id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.07387 | Isochrony-Controlled Speech-to-Text Translation: A study on translating
from Sino-Tibetan to Indo-European Languages | [
"cs.CL",
"eess.AS"
] | End-to-end speech translation (ST), which translates source language speech directly into target language text, has garnered significant attention in recent years. Many ST applications require strict length control to ensure that the translation duration matches the length of the source audio, including both speech and... | {
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2411.07388 | Firing Rate Models as Associative Memory: Excitatory-Inhibitory Balance
for Robust Retrieval | [
"q-bio.NC",
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.AI",
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] | Firing rate models are dynamical systems widely used in applied and theoretical neuroscience to describe local cortical dynamics in neuronal populations. By providing a macroscopic perspective of neuronal activity, these models are essential for investigating oscillatory phenomena, chaotic behavior, and associative mem... | {
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2411.07391 | Federated Learning Client Pruning for Noisy Labels | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.DC"
] | Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, existing FL methods often assume clean annotated datasets, impractical for resource-constrained edge devices. In reality, noisy labels are prevalent, posing significant challenges to FL... | {
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2411.07392 | Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set
Domain Generalization | [
"cs.CV",
"cs.AI"
] | Open-set domain generalization addresses a real-world challenge: training a model to generalize across unseen domains (domain generalization) while also detecting samples from unknown classes not encountered during training (open-set recognition). However, most existing approaches tackle these issues separately, limiti... | {
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2411.07395 | Data-Centric Learning Framework for Real-Time Detection of Aiming Beam
in Fluorescence Lifetime Imaging Guided Surgery | [
"cs.AI"
] | This study introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm). A key aspect of the methodology is the accurate detection of the aiming beam, which is essential for localizing points used to map FLIm measurements onto the tissue region w... | {
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2411.07396 | Toward Optimal Search and Retrieval for RAG | [
"cs.CL"
] | Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems form the RAG pipeline, the retriever and the reader, and the impact of each on downstream task performance is not well-understood. Here, we w... | {
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2411.07397 | Spiking Transformer Hardware Accelerators in 3D Integration | [
"cs.NE",
"cs.AR"
] | Spiking neural networks (SNNs) are powerful models of spatiotemporal computation and are well suited for deployment on resource-constrained edge devices and neuromorphic hardware due to their low power consumption. Leveraging attention mechanisms similar to those found in their artificial neural network counterparts, r... | {
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2411.07398 | Beyond Keywords: A Context-based Hybrid Approach to Mining Ethical
Concern-related App Reviews | [
"cs.CL",
"cs.AI",
"cs.SE"
] | With the increasing proliferation of mobile applications in our everyday experiences, the concerns surrounding ethics have surged significantly. Users generally communicate their feedback, report issues, and suggest new functionalities in application (app) reviews, frequently emphasizing safety, privacy, and accountabi... | {
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2411.07402 | Development of a Collaborative Robotic Arm-based Bimanual Haptic Display | [
"cs.RO",
"cs.HC"
] | This paper presents a bimanual haptic display based on collaborative robot arms. We address the limitations of existing robot arm-based haptic displays by optimizing the setup configuration and implementing inertia/friction compensation techniques. The optimized setup configuration maximizes workspace coverage, dexteri... | {
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2411.07404 | Controllable Context Sensitivity and the Knob Behind It | [
"cs.CL",
"cs.AI"
] | When making predictions, a language model must trade off how much it relies on its context vs. its prior knowledge. Choosing how sensitive the model is to its context is a fundamental functionality, as it enables the model to excel at tasks like retrieval-augmented generation and question-answering. In this paper, we s... | {
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2411.07405 | Quality of Control based Resource Dimensioning for Collaborative Edge
Robotics | [
"cs.RO",
"cs.SY",
"eess.SY"
] | With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, w... | {
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2411.07407 | Using Generative AI and Multi-Agents to Provide Automatic Feedback | [
"cs.CL"
] | This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments. The research addresses a key gap in the field by exploring how multi-agent systems, called AutoFeedback, can improve the ... | {
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2411.07413 | ODEStream: A Buffer-Free Online Learning Framework with ODE-based
Adaptor for Streaming Time Series Forecasting | [
"cs.LG"
] | Addressing the challenges of irregularity and concept drift in streaming time series is crucial in real-world predictive modelling. Previous studies in time series continual learning often propose models that require buffering of long sequences, potentially restricting the responsiveness of the inference system. Moreov... | {
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2411.07414 | Comparing Targeting Strategies for Maximizing Social Welfare with
Limited Resources | [
"cs.LG",
"stat.ML"
] | Machine learning is increasingly used to select which individuals receive limited-resource interventions in domains such as human services, education, development, and more. However, it is often not apparent what the right quantity is for models to predict. In particular, policymakers rarely have access to data from a ... | {
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2411.07416 | T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric
MR Imaging | [
"eess.IV",
"cs.CV"
] | Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement. However, measuring diffusion patterns in DWI sequences can be time-consuming, prone to artifacts and sensitive to ima... | {
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2411.07417 | Untangling Hate Speech Definitions: A Semantic Componential Analysis
Across Cultures and Domains | [
"cs.CL"
] | Hate speech relies heavily on cultural influences, leading to varying individual interpretations. For that reason, we propose a Semantic Componential Analysis (SCA) framework for a cross-cultural and cross-domain analysis of hate speech definitions. We create the first dataset of definitions derived from five domains: ... | {
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2411.07419 | Machine Learning Based Cyber System Restoration for IEC 61850 Based
Digital Substations | [
"eess.SY",
"cs.SY"
] | Substation Automation Systems (SAS) that adhere to the International Electrotechnical Commission (IEC) 61850 standard have already been widely implemented across various on-site local substations. However, the digitalization of substations, which involves the use of cyber system, inherently increases their vulnerabilit... | {
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2411.07425 | Predicting BWR Criticality with Data-Driven Machine Learning Model | [
"cs.LG",
"cs.AI"
] | One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full power. Economically, a nuclear power plant should burn enough fuel to maintain critic... | {
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2411.07426 | Evaluating Detection Thresholds: The Impact of False Positives and
Negatives on Super-Resolution Ultrasound Localization Microscopy | [
"cs.AI"
] | Super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) offers a high-resolution view of microvascular structures. Yet, ULM image quality heavily relies on precise microbubble (MB) detection. Despite the crucial role of localization algorithms, there has been limited focus on the practical pit... | {
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2411.07428 | Just Label the Repeats for In-The-Wild Audio-to-Score Alignment | [
"cs.SD",
"cs.LG",
"cs.MM",
"eess.AS"
] | We propose an efficient workflow for high-quality offline alignment of in-the-wild performance audio and corresponding sheet music scans (images). Recent work on audio-to-score alignment extends dynamic time warping (DTW) to be theoretically able to handle jumps in sheet music induced by repeat signs-this method requir... | {
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2411.07430 | XPoint: A Self-Supervised Visual-State-Space based Architecture for
Multispectral Image Registration | [
"cs.CV"
] | Accurate multispectral image matching presents significant challenges due to non-linear intensity variations across spectral modalities, extreme viewpoint changes, and the scarcity of labeled datasets. Current state-of-the-art methods are typically specialized for a single spectral difference, such as visibleinfrared, ... | {
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2411.07432 | Fast unsupervised ground metric learning with tree-Wasserstein distance | [
"cs.LG"
] | The performance of unsupervised methods such as clustering depends on the choice of distance metric between features, or ground metric. Commonly, ground metrics are decided with heuristics or learned via supervised algorithms. However, since many interesting datasets are unlabelled, unsupervised ground metric learning ... | {
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2411.07439 | Music Discovery Dialogue Generation Using Human Intent Analysis and
Large Language Models | [
"cs.SD",
"cs.IR",
"eess.AS"
] | A conversational music retrieval system can help users discover music that matches their preferences through dialogue. To achieve this, a conversational music retrieval system should seamlessly engage in multi-turn conversation by 1) understanding user queries and 2) responding with natural language and retrieved music... | {
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2411.07441 | Automatically Detecting Online Deceptive Patterns in Real-time | [
"cs.HC",
"cs.AI",
"cs.CY"
] | Deceptive patterns (DPs) in digital interfaces manipulate users into making unintended decisions, exploiting cognitive biases and psychological vulnerabilities. These patterns have become ubiquitous across various digital platforms. While efforts to mitigate DPs have emerged from legal and technical perspectives, a sig... | {
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2411.07442 | Learned Slip-Detection-Severity Framework using Tactile Deformation
Field Feedback for Robotic Manipulation | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Safely handling objects and avoiding slippage are fundamental challenges in robotic manipulation, yet traditional techniques often oversimplify the issue by treating slippage as a binary occurrence. Our research presents a framework that both identifies slip incidents and measures their severity. We introduce a set of ... | {
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2411.07444 | Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of
Serverless Computing Functions | [
"cs.DC",
"cs.AI",
"cs.SY",
"eess.SY"
] | In today's Function-as-a-Service offerings, a programmer is usually responsible for configuring function memory for its successful execution, which allocates proportional function resources such as CPU and network. However, right-sizing the function memory force developers to speculate performance and make ad-hoc confi... | {
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2411.07445 | All-in-one Weather-degraded Image Restoration via Adaptive
Degradation-aware Self-prompting Model | [
"cs.CV"
] | Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weat... | {
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2411.07446 | Efficient and Accurate Prompt Optimization: the Benefit of Memory in
Exemplar-Guided Reflection | [
"cs.CL"
] | Automatic prompt engineering aims to enhance the generation quality of large language models (LLMs). Recent works utilize feedbacks generated from erroneous cases to guide the prompt optimization. During inference, they may further retrieve several semantically-related exemplars and concatenate them to the optimized pr... | {
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2411.07447 | The Effect of Scheduling and Preemption on the Efficiency of LLM
Inference Serving | [
"cs.PF",
"cs.AI"
] | The growing usage of Large Language Models (LLMs) highlights the demands and challenges in scalable LLM inference systems, affecting deployment and development processes. On the deployment side, there is a lack of comprehensive analysis on the conditions under which a particular scheduler performs better or worse, with... | {
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2411.07449 | Tracing the Roots: Leveraging Temporal Dynamics in Diffusion
Trajectories for Origin Attribution | [
"cs.CV"
] | Diffusion models have revolutionized image synthesis, garnering significant research interest in recent years. Diffusion is an iterative algorithm in which samples are generated step-by-step, starting from pure noise. This process introduces the notion of diffusion trajectories, i.e., paths from the standard Gaussian d... | {
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2411.07451 | Optimizing Data Delivery: Insights from User Preferences on Visuals,
Tables, and Text | [
"cs.HC",
"cs.AI",
"cs.LG"
] | In this work, we research user preferences to see a chart, table, or text given a question asked by the user. This enables us to understand when it is best to show a chart, table, or text to the user for the specific question. For this, we conduct a user study where users are shown a question and asked what they would ... | {
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2411.07453 | Research on fault diagnosis of nuclear power first-second circuit based
on hierarchical multi-granularity classification network | [
"eess.SY",
"cs.AI",
"cs.SY"
] | The safe and reliable operation of complex electromechanical systems in nuclear power plants is crucial for the safe production of nuclear power plants and their nuclear power unit. Therefore, accurate and timely fault diagnosis of nuclear power systems is of great significance for ensuring the safe and reliable operat... | {
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2411.07457 | DecoPrompt : Decoding Prompts Reduces Hallucinations when Large Language
Models Meet False Premises | [
"cs.CL"
] | While large language models (LLMs) have demonstrated increasing power, they have also called upon studies on their hallucinated outputs that deviate from factually correct statements. In this paper, we focus on one important scenario of false premises, where LLMs are distracted by misaligned claims although the model p... | {
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2411.07461 | BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions | [
"cs.CV",
"cs.AI"
] | We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that bridges the gap between descriptive synthetic captions and factual web-scale alt-text. KALE augments synthetic dense image captions with web-scale alt-text to generate factually grounded image captions. Our two-stage approach leverages large vision... | {
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2411.07462 | MureObjectStitch: Multi-reference Image Composition | [
"cs.CV"
] | Generative image composition aims to regenerate the given foreground object in the background image to produce a realistic composite image. In this work, we propose an effective finetuning strategy for generative image composition model, in which we finetune a pretrained model using one or more images containing the sa... | {
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2411.07463 | MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation
Models, Convolutional Neural Networks, and Uncertainty Quantification for
High-Speed Video Phase Detection Data | [
"cs.CV",
"cs.LG",
"eess.IV"
] | High-speed video (HSV) phase detection (PD) segmentation is crucial for monitoring vapor, liquid, and microlayer phases in industrial processes. While CNN-based models like U-Net have shown success in simplified shadowgraphy-based two-phase flow (TPF) analysis, their application to complex HSV PD tasks remains unexplor... | {
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2411.07464 | BudgetMLAgent: A Cost-Effective LLM Multi-Agent system for Automating
Machine Learning Tasks | [
"cs.MA",
"cs.AI",
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) excel in diverse applications including generation of code snippets, but often struggle with generating code for complex Machine Learning (ML) tasks. Although existing LLM single-agent based systems give varying performance depending on the task complexity, they purely rely on larger and ex... | {
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2411.07466 | IdentifyMe: A Challenging Long-Context Mention Resolution Benchmark | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recent evaluations of LLMs on coreference resolution have revealed that traditional output formats and evaluation metrics do not fully capture the models' referential understanding. To address this, we introduce IdentifyMe, a new benchmark for mention resolution presented in a multiple-choice question (MCQ) format, com... | {
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2411.07467 | Machines and Mathematical Mutations: Using GNNs to Characterize Quiver
Mutation Classes | [
"cs.LG",
"hep-th",
"math.CO"
] | Machine learning is becoming an increasingly valuable tool in mathematics, enabling one to identify subtle patterns across collections of examples so vast that they would be impossible for a single researcher to feasibly review and analyze. In this work, we use graph neural networks to investigate quiver mutation -- an... | {
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2411.07468 | Privacy-Preserving Verifiable Neural Network Inference Service | [
"cs.CR",
"cs.LG"
] | Machine learning has revolutionized data analysis and pattern recognition, but its resource-intensive training has limited accessibility. Machine Learning as a Service (MLaaS) simplifies this by enabling users to delegate their data samples to an MLaaS provider and obtain the inference result using a pre-trained model.... | {
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2411.07470 | Two-Layer Attention Optimization for Bimanual Coordination | [
"eess.SY",
"cs.SY"
] | Bimanual tasks performed by human agents present unique optimal control considerations compared to cyberphysical agents. These considerations include minimizing attention, distributing attention across two isolated hands, and coordinating the two hands to reach a broader goal. In this work, we propose a two-layer contr... | {
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2411.07472 | Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating
Robustness of AI-Generated Image detectors | [
"cs.CV"
] | Text-to-image diffusion models have impactful applications in art, design, and entertainment, yet these technologies also pose significant risks by enabling the creation and dissemination of misinformation. Although recent advancements have produced AI-generated image detectors that claim robustness against various aug... | {
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2411.07473 | Nearly-Linear Time Seeded Extractors with Short Seeds | [
"cs.CC",
"cs.CR",
"cs.IT",
"math.IT"
] | (abstract shortened due to space constraints) Existing constructions of seeded extractors with short seed length and large output length run in time $\Omega(n \log(1/\varepsilon))$ and often slower, where $n$ is the input source length and $\varepsilon$ is the error of the extractor. Since cryptographic applications ... | {
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2411.07474 | Controlled Evaluation of Syntactic Knowledge in Multilingual Language
Models | [
"cs.CL"
] | Language models (LMs) are capable of acquiring elements of human-like syntactic knowledge. Targeted syntactic evaluation tests have been employed to measure how well they form generalizations about syntactic phenomena in high-resource languages such as English. However, we still lack a thorough understanding of LMs' ca... | {
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2411.07475 | Degree Matrix Comparison for Graph Alignment | [
"cs.SI",
"math.OC"
] | Graph alignment considers the optimal node correspondence across networks. To advance unsupervised graph alignment algorithms on plain graphs, we propose Degree Matrix Comparison (DMC). Through extensive experiments and mathematical motivations, we demonstrate the potential of this method. Remarkably, DMC achieves up t... | {
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2411.07478 | GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering | [
"cs.CV"
] | Recovering the intrinsic physical attributes of a scene from images, generally termed as the inverse rendering problem, has been a central and challenging task in computer vision and computer graphics. In this paper, we present GUS-IR, a novel framework designed to address the inverse rendering problem for complicated ... | {
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2411.07482 | Enhancing Link Prediction with Fuzzy Graph Attention Networks and
Dynamic Negative Sampling | [
"cs.LG",
"cs.AI",
"cs.IR"
] | Link prediction is crucial for understanding complex networks but traditional Graph Neural Networks (GNNs) often rely on random negative sampling, leading to suboptimal performance. This paper introduces Fuzzy Graph Attention Networks (FGAT), a novel approach integrating fuzzy rough sets for dynamic negative sampling a... | {
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2411.07483 | Quantifying Knowledge Distillation Using Partial Information
Decomposition | [
"stat.ML",
"cs.CV",
"cs.IT",
"cs.LG",
"eess.IV",
"math.IT"
] | Knowledge distillation provides an effective method for deploying complex machine learning models in resource-constrained environments. It typically involves training a smaller student model to emulate either the probabilistic outputs or the internal feature representations of a larger teacher model. By doing so, the s... | {
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2411.07484 | Differentiable Optimization-based Control Policy with Convergence
Analysis | [
"eess.SY",
"cs.SY"
] | Real-world system control requires both high-performing and interpretable controllers. Model-based control policies have gained popularity by using historical data to learn system costs and dynamics before implementation. However, this two-phase approach prevents these policies from achieving optimal control as the met... | {
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2411.07490 | $\textit{Dirigo}$: A Method to Extract Event Logs for Object-Centric
Processes | [
"cs.DB"
] | Real-world processes involve multiple object types with intricate interrelationships. Traditional event logs (in XES format), which record process execution centred around the case notion, are restricted to a single-object perspective, making it difficult to capture the behaviour of multiple objects and their interacti... | {
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2411.07494 | Rapid Response: Mitigating LLM Jailbreaks with a Few Examples | [
"cs.CL"
] | As large language models (LLMs) grow more powerful, ensuring their safety against misuse becomes crucial. While researchers have focused on developing robust defenses, no method has yet achieved complete invulnerability to attacks. We propose an alternative approach: instead of seeking perfect adversarial robustness, w... | {
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2411.07496 | ADMM for Structured Fractional Minimization | [
"math.OC",
"cs.LG",
"cs.NA",
"math.NA"
] | We consider a class of structured fractional minimization problems, where the numerator includes a differentiable function, a simple nonconvex nonsmooth function, a concave nonsmooth function, and a convex nonsmooth function composed with a linear operator, while the denominator is a continuous function that is either ... | {
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2411.07501 | LAuReL: Learned Augmented Residual Layer | [
"cs.LG",
"cs.AI",
"cs.CV"
] | One of the core pillars of efficient deep learning methods is architectural improvements such as the residual/skip connection, which has led to significantly better model convergence and quality. Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-base... | {
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2411.07503 | A Novel Automatic Real-time Motion Tracking Method for Magnetic
Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced
Tracking-Learning-Detection Framework with Automatic Segmentation | [
"eess.IV",
"cs.CV",
"cs.LG",
"physics.med-ph",
"q-bio.TO"
] | Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework wit... | {
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2411.07504 | AdaS&S: a One-Shot Supernet Approach for Automatic Embedding Size Search
in Deep Recommender System | [
"cs.IR",
"cs.LG"
] | Deep Learning Recommendation Model(DLRM)s utilize the embedding layer to represent various categorical features. Traditional DLRMs adopt unified embedding size for all features, leading to suboptimal performance and redundant parameters. Thus, lots of Automatic Embedding size Search (AES) works focus on obtaining mixed... | {
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2411.07506 | FlowTS: Time Series Generation via Rectified Flow | [
"cs.LG",
"cs.AI"
] | Diffusion-based models have significant achievements in time series generation but suffer from inefficient computation: solving high-dimensional ODEs/SDEs via iterative numerical solvers demands hundreds to thousands of drift function evaluations per sample, incurring prohibitive costs. To resolve this, we propose Flow... | {
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2411.07508 | Feature Interaction Fusion Self-Distillation Network For CTR Prediction | [
"cs.IR"
] | Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions through stacked or parallel structures, with some employing knowledge distillation for model compression. However, we observe some limitations ... | {
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2411.07510 | An Attack Traffic Identification Method Based on Temporal Spectrum | [
"cs.AI",
"cs.CR"
] | To address the issues of insufficient robustness, unstable features, and data noise interference in existing network attack detection and identification models, this paper proposes an attack traffic detection and identification method based on temporal spectrum. First, traffic data is segmented by a sliding window to c... | {
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2411.07514 | Robust Offline Reinforcement Learning for Non-Markovian Decision
Processes | [
"cs.LG",
"stat.ML"
] | Distributionally robust offline reinforcement learning (RL) aims to find a policy that performs the best under the worst environment within an uncertainty set using an offline dataset collected from a nominal model. While recent advances in robust RL focus on Markov decision processes (MDPs), robust non-Markovian RL is... | {
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2411.07515 | Bayesian Deep Learning Approach for Real-time Lane-based Arrival Curve
Reconstruction at Intersection using License Plate Recognition Data | [
"cs.LG"
] | The acquisition of real-time and accurate traffic arrival information is of vital importance for proactive traffic control systems, especially in partially connected vehicle environments. License plate recognition (LPR) data that record both vehicle departures and identities are proven to be desirable in reconstructing... | {
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2411.07516 | SparrowVQE: Visual Question Explanation for Course Content Understanding | [
"cs.CV",
"cs.CL"
] | Visual Question Answering (VQA) research seeks to create AI systems to answer natural language questions in images, yet VQA methods often yield overly simplistic and short answers. This paper aims to advance the field by introducing Visual Question Explanation (VQE), which enhances the ability of VQA to provide detaile... | {
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2411.07518 | LLM App Squatting and Cloning | [
"cs.AI",
"cs.CR"
] | Impersonation tactics, such as app squatting and app cloning, have posed longstanding challenges in mobile app stores, where malicious actors exploit the names and reputations of popular apps to deceive users. With the rapid growth of Large Language Model (LLM) stores like GPT Store and FlowGPT, these issues have simil... | {
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2411.07519 | TIPS: Threat Actor Informed Prioritization of Applications using
SecEncoder | [
"cs.CR",
"cs.AI"
] | This paper introduces TIPS: Threat Actor Informed Prioritization using SecEncoder, a specialized language model for security. TIPS combines the strengths of both encoder and decoder language models to detect and prioritize compromised applications. By integrating threat actor intelligence, TIPS enhances the accuracy an... | {
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2411.07521 | Fair Summarization: Bridging Quality and Diversity in Extractive
Summaries | [
"cs.CL",
"cs.AI"
] | Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to ensure equitable representation across different social groups, leading to biased outputs. In this paper, we introduce two novel methods for ... | {
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2411.07523 | Collaborative and Federated Black-box Optimization: A Bayesian
Optimization Perspective | [
"cs.LG",
"stat.ML"
] | We focus on collaborative and federated black-box optimization (BBOpt), where agents optimize their heterogeneous black-box functions through collaborative sequential experimentation. From a Bayesian optimization perspective, we address the fundamental challenges of distributed experimentation, heterogeneity, and priva... | {
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2411.07527 | Prompt-enhanced Network for Hateful Meme Classification | [
"cs.CL"
] | The dynamic expansion of social media has led to an inundation of hateful memes on media platforms, accentuating the growing need for efficient identification and removal. Acknowledging the constraints of conventional multimodal hateful meme classification, which heavily depends on external knowledge and poses the risk... | {
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2411.07528 | SecEncoder: Logs are All You Need in Security | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | Large and Small Language Models (LMs) are typically pretrained using extensive volumes of text, which are sourced from publicly accessible platforms such as Wikipedia, Book Corpus, or through web scraping. These models, due to their exposure to a wide range of language data, exhibit impressive generalization capabiliti... | {
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2411.07529 | Evaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of
Different Complexity Levels: An Empirical Analysis | [
"cs.SE",
"cs.AI"
] | ChatGPT and other large language models (LLMs) promise to revolutionize software development by automatically generating code from program specifications. We assess the performance of ChatGPT's GPT-3.5-turbo model on LeetCode, a popular platform with algorithmic coding challenges for technical interview practice, acros... | {
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2411.07533 | Large Language Models as Neurolinguistic Subjects: Identifying Internal
Representations for Form and Meaning | [
"cs.CL"
] | This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM evaluation paradigms: psycholinguistic and neurolinguistic. Traditional psycholinguistic evaluations often reflect statistical biases that may misrepresent LL... | {
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2411.07534 | Effective Virtual Reality Teleoperation of an Upper-body Humanoid with
Modified Task Jacobians and Relaxed Barrier Functions for Self-Collision
Avoidance | [
"cs.RO",
"cs.LG"
] | We present an approach for retartgeting off-the-shelf Virtual Reality (VR) trackers to effectively teleoperate an upper-body humanoid while ensuring self-collision-free motions. Key to the effectiveness was the proper assignment of trackers to joint sets via modified task Jacobians and relaxed barrier functions for sel... | {
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2411.07536 | Model Stealing for Any Low-Rank Language Model | [
"cs.LG",
"cs.AI",
"cs.DS",
"stat.ML"
] | Model stealing, where a learner tries to recover an unknown model via carefully chosen queries, is a critical problem in machine learning, as it threatens the security of proprietary models and the privacy of data they are trained on. In recent years, there has been particular interest in stealing large language models... | {
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2411.07537 | Accident Impact Prediction based on a deep convolutional and recurrent
neural network model | [
"cs.LG"
] | Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of real-time forecasting of post-accident impact using readily available data can play a crucial role in preventing adverse ... | {
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2411.07538 | Unraveling the Gradient Descent Dynamics of Transformers | [
"cs.LG",
"math.OC"
] | While the Transformer architecture has achieved remarkable success across various domains, a thorough theoretical foundation explaining its optimization dynamics is yet to be fully developed. In this study, we aim to bridge this understanding gap by answering the following two core questions: (1) Which types of Transfo... | {
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2411.07540 | Lateral String Stability in Autonomous & Connected Vehicle Platoons | [
"eess.SY",
"cs.SY"
] | This paper addresses the lateral control of Autonomous and Connected Vehicles (ACVs) in a platoon executing an Emergency Lane Change (ELC) maneuver. These maneuvers are typically triggered by emergency signals from the front or rear of the platoon in response to the need to avoid obstacles or allow other vehicles to pa... | {
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2411.07541 | HiCoM: Hierarchical Coherent Motion for Streamable Dynamic Scene with 3D
Gaussian Splatting | [
"cs.CV"
] | The online reconstruction of dynamic scenes from multi-view streaming videos faces significant challenges in training, rendering and storage efficiency. Harnessing superior learning speed and real-time rendering capabilities, 3D Gaussian Splatting (3DGS) has recently demonstrated considerable potential in this field. H... | {
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2411.07544 | Depthwise Separable Convolutions with Deep Residual Convolutions | [
"cs.CV"
] | The recent advancement of edge computing enables researchers to optimize various deep learning architectures to employ them in edge devices. In this study, we aim to optimize Xception architecture which is one of the most popular deep learning algorithms for computer vision applications. The Xception architecture is hi... | {
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2411.07546 | Contrastive Language Prompting to Ease False Positives in Medical
Anomaly Detection | [
"cs.CV",
"cs.AI",
"cs.CL"
] | A pre-trained visual-language model, contrastive language-image pre-training (CLIP), successfully accomplishes various downstream tasks with text prompts, such as finding images or localizing regions within the image. Despite CLIP's strong multi-modal data capabilities, it remains limited in specialized environments, s... | {
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2411.07550 | Learning Autonomous Docking Operation of Fully Actuated Autonomous
Surface Vessel from Expert data | [
"cs.RO"
] | This paper presents an approach for autonomous docking of a fully actuated autonomous surface vessel using expert demonstration data. We frame the docking problem as an imitation learning task and employ inverse reinforcement learning (IRL) to learn a reward function from expert trajectories. A two-stage neural network... | {
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2411.07551 | SP-VIO: Robust and Efficient Filter-Based Visual Inertial Odometry with
State Transformation Model and Pose-Only Visual Description | [
"cs.RO"
] | Due to the advantages of high computational efficiency and small memory requirements, filter-based visual inertial odometry (VIO) has a good application prospect in miniaturized and payload-constrained embedded systems. However, the filter-based method has the problem of insufficient accuracy. To this end, we propose t... | {
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2411.07554 | Exogenous Randomness Empowering Random Forests | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | We offer theoretical and empirical insights into the impact of exogenous randomness on the effectiveness of random forests with tree-building rules independent of training data. We formally introduce the concept of exogenous randomness and identify two types of commonly existing randomness: Type I from feature subsampl... | {
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2411.07555 | GaussianCut: Interactive segmentation via graph cut for 3D Gaussian
Splatting | [
"cs.CV"
] | We introduce GaussianCut, a new method for interactive multiview segmentation of scenes represented as 3D Gaussians. Our approach allows for selecting the objects to be segmented by interacting with a single view. It accepts intuitive user input, such as point clicks, coarse scribbles, or text. Using 3D Gaussian Splatt... | {
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2411.07556 | Multi-task Feature Enhancement Network for No-Reference Image Quality
Assessment | [
"cs.CV",
"eess.IV"
] | Due to the scarcity of labeled samples in Image Quality Assessment (IQA) datasets, numerous recent studies have proposed multi-task based strategies, which explore feature information from other tasks or domains to boost the IQA task. Nevertheless, multi-task strategies based No-Reference Image Quality Assessment (NR-I... | {
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2411.07558 | Discrete-Valued Signal Estimation via Low-Complexity Message Passing
Algorithm for Highly Correlated Measurements | [
"eess.SP",
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | This paper considers a discrete-valued signal estimation scheme based on a low-complexity Bayesian optimal message passing algorithm (MPA) for solving massive linear inverse problems under highly correlated measurements. Gaussian belief propagation (GaBP) can be derived by applying the central limit theorem (CLT)-based... | {
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2411.07559 | Zer0-Jack: A Memory-efficient Gradient-based Jailbreaking Method for
Black-box Multi-modal Large Language Models | [
"cs.LG",
"cs.AI"
] | Jailbreaking methods, which induce Multi-modal Large Language Models (MLLMs) to output harmful responses, raise significant safety concerns. Among these methods, gradient-based approaches, which use gradients to generate malicious prompts, have been widely studied due to their high success rates in white-box settings, ... | {
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2411.07560 | EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on
Pre-trained Language Models and Deep Learning Methods | [
"cs.CE",
"cs.AI"
] | This study introduces a novel approach for EUR/USD exchange rate forecasting that integrates deep learning, textual analysis, and particle swarm optimization (PSO). By incorporating online news and analysis texts as qualitative data, the proposed PSO-LSTM model demonstrates superior performance compared to traditional ... | {
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2411.07563 | Improving Grapheme-to-Phoneme Conversion through In-Context Knowledge
Retrieval with Large Language Models | [
"cs.AI"
] | Grapheme-to-phoneme (G2P) conversion is a crucial step in Text-to-Speech (TTS) systems, responsible for mapping grapheme to corresponding phonetic representations. However, it faces ambiguities problems where the same grapheme can represent multiple phonemes depending on contexts, posing a challenge for G2P conversion.... | {
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2411.07567 | Uncertainty-Aware Test-Time Adaptation for Inverse Consistent
Diffeomorphic Lung Image Registration | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Diffeomorphic deformable image registration ensures smooth invertible transformations across inspiratory and expiratory chest CT scans. Yet, in practice, deep learning-based diffeomorphic methods struggle to capture large deformations between inspiratory and expiratory volumes, and therefore lack inverse consistency. E... | {
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2411.07569 | Towards Automated Model Design on Recommender Systems | [
"cs.IR"
] | The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architectur... | {
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2411.07570 | Constructive RNNs: An Error-Recurrence Perspective on Time-Variant Zero
Finding Problem Solving Under Uncertainty | [
"eess.SY",
"cs.SY"
] | When facing time-variant problems in analog computing, the desirable RNN design requires finite-time convergence and robustness with respect to various types of uncertainties, due to the time-variant nature and difficulties in implementation. It is very worthwhile to explore terminal zeroing neural networks, through ex... | {
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2411.07573 | Robotic Control Optimization Through Kernel Selection in Safe Bayesian
Optimization | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with mul... | {
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} |
2411.07574 | Disentangling Tabular Data Towards Better One-Class Anomaly Detection | [
"cs.LG",
"cs.AI"
] | Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples p... | {
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} |
2411.07575 | G\'en\'eration de bases de donn\'ees images IR sous contraintes avec
variabilit\'e thermique intrins\`eque des cibles | [
"cs.CV"
] | In this communication, we propose a method which permits to simulate images of targets in infrared imagery by superimposition of vehicle signatures in background, eventually with occultants. We develop a principle which authorizes us to generate different thermal configurations of target signatures. This method enables... | {
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} |
2411.07577 | IR image databases generation under target intrinsic thermal variability
constraints | [
"cs.CV"
] | This paper deals with the problem of infrared image database generation for ATR assessment purposes. Huge databases are required to have quantitative and objective performance evaluations. We propose a method which superimpose targets and occultants on background under image quality metrics constraints to generate real... | {
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} |
2411.07578 | Atmospheric turbulence restoration by diffeomorphic image registration
and blind deconvolution | [
"cs.CV"
] | A novel approach is presented in this paper to improve images which are altered by atmospheric turbulence. Two new algorithms are presented based on two combinations of a blind deconvolution block, an elastic registration block and a temporal filter block. The algorithms are tested on real images acquired in the desert... | {
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} |
2411.07579 | Projecting Gaussian Ellipsoids While Avoiding Affine Projection
Approximation | [
"cs.CV",
"cs.GR"
] | Recently, 3D Gaussian Splatting has dominated novel-view synthesis with its real-time rendering speed and state-of-the-art rendering quality. However, during the rendering process, the use of the Jacobian of the affine approximation of the projection transformation leads to inevitable errors, resulting in blurriness, a... | {
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} |
2411.07581 | Semantic segmentation on multi-resolution optical and microwave data
using deep learning | [
"cs.CV",
"eess.IV"
] | Presently, deep learning and convolutional neural networks (CNNs) are widely used in the fields of image processing, image classification, object identification and many more. In this work, we implemented convolutional neural network based modified U-Net model and VGG-UNet model to automatically identify objects from s... | {
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} |
2411.07584 | Grounded Video Caption Generation | [
"cs.CV"
] | We propose a new task, dataset and model for grounded video caption generation. This task unifies captioning and object grounding in video, where the objects in the caption are grounded in the video via temporally consistent bounding boxes. We introduce the following contributions. First, we present a task definition a... | {
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} |
2411.07585 | Reinforcement Learning Framework for Quantitative Trading | [
"q-fin.TR",
"cs.AI",
"q-fin.CP"
] | The inherent volatility and dynamic fluctuations within the financial stock market underscore the necessity for investors to employ a comprehensive and reliable approach that integrates risk management strategies, market trends, and the movement trends of individual securities. By evaluating specific data, investors ca... | {
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} |
2411.07586 | A Comprehensive Survey of AI-Driven Advancements and Techniques in
Automated Program Repair and Code Generation | [
"cs.AI"
] | Bug fixing and code generation have been core research topics in software development for many years. The recent explosive growth in Large Language Models has completely transformed these spaces, putting in reach incredibly powerful tools for both. In this survey, 27 recent papers have been reviewed and split into two ... | {
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} |
2411.07588 | A High-frequency Pneumatic Oscillator for Soft Robotics | [
"cs.RO"
] | Soft robots, while highly adaptable to diverse environments through various actuation methods, still face significant performance boundary due to the inherent properties of materials. These limitations manifest in the challenge of guaranteeing rapid response and large-scale movements simultaneously, ultimately restrict... | {
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} |
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