id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.05265 | Image Decomposition: Theory, Numerical Schemes, and Performance
Evaluation | [
"eess.IV",
"cs.CV",
"math.FA"
] | This paper describes the many image decomposition models that allow to separate structures and textures or structures, textures, and noise. These models combined a total variation approach with different adapted functional spaces such as Besov or Contourlet spaces or a special oscillating function space based on the wo... | {
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2411.05267 | Optimal Design to Dual-Scale Channel Estimation for Sensing-Assisted
Communication Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | Sensing-assisted communication is critical to enhance the system efficiency in integrated sensing and communication (ISAC) systems. However, most existing literature focuses on large-scale channel sensing, without considering the impacts of small-scale channel aging. In this paper, we investigate a dual-scale channel e... | {
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2411.05269 | Cancer-Net SCa-Synth: An Open Access Synthetically Generated 2D Skin
Lesion Dataset for Skin Cancer Classification | [
"cs.CV",
"cs.LG"
] | In the United States, skin cancer ranks as the most commonly diagnosed cancer, presenting a significant public health issue due to its high rates of occurrence and the risk of serious complications if not caught early. Recent advancements in dataset curation and deep learning have shown promise in quick and accurate de... | {
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2411.05270 | Seeing Through the Fog: A Cost-Effectiveness Analysis of Hallucination
Detection Systems | [
"cs.CL",
"cs.AI"
] | This paper presents a comparative analysis of hallucination detection systems for AI, focusing on automatic summarization and question answering tasks for Large Language Models (LLMs). We evaluate different hallucination detection systems using the diagnostic odds ratio (DOR) and cost-effectiveness metrics. Our results... | {
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2411.05273 | Real-World Offline Reinforcement Learning from Vision Language Model
Feedback | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Offline reinforcement learning can enable policy learning from pre-collected, sub-optimal datasets without online interactions. This makes it ideal for real-world robots and safety-critical scenarios, where collecting online data or expert demonstrations is slow, costly, and risky. However, most existing offline RL wor... | {
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2411.05274 | Distributed-Order Fractional Graph Operating Network | [
"cs.LG"
] | We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus. Unlike traditional continuous GNNs that utilize integer-order or single fractional-order differential equations, DRAGON uses a ... | {
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2411.05276 | GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic
Embedding Caching | [
"cs.LG"
] | Large Language Models (LLMs), such as GPT, have revolutionized artificial intelligence by enabling nuanced understanding and generation of human-like text across a wide range of applications. However, the high computational and financial costs associated with frequent API calls to these models present a substantial bot... | {
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2411.05277 | Revisiting the Robustness of Watermarking to Paraphrasing Attacks | [
"cs.CR",
"cs.CL",
"cs.LG"
] | Amidst rising concerns about the internet being proliferated with content generated from language models (LMs), watermarking is seen as a principled way to certify whether text was generated from a model. Many recent watermarking techniques slightly modify the output probabilities of LMs to embed a signal in the genera... | {
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2411.05278 | Integrated Location Sensing and Communication for Ultra-Massive MIMO
With Hybrid-Field Beam-Squint Effect | [
"eess.SP",
"cs.IT",
"math.IT"
] | The advent of ultra-massive multiple-input-multiple output systems holds great promise for next-generation communications, yet their channels exhibit hybrid far- and near- field beam-squint (HFBS) effect. In this paper, we not only overcome but also harness the HFBS effect to propose an integrated location sensing and ... | {
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2411.05279 | Path Planning in Complex Environments with Superquadrics and
Voronoi-Based Orientation | [
"cs.RO"
] | Path planning in narrow passages is a challenging problem in various applications. Traditional planning algorithms often face challenges in complex environments like mazes and traps, where narrow entrances require special orientation control for successful navigation. In this work, we present a novel approach that comb... | {
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2411.05281 | Fox-1 Technical Report | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We present Fox-1, a series of small language models (SLMs) consisting of Fox-1-1.6B and Fox-1-1.6B-Instruct-v0.1. These models are pre-trained on 3 trillion tokens of web-scraped document data and fine-tuned with 5 billion tokens of instruction-following and multi-turn conversation data. Aiming to improve the pre-train... | {
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2411.05282 | MicroScopiQ: Accelerating Foundational Models through Outlier-Aware
Microscaling Quantization | [
"cs.AR",
"cs.AI",
"cs.LG"
] | Quantization of foundational models (FMs) is significantly more challenging than traditional DNNs due to the emergence of large magnitude features called outliers. Existing outlier-aware algorithm/architecture co-design techniques either use mixed-precision, retaining outliers at high precision but compromise hardware ... | {
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2411.05285 | AgentOps: Enabling Observability of LLM Agents | [
"cs.AI",
"cs.SE"
] | Large language model (LLM) agents have demonstrated remarkable capabilities across various domains, gaining extensive attention from academia and industry. However, these agents raise significant concerns on AI safety due to their autonomous and non-deterministic behavior, as well as continuous evolving nature . From a... | {
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2411.05286 | Metrology and Manufacturing-Integrated Digital Twin (MM-DT) for Advanced
Manufacturing: Insights from CMM and FARO Arm Measurements | [
"cs.CE"
] | Metrology, the science of measurement, plays a key role in Advanced Manufacturing (AM) to ensure quality control, process optimization, and predictive maintenance. However, it has often been overlooked in AM domains due to the current focus on automation and the complexity of integrated precise measurement systems. Ove... | {
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2411.05289 | SpecHub: Provable Acceleration to Multi-Draft Speculative Decoding | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have become essential in advancing natural language processing (NLP) tasks, but their sequential token generation limits inference speed. Multi-Draft Speculative Decoding (MDSD) offers a promising solution by using a smaller draft model to generate multiple token sequences, which the target... | {
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2411.05292 | SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object
Detection | [
"cs.CV",
"cs.AI"
] | More and more research works fuse the LiDAR and camera information to improve the 3D object detection of the autonomous driving system. Recently, a simple yet effective fusion framework has achieved an excellent detection performance, fusing the LiDAR and camera features in a unified bird's-eye-view (BEV) space. In thi... | {
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2411.05296 | On Training of Kolmogorov-Arnold Networks | [
"cs.LG",
"cs.AI"
] | Kolmogorov-Arnold Networks have recently been introduced as a flexible alternative to multi-layer Perceptron architectures. In this paper, we examine the training dynamics of different KAN architectures and compare them with corresponding MLP formulations. We train with a variety of different initialization schemes, op... | {
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2411.05298 | Integrated Power and Thermal Management for Enhancing Energy Efficiency
and Battery Life in Connected and Automated Electric Vehicles | [
"eess.SY",
"cs.SY"
] | Effective power and thermal management are essential for ensuring battery efficiency, safety, and longevity in Connected and Automated Electric Vehicles (CAEVs). However, real-time implementation is challenging due to the multi-timescale dynamics and complex trade-offs between energy consumption, battery degradation, t... | {
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2411.05302 | Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with
ControlNet | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | Positron Emission Tomography (PET) is a vital imaging modality widely used in clinical diagnosis and preclinical research but faces limitations in image resolution and signal-to-noise ratio due to inherent physical degradation factors. Current deep learning-based denoising methods face challenges in adapting to the var... | {
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2411.05307 | Revisiting Network Perturbation for Semi-Supervised Semantic
Segmentation | [
"cs.CV",
"cs.AI"
] | In semi-supervised semantic segmentation (SSS), weak-to-strong consistency regularization techniques are widely utilized in recent works, typically combined with input-level and feature-level perturbations. However, the integration between weak-to-strong consistency regularization and network perturbation has been rela... | {
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2411.05311 | ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for
Autonomous Driving | [
"cs.CV",
"cs.RO"
] | Offboard perception aims to automatically generate high-quality 3D labels for autonomous driving (AD) scenes. Existing offboard methods focus on 3D object detection with closed-set taxonomy and fail to match human-level recognition capability on the rapidly evolving perception tasks. Due to heavy reliance on human labe... | {
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2411.05312 | A Real-time Face Mask Detection and Social Distancing System for
COVID-19 using Attention-InceptionV3 Model | [
"cs.CV"
] | One of the deadliest pandemics is now happening in the current world due to COVID-19. This contagious virus is spreading like wildfire around the whole world. To minimize the spreading of this virus, World Health Organization (WHO) has made protocols mandatory for wearing face masks and maintaining 6 feet physical dist... | {
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2411.05315 | Differentiable Calibration of Inexact Stochastic Simulation Models via
Kernel Score Minimization | [
"stat.ME",
"cs.LG",
"stat.CO"
] | Stochastic simulation models are generative models that mimic complex systems to help with decision-making. The reliability of these models heavily depends on well-calibrated input model parameters. However, in many practical scenarios, only output-level data are available to learn the input model parameters, which is ... | {
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2411.05316 | Exploring the Alignment Landscape: LLMs and Geometric Deep Models in
Protein Representation | [
"cs.LG",
"cs.AI",
"cs.CE",
"q-bio.BM"
] | Latent representation alignment has become a foundational technique for constructing multimodal large language models (MLLM) by mapping embeddings from different modalities into a shared space, often aligned with the embedding space of large language models (LLMs) to enable effective cross-modal understanding. While pr... | {
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2411.05317 | SeqRFM: Fast RFM Analysis in Sequence Data | [
"cs.DB"
] | In recent years, data mining technologies have been well applied to many domains, including e-commerce. In customer relationship management (CRM), the RFM analysis model is one of the most effective approaches to increase the profits of major enterprises. However, with the rapid development of e-commerce, the diversity... | {
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2411.05318 | Fairness in Monotone $k$-submodular Maximization: Algorithms and
Applications | [
"cs.LG",
"cs.DS"
] | Submodular optimization has become increasingly prominent in machine learning and fairness has drawn much attention. In this paper, we propose to study the fair $k$-submodular maximization problem and develop a $\frac{1}{3}$-approximation greedy algorithm with a running time of $\mathcal{O}(knB)$. To the best of our kn... | {
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2411.05322 | Rate-aware Compression for NeRF-based Volumetric Video | [
"cs.MM",
"cs.CV"
] | The neural radiance fields (NeRF) have advanced the development of 3D volumetric video technology, but the large data volumes they involve pose significant challenges for storage and transmission. To address these problems, the existing solutions typically compress these NeRF representations after the training stage, l... | {
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2411.05324 | SASWISE-UE: Segmentation and Synthesis with Interpretable Scalable
Ensembles for Uncertainty Estimation | [
"cs.LG",
"cs.CV",
"stat.ME"
] | This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to evaluate the reliability of model outputs. We developed a strategy to... | {
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2411.05328 | Content Quality vs. Attention Allocation: An LLM-Based Case Study in
Peer-to-peer Mental Health Networks | [
"cs.SI"
] | With the rise of social media and peer-to-peer networks, users increasingly rely on crowdsourced responses for information and assistance. However, the mechanisms used to rank and promote responses often prioritize and end up biasing in favor of timeliness over quality, which may result in suboptimal support for help-s... | {
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2411.05330 | Inversion-based Latent Bayesian Optimization | [
"cs.LG",
"cs.AI"
] | Latent Bayesian optimization (LBO) approaches have successfully adopted Bayesian optimization over a continuous latent space by employing an encoder-decoder architecture to address the challenge of optimization in a high dimensional or discrete input space. LBO learns a surrogate model to approximate the black-box obje... | {
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2411.05331 | Discovering Latent Structural Causal Models from Spatio-Temporal Data | [
"cs.LG",
"stat.ML"
] | Many important phenomena in scientific fields such as climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. For example, in climate science, researchers aim to uncover how large-scale events, such as the North Atlantic Oscillation (NAO) and the Antar... | {
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2411.05335 | A Quality-Centric Framework for Generic Deepfake Detection | [
"cs.CV",
"cs.CR",
"cs.LG"
] | This paper addresses the generalization issue in deepfake detection by harnessing forgery quality in training data. Generally, the forgery quality of different deepfakes varies: some have easily recognizable forgery clues, while others are highly realistic. Existing works often train detectors on a mix of deepfakes wit... | {
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2411.05337 | Development of an indoor localization and navigation system based on
monocular SLAM for mobile robots | [
"cs.RO"
] | Localization and navigation are two crucial issues for mobile robots. In this paper, we propose an approach for localization and navigation systems for a differential-drive robot based on monocular SLAM. The system is implemented on the Robot Operating System (ROS). The hardware includes a differential-drive robot with... | {
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2411.05338 | SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers | [
"cs.CL"
] | Scientific literature is typically dense, requiring significant background knowledge and deep comprehension for effective engagement. We introduce SciDQA, a new dataset for reading comprehension that challenges LLMs for a deep understanding of scientific articles, consisting of 2,937 QA pairs. Unlike other scientific Q... | {
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2411.05340 | Improving Multi-Domain Task-Oriented Dialogue System with Offline
Reinforcement Learning | [
"cs.CL",
"cs.AI",
"cs.HC",
"cs.IR"
] | Task-oriented dialogue (TOD) system is designed to accomplish user-defined tasks through dialogues. The TOD system has progressed towards end-to-end modeling by leveraging pre-trained large language models. Fine-tuning the pre-trained language models using only supervised learning leads to the exposure bias and token l... | {
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2411.05342 | Development of a Human-Robot Interaction Platform for Dual-Arm Robots
Based on ROS and Multimodal Artificial Intelligence | [
"cs.RO"
] | In this paper, we propose the development of an interactive platform between humans and a dual-arm robotic system based on the Robot Operating System (ROS) and a multimodal artificial intelligence model. Our proposed platform consists of two main components: a dual-arm robotic hardware system and software that includes... | {
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2411.05344 | Enhancing Depth Image Estimation for Underwater Robots by Combining
Image Processing and Machine Learning | [
"cs.RO"
] | Depth information plays a crucial role in autonomous systems for environmental perception and robot state estimation. With the rapid development of deep neural network technology, depth estimation has been extensively studied and shown potential for practical applications. However, in particularly challenging environme... | {
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2411.05345 | Reasoning Robustness of LLMs to Adversarial Typographical Errors | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning using Chain-of-Thought (CoT) prompting. However, CoT can be biased by users' instruction. In this work, we study the reasoning robustness of LLMs to typographical errors, which can naturally occur in users' queries. We design an Adversa... | {
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2411.05346 | Reinforcement Learning for Adaptive Resource Scheduling in Complex
System Environments | [
"cs.LG",
"cs.DC"
] | This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, task complexity, and dynamic workloads, traditional static scheduling methods such as Round-Robin an... | {
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2411.05348 | LLM-PySC2: Starcraft II learning environment for Large Language Models | [
"cs.AI"
] | This paper introduces a new environment LLM-PySC2 (the Large Language Model StarCraft II Learning Environment), a platform derived from DeepMind's StarCraft II Learning Environment that serves to develop Large Language Models (LLMs) based decision-making methodologies. This environment is the first to offer the complet... | {
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2411.05349 | Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent
Cluster Diagnosis System and Evaluation Framework | [
"cs.AI",
"cs.DC"
] | Recent advancements in Large Language Models (LLMs) and related technologies such as Retrieval-Augmented Generation (RAG) and Diagram of Thought (DoT) have enabled the creation of autonomous intelligent systems capable of performing cluster diagnostics and troubleshooting. By integrating these technologies with self-pl... | {
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2411.05353 | Controlling Grokking with Nonlinearity and Data Symmetry | [
"cs.LG",
"cs.AI"
] | This paper demonstrates that grokking behavior in modular arithmetic with a modulus P in a neural network can be controlled by modifying the profile of the activation function as well as the depth and width of the model. Plotting the even PCA projections of the weights of the last NN layer against their odd projections... | {
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2411.05354 | RED: Residual Estimation Diffusion for Low-Dose PET Sinogram
Reconstruction | [
"cs.LG"
] | Recent advances in diffusion models have demonstrated exceptional performance in generative tasks across vari-ous fields. In positron emission tomography (PET), the reduction in tracer dose leads to information loss in sino-grams. Using diffusion models to reconstruct missing in-formation can improve imaging quality. T... | {
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2411.05355 | Improving Computational Cost of Bayesian Optimization for Controller
Tuning with a Multi-stage Tuning Framework | [
"cs.CE"
] | Control auto-tuning for industrial and robotic systems, when framed as an optimization problem, provides an excellent means to tune these systems. However, most optimization methods are computationally costly, and this is problematic for high-dimension control parameter spaces. In this paper, we present a multi-stage c... | {
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2411.05357 | Enhancing Visual Classification using Comparative Descriptors | [
"cs.CV"
] | The performance of vision-language models (VLMs), such as CLIP, in visual classification tasks, has been enhanced by leveraging semantic knowledge from large language models (LLMs), including GPT. Recent studies have shown that in zero-shot classification tasks, descriptors incorporating additional cues, high-level con... | {
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2411.05359 | Agricultural Landscape Understanding At Country-Scale | [
"cs.CV",
"cs.AI",
"cs.CY"
] | Agricultural landscapes are quite complex, especially in the Global South where fields are smaller, and agricultural practices are more varied. In this paper we report on our progress in digitizing the agricultural landscape (natural and man-made) in our study region of India. We use high resolution imagery and a UNet ... | {
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2411.05361 | Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for
Measuring the Capabilities of Spoken Language Models with 180 Tasks | [
"cs.CL",
"eess.AS"
] | Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spoken language model that comprehends a wide range of natural language instructions is critical for bridging communication gaps and facilitati... | {
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2411.05362 | From Transparent to Opaque: Rethinking Neural Implicit Surfaces with
$\alpha$-NeuS | [
"cs.CV"
] | Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. Recent advances in neural radiance fields and its variants primarily address opaque or transparent objects, encountering difficulties to reco... | {
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2411.05375 | Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | Current automated fact-checking (AFC) approaches commonly evaluate evidence either implicitly via the predicted verdicts or by comparing retrieved evidence with a predefined closed knowledge source, such as Wikipedia. However, these methods suffer from limitations, resulting from their reliance on evaluation metrics de... | {
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2411.05378 | Machine learning for prediction of dose-volume histograms of
organs-at-risk in prostate cancer from simple structure volume parameters | [
"cs.LG"
] | Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of target, at-risk structure organs and their overlap regions using machine l... | {
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2411.05379 | Word reuse and combination support efficient communication of emerging
concepts | [
"cs.CL"
] | A key function of the lexicon is to express novel concepts as they emerge over time through a process known as lexicalization. The most common lexicalization strategies are the reuse and combination of existing words, but they have typically been studied separately in the areas of word meaning extension and word format... | {
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2411.05383 | Towards Low-Resource Harmful Meme Detection with LMM Agents | [
"cs.CL"
] | The proliferation of Internet memes in the age of social media necessitates effective identification of harmful ones. Due to the dynamic nature of memes, existing data-driven models may struggle in low-resource scenarios where only a few labeled examples are available. In this paper, we propose an agency-driven framewo... | {
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2411.05384 | Advancing Meteorological Forecasting: AI-based Approach to Synoptic
Weather Map Analysis | [
"cs.LG",
"cs.AI",
"cs.CV"
] | As global warming increases the complexity of weather patterns; the precision of weather forecasting becomes increasingly important. Our study proposes a novel preprocessing method and convolutional autoencoder model developed to improve the interpretation of synoptic weather maps. These are critical for meteorologists... | {
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2411.05395 | AuthFormer: Adaptive Multimodal biometric authentication transformer for
middle-aged and elderly people | [
"cs.CV"
] | Multimodal biometric authentication methods address the limitations of unimodal biometric technologies in security, robustness, and user adaptability. However, most existing methods depend on fixed combinations and numbers of biometric modalities, which restricts flexibility and adaptability in real-world applications.... | {
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2411.05399 | Post-Hoc Robustness Enhancement in Graph Neural Networks with
Conditional Random Fields | [
"cs.LG",
"cs.SI",
"stat.AP",
"stat.ML"
] | Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph representation learning, have been shown to be vulnerable to adversarial attacks, raising concerns about their real-world applicability. While existing defense techniques primarily concentrate on the training phase of GNNs, involving adjus... | {
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2411.05403 | Benchmarking Distributional Alignment of Large Language Models | [
"cs.CL",
"cs.AI"
] | Language models (LMs) are increasingly used as simulacra for people, yet their ability to match the distribution of views of a specific demographic group and be \textit{distributionally aligned} remains uncertain. This notion of distributional alignment is complex, as there is significant variation in the types of attr... | {
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2411.05407 | Gap-Filling Prompting Enhances Code-Assisted Mathematical Reasoning | [
"cs.CL"
] | Despite the strong performance of large language models (LLMs) in tasks like mathematical reasoning, their practical use is limited by high computational demands and proprietary restrictions. Chain-of-thought (CoT) and program-of-thought (PoT) fine-tuning are common methods to transfer LLM knowledge to small language m... | {
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2411.05409 | Web Archives Metadata Generation with GPT-4o: Challenges and Insights | [
"cs.DL",
"cs.AI"
] | Current metadata creation for web archives is time consuming and costly due to reliance on human effort. This paper explores the use of gpt-4o for metadata generation within the Web Archive Singapore, focusing on scalability, efficiency, and cost effectiveness. We processed 112 Web ARChive (WARC) files using data reduc... | {
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2411.05418 | Development of Underactuated Geometric Compliant (UGC) Module with
Variable Radial for Robotic Applications | [
"cs.RO"
] | This paper introduces a novel underactuated geometric compliant (UGC) robot and investigates the behaviors of underactuated compliant modules with variable radial stiffness, aiming to enhance the versatility and functionality of UGC robots. We initiate the study by designing and fabricating various compliant semi-rigid... | {
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2411.05419 | POC-SLT: Partial Object Completion with SDF Latent Transformers | [
"cs.CV"
] | 3D geometric shape completion hinges on representation learning and a deep understanding of geometric data. Without profound insights into the three-dimensional nature of the data, this task remains unattainable. Our work addresses this challenge of 3D shape completion given partial observations by proposing a transfor... | {
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2411.05420 | WeatherGFM: Learning A Weather Generalist Foundation Model via
In-context Learning | [
"cs.LG",
"cs.AI",
"cs.CV",
"physics.ao-ph"
] | The Earth's weather system encompasses intricate weather data modalities and diverse weather understanding tasks, which hold significant value to human life. Existing data-driven models focus on single weather understanding tasks (e.g., weather forecasting). Although these models have achieved promising results, they f... | {
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2411.05421 | Learning the rules of peptide self-assembly through data mining with
large language models | [
"cond-mat.soft",
"cond-mat.dis-nn",
"cond-mat.mes-hall",
"cs.AI",
"cs.CL"
] | Peptides are ubiquitous and important biologically derived molecules, that have been found to self-assemble to form a wide array of structures. Extensive research has explored the impacts of both internal chemical composition and external environmental stimuli on the self-assembly behaviour of these systems. However, t... | {
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2411.05423 | VISTA: Visual Integrated System for Tailored Automation in Math Problem
Generation Using LLM | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to aut... | {
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2411.05424 | ICE-T: A Multi-Faceted Concept for Teaching Machine Learning | [
"cs.CY",
"cs.AI",
"cs.LG"
] | The topics of Artificial intelligence (AI) and especially Machine Learning (ML) are increasingly making their way into educational curricula. To facilitate the access for students, a variety of platforms, visual tools, and digital games are already being used to introduce ML concepts and strengthen the understanding of... | {
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2411.05433 | Computing the Low-Weight codewords of Punctured and Shortened
Pre-Transformed polar Codes | [
"cs.IT",
"math.IT"
] | In this paper, we present a deterministic algorithm to count the low-weight codewords of punctured and shortened pure and pre-transformed polar codes. The method first evaluates the weight properties of punctured/shortened polar cosets. Then, a method that discards the cosets that have no impact on the computation of t... | {
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2411.05442 | IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge
Delivery | [
"cs.IR"
] | In the rapidly evolving landscape of cyber security, intelligent chatbots are gaining prominence. Artificial Intelligence, Machine Learning, and Natural Language Processing empower these chatbots to handle user inquiries and deliver threat intelligence. This helps cyber security knowledge readily available to both prof... | {
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2411.05445 | Agile UAV landing control on moving ship in adverse conditions | [
"eess.SY",
"cs.SY"
] | This paper presents an agile Unmanned Aerial Vehicle (UAV) landing control by considering the effect of ship's oscillations and moving, and also disturbance (i.e., crosswind) is considered. The presented control system can make the quadrotor UAV autonomously land whilst overcoming these adverse conditions, and the addi... | {
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2411.05448 | Influencers' Reposts and Viral Diffusion: Prestige Bias in Online
Communities | [
"cs.SI",
"cs.CY"
] | Cultural evolution theory suggests that prestige bias (whereby individuals preferentially learn from prestigious figures) has played a key role in human ecological success. However, its impact within online environments remains unclear, particularly regarding whether reposts by prestigious individuals amplify diffusion... | {
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2411.05449 | Unmanned F/A-18 Aircraft Landing Control on Aircraft Carrier in Adverse
Conditions | [
"eess.SY",
"cs.SY"
] | Carrier landing of aircrafts is a challenge for control due to the existence of nonlinear wind disturbances and the requirements of changing reference trajectories. In this paper, a robust landing control system is presented for carrier landing of unmanned F/A-18 aircraft. In the control system, an augmented observer i... | {
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2411.05450 | Analysing control-theoretic properties of nonlinear synthetic biology
circuits | [
"eess.SY",
"cs.SY",
"q-bio.QM"
] | Synthetic biology is a recent area of biological engineering, whose aim is to provide cells with novel functionalities. A number of important results regarding the development of control circuits in synthetic biology have been achieved during the last decade. A differential geometry approach can be used for the analysi... | {
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2411.05451 | WorkflowLLM: Enhancing Workflow Orchestration Capability of Large
Language Models | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Recent advancements in large language models (LLMs) have driven a revolutionary paradigm shift in process automation from Robotic Process Automation to Agentic Process Automation by automating the workflow orchestration procedure based on LLMs. However, existing LLMs (even the advanced OpenAI GPT-4o) are confined to ac... | {
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2411.05453 | The sampling complexity of learning invertible residual neural networks | [
"stat.ML",
"cs.LG"
] | In recent work it has been shown that determining a feedforward ReLU neural network to within high uniform accuracy from point samples suffers from the curse of dimensionality in terms of the number of samples needed. As a consequence, feedforward ReLU neural networks are of limited use for applications where guarantee... | {
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2411.05454 | Emergent Cooperative Strategies for Multi-Agent Shepherding via
Reinforcement Learning | [
"eess.SY",
"cs.SY"
] | We present a decentralized reinforcement learning (RL) approach to address the multi-agent shepherding control problem, departing from the conventional assumption of cohesive target groups. Our two-layer control architecture consists of a low-level controller that guides each herder to contain a specific target within ... | {
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2411.05456 | Comparative Study of Probabilistic Atlas and Deep Learning Approaches
for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field
Correction and Anisotropic Diffusion Pre-processing Techniques | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Automatic brain tissue segmentation from Magnetic Resonance Imaging (MRI) images is vital for accurate diagnosis and further analysis in medical imaging. Despite advancements in segmentation techniques, a comprehensive comparison between traditional statistical methods and modern deep learning approaches using pre-proc... | {
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2411.05460 | Supporting Automated Fact-checking across Topics: Similarity-driven
Gradual Topic Learning for Claim Detection | [
"cs.CL"
] | Selecting check-worthy claims for fact-checking is considered a crucial part of expediting the fact-checking process by filtering out and ranking the check-worthy claims for being validated among the impressive amount of claims could be found online. The check-worthy claim detection task, however, becomes more challeng... | {
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2411.05464 | Generalization, Expressivity, and Universality of Graph Neural Networks
on Attributed Graphs | [
"cs.LG"
] | We analyze the universality and generalization of graph neural networks (GNNs) on attributed graphs, i.e., with node attributes. To this end, we propose pseudometrics over the space of all attributed graphs that describe the fine-grained expressivity of GNNs. Namely, GNNs are both Lipschitz continuous with respect to o... | {
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2411.05471 | Some notes on the pseudorandomness of Legendre symbol and Liouville
function | [
"math.NT",
"cs.IT",
"math.IT"
] | We improve bounds on the degree and sparsity of Boolean functions representing the Legendre symbol as well as on the $N$th linear complexity of the Legendre sequence. We also prove similar results for both the Liouville function for integers and its analog for polynomials over $\mathbb{F}_2$, or more general for any (b... | {
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2411.05472 | Bridging the Gap between Learning and Inference for Diffusion-Based
Molecule Generation | [
"cs.LG"
] | The efficacy of diffusion models in generating a spectrum of data modalities, including images, text, and videos, has spurred inquiries into their utility in molecular generation, yielding significant advancements in the field. However, the molecular generation process with diffusion models involves multiple autoregres... | {
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2411.05473 | Improving image synthesis with diffusion-negative sampling | [
"cs.CV"
] | For image generation with diffusion models (DMs), a negative prompt n can be used to complement the text prompt p, helping define properties not desired in the synthesized image. While this improves prompt adherence and image quality, finding good negative prompts is challenging. We argue that this is due to a semantic... | {
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2411.05474 | Enhancing Robustness in Language-Driven Robotics: A Modular Approach to
Failure Reduction | [
"cs.RO"
] | Recent advances in large language models (LLMs) have led to significant progress in robotics, enabling embodied agents to better understand and execute open-ended tasks. However, existing approaches using LLMs face limitations in grounding their outputs within the physical environment and aligning with the capabilities... | {
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2411.05475 | 3D-Printed Dual-Polarized Magneto-Electric Dipole Antenna with Wideband
High Isolation for Full-Duplex Applications | [
"physics.app-ph",
"cs.SY",
"eess.SY"
] | The paper introduces a novel dual-port dual-polarized magneto-electric dipole (MED) antenna with orthogonal Gamma and inverted-Gamma shape probes, which was fabricated by means of an additive 3D metal printing process. Electromagnetic wave simulation and RF measurement report a resonance bandwidth from 3 GHz to 4 GHz a... | {
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2411.05478 | Cell Balancing Paradigms: Advanced Types, Algorithms, and Optimization
Frameworks | [
"eess.SY",
"cs.SY"
] | The operation efficiency of the electric transportation, energy storage, and grids mainly depends on the fundamental characteristics of the employed batteries. Fundamental variables like voltage, current, temperature, and estimated parameters, like the State of Charge (SoC) of the battery pack, influence the functional... | {
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2411.05479 | EUREKHA: Enhancing User Representation for Key Hackers Identification in
Underground Forums | [
"cs.CR",
"cs.CL",
"cs.SI"
] | Underground forums serve as hubs for cybercriminal activities, offering a space for anonymity and evasion of conventional online oversight. In these hidden communities, malicious actors collaborate to exchange illicit knowledge, tools, and tactics, driving a range of cyber threats from hacking techniques to the sale of... | {
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2411.05481 | Relative Pose Estimation for Nonholonomic Robot Formation with UWB-IO
Measurements | [
"cs.RO"
] | This article studies the problem of distributed formation control for multiple robots by using onboard ultra wide band (UWB) ranging and inertial odometer (IO) measurements. Although this problem has been widely studied, a fundamental limitation of most works is that they require each robot's pose and sensor measuremen... | {
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2411.05482 | Soft Gripping System for Space Exploration Legged Robots | [
"cs.RO"
] | Although wheeled robots have been predominant for planetary exploration, their geometry limits their capabilities when traveling over steep slopes, through rocky terrains, and in microgravity. Legged robots equipped with grippers are a viable alternative to overcome these obstacles. This paper proposes a gripping syste... | {
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2411.05483 | The Limits of Differential Privacy in Online Learning | [
"cs.LG"
] | Differential privacy (DP) is a formal notion that restricts the privacy leakage of an algorithm when running on sensitive data, in which privacy-utility trade-off is one of the central problems in private data analysis. In this work, we investigate the fundamental limits of differential privacy in online learning algor... | {
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2411.05486 | Handling geometrical variability in nonlinear reduced order modeling
through Continuous Geometry-Aware DL-ROMs | [
"math.NA",
"cs.LG",
"cs.NA"
] | Deep Learning-based Reduced Order Models (DL-ROMs) provide nowadays a well-established class of accurate surrogate models for complex physical systems described by parametrized PDEs, by nonlinearly compressing the solution manifold into a handful of latent coordinates. Until now, design and application of DL-ROMs mainl... | {
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2411.05489 | Do Histopathological Foundation Models Eliminate Batch Effects? A
Comparative Study | [
"cs.LG",
"cs.CV"
] | Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis. Yet, the lack of annotated data and the impact of batch effects, e.g., systematic technical data differences across hospitals, hamper model robustness and generalization. ... | {
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2411.05492 | Covariance-Based Device Activity Detection with Massive MIMO for
Near-Field Correlated Channels | [
"cs.IT",
"eess.SP",
"math.IT",
"math.OC"
] | This paper studies the device activity detection problem in a massive multiple-input multiple-output (MIMO) system for near-field communications (NFC). In this system, active devices transmit their signature sequences to the base station (BS), which detects the active devices based on the received signal. In this paper... | {
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2411.05497 | Tightly-Coupled, Speed-aided Monocular Visual-Inertial Localization in
Topological Map | [
"cs.CV",
"cs.RO"
] | This paper proposes a novel algorithm for vehicle speed-aided monocular visual-inertial localization using a topological map. The proposed system aims to address the limitations of existing methods that rely heavily on expensive sensors like GPS and LiDAR by leveraging relatively inexpensive camera-based pose estimatio... | {
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2411.05500 | FGGP: Fixed-Rate Gradient-First Gradual Pruning | [
"cs.CV",
"cs.LG"
] | In recent years, the increasing size of deep learning models and their growing demand for computational resources have drawn significant attention to the practice of pruning neural networks, while aiming to preserve their accuracy. In unstructured gradual pruning, which sparsifies a network by gradually removing indivi... | {
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} |
2411.05503 | KyrgyzNLP: Challenges, Progress, and Future | [
"cs.CL"
] | Large language models (LLMs) have excelled in numerous benchmarks, advancing AI applications in both linguistic and non-linguistic tasks. However, this has primarily benefited well-resourced languages, leaving less-resourced ones (LRLs) at a disadvantage. In this paper, we highlight the current state of the NLP field i... | {
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2411.05504 | LBPE: Long-token-first Tokenization to Improve Large Language Models | [
"cs.CL"
] | The prevalent use of Byte Pair Encoding (BPE) in Large Language Models (LLMs) facilitates robust handling of subword units and avoids issues of out-of-vocabulary words. Despite its success, a critical challenge persists: long tokens, rich in semantic information, have fewer occurrences in tokenized datasets compared to... | {
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2411.05508 | An Early FIRST Reproduction and Improvements to Single-Token Decoding
for Fast Listwise Reranking | [
"cs.IR",
"cs.CL"
] | Recent advances have demonstrated that large language models (LLMs) excel as listwise rerankers, but their high computational demands remain a barrier to widespread adoption. Further, the traditional language modeling (LM) objective is not ideally suited for reranking tasks. FIRST is a novel approach that addresses the... | {
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} |
2411.05510 | Fast Stochastic Subspace Identification of Densely Instrumented Bridges
Using Randomized SVD | [
"cs.CE"
] | The rising number of bridge collapses worldwide has compelled governments to introduce predictive maintenance strategies to extend structural lifespan. In this context, vibration-based Structural Health Monitoring (SHM) techniques utilizing Operational Modal Analysis (OMA) are favored for their non-destructive and glob... | {
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} |
2411.05514 | Towards Scalable Foundation Models for Digital Dermatology | [
"cs.CV",
"cs.AI"
] | The growing demand for accurate and equitable AI models in digital dermatology faces a significant challenge: the lack of diverse, high-quality labeled data. In this work, we investigate the potential of domain-specific foundation models for dermatology in addressing this challenge. We utilize self-supervised learning ... | {
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} |
2411.05516 | EROAS: 3D Efficient Reactive Obstacle Avoidance System for Autonomous
Underwater Vehicles using 2.5D Forward-Looking Sonar | [
"cs.RO"
] | Advances in Autonomous Underwater Vehicles (AUVs) have evolved vastly in short period of time. While advancements in sonar and camera technology with deep learning aid the obstacle detection and path planning to a great extent, achieving the right balance between computational resources , precision and safety maintaine... | {
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} |
2411.05521 | SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmark | [
"cs.DB",
"cs.AI"
] | Electronic health records (EHRs) are stored in various database systems with different database models on heterogeneous storage architectures, such as relational databases, document stores, or graph databases. These different database models have a big impact on query complexity and performance. While this has been a k... | {
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} |
2411.05524 | Alignment of 3D woodblock geometrical models and 2D orthographic
projection image | [
"cs.CV",
"cs.GR"
] | The accurate alignment of 3D woodblock geometrical models with 2D orthographic projection images presents a significant challenge in the digital preservation of Vietnamese cultural heritage. This paper proposes a unified image processing algorithm to address this issue, enhancing the registration quality between 3D woo... | {
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} |
2411.05527 | How Good is Your Wikipedia? | [
"cs.CL"
] | Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in multilingual NLP. In the context of low-resource languages, however, these quality assumptions are increasingly being scrutinised. This paper critically examines the data quality of Wikipedia in a non-English... | {
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} |
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