pruning informative tokens in Mamba leads to a high loss of key knowledge... | {
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} |
2412.12497 | NLSR: Neuron-Level Safety Realignment of Large Language Models Against
Harmful Fine-Tuning | [
"cs.CL"
] | The emergence of finetuning-as-a-service has revealed a new vulnerability in large language models (LLMs). A mere handful of malicious data uploaded by users can subtly manipulate the finetuning process, resulting in an alignment-broken model. Existing methods to counteract fine-tuning attacks typically require substan... | {
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} |
2412.12499 | LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for
Low-Resource Language Reasoning | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have exhibited impressive multilingual reasoning capabilities, driven by extensive multilingual pre-training corpora and instruction fine-tuning data. However, a performance gap exists between high- and low-resource language reasoning tasks due to the language imbalance in the pre-training ... | {
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} |
2412.12500 | Beyond Data Quantity: Key Factors Driving Performance in Multilingual
Language Models | [
"cs.CL",
"cs.AI"
] | Multilingual language models (MLLMs) are crucial for handling text across various languages, yet they often show performance disparities due to differences in resource availability and linguistic characteristics. While the impact of pre-train data percentage and model size on performance is well-known, our study reveal... | {
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} |
2412.12501 | Unleashing the Potential of Model Bias for Generalized Category
Discovery | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Generalized Category Discovery is a significant and complex task that aims to identify both known and undefined novel categories from a set of unlabeled data, leveraging another labeled dataset containing only known categories. The primary challenges stem from model bias induced by pre-training on only known categories... | {
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} |
2412.12502 | Track the Answer: Extending TextVQA from Image to Video with
Spatio-Temporal Clues | [
"cs.CV"
] | Video text-based visual question answering (Video TextVQA) is a practical task that aims to answer questions by jointly reasoning textual and visual information in a given video. Inspired by the development of TextVQA in image domain, existing Video TextVQA approaches leverage a language model (e.g. T5) to process text... | {
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} |
2412.12503 | Multi-Scale Cross-Fusion and Edge-Supervision Network for Image Splicing
Localization | [
"cs.CV"
] | Image Splicing Localization (ISL) is a fundamental yet challenging task in digital forensics. Although current approaches have achieved promising performance, the edge information is insufficiently exploited, resulting in poor integrality and high false alarms. To tackle this problem, we propose a multi-scale cross-fus... | {
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} |
2412.12504 | Boosting LLM-based Relevance Modeling with Distribution-Aware Robust
Learning | [
"cs.IR"
] | With the rapid advancement of pre-trained large language models (LLMs), recent endeavors have leveraged the capabilities of LLMs in relevance modeling, resulting in enhanced performance. This is usually done through the process of fine-tuning LLMs on specifically annotated datasets to determine the relevance between qu... | {
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} |
2412.12505 | DocFusion: A Unified Framework for Document Parsing Tasks | [
"cs.CL"
] | Document parsing is essential for analyzing complex document structures and extracting fine-grained information, supporting numerous downstream applications. However, existing methods often require integrating multiple independent models to handle various parsing tasks, leading to high complexity and maintenance overhe... | {
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} |
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