id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.06052 | A Comprehensive Energy Management Application Method considering Smart
Home Occupant Behavior using IoT and Real Big Data | [
"eess.SY",
"cs.SY"
] | One of the most far-reaching use cases of the internet of things is in smart grid and smart home operation. The smart home concept allows residents to control, monitor, and manage their energy consumption with minimum loss and self-involvement. Since each household's lifestyle and energy consumption is unique, the mana... | {
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2502.06058 | Regular LDPC codes on BMS wiretap channels: Security bounds | [
"cs.IT",
"math.IT"
] | We improve the secrecy guarantees for transmission over general binary memoryless symmetric wiretap channels that relies on regular LDPC codes. Previous works showed that LDPC codes achieve secrecy capacity of some classes of wiretap channels while leaking $o(n)$ bits of information over $n$ uses of the channel. In thi... | {
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2502.06060 | Training Language Models for Social Deduction with Multi-Agent
Reinforcement Learning | [
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MA"
] | Communicating in natural language is a powerful tool in multi-agent settings, as it enables independent agents to share information in partially observable settings and allows zero-shot coordination with humans. However, most prior works are limited as they either rely on training with large amounts of human demonstrat... | {
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2502.06061 | Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein
Regularization | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | Recent advancements in reinforcement learning (RL) have achieved great success in fine-tuning diffusion-based generative models. However, fine-tuning continuous flow-based generative models to align with arbitrary user-defined reward functions remains challenging, particularly due to issues such as policy collapse from... | {
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2502.06062 | Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop
Yield Prediction | [
"eess.IV",
"cs.AI"
] | This study introduces RicEns-Net, a novel Deep Ensemble model designed to predict crop yields by integrating diverse data sources through multimodal data fusion techniques. The research focuses specifically on the use of synthetic aperture radar (SAR), optical remote sensing data from Sentinel 1, 2, and 3 satellites, a... | {
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2502.06065 | Benchmarking Prompt Sensitivity in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Large language Models (LLMs) are highly sensitive to variations in prompt formulation, which can significantly impact their ability to generate accurate responses. In this paper, we introduce a new task, Prompt Sensitivity Prediction, and a dataset PromptSET designed to investigate the effects of slight prompt variatio... | {
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2502.06067 | Lipschitz-Driven Inference: Bias-corrected Confidence Intervals for
Spatial Linear Models | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Linear models remain ubiquitous in modern spatial applications - including climate science, public health, and economics - due to their interpretability, speed, and reproducibility. While practitioners generally report a form of uncertainty, popular spatial uncertainty quantification methods do not jointly handle model... | {
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2502.06072 | ID policy (with reassignment) is asymptotically optimal for
heterogeneous weakly-coupled MDPs | [
"cs.LG",
"math.OC",
"math.PR"
] | Heterogeneity poses a fundamental challenge for many real-world large-scale decision-making problems but remains largely understudied. In this paper, we study the fully heterogeneous setting of a prominent class of such problems, known as weakly-coupled Markov decision processes (WCMDPs). Each WCMDP consists of $N$ arm... | {
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2502.06075 | Deconstructing Depression Stigma: Integrating AI-driven Data Collection
and Analysis with Causal Knowledge Graphs | [
"cs.HC",
"cs.CL",
"cs.CY"
] | Mental-illness stigma is a persistent social problem, hampering both treatment-seeking and recovery. Accordingly, there is a pressing need to understand it more clearly, but analyzing the relevant data is highly labor-intensive. Therefore, we designed a chatbot to engage participants in conversations; coded those conve... | {
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2502.06076 | A Planning Framework for Adaptive Labeling | [
"cs.LG"
] | Ground truth labels/outcomes are critical for advancing scientific and engineering applications, e.g., evaluating the treatment effect of an intervention or performance of a predictive model. Since randomly sampling inputs for labeling can be prohibitively expensive, we introduce an adaptive labeling framework where me... | {
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2502.06079 | Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo | [
"cs.LG"
] | Discrete diffusion models are a class of generative models that produce samples from an approximated data distribution within a discrete state space. Often, there is a need to target specific regions of the data distribution. Current guidance methods aim to sample from a distribution with mass proportional to $p_0(x_0)... | {
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2502.06084 | Physics-Guided Foundation Model for Scientific Discovery: An Application
to Aquatic Science | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Physics-guided machine learning (PGML) has become a prevalent approach in studying scientific systems due to its ability to integrate scientific theories for enhancing machine learning (ML) models. However, most PGML approaches are tailored to isolated and relatively simple tasks, which limits their applicability to co... | {
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2502.06086 | Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual
Combination with Property Type | [
"cs.CL"
] | Conceptual combination is a cognitive process that merges basic concepts, enabling the creation of complex expressions. During this process, the properties of combination (e.g., the whiteness of a peeled apple) can be inherited from basic concepts, newly emerge, or be canceled. However, previous studies have evaluated ... | {
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2502.06087 | ConMeC: A Dataset for Metonymy Resolution with Common Nouns | [
"cs.CL"
] | Metonymy plays an important role in our daily communication. People naturally think about things using their most salient properties or commonly related concepts. For example, by saying "The bus decided to skip our stop today," we actually mean that the bus driver made the decision, not the bus. Prior work on metonymy ... | {
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2502.06089 | On the Computability of Multiclass PAC Learning | [
"cs.LG",
"stat.ML"
] | We study the problem of computable multiclass learnability within the Probably Approximately Correct (PAC) learning framework of Valiant (1984). In the recently introduced computable PAC (CPAC) learning framework of Agarwal et al. (2020), both learners and the functions they output are required to be computable. We foc... | {
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2502.06094 | Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models | [
"cs.CV"
] | Fairness is a fundamental principle in medical ethics. Vision Language Models (VLMs) have shown significant potential in the medical field due to their ability to leverage both visual and linguistic contexts, reducing the need for large datasets and enabling the performance of complex tasks. However, the exploration of... | {
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2502.06095 | Rateless Joint Source-Channel Coding, and a Blueprint for 6G Semantic
Communications System Design | [
"cs.IT",
"cs.AI",
"math.IT"
] | This paper introduces rateless joint source-channel coding (rateless JSCC). The code is rateless in that it is designed and optimized for a continuum of coding rates such that it achieves a desired distortion for any rate in that continuum. We further introduce rate-adaptive and stable communication link operation to a... | {
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2502.06096 | Post-detection inference for sequential changepoint localization | [
"stat.ML",
"cs.AI",
"cs.LG",
"stat.ME"
] | This paper addresses a fundamental but largely unexplored challenge in sequential changepoint analysis: conducting inference following a detected change. We study the problem of localizing the changepoint using only the data observed up to a data-dependent stopping time at which a sequential detection algorithm $\mathc... | {
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2502.06097 | NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized
Recommendation Systems | [
"cs.IR",
"cs.AI"
] | Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list. Due to the inherent challenges of combinatorial search spaces, some current research adopts an evaluator-generator paradigm, with a generator generating feasible sequences and an evaluator selecting the bes... | {
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2502.06099 | Fine-Tuning Federated Learning-Based Intrusion Detection Systems for
Transportation IoT | [
"cs.LG"
] | The rapid advancement of machine learning (ML) and on-device computing has revolutionized various industries, including transportation, through the development of Connected and Autonomous Vehicles (CAVs) and Intelligent Transportation Systems (ITS). These technologies improve traffic management and vehicle safety, but ... | {
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2502.06100 | Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text
Recognition | [
"cs.CV",
"eess.SP"
] | Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attention-based recognition ... | {
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2502.06101 | RALLRec: Improving Retrieval Augmented Large Language Model
Recommendation with Representation Learning | [
"cs.IR",
"cs.CL"
] | Large Language Models (LLMs) have been integrated into recommendation systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further incorporated into these systems to retrieve more relevant items and improve system performance. However, existing RAG methods rely primarily... | {
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2502.06105 | Comprehensive Framework for Evaluating Conversational AI Chatbots | [
"cs.CY",
"cs.AI"
] | Conversational AI chatbots are transforming industries by streamlining customer service, automating transactions, and enhancing user engagement. However, evaluating these systems remains a challenge, particularly in financial services, where compliance, user trust, and operational efficiency are critical. This paper in... | {
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2502.06106 | Circuit-tuning: A Mechanistic Approach for Identifying Parameter
Redundancy and Fine-tuning Neural Networks | [
"cs.LG",
"cs.AI",
"cs.CL"
] | The study of mechanistic interpretability aims to reverse-engineer a model to explain its behaviors. While recent studies have focused on the static mechanism of a certain behavior, the training dynamics inside a model remain to be explored. In this work, we develop an interpretable method for fine-tuning and reveal th... | {
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2502.06109 | CDM: Contact Diffusion Model for Multi-Contact Point Localization | [
"cs.RO"
] | In this paper, we propose a Contact Diffusion Model (CDM), a novel learning-based approach for multi-contact point localization. We consider a robot equipped with joint torque sensors and a force/torque sensor at the base. By leveraging a diffusion model, CDM addresses the singularity where multiple pairs of contact po... | {
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2502.06111 | CSR-Bench: Benchmarking LLM Agents in Deployment of Computer Science
Research Repositories | [
"cs.SE",
"cs.AI",
"cs.LG"
] | The increasing complexity of computer science research projects demands more effective tools for deploying code repositories. Large Language Models (LLMs), such as Anthropic Claude and Meta Llama, have demonstrated significant advancements across various fields of computer science research, including the automation of ... | {
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2502.06112 | Pcodec: Better Compression for Numerical Sequences | [
"cs.IT",
"cs.DS",
"math.IT"
] | We present Pcodec (Pco), a format and algorithm for losslessly compressing numerical sequences. Pco's core and most novel component is a binning algorithm that quickly converges to the true entropy of smoothly, independently, and identically distributed (SIID) data. To automatically handle more general data, Pco has tw... | {
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2502.06113 | Towards Bio-inspired Heuristically Accelerated Reinforcement Learning
for Adaptive Underwater Multi-Agents Behaviour | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper describes the problem of coordination of an autonomous Multi-Agent System which aims to solve the coverage planning problem in a complex environment. The considered applications are the detection and identification of objects of interest while covering an area. These tasks, which are highly relevant for spac... | {
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2502.06114 | A Novel Multi-Teacher Knowledge Distillation for Real-Time Object
Detection using 4D Radar | [
"cs.CV"
] | Accurate 3D object detection is crucial for safe autonomous navigation, requiring reliable performance across diverse weather conditions. While LiDAR performance deteriorates in challenging weather, Radar systems maintain their reliability. Traditional Radars have limitations due to their lack of elevation data, but th... | {
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2502.06115 | Task-driven Layerwise Additive Activation Intervention | [
"cs.CL",
"cs.LG"
] | Modern language models (LMs) have significantly advanced generative modeling in natural language processing (NLP). Despite their success, LMs often struggle with adaptation to new contexts in real-time applications. A promising approach to task adaptation is activation intervention, which steers the LMs' generation pro... | {
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2502.06116 | Event Vision Sensor: A Review | [
"physics.ins-det",
"cs.CV"
] | By monitoring temporal contrast, event-based vision sensors can provide high temporal resolution and low latency while maintaining low power consumption and simplicity in circuit structure. These characteristics have garnered significant attention in both academia and industry. In recent years, the application of back-... | {
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2502.06117 | Revisiting Dynamic Graph Clustering via Matrix Factorization | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix factorization-based methods are promising approaches for this task; however, these methods often struggle with scalability and can be time-consum... | {
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2502.06118 | Token-Domain Multiple Access: Exploiting Semantic Orthogonality for
Collision Mitigation | [
"cs.IT",
"eess.SP",
"math.IT"
] | Token communications is an emerging generative semantic communication concept that reduces transmission rates by using context and transformer-based token processing, with tokens serving as universal semantic units. In this paper, we propose a semantic multiple access scheme in the token domain, referred to as ToDMA, w... | {
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2502.06119 | An Appearance Defect Detection Method for Cigarettes Based on
C-CenterNet | [
"cs.CV"
] | Due to the poor adaptability of traditional methods in the cigarette detection task on the automatic cigarette production line, it is difficult to accurately identify whether a cigarette has defects and the types of defects; thus, a cigarette appearance defect detection method based on C-CenterNet is proposed. This det... | {
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2502.06123 | Real-Time LiDAR Point Cloud Compression and Transmission for
Resource-constrained Robots | [
"cs.RO"
] | LiDARs are widely used in autonomous robots due to their ability to provide accurate environment structural information. However, the large size of point clouds poses challenges in terms of data storage and transmission. In this paper, we propose a novel point cloud compression and transmission framework for resource-c... | {
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2502.06124 | Foundation Model of Electronic Medical Records for Adaptive Risk
Estimation | [
"cs.LG",
"cs.AI"
] | We developed the Enhanced Transformer for Health Outcome Simulation (ETHOS), an AI model that tokenizes patient health timelines (PHTs) from EHRs. ETHOS predicts future PHTs using transformer-based architectures. The Adaptive Risk Estimation System (ARES) employs ETHOS to compute dynamic and personalized risk probabili... | {
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2502.06126 | Graph Pseudotime Analysis and Neural Stochastic Differential Equations
for Analyzing Retinal Degeneration Dynamics and Beyond | [
"cs.LG"
] | Understanding disease progression at the molecular pathway level usually requires capturing both structural dependencies between pathways and the temporal dynamics of disease evolution. In this work, we solve the former challenge by developing a biologically informed graph-forming method to efficiently construct pathwa... | {
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2502.06127 | Improved YOLOv5s model for key components detection of power
transmission lines | [
"cs.CV",
"cs.AI"
] | High-voltage transmission lines are located far from the road, resulting in inconvenient inspection work and rising maintenance costs. Intelligent inspection of power transmission lines has become increasingly important. However, subsequent intelligent inspection relies on accurately detecting various key components. D... | {
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2502.06128 | Intelligent Reconfigurable Optical Wireless Ether | [
"eess.SY",
"cs.SY",
"eess.SP"
] | Optical wireless communication (OWC) uses light for wireless data transmission, potentially providing faster and more secure communication than traditional radio-frequency-based techniques like Wi-Fi. However, light's high directionality and its limited penetration ability restrict the signal coverage. To address this ... | {
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2502.06130 | Self-Correcting Decoding with Generative Feedback for Mitigating
Hallucinations in Large Vision-Language Models | [
"cs.CV",
"cs.CL"
] | While recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone to generating hallucinatory text responses that do not align with the given visual input, which restricts their practical applicability in real-world scenarios. In this work, inspired by the observat... | {
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2502.06132 | Enhancing Document Key Information Localization Through Data
Augmentation | [
"cs.CV",
"cs.CL"
] | The Visually Rich Form Document Intelligence and Understanding (VRDIU) Track B focuses on the localization of key information in document images. The goal is to develop a method capable of localizing objects in both digital and handwritten documents, using only digital documents for training. This paper presents a simp... | {
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2502.06134 | Integrating Sequence and Image Modeling in Irregular Medical Time Series
Through Self-Supervised Learning | [
"cs.CV",
"cs.AI"
] | Medical time series are often irregular and face significant missingness, posing challenges for data analysis and clinical decision-making. Existing methods typically adopt a single modeling perspective, either treating series data as sequences or transforming them into image representations for further classification.... | {
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2502.06136 | Graph Neural Networks at a Fraction | [
"cs.LG",
"cs.AI"
] | Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data. In addition to real-valued GNNs, quaternion GNNs also perform well on tasks on graph-structured data. With the aim of reducing the energy footprint, we reduce the model size while maintaining accuracy comp... | {
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2502.06138 | Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in
IoT Environment | [
"cs.CR",
"cs.CV"
] | Cyberattacks in an Internet of Things (IoT) environment can have significant impacts because of the interconnected nature of devices and systems. An attacker uses a network of compromised IoT devices in a botnet attack to carry out various harmful activities. Detecting botnet attacks poses several challenges because of... | {
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2502.06139 | LCIRC: A Recurrent Compression Approach for Efficient Long-form Context
and Query Dependent Modeling in LLMs | [
"cs.CL"
] | While large language models (LLMs) excel in generating coherent and contextually rich outputs, their capacity to efficiently handle long-form contexts is limited by fixed-length position embeddings. Additionally, the computational cost of processing long sequences increases quadratically, making it challenging to exten... | {
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2502.06141 | Mixed Reality Outperforms Virtual Reality for Remote Error Resolution in
Pick-and-Place Tasks | [
"cs.RO",
"cs.HC"
] | This study evaluates the performance and usability of Mixed Reality (MR), Virtual Reality (VR), and camera stream interfaces for remote error resolution tasks, such as correcting warehouse packaging errors. Specifically, we consider a scenario where a robotic arm halts after detecting an error, requiring a remote opera... | {
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2502.06142 | Linear Bandits with Partially Observable Features | [
"stat.ML",
"cs.LG"
] | We introduce a novel linear bandit problem with partially observable features, resulting in partial reward information and spurious estimates. Without proper address for latent part, regret possibly grows linearly in decision horizon $T$, as their influence on rewards are unknown. To tackle this, we propose a novel ana... | {
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2502.06145 | Animate Anyone 2: High-Fidelity Character Image Animation with
Environment Affordance | [
"cs.CV"
] | Recent character image animation methods based on diffusion models, such as Animate Anyone, have made significant progress in generating consistent and generalizable character animations. However, these approaches fail to produce reasonable associations between characters and their environments. To address this limitat... | {
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2502.06146 | Guided Exploration for Efficient Relational Model Learning | [
"cs.LG",
"cs.AI"
] | Efficient exploration is critical for learning relational models in large-scale environments with complex, long-horizon tasks. Random exploration methods often collect redundant or irrelevant data, limiting their ability to learn accurate relational models of the environment. Goal-literal babbling (GLIB) improves upon ... | {
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2502.06147 | LegalViz: Legal Text Visualization by Text To Diagram Generation | [
"cs.CL"
] | Legal documents including judgments and court orders require highly sophisticated legal knowledge for understanding. To disclose expert knowledge for non-experts, we explore the problem of visualizing legal texts with easy-to-understand diagrams and propose a novel dataset of LegalViz with 23 languages and 7,010 cases ... | {
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2502.06148 | Optimizing Knowledge Integration in Retrieval-Augmented Generation with
Self-Selection | [
"cs.CL",
"cs.IR"
] | Retrieval-Augmented Generation (RAG), which integrates external knowledge into Large Language Models (LLMs), has proven effective in enabling LLMs to produce more accurate and reliable responses. However, it remains a significant challenge how to effectively integrate external retrieved knowledge with internal parametr... | {
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2502.06149 | Reward-Based Collision-Free Algorithm for Trajectory Planning of
Autonomous Robots | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper introduces a new mission planning algorithm for autonomous robots that enables the reward-based selection of an optimal waypoint sequence from a predefined set. The algorithm computes a feasible trajectory and corresponding control inputs for a robot to navigate between waypoints while avoiding obstacles, ma... | {
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2502.06150 | Scaling Public Health Text Annotation: Zero-Shot Learning vs.
Crowdsourcing for Improved Efficiency and Labeling Accuracy | [
"cs.CL"
] | Public health researchers are increasingly interested in using social media data to study health-related behaviors, but manually labeling this data can be labor-intensive and costly. This study explores whether zero-shot labeling using large language models (LLMs) can match or surpass conventional crowd-sourced annotat... | {
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2502.06151 | Powerformer: A Transformer with Weighted Causal Attention for
Time-series Forecasting | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Transformers have recently shown strong performance in time-series forecasting, but their all-to-all attention mechanism overlooks the (temporal) causal and often (temporally) local nature of data. We introduce Powerformer, a novel Transformer variant that replaces noncausal attention weights with causal weights that a... | {
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2502.06152 | The Value of Information in Human-AI Decision-making | [
"cs.AI",
"cs.LG"
] | Humans and AIs are often paired on decision tasks with the expectation of achieving complementary performance, where the combination of human and AI outperforms either one alone. However, how to improve performance of a human-AI team is often not clear without knowing more about what particular information and strategi... | {
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2502.06153 | Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks | [
"cs.LG",
"cs.AI"
] | Kolmogorov--Arnold networks (KANs) have demonstrated their potential as an alternative to multi-layer perceptions (MLPs) in various domains, especially for science-related tasks. However, transfer learning of KANs remains a relatively unexplored area. In this paper, inspired by Tucker decomposition of tensors and evide... | {
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2502.06155 | Efficient-vDiT: Efficient Video Diffusion Transformers With Attention
Tile | [
"cs.CV"
] | Despite the promise of synthesizing high-fidelity videos, Diffusion Transformers (DiTs) with 3D full attention suffer from expensive inference due to the complexity of attention computation and numerous sampling steps. For example, the popular Open-Sora-Plan model consumes more than 9 minutes for generating a single vi... | {
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2502.06156 | Axial current as the origin of quantum intrinsic orbital angular
momentum | [
"hep-ph",
"cs.SY",
"eess.SY"
] | We show that it is impossible to experimentally observe the quantum intrinsic orbital angular momentum (IOAM) effect without its axial current. Broadly speaking, we argue that the spiral or interference characteristics of the axial current density determine the occurrence of nonlinear or tunneling effects in any spacet... | {
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2502.06159 | Analysis and Optimization of Robustness in Multiplex Flow Networks
Against Cascading Failures | [
"eess.SY",
"cs.SY"
] | Networked systems are susceptible to cascading failures, where the failure of an initial set of nodes propagates through the network, often leading to system-wide failures. In this work, we propose a multiplex flow network model to study robustness against cascading failures triggered by random failures. The model is i... | {
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2502.06163 | Scalable k-Means Clustering for Large k via Seeded Approximate
Nearest-Neighbor Search | [
"cs.LG",
"cs.CG",
"stat.ML"
] | For very large values of $k$, we consider methods for fast $k$-means clustering of massive datasets with $10^7\sim10^9$ points in high-dimensions ($d\geq100$). All current practical methods for this problem have runtimes at least $\Omega(k^2)$. We find that initialization routines are not a bottleneck for this case. In... | {
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2502.06164 | Generalized Temporal Tensor Decomposition with Rank-revealing Latent-ODE | [
"cs.LG",
"stat.ML"
] | Tensor decomposition is a fundamental tool for analyzing multi-dimensional data by learning low-rank factors to represent high-order interactions. While recent works on temporal tensor decomposition have made significant progress by incorporating continuous timestamps in latent factors, they still struggle with general... | {
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2502.06166 | Portable, High-Frequency, and High-Voltage Control Circuits for
Untethered Miniature Robots Driven by Dielectric Elastomer Actuators | [
"cs.RO"
] | In this work, we propose a high-voltage, high-frequency control circuit for the untethered applications of dielectric elastomer actuators (DEAs). The circuit board leverages low-voltage resistive components connected in series to control voltages of up to 1.8 kV within a compact size, suitable for frequencies ranging f... | {
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2502.06167 | Universal Approximation of Visual Autoregressive Transformers | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | We investigate the fundamental limits of transformer-based foundation models, extending our analysis to include Visual Autoregressive (VAR) transformers. VAR represents a big step toward generating images using a novel, scalable, coarse-to-fine ``next-scale prediction'' framework. These models set a new quality bar, ou... | {
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2502.06168 | Dynamic Pricing with Adversarially-Censored Demands | [
"stat.ML",
"cs.LG",
"econ.EM",
"math.OC"
] | We study an online dynamic pricing problem where the potential demand at each time period $t=1,2,\ldots, T$ is stochastic and dependent on the price. However, a perishable inventory is imposed at the beginning of each time $t$, censoring the potential demand if it exceeds the inventory level. To address this problem, w... | {
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2502.06170 | An Interpretable Implicit-Based Approach for Modeling Local Spatial
Effects: A Case Study of Global Gross Primary Productivity | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In Earth sciences, unobserved factors exhibit non-stationary spatial distributions, causing the relationships between features and targets to display spatial heterogeneity. In geographic machine learning tasks, conventional statistical learning methods often struggle to capture spatial heterogeneity, leading to unsatis... | {
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2502.06171 | A Data-Efficient Pan-Tumor Foundation Model for Oncology CT
Interpretation | [
"eess.IV",
"cs.CV"
] | Artificial intelligence-assisted imaging analysis has made substantial strides in tumor diagnosis and management. Here we present PASTA, a pan-tumor CT foundation model that achieves state-of-the-art performance on 45 of 46 representative oncology tasks -- including lesion segmentation, tumor detection in plain CT, tum... | {
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2502.06172 | PLATTER: A Page-Level Handwritten Text Recognition System for Indic
Scripts | [
"cs.CV"
] | In recent years, the field of Handwritten Text Recognition (HTR) has seen the emergence of various new models, each claiming to perform competitively better than the other in specific scenarios. However, making a fair comparison of these models is challenging due to inconsistent choices and diversity in test sets. Furt... | {
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2502.06173 | Uncertainty-Aware Adaptation of Large Language Models for
Protein-Protein Interaction Analysis | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.AP",
"stat.ML"
] | Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer. Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structu... | {
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2502.06178 | Bayesian Optimization by Kernel Regression and Density-based Exploration | [
"math.OC",
"cs.LG",
"stat.ML"
] | Bayesian optimization is highly effective for optimizing expensive-to-evaluate black-box functions, but it faces significant computational challenges due to the high computational complexity of Gaussian processes, which results in a total time complexity that is quartic with respect to the number of iterations. To addr... | {
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2502.06180 | RideKE: Leveraging Low-Resource, User-Generated Twitter Content for
Sentiment and Emotion Detection in Kenyan Code-Switched Dataset | [
"cs.CL",
"cs.AI"
] | Social media has become a crucial open-access platform for individuals to express opinions and share experiences. However, leveraging low-resource language data from Twitter is challenging due to scarce, poor-quality content and the major variations in language use, such as slang and code-switching. Identifying tweets ... | {
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2502.06181 | CANeRV: Content Adaptive Neural Representation for Video Compression | [
"cs.CV"
] | Recent advances in video compression introduce implicit neural representation (INR) based methods, which effectively capture global dependencies and characteristics of entire video sequences. Unlike traditional and deep learning based approaches, INR-based methods optimize network parameters from a global perspective, ... | {
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2502.06185 | Discourse-Driven Evaluation: Unveiling Factual Inconsistency in Long
Document Summarization | [
"cs.CL",
"cs.AI"
] | Detecting factual inconsistency for long document summarization remains challenging, given the complex structure of the source article and long summary length. In this work, we study factual inconsistency errors and connect them with a line of discourse analysis. We find that errors are more common in complex sentences... | {
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2502.06186 | Learning the Frequency Dynamics of the Power System Using Higher-order
Dynamic Mode Decomposition | [
"eess.SY",
"cs.SY"
] | The increasing penetration of renewable energy sources, characterised by low inertia and intermittent disturbances, presents substantial challenges to power system stability. As critical indicators of system stability, frequency dynamics and associated oscillatory phenomena have attracted significant research attention... | {
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2502.06189 | Multi-Level Decoupled Relational Distillation for Heterogeneous
Architectures | [
"cs.CV"
] | Heterogeneous distillation is an effective way to transfer knowledge from cross-architecture teacher models to student models. However, existing heterogeneous distillation methods do not take full advantage of the dark knowledge hidden in the teacher's output, limiting their performance.To this end, we propose a novel ... | {
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2502.06190 | Is Science Inevitable? | [
"cs.DL",
"cs.SI"
] | Using large-scale citation data and a breakthrough metric, the study systematically evaluates the inevitability of scientific breakthroughs. We find that scientific breakthroughs emerge as multiple discoveries rather than singular events. Through analysis of over 40 million journal articles, we identify multiple discov... | {
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2502.06192 | Right Time to Learn:Promoting Generalization via Bio-inspired Spacing
Effect in Knowledge Distillation | [
"cs.LG",
"cs.AI"
] | Knowledge distillation (KD) is a powerful strategy for training deep neural networks (DNNs). Although it was originally proposed to train a more compact ``student'' model from a large ``teacher'' model, many recent efforts have focused on adapting it to promote generalization of the model itself, such as online KD and ... | {
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2502.06193 | Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge
in Software Engineering | [
"cs.SE",
"cs.AI"
] | Recently, large language models (LLMs) have been deployed to tackle various software engineering (SE) tasks like code generation, significantly advancing the automation of SE tasks. However, assessing the quality of these LLM-generated code and text remains challenging. The commonly used Pass@k metric necessitates exte... | {
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2502.06194 | Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting
in Anomaly Detection | [
"cs.CV"
] | Unsupervised Continuous Anomaly Detection (UCAD) faces significant challenges in multi-task representation learning, with existing methods suffering from incomplete representation and catastrophic forgetting. Unlike supervised models, unsupervised scenarios lack prior information, making it difficult to effectively dis... | {
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2502.06195 | Calibration of Multiple Asynchronous Microphone Arrays using Hybrid TDOA | [
"cs.SD",
"cs.RO"
] | Accurate calibration of acoustic sensing systems made of multiple asynchronous microphone arrays is essential for satisfactory performance in sound source localization and tracking. State-of-the-art calibration methods for this type of system rely on the time difference of arrival and direction of arrival measurements ... | {
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2502.06196 | Improved Extrinsic Calibration of Acoustic Cameras via Batch
Optimization | [
"cs.RO",
"cs.SD"
] | Acoustic cameras have found many applications in practice. Accurate and reliable extrinsic calibration of the microphone array and visual sensors within acoustic cameras is crucial for fusing visual and auditory measurements. Existing calibration methods either require prior knowledge of the microphone array geometry o... | {
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2502.06200 | On the query complexity of sampling from non-log-concave distributions | [
"cs.DS",
"cs.LG",
"stat.ML"
] | We study the problem of sampling from a $d$-dimensional distribution with density $p(x)\propto e^{-f(x)}$, which does not necessarily satisfy good isoperimetric conditions. Specifically, we show that for any $L,M$ satisfying $LM\ge d\ge 5$, $\epsilon\in \left(0,\frac{1}{32}\right)$, and any algorithm with query acces... | {
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2502.06201 | Comparing Image Segmentation Algorithms | [
"cs.CV"
] | This paper presents a novel approach for denoising binary images using simulated annealing (SA), a global optimization technique that addresses the inherent challenges of non convex energy functions. Binary images are often corrupted by noise, necessitating effective restoration methods. We propose an energy function E... | {
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2502.06204 | Non-literal Understanding of Number Words by Language Models | [
"cs.CL"
] | Humans naturally interpret numbers non-literally, effortlessly combining context, world knowledge, and speaker intent. We investigate whether large language models (LLMs) interpret numbers similarly, focusing on hyperbole and pragmatic halo effects. Through systematic comparison with human data and computational models... | {
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2502.06205 | C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like
Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Retrieval-augmented generation (RAG) systems face a fundamental challenge in aligning independently developed retrievers and large language models (LLMs). Existing approaches typically involve modifying either component or introducing simple intermediate modules, resulting in practical limitations and sub-optimal perfo... | {
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2502.06207 | Unveiling the Capabilities of Large Language Models in Detecting
Offensive Language with Annotation Disagreement | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have become essential for offensive language detection, yet their ability to handle annotation disagreement remains underexplored. Disagreement samples, which arise from subjective interpretations, pose a unique challenge due to their ambiguous nature. Understanding how LLMs process these c... | {
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2502.06208 | Product gales and Finite state dimension | [
"cs.IT",
"math.IT"
] | In this work, we introduce the notion of product gales, which is the modification of an $s$-gale such that $k$ separate bets can be placed at each symbol. The product of the bets placed are taken into the capital function of the product-gale. We show that Hausdorff dimension can be characterised using product gales. ... | {
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2502.06209 | Enhancing Cost Efficiency in Active Learning with Candidate Set Query | [
"cs.LG",
"cs.CV"
] | This paper introduces a cost-efficient active learning (AL) framework for classification, featuring a novel query design called candidate set query. Unlike traditional AL queries requiring the oracle to examine all possible classes, our method narrows down the set of candidate classes likely to include the ground-truth... | {
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2502.06210 | Position: Continual Learning Benefits from An Evolving Population over
An Unified Model | [
"cs.LG"
] | Deep neural networks have demonstrated remarkable success in machine learning; however, they remain fundamentally ill-suited for Continual Learning (CL). Recent research has increasingly focused on achieving CL without the need for rehearsal. Among these, parameter isolation-based methods have proven particularly effec... | {
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2502.06212 | AVSim -- Realistic Simulation Framework for Airborne and Vector-Borne
Disease Dynamics | [
"eess.SY",
"cs.SY"
] | The COVID-19 pandemic underscored the critical need for rapid epidemic trend identification and effective intervention strategies to mitigate disease progression and its socio-economic impact. Concurrent with emerging threats, endemic diseases like dengue continue to strain healthcare systems, particularly in populous,... | {
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2502.06215 | LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83
Software Engineering Benchmarks | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) are widely utilized in software engineering (SE) tasks, such as code generation and automated program repair. However, their reliance on extensive and often undisclosed pre-training datasets raises significant concerns about data leakage, where the evaluation benchmark data is unintentional... | {
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2502.06217 | Examining False Positives under Inference Scaling for Mathematical
Reasoning | [
"cs.CL",
"cs.AI"
] | Recent advancements in language models have led to significant improvements in mathematical reasoning across various benchmarks. However, most of these benchmarks rely on automatic evaluation methods that only compare final answers using heuristics, without verifying the underlying reasoning steps. This limitation resu... | {
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} |
2502.06219 | Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for
Generalizable RGB-Depth Driving Scene Parsing | [
"cs.CV"
] | Recent vision foundation models (VFMs), typically based on Vision Transformer (ViT), have significantly advanced numerous computer vision tasks. Despite their success in tasks focused solely on RGB images, the potential of VFMs in RGB-depth driving scene parsing remains largely under-explored. In this article, we take ... | {
"Other": 0,
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} |
2502.06220 | FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and
Cup Segmentation in Fundus Images | [
"cs.CV",
"cs.IR"
] | The Segment Anything Model (SAM) has gained popularity as a versatile image segmentation method, thanks to its strong generalization capabilities across various domains. However, when applied to optic disc (OD) and optic cup (OC) segmentation tasks, SAM encounters challenges due to the complex structures, low contrast,... | {
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} |
2502.06221 | Interaction-aware Conformal Prediction for Crowd Navigation | [
"cs.RO"
] | During crowd navigation, robot motion plan needs to consider human motion uncertainty, and the human motion uncertainty is dependent on the robot motion plan. We introduce Interaction-aware Conformal Prediction (ICP) to alternate uncertainty-aware robot motion planning and decision-dependent human motion uncertainty qu... | {
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} |
2502.06227 | Unsupervised deep learning for semantic segmentation of multispectral
LiDAR forest point clouds | [
"cs.CV"
] | Point clouds captured with laser scanning systems from forest environments can be utilized in a wide variety of applications within forestry and plant ecology, such as the estimation of tree stem attributes, leaf angle distribution, and above-ground biomass. However, effectively utilizing the data in such tasks require... | {
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} |
2502.06231 | Falsification of Unconfoundedness by Testing Independence of Causal
Mechanisms | [
"stat.ME",
"cs.LG",
"stat.ML"
] | A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions such as the assumption of no unmeasured confounding. In this work, we propose an algorithm that can falsify the assumption of no unmeasured confounding in a setting with observational data from multiple h... | {
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} |
2502.06233 | Confidence Improves Self-Consistency in LLMs | [
"cs.CL",
"cs.AI"
] | Self-consistency decoding enhances LLMs' performance on reasoning tasks by sampling diverse reasoning paths and selecting the most frequent answer. However, it is computationally expensive, as sampling many of these (lengthy) paths is required to increase the chances that the correct answer emerges as the most frequent... | {
"Other": 0,
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} |
2502.06235 | Conditioning and AGM-like belief change in the Desirability-Indifference
framework | [
"cs.AI",
"math.PR",
"quant-ph"
] | We show how the AGM framework for belief change (expansion, revision, contraction) can be extended to deal with conditioning in the so-called Desirability-Indifference framework, based on abstract notions of accepting and rejecting options, as well as on abstract notions of events. This level of abstraction allows us t... | {
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} |
2502.06238 | XNet-Enhanced Deep BSDE Method and Numerical Analysis | [
"cs.CE"
] | Solving high-dimensional semilinear parabolic partial differential equations (PDEs) challenges traditional numerical methods due to the "curse of dimensionality." Deep learning, particularly through the Deep BSDE method, offers a promising alternative by leveraging neural networks' capability to approximate high-dimens... | {
"Other": 0,
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} |
2502.06239 | Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System | [
"eess.SP",
"cs.IT",
"math.IT"
] | The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of... | {
"Other": 0,
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"cs.SY": 0
} |
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