id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.06948 | The Einstein Test: Towards a Practical Test of a Machine's Ability to
Exhibit Superintelligence | [
"cs.AI"
] | Creative and disruptive insights (CDIs), such as the development of the theory of relativity, have punctuated human history, marking pivotal shifts in our intellectual trajectory. Recent advancements in artificial intelligence (AI) have sparked debates over whether state of the art models possess the capacity to genera... | {
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2501.06954 | A Hessian-informed hyperparameter optimization for differential learning
rate | [
"cs.LG"
] | Differential learning rate (DLR), a technique that applies different learning rates to different model parameters, has been widely used in deep learning and achieved empirical success via its various forms. For example, parameter-efficient fine-tuning (PEFT) applies zero learning rates to most parameters so as to signi... | {
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2501.06956 | Patent Novelty Assessment Accelerating Innovation and Patent Prosecution | [
"cs.DL",
"cs.AI",
"cs.IR"
] | In the rapidly evolving landscape of technological innovation, safeguarding intellectual property rights through patents is crucial for fostering progress and stimulating research and development investments. This report introduces a ground-breaking Patent Novelty Assessment and Claim Generation System, meticulously cr... | {
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2501.06959 | Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music | [
"cs.SD",
"cs.DL",
"cs.LG",
"eess.AS"
] | Music source separation demixes a piece of music into its individual sound sources (vocals, percussion, melodic instruments, etc.), a task with no simple mathematical solution. It requires deep learning methods involving training on large datasets of isolated music stems. The most commonly available datasets are made f... | {
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2501.06962 | Compact Bayesian Neural Networks via pruned MCMC sampling | [
"cs.LG",
"cs.AI"
] | Bayesian Neural Networks (BNNs) offer robust uncertainty quantification in model predictions, but training them presents a significant computational challenge. This is mainly due to the problem of sampling multimodal posterior distributions using Markov Chain Monte Carlo (MCMC) sampling and variational inference algori... | {
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2501.06963 | Generative Artificial Intelligence-Supported Pentesting: A Comparison
between Claude Opus, GPT-4, and Copilot | [
"cs.CR",
"cs.AI"
] | The advent of Generative Artificial Intelligence (GenAI) has brought a significant change to our society. GenAI can be applied across numerous fields, with particular relevance in cybersecurity. Among the various areas of application, its use in penetration testing (pentesting) or ethical hacking processes is of specia... | {
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2501.06964 | Enhancing Patient-Centric Communication: Leveraging LLMs to Simulate
Patient Perspectives | [
"cs.AI",
"cs.HC"
] | Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing scenarios, particularly in simulating domain-specific experts using tailored prompts. This ability enables LLMs to adopt the persona of individuals with specific backgrounds, offering a cost-effective and efficient alternative to tra... | {
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2501.06965 | Kolmogorov-Arnold Recurrent Network for Short Term Load Forecasting
Across Diverse Consumers | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Load forecasting plays a crucial role in energy management, directly impacting grid stability, operational efficiency, cost reduction, and environmental sustainability. Traditional Vanilla Recurrent Neural Networks (RNNs) face issues such as vanishing and exploding gradients, whereas sophisticated RNNs such as LSTMs ha... | {
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2501.06970 | Next-Gen Space-Based Surveillance: Blockchain for Trusted and Efficient
Debris Tracking | [
"cs.IT",
"math.IT"
] | This paper presents a novel blockchain-enabled architecture for efficient decentralized space surveillance. Our simulation results indicate that a network under 30 nodes achieves optimal throughput and response time. We also compare our architecture with a fully participatory consensus model, where all nodes perform bo... | {
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2501.06974 | Downlink OFDM-FAMA in 5G-NR Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division... | {
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2501.06976 | TensorConvolutionPlus: A python package for distribution system
flexibility area estimation | [
"cs.SE",
"cs.SY",
"eess.SY"
] | Power system operators need new, efficient operational tools to use the flexibility of distributed resources and deal with the challenges of highly uncertain and variable power systems. Transmission system operators can consider the available flexibility in distribution systems (DSs) without breaching the DS constraint... | {
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2501.06978 | Towards a visually interpretable analysis of Two-Phase Locking
membership | [
"cs.DB"
] | Two-phase locking (2PL) is a consolidated policy commonly adopted by Database Management Systems to enforce serializability of a schedule. While the policy is well understood, both in its standard and in the strict version, automatically deriving a suitable tabular/graphical analysis of schedules with respect to 2PL is... | {
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2501.06980 | Combining LLM decision and RL action selection to improve RL policy for
adaptive interventions | [
"cs.LG",
"cs.AI"
] | Reinforcement learning (RL) is increasingly being used in the healthcare domain, particularly for the development of personalized health adaptive interventions. Inspired by the success of Large Language Models (LLMs), we are interested in using LLMs to update the RL policy in real time, with the goal of accelerating pe... | {
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2501.06981 | Data Enrichment Work and AI Labor in Latin America and the Caribbean | [
"cs.CY",
"cs.AI",
"cs.HC"
] | The global AI surge demands crowdworkers from diverse languages and cultures. They are pivotal in labeling data for enabling global AI systems. Despite global significance, research has primarily focused on understanding the perspectives and experiences of US and India crowdworkers, leaving a notable gap. To bridge thi... | {
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2501.06985 | Graph Contrastive Learning on Multi-label Classification for
Recommendations | [
"cs.IR",
"cs.AI"
] | In business analysis, providing effective recommendations is essential for enhancing company profits. The utilization of graph-based structures, such as bipartite graphs, has gained popularity for their ability to analyze complex data relationships. Link prediction is crucial for recommending specific items to users. T... | {
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2501.06986 | LEO: Boosting Mixture of Vision Encoders for Multimodal Large Language
Models | [
"cs.CV",
"cs.CL"
] | Enhanced visual understanding serves as a cornerstone for multimodal large language models (MLLMs). Recent hybrid MLLMs incorporate a mixture of vision experts to address the limitations of using a single vision encoder and excessively long visual tokens. Despite the progress of these MLLMs, a research gap remains in e... | {
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2501.06987 | Hand-Object Contact Detection using Grasp Quality Metrics | [
"cs.RO"
] | We propose a novel hand-object contact detection system based on grasp quality metrics extracted from object and hand poses, and evaluated its performance using the DexYCB dataset. Our evaluation demonstrated the system's high accuracy (approaching 90%). Future work will focus on a real-time implementation using vision... | {
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2501.06988 | Fully Differentiable Boundary Element Solver for Hydrodynamic
Sensitivity Analysis of Wave-Structure Interactions | [
"cs.CE"
] | Accurately predicting wave-structure interactions is critical for the effective design and analysis of marine structures. This is typically achieved using solvers that employ the boundary element method (BEM), which relies on linear potential flow theory. Precise estimation of the sensitivity of these interactions is e... | {
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2501.06994 | Motion Tracks: A Unified Representation for Human-Robot Transfer in
Few-Shot Imitation Learning | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Teaching robots to autonomously complete everyday tasks remains a challenge. Imitation Learning (IL) is a powerful approach that imbues robots with skills via demonstrations, but is limited by the labor-intensive process of collecting teleoperated robot data. Human videos offer a scalable alternative, but it remains di... | {
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2501.06999 | Likelihood Training of Cascaded Diffusion Models via Hierarchical
Volume-preserving Maps | [
"cs.LG",
"cs.AI"
] | Cascaded models are multi-scale generative models with a marked capacity for producing perceptually impressive samples at high resolutions. In this work, we show that they can also be excellent likelihood models, so long as we overcome a fundamental difficulty with probabilistic multi-scale models: the intractability o... | {
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2501.07000 | Multiple-gain Estimation for Running Time of Evolutionary Combinatorial
Optimization | [
"cs.NE"
] | The running-time analysis of evolutionary combinatorial optimization is a fundamental topic in evolutionary computation. Its current research mainly focuses on specific algorithms for simplified problems due to the challenge posed by fluctuating fitness values. This paper proposes a multiple-gain model to estimate the ... | {
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2501.07005 | Global Search for Optimal Low Thrust Spacecraft Trajectories using
Diffusion Models and the Indirect Method | [
"eess.SY",
"cs.LG",
"cs.SY",
"math.OC"
] | Long time-duration low-thrust nonlinear optimal spacecraft trajectory global search is a computationally and time expensive problem characterized by clustering patterns in locally optimal solutions. During preliminary mission design, mission parameters are subject to frequent changes, necessitating that trajectory desi... | {
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2501.07013 | Sthymuli: a Static Educational Robot. Leveraging the Thymio II Platform | [
"cs.RO"
] | The use of robots in education represents a challenge for teachers and a fixed vision of what robots can do for students. This paper presents the development of Sthymuli, a static educational robot designed to explore new classroom interactions between robots, students and teachers. We propose the use of the Thymio II ... | {
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2501.07014 | AlgoRxplorers | Precision in Mutation: Enhancing Drug Design with
Advanced Protein Stability Prediction Tools | [
"cs.LG",
"cs.AI"
] | Predicting the impact of single-point amino acid mutations on protein stability is essential for understanding disease mechanisms and advancing drug development. Protein stability, quantified by changes in Gibbs free energy ($\Delta\Delta G$), is influenced by these mutations. However, the scarcity of data and the comp... | {
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2501.07015 | SplatMAP: Online Dense Monocular SLAM with 3D Gaussian Splatting | [
"cs.CV"
] | Achieving high-fidelity 3D reconstruction from monocular video remains challenging due to the inherent limitations of traditional methods like Structure-from-Motion (SfM) and monocular SLAM in accurately capturing scene details. While differentiable rendering techniques such as Neural Radiance Fields (NeRF) address som... | {
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2501.07016 | A Multi-Modal Deep Learning Framework for Pan-Cancer Prognosis | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Prognostic task is of great importance as it closely related to the survival analysis of patients, the optimization of treatment plans and the allocation of resources. The existing prognostic models have shown promising results on specific datasets, but there are limitations in two aspects. On the one hand, they merely... | {
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2501.07017 | UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN
Powered Vision-LSTM | [
"cs.CV",
"cs.AI"
] | 3D medical image segmentation has progressed considerably due to Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), yet these methods struggle to balance long-range dependency acquisition with computational efficiency. To address this challenge, we propose UNETVL (U-Net Vision-LSTM), a novel architect... | {
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2501.07020 | ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical
Normalization | [
"cs.CL",
"cs.AI"
] | ViSoLex is an open-source system designed to address the unique challenges of lexical normalization for Vietnamese social media text. The platform provides two core services: Non-Standard Word (NSW) Lookup and Lexical Normalization, enabling users to retrieve standard forms of informal language and standardize text con... | {
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2501.07021 | Neural Probabilistic Circuits: Enabling Compositional and Interpretable
Predictions through Logical Reasoning | [
"cs.LG",
"cs.AI"
] | End-to-end deep neural networks have achieved remarkable success across various domains but are often criticized for their lack of interpretability. While post hoc explanation methods attempt to address this issue, they often fail to accurately represent these black-box models, resulting in misleading or incomplete exp... | {
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2501.07022 | Improved Regret Bounds for Online Fair Division with Bandit Learning | [
"cs.GT",
"cs.LG"
] | We study online fair division when there are a finite number of item types and the player values for the items are drawn randomly from distributions with unknown means. In this setting, a sequence of indivisible items arrives according to a random online process, and each item must be allocated to a single player. The ... | {
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2501.07024 | A Proposed Large Language Model-Based Smart Search for Archive System | [
"cs.AI",
"cs.IR"
] | This study presents a novel framework for smart search in digital archival systems, leveraging the capabilities of Large Language Models (LLMs) to enhance information retrieval. By employing a Retrieval-Augmented Generation (RAG) approach, the framework enables the processing of natural language queries and transformin... | {
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2501.07025 | A Weighted Similarity Metric for Community Detection in Sparse Data | [
"stat.ME",
"cs.SI"
] | Many Natural Language Processing (NLP) related applications involves topics and sentiments derived from short documents such as consumer reviews and social media posts. Topics and sentiments of short documents are highly sparse because a short document generally covers a few topics among hundreds of candidates. Imputat... | {
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2501.07026 | IEEE_TIE25: Analysis and Synthesis of DOb-based Robust Motion
Controllers | [
"eess.SY",
"cs.SY"
] | By employing a unified state-space design framework, this paper proposes a novel systematic analysis and synthesis method that facilitates the implementation of both conventional zero-order (ZO) and high-order (HO) DObs. Furthermore, this design method supports the development of advanced DObs (e.g., the proposed High-... | {
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2501.07027 | Necessary and sufficient condition for constructing a single qudit
insertion/deletion code and its decoding algorithm | [
"quant-ph",
"cs.IT",
"math.IT"
] | This paper shows that Knill-Laflamme condition, known as a necessary and sufficient condition for quantum error-correction, can be applied to quantum errors where the number of particles changes before and after the error. This fact shows that correctabilities of single deletion errors and single insertion errors are e... | {
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2501.07030 | Erasing Noise in Signal Detection with Diffusion Model: From Theory to
Application | [
"eess.SY",
"cs.LG",
"cs.SY",
"eess.SP"
] | In this paper, a signal detection method based on the denoise diffusion model (DM) is proposed, which outperforms the maximum likelihood (ML) estimation method that has long been regarded as the optimal signal detection technique. Theoretically, a novel mathematical theory for intelligent signal detection based on stoc... | {
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2501.07032 | PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks | [
"cs.LG"
] | Kolmogorov-Arnold Networks (KANs) represent an innovation in neural network architectures, offering a compelling alternative to Multi-Layer Perceptrons (MLPs) in models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. By advancing network design, KANs drive groundbreakin... | {
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2501.07033 | Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based
Models | [
"cs.LG",
"cs.CR",
"cs.CV"
] | This study explores the use of Generative Adversarial Networks (GANs) to detect AI deepfakes and fraudulent activities in online payment systems. With the growing prevalence of deepfake technology, which can manipulate facial features in images and videos, the potential for fraud in online transactions has escalated. T... | {
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2501.07034 | Explore the Use of Time Series Foundation Model for Car-Following
Behavior Analysis | [
"cs.LG"
] | Modeling car-following behavior is essential for traffic simulation, analyzing driving patterns, and understanding complex traffic flows with varying levels of autonomous vehicles. Traditional models like the Safe Distance Model and Intelligent Driver Model (IDM) require precise parameter calibration and often lack gen... | {
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2501.07039 | IoT-Based Real-Time Medical-Related Human Activity Recognition Using
Skeletons and Multi-Stage Deep Learning for Healthcare | [
"cs.CV"
] | The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing medical-related human activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions that are critical to patient well-being. Ho... | {
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2501.07040 | Rethinking Knowledge in Distillation: An In-context Sample Retrieval
Perspective | [
"cs.CV"
] | Conventional knowledge distillation (KD) approaches are designed for the student model to predict similar output as the teacher model for each sample. Unfortunately, the relationship across samples with same class is often neglected. In this paper, we explore to redefine the knowledge in distillation, capturing the rel... | {
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2501.07041 | Beam Structured Turbo Receiver for HF Skywave Massive MIMO | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we investigate receiver design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. We first establish a modified beam based channel model (BBCM) by performing uniform sampling for directional cosine with deterministic sampling interval, where the beam matrix is c... | {
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2501.07044 | Protego: Detecting Adversarial Examples for Vision Transformers via
Intrinsic Capabilities | [
"cs.CV",
"cs.LG"
] | Transformer models have excelled in natural language tasks, prompting the vision community to explore their implementation in computer vision problems. However, these models are still influenced by adversarial examples. In this paper, we investigate the attack capabilities of six common adversarial attacks on three pre... | {
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2501.07045 | ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression | [
"cs.LG",
"cs.AI"
] | In deep regression, capturing the relationship among continuous labels in feature space is a fundamental challenge that has attracted increasing interest. Addressing this issue can prevent models from converging to suboptimal solutions across various regression tasks, leading to improved performance, especially for imb... | {
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2501.07046 | Differentially Private Kernelized Contextual Bandits | [
"stat.ML",
"cs.LG"
] | We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space (RKHS). We study this problem under the additional constraint of joint differential privacy, where the agents needs to ensure that the sequence of query... | {
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2501.07047 | Leveraging ASIC AI Chips for Homomorphic Encryption | [
"cs.CR",
"cs.AR",
"cs.CL",
"cs.PL"
] | Cloud-based services are making the outsourcing of sensitive client data increasingly common. Although homomorphic encryption (HE) offers strong privacy guarantee, it requires substantially more resources than computing on plaintext, often leading to unacceptably large latencies in getting the results. HE accelerators ... | {
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2501.07048 | Unveiling the Potential of Text in High-Dimensional Time Series
Forecasting | [
"cs.AI"
] | Time series forecasting has traditionally focused on univariate and multivariate numerical data, often overlooking the benefits of incorporating multimodal information, particularly textual data. In this paper, we propose a novel framework that integrates time series models with Large Language Models to improve high-di... | {
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2501.07051 | ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot
Interaction | [
"cs.RO",
"cs.HC"
] | Human-robot interaction (HRI) is an interdisciplinary field that utilises both quantitative and qualitative methods. While ROSBags, a file format within the Robot Operating System (ROS), offer an efficient means of collecting temporally synched multimodal data in empirical studies with real robots, there is a lack of t... | {
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2501.07054 | PoAct: Policy and Action Dual-Control Agent for Generalized Applications | [
"cs.AI"
] | Based on their superior comprehension and reasoning capabilities, Large Language Model (LLM) driven agent frameworks have achieved significant success in numerous complex reasoning tasks. ReAct-like agents can solve various intricate problems step-by-step through progressive planning and tool calls, iteratively optimiz... | {
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2501.07055 | SFC-GAN: A Generative Adversarial Network for Brain Functional and
Structural Connectome Translation | [
"cs.CV",
"cs.LG"
] | Modern brain imaging technologies have enabled the detailed reconstruction of human brain connectomes, capturing structural connectivity (SC) from diffusion MRI and functional connectivity (FC) from functional MRI. Understanding the intricate relationships between SC and FC is vital for gaining deeper insights into the... | {
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2501.07057 | Optimization with Multi-sourced Reference Information and Unknown Trust:
A Distributionally Robust Approach | [
"math.OC",
"cs.SY",
"eess.SY"
] | In problems that involve input parameter information gathered from multiple data sources with varying reliability, incorporating users' trust about different sources in decision-optimization models can potentially improve solution performance and reliability. In this work, we propose a novel multi-reference distributio... | {
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2501.07058 | Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities | [
"cs.CR",
"cs.AI"
] | Smart contract vulnerabilities caused significant economic losses in blockchain applications. Large Language Models (LLMs) provide new possibilities for addressing this time-consuming task. However, state-of-the-art LLM-based detection solutions are often plagued by high false-positive rates. In this paper, we push t... | {
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2501.07063 | Research on the Online Update Method for Retrieval-Augmented Generation
(RAG) Model with Incremental Learning | [
"cs.IR",
"cs.CL"
] | In the contemporary context of rapid advancements in information technology and the exponential growth of data volume, language models are confronted with significant challenges in effectively navigating the dynamic and ever-evolving information landscape to update and adapt to novel knowledge in real time. In this wor... | {
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2501.07069 | Hierarchical Superpixel Segmentation via Structural Information Theory | [
"cs.CV"
] | Superpixel segmentation is a foundation for many higher-level computer vision tasks, such as image segmentation, object recognition, and scene understanding. Existing graph-based superpixel segmentation methods typically concentrate on the relationships between a given pixel and its directly adjacent pixels while overl... | {
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2501.07070 | Enhancing Image Generation Fidelity via Progressive Prompts | [
"cs.CV"
] | The diffusion transformer (DiT) architecture has attracted significant attention in image generation, achieving better fidelity, performance, and diversity. However, most existing DiT - based image generation methods focus on global - aware synthesis, and regional prompt control has been less explored. In this paper, w... | {
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2501.07071 | Value Compass Leaderboard: A Platform for Fundamental and Validated
Evaluation of LLMs Values | [
"cs.AI"
] | As Large Language Models (LLMs) achieve remarkable breakthroughs, aligning their values with humans has become imperative for their responsible development and customized applications. However, there still lack evaluations of LLMs values that fulfill three desirable goals. (1) Value Clarification: We expect to clarify ... | {
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2501.07072 | Label Calibration in Source Free Domain Adaptation | [
"cs.CV"
] | Source-free domain adaptation (SFDA) utilizes a pre-trained source model with unlabeled target data. Self-supervised SFDA techniques generate pseudolabels from the pre-trained source model, but these pseudolabels often contain noise due to domain discrepancies between the source and target domains. Traditional self-sup... | {
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2501.07076 | Representation Learning of Point Cloud Upsampling in Global and Local
Inputs | [
"cs.CV",
"cs.AI"
] | In recent years, point cloud upsampling has been widely applied in fields such as 3D reconstruction. Our study investigates the factors influencing point cloud upsampling on both global and local levels through representation learning. Specifically, the paper inputs global and local information of the same point cloud ... | {
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2501.07077 | D3MES: Diffusion Transformer with multihead equivariant self-attention
for 3D molecule generation | [
"cs.LG",
"physics.chem-ph"
] | Understanding and predicting the diverse conformational states of molecules is crucial for advancing fields such as chemistry, material science, and drug development. Despite significant progress in generative models, accurately generating complex and biologically or material-relevant molecular structures remains a maj... | {
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2501.07078 | ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training | [
"cs.AI",
"cs.DB"
] | In the current development of large language models (LLMs), it is important to ensure the accuracy and reliability of the underlying data sources. LLMs are critical for various applications, but they often suffer from hallucinations and inaccuracies due to knowledge gaps in the training data. Knowledge graphs (KGs), as... | {
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2501.07086 | Boosting Text-To-Image Generation via Multilingual Prompting in Large
Multimodal Models | [
"cs.CL"
] | Previous work on augmenting large multimodal models (LMMs) for text-to-image (T2I) generation has focused on enriching the input space of in-context learning (ICL). This includes providing a few demonstrations and optimizing image descriptions to be more detailed and logical. However, as demand for more complex and fle... | {
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2501.07087 | Video Quality Assessment for Online Processing: From Spatial to Temporal
Sampling | [
"cs.CV",
"cs.AI"
] | With the rapid development of multimedia processing and deep learning technologies, especially in the field of video understanding, video quality assessment (VQA) has achieved significant progress. Although researchers have moved from designing efficient video quality mapping models to various research directions, in-d... | {
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2501.07088 | MathReader : Text-to-Speech for Mathematical Documents | [
"cs.AI",
"cs.SD",
"eess.AS"
] | TTS (Text-to-Speech) document reader from Microsoft, Adobe, Apple, and OpenAI have been serviced worldwide. They provide relatively good TTS results for general plain text, but sometimes skip contents or provide unsatisfactory results for mathematical expressions. This is because most modern academic papers are written... | {
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2501.07096 | Intent-Interest Disentanglement and Item-Aware Intent Contrastive
Learning for Sequential Recommendation | [
"cs.IR"
] | Recommender systems aim to provide personalized item recommendations by capturing user behaviors derived from their interaction history. Considering that user interactions naturally occur sequentially based on users' intents in mind, user behaviors can be interpreted as user intents. Therefore, intent-based sequential ... | {
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2501.07100 | Collaborative Learning for 3D Hand-Object Reconstruction and
Compositional Action Recognition from Egocentric RGB Videos Using
Superquadrics | [
"cs.CV",
"cs.AI"
] | With the availability of egocentric 3D hand-object interaction datasets, there is increasing interest in developing unified models for hand-object pose estimation and action recognition. However, existing methods still struggle to recognise seen actions on unseen objects due to the limitations in representing object sh... | {
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2501.07101 | Dual Scale-aware Adaptive Masked Knowledge Distillation for Object
Detection | [
"cs.CV"
] | Recent feature masking knowledge distillation methods make use of attention mechanisms to identify either important spatial regions or channel clues for discriminative feature reconstruction. However, most of existing strategies perform global attention-guided feature masking distillation without delving into fine-grai... | {
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2501.07102 | AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] | Intra-sentential code-switching (CS) refers to the alternation between languages that happens within a single utterance and is a significant challenge for Automatic Speech Recognition (ASR) systems. For example, when a Vietnamese speaker uses foreign proper names or specialized terms within their speech. ASR systems of... | {
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2501.07104 | RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular
Video Based on Rectified Mesh-embedded Gaussians | [
"cs.CV"
] | We introduce RMAvatar, a novel human avatar representation with Gaussian splatting embedded on mesh to learn clothed avatar from a monocular video. We utilize the explicit mesh geometry to represent motion and shape of a virtual human and implicit appearance rendering with Gaussian Splatting. Our method consists of two... | {
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2501.07106 | Efficient Multiple Temporal Network Kernel Density Estimation | [
"cs.DB"
] | Kernel density estimation (KDE) has become a popular method for visual analysis in various fields, such as financial risk forecasting, crime clustering, and traffic monitoring. KDE can identify high-density areas from discrete datasets. However, most existing works only consider planar distance and spatial data. In thi... | {
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2501.07108 | How GPT learns layer by layer | [
"cs.AI"
] | Large Language Models (LLMs) excel at tasks like language processing, strategy games, and reasoning but struggle to build generalizable internal representations essential for adaptive decision-making in agents. For agents to effectively navigate complex environments, they must construct reliable world models. While LLM... | {
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2501.07109 | The Quest for Visual Understanding: A Journey Through the Evolution of
Visual Question Answering | [
"cs.CV"
] | Visual Question Answering (VQA) is an interdisciplinary field that bridges the gap between computer vision (CV) and natural language processing(NLP), enabling Artificial Intelligence(AI) systems to answer questions about images. Since its inception in 2015, VQA has rapidly evolved, driven by advances in deep learning, ... | {
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2501.07110 | Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video
Recommendation | [
"cs.CV",
"cs.IR",
"cs.MM"
] | Multimodal information (e.g., visual, acoustic, and textual) has been widely used to enhance representation learning for micro-video recommendation. For integrating multimodal information into a joint representation of micro-video, multimodal fusion plays a vital role in the existing micro-video recommendation approach... | {
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2501.07111 | ListConRanker: A Contrastive Text Reranker with Listwise Encoding | [
"cs.CL",
"cs.IR"
] | Reranker models aim to re-rank the passages based on the semantics similarity between the given query and passages, which have recently received more attention due to the wide application of the Retrieval-Augmented Generation. Most previous methods apply pointwise encoding, meaning that it can only encode the context o... | {
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2501.07113 | Matching Free Depth Recovery from Structured Light | [
"cs.CV"
] | We present a novel approach for depth estimation from images captured by structured light systems. Unlike many previous methods that rely on image matching process, our approach uses a density voxel grid to represent scene geometry, which is trained via self-supervised differentiable volume rendering. Our method levera... | {
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2501.07114 | Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning | [
"cs.CV"
] | Compositional Zero-Shot Learning (CZSL) aims to enable models to recognize novel compositions of visual states and objects that were absent during training. Existing methods predominantly focus on learning semantic representations of seen compositions but often fail to disentangle the independent features of states and... | {
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2501.07120 | MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography
Segmentation | [
"eess.IV",
"cs.CV"
] | Ultrasound imaging frequently encounters challenges, such as those related to elevated noise levels, diminished spatiotemporal resolution, and the complexity of anatomical structures. These factors significantly hinder the model's ability to accurately capture and analyze structural relationships and dynamic patterns a... | {
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2501.07121 | The Value of Battery Energy Storage in the Continuous Intraday Market:
Forecast vs. Perfect Foresight Strategies | [
"cs.CE"
] | Grid-scale battery energy storage systems (BESSs) can provide flexibility to the power system and capture shortterm price volatility by shifting energy in time through controlled charging and discharging. The highly volatile European continuous intraday (CID) market allows trading until just a few minutes before physic... | {
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2501.07123 | Inferring Interpretable Models of Fragmentation Functions using Symbolic
Regression | [
"hep-ph",
"cs.LG",
"cs.SC",
"hep-th"
] | Machine learning is rapidly making its path into natural sciences, including high-energy physics. We present the first study that infers, directly from experimental data, a functional form of fragmentation functions. The latter represent a key ingredient to describe physical observables measured in high-energy physics ... | {
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2501.07124 | LLM360 K2: Building a 65B 360-Open-Source Large Language Model from
Scratch | [
"cs.LG"
] | We detail the training of the LLM360 K2-65B model, scaling up our 360-degree OPEN SOURCE approach to the largest and most powerful models under project LLM360. While open-source LLMs continue to advance, the answer to "How are the largest LLMs trained?" remains unclear within the community. The implementation details f... | {
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2501.07126 | A Federated Deep Learning Framework for Cell-Free RSMA Networks | [
"eess.SY",
"cs.SY"
] | Next-generation wireless networks are poised to benefit significantly from the integration of three key technologies (KTs): Rate-Splitting Multiple Access (RSMA), cell-free architectures, and federated learning. Each of these technologies offers distinct advantages in terms of security, robustness, and distributed stru... | {
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2501.07133 | Robust Single Object Tracking in LiDAR Point Clouds under Adverse
Weather Conditions | [
"cs.CV"
] | 3D single object tracking (3DSOT) in LiDAR point clouds is a critical task for outdoor perception, enabling real-time perception of object location, orientation, and motion. Despite the impressive performance of current 3DSOT methods, evaluating them on clean datasets inadequately reflects their comprehensive performan... | {
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2501.07139 | FlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge
Devices | [
"cs.AI",
"cs.PF"
] | Deploying LLMs on edge devices presents serious technical challenges. Memory elasticity is crucial for edge devices with unified memory, where memory is shared and fluctuates dynamically. Existing solutions suffer from either poor transition granularity or high storage costs. We propose FlexQuant, a novel elasticity fr... | {
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2501.07145 | A User's Guide to $\texttt{KSig}$: GPU-Accelerated Computation of the
Signature Kernel | [
"stat.ML",
"cs.LG"
] | The signature kernel is a positive definite kernel for sequential and temporal data that has become increasingly popular in machine learning applications due to powerful theoretical guarantees, strong empirical performance, and recently introduced various scalable variations. In this chapter, we give a short introducti... | {
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2501.07146 | TIMRL: A Novel Meta-Reinforcement Learning Framework for Non-Stationary
and Multi-Task Environments | [
"cs.LG",
"cs.AI"
] | In recent years, meta-reinforcement learning (meta-RL) algorithm has been proposed to improve sample efficiency in the field of decision-making and control, enabling agents to learn new knowledge from a small number of samples. However, most research uses the Gaussian distribution to extract task representation, which ... | {
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2501.07148 | Implementing LoRa MIMO System for Internet of Things | [
"cs.CY",
"cs.AR",
"cs.NI",
"cs.SY",
"eess.SY"
] | Bandwidth constraints limit LoRa implementations. Contemporary IoT applications require higher throughput than that provided by LoRa. This work introduces a LoRa Multiple Input Multiple Output (MIMO) system and a spatial multiplexing algorithm to address LoRa's bandwidth limitation. The transceivers in the proposed app... | {
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2501.07154 | Privacy-Preserving Data Quality Assessment for Time-Series IoT Sensors | [
"cs.IT",
"math.IT"
] | Data from Internet of Things (IoT) sensors has emerged as a key contributor to decision-making processes in various domains. However, the quality of the data is crucial to the effectiveness of applications built on it, and assessment of the data quality is heavily context-dependent. Further, preserving the privacy of t... | {
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2501.07155 | AlphaNet: Scaling Up Local Frame-based Atomistic Foundation Model | [
"cs.LG"
] | We present AlphaNet, a local frame-based equivariant model designed to achieve both accurate and efficient simulations for atomistic systems. Recently, machine learning force fields (MLFFs) have gained prominence in molecular dynamics simulations due to their advantageous efficiency-accuracy balance compared to classic... | {
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2501.07157 | CureGraph: Contrastive Multi-Modal Graph Representation Learning for
Urban Living Circle Health Profiling and Prediction | [
"cs.AI"
] | The early detection and prediction of health status decline among the elderly at the neighborhood level are of great significance for urban planning and public health policymaking. While existing studies affirm the connection between living environments and health outcomes, most rely on single data modalities or simpli... | {
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2501.07158 | Eye Sclera for Fair Face Image Quality Assessment | [
"cs.CV",
"cs.AI"
] | Fair operational systems are crucial in gaining and maintaining society's trust in face recognition systems (FRS). FRS start with capturing an image and assessing its quality before using it further for enrollment or verification. Fair Face Image Quality Assessment (FIQA) schemes therefore become equally important in t... | {
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2501.07161 | QuantuneV2: Compiler-Based Local Metric-Driven Mixed Precision
Quantization for Practical Embedded AI Applications | [
"cs.AI"
] | Mixed-precision quantization methods have been proposed to reduce model size while minimizing accuracy degradation. However, existing studies require retraining and do not consider the computational overhead and intermediate representations (IR) generated during the compilation process, limiting their application at th... | {
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2501.07163 | Adaptive Noise-Tolerant Network for Image Segmentation | [
"cs.CV"
] | Unlike image classification and annotation, for which deep network models have achieved dominating superior performances compared to traditional computer vision algorithms, deep learning for automatic image segmentation still faces critical challenges. One of such hurdles is to obtain ground-truth segmentations as the ... | {
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2501.07166 | Natural Language-Assisted Multi-modal Medication Recommendation | [
"cs.AI"
] | Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patients with intricate health conditions, particularly in the scenarios of long-term medical care. Previous research efforts have sought to extr... | {
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"cs.SY": 0
} |
2501.07171 | BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and
Vision-Language Models Derived from Scientific Literature | [
"cs.CV",
"cs.CL"
] | The development of vision-language models (VLMs) is driven by large-scale and diverse multimodal datasets. However, progress toward generalist biomedical VLMs is limited by the lack of annotated, publicly accessible datasets across biology and medicine. Existing efforts are restricted to narrow domains, missing the ful... | {
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} |
2501.07172 | Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in
Correlated, Multivariate Time Series Data | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Detecting and classifying abnormal system states is critical for condition monitoring, but supervised methods often fall short due to the rarity of anomalies and the lack of labeled data. Therefore, clustering is often used to group similar abnormal behavior. However, evaluating cluster quality without ground truth is ... | {
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} |
2501.07173 | Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph
Convolutional Network for Resource-Constrained Bearing Fault Diagnosis | [
"cs.LG",
"eess.SP"
] | Bearing fault diagnosis under varying working conditions faces challenges, including a lack of labeled data, distribution discrepancies, and resource constraints. To address these issues, we propose a progressive knowledge distillation framework that transfers knowledge from a complex teacher model, utilizing a Graph C... | {
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} |
2501.07178 | The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric
Firms | [
"econ.GN",
"cs.AI",
"q-fin.EC"
] | We study the propensity of independent algorithms to collude in repeated Cournot duopoly games. Specifically, we investigate the predictive power of different oligopoly and bargaining solutions regarding the effect of asymmetry between firms. We find that both consumers and firms can benefit from asymmetry. Algorithms ... | {
"Other": 0,
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} |
2501.07179 | Radial Distortion in Face Images: Detection and Impact | [
"cs.CV"
] | Acquiring face images of sufficiently high quality is important for online ID and travel document issuance applications using face recognition systems (FRS). Low-quality, manipulated (intentionally or unintentionally), or distorted images degrade the FRS performance and facilitate documents' misuse. Securing quality fo... | {
"Other": 0,
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} |
2501.07180 | Evaluating Robotic Approach Techniques for the Insertion of a Straight
Instrument into a Vitreoretinal Surgery Trocar | [
"cs.RO",
"cs.HC",
"cs.SY",
"eess.SY"
] | Advances in vitreoretinal robotic surgery enable precise techniques for gene therapies. This study evaluates three robotic approaches using the 7-DoF robotic arm for docking a micro-precise tool to a trocar: fully co-manipulated, hybrid co-manipulated/teleoperated, and hybrid with camera assistance. The fully co-manipu... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.07182 | Unveiling Voices: A Co-Hashtag Analysis of TikTok Discourse on the 2023
Israel-Palestine Crisis | [
"cs.SI",
"cs.HC"
] | TikTok has gradually become one of the most pervasive social media platforms in our daily lives. In this research article, I explore how users on TikTok discussed the crisis in Palestine that worsened in 2023. Using network analysis, I situate keywords representing the conflict and categorize them thematically based on... | {
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"cs.SY": 0
} |
2501.07183 | Kriging and Gaussian Process Interpolation for Georeferenced Data
Augmentation | [
"cs.AI"
] | Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in... | {
"Other": 0,
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} |
2501.07185 | Uncertainty Guarantees on Automated Precision Weeding using Conformal
Prediction | [
"cs.CV",
"cs.LG",
"stat.AP",
"stat.ML"
] | Precision agriculture in general, and precision weeding in particular, have greatly benefited from the major advancements in deep learning and computer vision. A large variety of commercial robotic solutions are already available and deployed. However, the adoption by farmers of such solutions is still low for many rea... | {
"Other": 0,
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} |
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