id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.06642 | Antenna Coding Empowered by Pixel Antennas | [
"eess.SP",
"cs.IT",
"math.IT"
] | Pixel antennas, based on discretizing a continuous radiation surface into small elements called pixels, are a flexible reconfigurable antenna technology. By controlling the connections between pixels via switches, the characteristics of pixel antennas can be adjusted to enhance the wireless channel. Inspired by this, w... | {
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2411.06643 | Flight Demonstration and Model Validation of a Prototype
Variable-Altitude Venus Aerobot | [
"cs.RO"
] | This paper details a significant milestone towards maturing a buoyant aerial robotic platform, or aerobot, for flight in the Venus clouds. We describe two flights of our subscale altitude-controlled aerobot, fabricated from the materials necessary to survive Venus conditions. During these flights over the Nevada Black ... | {
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2411.06646 | Understanding Scaling Laws with Statistical and Approximation Theory for
Transformer Neural Networks on Intrinsically Low-dimensional Data | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] | When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the data size. Perhaps the best known example of such scaling laws are for transformer-based large language models, where networks with billions of parameters are train... | {
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2411.06649 | A Novel Combined Data-Driven Approach for Electricity Theft Detection | [
"eess.SY",
"cs.LG",
"cs.SY",
"eess.SP"
] | The two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnorma... | {
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2411.06650 | Quantum Policy Gradient in Reproducing Kernel Hilbert Space | [
"quant-ph",
"cs.LG"
] | Parametrised quantum circuits offer expressive and data-efficient representations for machine learning. Due to quantum states residing in a high-dimensional Hilbert space, parametrised quantum circuits have a natural interpretation in terms of kernel methods. The representation of quantum circuits in terms of quantum k... | {
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2411.06651 | Machine learning-enabled velocity model building with uncertainty
quantification | [
"cs.LG",
"cs.CV"
] | Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional velocity model building methods such as Full-Waveform Inversion (FWI) are powerful but often struggle with the inherent compl... | {
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2411.06652 | LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection | [
"cs.CV"
] | A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. In this work, a state-of-the-art salient object detection model for multi-focus light field images, c... | {
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2411.06655 | Explore the Reasoning Capability of LLMs in the Chess Testbed | [
"cs.CL",
"cs.AI"
] | Reasoning is a central capability of human intelligence. In recent years, with the advent of large-scale datasets, pretrained large language models have emerged with new capabilities, including reasoning. However, these models still struggle with long-term, complex reasoning tasks, such as playing chess. Based on the o... | {
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2411.06657 | Renaissance: Investigating the Pretraining of Vision-Language Encoders | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | In the past several years there has been an explosion of available models for vision-language tasks. Unfortunately, the literature still leaves open a number of questions related to best practices in designing and training such models. In this paper we seek to answer several questions related to the pretraining of visi... | {
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2411.06659 | An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning | [
"cs.LG",
"cs.AI"
] | Incremental graph learning has gained significant attention for its ability to address the catastrophic forgetting problem in graph representation learning. However, traditional methods often rely on a large number of labels for node classification, which is impractical in real-world applications. This makes few-shot i... | {
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2411.06660 | Bridge: A Unified Framework to Knowledge Graph Completion via Language
Models and Knowledge Representation | [
"cs.CL",
"cs.LG"
] | Knowledge graph completion (KGC) is a task of inferring missing triples based on existing Knowledge Graphs (KGs). Both structural and semantic information are vital for successful KGC. However, existing methods only use either the structural knowledge from the KG embeddings or the semantic information from pre-trained ... | {
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2411.06665 | Learning from Different Samples: A Source-free Framework for
Semi-supervised Domain Adaptation | [
"cs.CV"
] | Semi-supervised domain adaptation (SSDA) has been widely studied due to its ability to utilize a few labeled target data to improve the generalization ability of the model. However, existing methods only consider designing certain strategies for target samples to adapt, ignoring the exploration of customized learning f... | {
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2411.06666 | Adversarial Detection with a Dynamically Stable System | [
"cs.AI"
] | Adversarial detection is designed to identify and reject maliciously crafted adversarial examples(AEs) which are generated to disrupt the classification of target models. Presently, various input transformation-based methods have been developed on adversarial example detection, which typically rely on empirical exper... | {
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2411.06672 | What Should Baby Models Read? Exploring Sample-Efficient Data
Composition on Model Performance | [
"cs.CL",
"cs.AI"
] | We explore the impact of pre-training data composition on the performance of small language models in a sample-efficient setting. Using datasets limited to 10 million words, we evaluate several dataset sources, including child-directed speech (CHILDES), classic books (Gutenberg), synthetic data (TinyStories), and a mix... | {
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2411.06681 | WDMoE: Wireless Distributed Mixture of Experts for Large Language Models | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.IT",
"math.IT"
] | Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but the role of wireless networks in supporting LLMs has not been thoroughly explored. In this paper, we propose a wireless distributed Mixture of Experts (WDMoE) architecture to enable collaborative deployment ... | {
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2411.06685 | High-Frequency Enhanced Hybrid Neural Representation for Video
Compression | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Neural Representations for Videos (NeRV) have simplified the video codec process and achieved swift decoding speeds by encoding video content into a neural network, presenting a promising solution for video compression. However, existing work overlooks the crucial issue that videos reconstructed by these methods lack h... | {
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2411.06686 | SeedEdit: Align Image Re-Generation to Image Editing | [
"cs.CV"
] | We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance between maintaining the original image, i.e. image reconstruction, and generating a new image, i.e. image re-generation. To this end, we start fr... | {
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2411.06688 | Shedding Light on Problems with Hyperbolic Graph Learning | [
"cs.LG",
"stat.ML"
] | Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance on a variety of graph tasks, node classification and link prediction included. Claims have also been made about the geometric suitability o... | {
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2411.06689 | Resilient control under denial-of-service and uncertainty: An adaptive
dynamic programming approach | [
"eess.SY",
"cs.SY"
] | In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly fr... | {
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2411.06690 | Polarization Aware Movable Antenna | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper presents a polarization-aware movable antenna (PAMA) framework that integrates polarization effects into the design and optimization of movable antennas (MAs). While MAs have proven effective at boosting wireless communication performance, existing studies primarily focus on phase variations caused by differ... | {
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2411.06691 | Autonomous Droplet Microfluidic Design Framework with Large Language
Models | [
"cs.AI"
] | Droplet-based microfluidic devices have substantial promise as cost-effective alternatives to current assessment tools in biological research. Moreover, machine learning models that leverage tabular data, including input design parameters and their corresponding efficiency outputs, are increasingly utilised to automate... | {
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2411.06692 | Layout Control and Semantic Guidance with Attention Loss Backward for
T2I Diffusion Model | [
"cs.CV"
] | Controllable image generation has always been one of the core demands in image generation, aiming to create images that are both creative and logical while satisfying additional specified conditions. In the post-AIGC era, controllable generation relies on diffusion models and is accomplished by maintaining certain comp... | {
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2411.06695 | METRIC: a complete methodology for performances evaluation of automatic
target Detection, Recognition and Tracking algorithms in infrared imagery | [
"eess.IV",
"cs.CV"
] | In this communication, we deal with the question of automatic target detection, recognition and tracking (ATD/R/T) algorithms performance assessment. We propose a complete methodology of evaluation which approaches objective image datasets development and adapted metrics definition for the different tasks (detection, r... | {
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2411.06696 | S\'eparation en composantes structures, textures et bruit d'une image,
apport de l'utilisation des contourlettes | [
"eess.IV",
"cs.CV"
] | In this paper, we propose to improve image decomposition algorithms in the case of noisy images. In \cite{gilles1,aujoluvw}, the authors propose to separate structures, textures and noise from an image. Unfortunately, the use of separable wavelets shows some artefacts. In this paper, we propose to replace the wavelet t... | {
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2411.06697 | Learning a Single Neuron Robustly to Distributional Shifts and
Adversarial Label Noise | [
"cs.LG",
"cs.DS",
"math.OC",
"stat.ML"
] | We study the problem of learning a single neuron with respect to the $L_2^2$-loss in the presence of adversarial distribution shifts, where the labels can be arbitrary, and the goal is to find a ``best-fit'' function. More precisely, given training samples from a reference distribution $\mathcal{p}_0$, the goal is to a... | {
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2411.06700 | HomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency
by Homography Estimation | [
"cs.CV"
] | Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. However, the majority of existing methods f... | {
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2411.06702 | Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs | [
"cs.CV"
] | In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP leverages the zero-shot detection capabilities of vision-language foundation models like Grounding DINO to automatically generate pseudo-labe... | {
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2411.06703 | United Domain Cognition Network for Salient Object Detection in Optical
Remote Sensing Images | [
"cs.CV"
] | Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objec... | {
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2411.06707 | Quadrotor Trajectory Tracking Using Linear and Nonlinear Model
Predictive Control | [
"cs.RO"
] | Accurate trajectory tracking is an essential characteristic for the safe navigation of a quadrotor in cluttered or disturbed environments. In this paper, we present in detail two state-of-the-art model-based control frameworks for trajectory tracking: the Linear Model Predictive Controller (LMPC) and the Nonlinear Mode... | {
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2411.06708 | Flight Time Improvement Using Adaptive Model Predictive Control for
Unmanned Aerial Vehicles | [
"cs.RO"
] | Intelligent aerial platforms such as Unmanned Aerial Vehicles (UAVs) are expected to revolutionize various fields, including transportation, traffic management, field monitoring, industrial production, and agricultural management. Among these, precise control is a critical task that determines the performance and capab... | {
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2411.06710 | Model Fusion through Bayesian Optimization in Language Model Fine-Tuning | [
"cs.AI",
"cs.CL"
] | Fine-tuning pre-trained models for downstream tasks is a widely adopted technique known for its adaptability and reliability across various domains. Despite its conceptual simplicity, fine-tuning entails several troublesome engineering choices, such as selecting hyperparameters and determining checkpoints from an optim... | {
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2411.06711 | Anytime Probabilistically Constrained Provably Convergent Online Belief
Space Planning | [
"cs.AI",
"cs.RO"
] | Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon the recently introduced formulation of probabilistic belief-dependent constraints. We present an anytime approach employing the Monte Carlo Tr... | {
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2411.06713 | Ambient AI Scribing Support: Comparing the Performance of Specialized AI
Agentic Architecture to Leading Foundational Models | [
"cs.AI"
] | This study compares Sporo Health's AI Scribe, a proprietary model fine-tuned for medical scribing, with various LLMs (GPT-4o, GPT-3.5, Gemma-9B, and Llama-3.2-3B) in clinical documentation. We analyzed de-identified patient transcripts from partner clinics, using clinician-provided SOAP notes as the ground truth. Each ... | {
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2411.06714 | DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from
Satellite Observations | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Weather radar data synthesis can fill in data for areas where ground observations are missing. Existing methods often employ reconstruction-based approaches with MSE loss to reconstruct radar data from satellite observation. However, such methods lead to over-smoothing, which hinders the generation of high-frequency de... | {
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2411.06718 | Truth, beauty, and goodness in grand unification: a machine learning
approach | [
"hep-ph",
"cs.LG",
"hep-th"
] | We investigate the flavour sector of the supersymmetric $SU(5)$ Grand Unified Theory (GUT) model using machine learning techniques. The minimal $SU(5)$ model is known to predict fermion masses that disagree with observed values in nature. There are two well-known approaches to address this issue: one involves introduci... | {
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2411.06719 | Shallow Signed Distance Functions for Kinematic Collision Bodies | [
"cs.CV",
"cs.LG"
] | We present learning-based implicit shape representations designed for real-time avatar collision queries arising in the simulation of clothing. Signed distance functions (SDFs) have been used for such queries for many years due to their computational efficiency. Recently deep neural networks have been used for implicit... | {
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2411.06720 | Real-time Monitoring and Analysis of Track and Field Athletes Based on
Edge Computing and Deep Reinforcement Learning Algorithm | [
"cs.LG",
"eess.SP"
] | This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that integrates edge computing and deep learning algorithms. Traditional systems often e... | {
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2411.06722 | Synthesize, Partition, then Adapt: Eliciting Diverse Samples from
Foundation Models | [
"cs.LG",
"cs.AI"
] | Presenting users with diverse responses from foundation models is crucial for enhancing user experience and accommodating varying preferences. However, generating multiple high-quality and diverse responses without sacrificing accuracy remains a challenge, especially when using greedy sampling. In this work, we propose... | {
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2411.06723 | Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted
Dialogue Scripts and Therapeutic Strategies for Psychotherapy | [
"cs.HC",
"cs.AI"
] | Chatbots or conversational agents (CAs) are increasingly used to improve access to digital psychotherapy. Many current systems rely on rigid, rule-based designs, heavily dependent on expert-crafted dialogue scripts for guiding therapeutic conversations. Although recent advances in large language models (LLMs) offer the... | {
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2411.06725 | GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time
Adolescent Sports Posture Correction | [
"cs.CV"
] | With the advancement of artificial intelligence, 3D human pose estimation-based systems for sports training and posture correction have gained significant attention in adolescent sports. However, existing methods face challenges in handling complex movements, providing real-time feedback, and accommodating diverse post... | {
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2411.06727 | Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in
Computer Vision | [
"cs.CV"
] | Kolmogorov-Arnold Networks(KANs), as a theoretically efficient neural network architecture, have garnered attention for their potential in capturing complex patterns. However, their application in computer vision remains relatively unexplored. This study first analyzes the potential of KAN in computer vision tasks, eva... | {
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2411.06728 | On the Principles of ReLU Networks with One Hidden Layer | [
"cs.LG",
"cs.AI",
"cs.NE"
] | A neural network with one hidden layer or a two-layer network (regardless of the input layer) is the simplest feedforward neural network, whose mechanism may be the basis of more general network architectures. However, even to this type of simple architecture, it is also a ``black box''; that is, it remains unclear how... | {
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2411.06729 | Reverse Prompt Engineering | [
"cs.CL"
] | We explore a new language model inversion problem under strict black-box, zero-shot, and limited data conditions. We propose a novel training-free framework that reconstructs prompts using only a limited number of text outputs from a language model. Existing methods rely on the availability of a large number of outputs... | {
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2411.06733 | GSL-PCD: Improving Generalist-Specialist Learning with Point Cloud
Feature-based Task Partitioning | [
"cs.LG",
"cs.RO"
] | Generalization in Deep Reinforcement Learning (DRL) across unseen environment variations often requires training over a diverse set of scenarios. Many existing DRL algorithms struggle with efficiency when handling numerous variations. The Generalist-Specialist Learning (GSL) framework addresses this by first training a... | {
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2411.06735 | Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data | [
"cs.AI"
] | Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset. In this work, we develop TimeText Corpus (TTC), a carefully curated, time-aligned text and time dataset for multimodal forecasting. Our data... | {
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2411.06736 | MrSteve: Instruction-Following Agents in Minecraft with What-Where-When
Memory | [
"cs.LG"
] | Significant advances have been made in developing general-purpose embodied AI in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches. While these approaches, which combine high-level planners with low-level controllers, show promise, low-level controllers frequently become performa... | {
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2411.06739 | Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit
Feedback | [
"cs.LG"
] | We consider regret minimization in low-rank MDPs with fixed transition and adversarial losses. Previous work has investigated this problem under either full-information loss feedback with unknown transitions (Zhao et al., 2024), or bandit loss feedback with known transition (Foster et al., 2022). First, we improve the ... | {
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2411.06740 | Dockformer: A transformer-based molecular docking paradigm for
large-scale virtual screening | [
"cs.LG",
"cs.AI"
] | Molecular docking is a crucial step in drug development, which enables the virtual screening of compound libraries to identify potential ligands that target proteins of interest. However, the computational complexity of traditional docking models increases as the size of the compound library increases. Recently, deep l... | {
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2411.06741 | Methane projections from Canada's oil sands tailings using scientific
deep learning reveal significant underestimation | [
"stat.AP",
"cs.LG",
"stat.ML"
] | Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands r... | {
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2411.06743 | Data-Driven Control of Large-Scale Networks with Formal Guarantees: A
Small-Gain Free Approach | [
"eess.SY",
"cs.SY"
] | This paper offers a data-driven divide-and-conquer strategy to analyze large-scale interconnected networks, characterized by both unknown mathematical models and interconnection topologies. Our data-driven scheme treats an unknown network as an interconnection of individual agents (a.k.a. subsystems) and aims at constr... | {
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2411.06746 | Neuromodulated Meta-Learning | [
"cs.LG"
] | Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple... | {
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2411.06749 | KLCBL: An Improved Police Incident Classification Model | [
"cs.AI"
] | Police incident data is crucial for public security intelligence, yet grassroots agencies struggle with efficient classification due to manual inefficiency and automated system limitations, especially in telecom and online fraud cases. This research proposes a multichannel neural network model, KLCBL, integrating Kolmo... | {
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2411.06750 | SynStitch: a Self-Supervised Learning Network for Ultrasound Image
Stitching Using Synthetic Training Pairs and Indirect Supervision | [
"eess.IV",
"cs.CV"
] | Ultrasound (US) image stitching can expand the field-of-view (FOV) by combining multiple US images from varied probe positions. However, registering US images with only partially overlapping anatomical contents is a challenging task. In this work, we introduce SynStitch, a self-supervised framework designed for 2DUS st... | {
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2411.06751 | DP and QP Based Decision-making and Planning for Autonomous Vehicle | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Autonomous driving technology is rapidly evolving and becoming a pivotal element of modern automation systems. Effective decision-making and planning are essential to ensuring autonomous vehicles operate safely and efficiently in complex environments. This paper introduces a decision-making and planning framework for a... | {
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2411.06752 | Learning from Feedback: Semantic Enhancement for Object SLAM Using
Foundation Models | [
"cs.RO"
] | Semantic Simultaneous Localization and Mapping (SLAM) systems struggle to map semantically similar objects in close proximity, especially in cluttered indoor environments. We introduce Semantic Enhancement for Object SLAM (SEO-SLAM), a novel SLAM system that leverages Vision-Language Models (VLMs) and Multimodal Large ... | {
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2411.06753 | Mode transition control of large-size tiltrotor aircraft | [
"eess.SY",
"cs.SY"
] | Tiltrotors are an aircraft concept with the ability to rotate their rotors freely, achieving vertical take-off and fast forward flight. The combination of helicopter and fixed-wing flight into one aircraft provides versatility in mission selection, yet challenges persist in their construction and control. Tiltrotor air... | {
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2411.06754 | Robust Sliding Mode Control for Air-to-Air Missile | [
"eess.SY",
"cs.SY"
] | Within the missile guidance and control system the autopilot must overcome an array of variables and uncertainties to maintain tracking trajectory. A large uncertainty explored in this paper is the difference between the assumed flight dynamics, the controller design relies on, and the true flight dynamics the missile ... | {
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2411.06757 | LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes | [
"cs.CV"
] | Neural Radiance Fields (NeRFs) have shown remarkable performances in producing novel-view images from high-quality scene images. However, hand-held low-light photography challenges NeRFs as the captured images may simultaneously suffer from low visibility, noise, and camera shakes. While existing NeRF methods may handl... | {
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2411.06762 | Precision Glass Thermoforming Assisted by Neural Networks | [
"cs.CE",
"cs.LG"
] | Glass with good processability, chemical inertness, and optical transparency has been widely used in optical and aesthetic products, many of which require curve pro-files with high precision. To meet the increasingly tightened geometrical tolerances and fast product updating rates, the traditional approach of developin... | {
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2411.06764 | Multi-Stage Knowledge Integration of Vision-Language Models for
Continual Learning | [
"cs.CV",
"cs.LG"
] | Vision Language Models (VLMs), pre-trained on large-scale image-text datasets, enable zero-shot predictions for unseen data but may underperform on specific unseen tasks. Continual learning (CL) can help VLMs effectively adapt to new data distributions without joint training, but faces challenges of catastrophic forget... | {
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2411.06765 | Research on an intelligent fault diagnosis method for nuclear power
plants based on ETCN-SSA combined algorithm | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Utilizing fault diagnosis methods is crucial for nuclear power professionals to achieve efficient and accurate fault diagnosis for nuclear power plants (NPPs). The performance of traditional methods is limited by their dependence on complex feature extraction and skilled expert knowledge, which can be time-consuming an... | {
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2411.06766 | GenZ-ICP: Generalizable and Degeneracy-Robust LiDAR Odometry Using an
Adaptive Weighting | [
"cs.RO"
] | Light detection and ranging (LiDAR)-based odometry has been widely utilized for pose estimation due to its use of high-accuracy range measurements and immunity to ambient light conditions. However, the performance of LiDAR odometry varies depending on the environment and deteriorates in degenerative environments such a... | {
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2411.06767 | PDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Code Large Language Models (Code LLMs), such as Code llama and DeepSeek-Coder, have demonstrated exceptional performance in the code generation tasks. However, most existing models focus on the abilities of generating correct code, but often struggle with bug repair. We introduce a suit of methods to enhance LLM's SQL ... | {
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2411.06770 | Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis | [
"cs.LG"
] | Combining gradient compression methods (e.g., CountSketch, quantization) and adaptive optimizers (e.g., Adam, AMSGrad) is a desirable goal in federated learning (FL), with potential benefits on both fewer communication rounds and less per-round communication. In spite of the preliminary empirical success of sketched ad... | {
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2411.06772 | A Text Classification Model Combining Adversarial Training with
Pre-trained Language Model and neural networks: A Case Study on Telecom Fraud
Incident Texts | [
"cs.AI"
] | Front-line police officers often categorize all police call reported cases of Telecom Fraud into 14 subcategories to facilitate targeted prevention measures, such as precise public education. However, the associated data is characterized by its large volume, diverse information content, and variations in expression. Cu... | {
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2411.06773 | Model Partition and Resource Allocation for Split Learning in Vehicular
Edge Networks | [
"cs.LG",
"cs.DC"
] | The integration of autonomous driving technologies with vehicular networks presents significant challenges in privacy preservation, communication efficiency, and resource allocation. This paper proposes a novel U-shaped split federated learning (U-SFL) framework to address these challenges on the way of realizing in ve... | {
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2411.06776 | Machine vision-aware quality metrics for compressed image and video
assessment | [
"cs.CV",
"cs.AI"
] | A main goal in developing video-compression algorithms is to enhance human-perceived visual quality while maintaining file size. But modern video-analysis efforts such as detection and recognition, which are integral to video surveillance and autonomous vehicles, involve so much data that they necessitate machine-visio... | {
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2411.06780 | SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive
Learning for End-to-End Multi-Camera 3D Tracking | [
"cs.CV"
] | While existing query-based 3D end-to-end visual trackers integrate detection and tracking via the tracking-by-attention paradigm, these two chicken-and-egg tasks encounter optimization difficulties when sharing the same parameters. Our findings reveal that these difficulties arise due to two inherent constraints on the... | {
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2411.06781 | MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic
Forecasting | [
"cs.AI",
"cs.LG"
] | Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family, which make strong assumptions about the underlying spreading process... | {
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2411.06782 | QuadWBG: Generalizable Quadrupedal Whole-Body Grasping | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | Legged robots with advanced manipulation capabilities have the potential to significantly improve household duties and urban maintenance. Despite considerable progress in developing robust locomotion and precise manipulation methods, seamlessly integrating these into cohesive whole-body control for real-world applicati... | {
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2411.06784 | Boosting the Targeted Transferability of Adversarial Examples via
Salient Region & Weighted Feature Drop | [
"cs.IR"
] | Deep neural networks can be vulnerable to adversarially crafted examples, presenting significant risks to practical applications. A prevalent approach for adversarial attacks relies on the transferability of adversarial examples, which are generated from a substitute model and leveraged to attack unknown black-box mode... | {
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2411.06785 | White-Box Diffusion Transformer for single-cell RNA-seq generation | [
"cs.LG",
"q-bio.GN"
] | As a powerful tool for characterizing cellular subpopulations and cellular heterogeneity, single cell RNA sequencing (scRNA-seq) technology offers advantages of high throughput and multidimensional analysis. However, the process of data acquisition is often constrained by high cost and limited sample availability. To o... | {
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2411.06786 | ScaleKD: Strong Vision Transformers Could Be Excellent Teachers | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In this paper, we question if well pre-trained vision transformer (ViT) models could be used as teachers that exhibit scalable properties to advance cross architecture knowledge distillation (KD) research, in the context of using large-scale datasets for evaluation. To make this possible, our analysis underlines the im... | {
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2411.06787 | A System Parametrization for Direct Data-Driven Analysis and Control
with Error-in-Variables | [
"eess.SY",
"cs.SY"
] | In this paper, we present a new parametrization to perform direct data-driven analysis and controller synthesis for the error-in-variables case. To achieve this, we employ the Sherman-Morrison-Woodbury formula to transform the problem into a linear fractional transformation (LFT) with unknown measurement errors and dis... | {
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2411.06789 | AV-PedAware: Self-Supervised Audio-Visual Fusion for Dynamic Pedestrian
Awareness | [
"cs.RO"
] | In this study, we introduce AV-PedAware, a self-supervised audio-visual fusion system designed to improve dynamic pedestrian awareness for robotics applications. Pedestrian awareness is a critical requirement in many robotics applications. However, traditional approaches that rely on cameras and LIDARs to cover multipl... | {
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2411.06790 | Large-scale moral machine experiment on large language models | [
"cs.CY",
"cs.CL",
"cs.HC"
] | The rapid advancement of Large Language Models (LLMs) and their potential integration into autonomous driving systems necessitates understanding their moral decision-making capabilities. While our previous study examined four prominent LLMs using the Moral Machine experimental framework, the dynamic landscape of LLM de... | {
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2411.06792 | Evolving Efficient Genetic Encoding for Deep Spiking Neural Networks | [
"cs.NE",
"cs.AI"
] | By exploiting discrete signal processing and simulating brain neuron communication, Spiking Neural Networks (SNNs) offer a low-energy alternative to Artificial Neural Networks (ANNs). However, existing SNN models, still face high computational costs due to the numerous time steps as well as network depth and scale. The... | {
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2411.06798 | LA4SR: illuminating the dark proteome with generative AI | [
"q-bio.GN",
"cs.AI",
"cs.CL",
"q-bio.QM"
] | AI language models (LMs) show promise for biological sequence analysis. We re-engineered open-source LMs (GPT-2, BLOOM, DistilRoBERTa, ELECTRA, and Mamba, ranging from 70M to 12B parameters) for microbial sequence classification. The models achieved F1 scores up to 95 and operated 16,580x faster and at 2.9x the recall ... | {
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2411.06799 | Structuring the Processing Frameworks for Data Stream Evaluation and
Application | [
"cs.LG",
"cs.DB"
] | The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications. The definition of structured frameworks stems from a need to reliably evaluate the data stream classification methods, considering the co... | {
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2411.06802 | Identifying the impact of local connectivity patterns on dynamics in
excitatory-inhibitory networks | [
"q-bio.NC",
"cond-mat.dis-nn",
"cs.NE"
] | Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are otherwise statistically independent. Recent synaptic physiology datas... | {
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2411.06804 | Predicting ionic conductivity in solids from the machine-learned
potential energy landscape | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalab... | {
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2411.06805 | AssistRAG: Boosting the Potential of Large Language Models with an
Intelligent Information Assistant | [
"cs.CL",
"cs.AI",
"cs.IR"
] | The emergence of Large Language Models (LLMs) has significantly advanced natural language processing, but these models often generate factually incorrect information, known as "hallucination". Initial retrieval-augmented generation (RAG) methods like the "Retrieve-Read" framework was inadequate for complex reasoning ta... | {
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2411.06808 | Modeling and Detection of Critical Slowing Down in Epileptic Dynamics | [
"eess.SY",
"cs.SY",
"math.OC"
] | Epilepsy is a common neurological disorder characterized by abrupt seizures. Although seizures may appear random, they are often preceded by early warning signs in neural signals, notably, critical slowing down, a phenomenon in which the system's recovery rate from perturbations declines when it approaches a critical p... | {
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2411.06810 | JPEG AI Image Compression Visual Artifacts: Detection Methods and
Dataset | [
"cs.AI",
"cs.CV",
"cs.MM"
] | Learning-based image compression methods have improved in recent years and started to outperform traditional codecs. However, neural-network approaches can unexpectedly introduce visual artifacts in some images. We therefore propose methods to separately detect three types of artifacts (texture and boundary degradation... | {
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2411.06812 | Generative midtended cognition and Artificial Intelligence. Thinging
with thinging things | [
"cs.AI",
"cs.CY",
"cs.LG"
] | This paper introduces the concept of ``generative midtended cognition'', exploring the integration of generative AI with human cognition. The term "generative" reflects AI's ability to iteratively produce structured outputs, while "midtended" captures the potential hybrid (human-AI) nature of the process. It stands bet... | {
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2411.06815 | Streetwise Agents: Empowering Offline RL Policies to Outsmart Exogenous
Stochastic Disturbances in RTC | [
"cs.LG"
] | The difficulty of exploring and training online on real production systems limits the scope of real-time online data/feedback-driven decision making. The most feasible approach is to adopt offline reinforcement learning from limited trajectory samples. However, after deployment, such policies fail due to exogenous fact... | {
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2411.06823 | Large Language Model in Medical Informatics: Direct Classification and
Enhanced Text Representations for Automatic ICD Coding | [
"cs.LG",
"cs.IR"
] | Addressing the complexity of accurately classifying International Classification of Diseases (ICD) codes from medical discharge summaries is challenging due to the intricate nature of medical documentation. This paper explores the use of Large Language Models (LLM), specifically the LLAMA architecture, to enhance ICD c... | {
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2411.06824 | Combining Domain and Alignment Vectors to Achieve Better
Knowledge-Safety Trade-offs in LLMs | [
"cs.AI"
] | There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solu... | {
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2411.06826 | Adaptive Conditional Expert Selection Network for Multi-domain
Recommendation | [
"cs.LG",
"cs.IR"
] | Mixture-of-Experts (MOE) has recently become the de facto standard in Multi-domain recommendation (MDR) due to its powerful expressive ability. However, such MOE-based method typically employs all experts for each instance, leading to scalability issue and low-discriminability between domains and experts. Furthermore, ... | {
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} |
2411.06832 | Optimized Quality of Service prediction in FSO Links over South Africa
using Ensemble Learning | [
"stat.ML",
"cs.LG",
"eess.SP",
"physics.optics"
] | Fibre optic communication system is expected to increase exponentially in terms of application due to the numerous advantages over copper wires. The optical network evolution presents several advantages such as over long-distance, low-power requirement, higher carrying capacity and high bandwidth among others Such netw... | {
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} |
2411.06833 | Learning Interpretable Network Dynamics via Universal Neural Symbolic
Regression | [
"cs.AI",
"cs.LG",
"cs.MA",
"cs.SC"
] | Discovering governing equations of complex network dynamics is a fundamental challenge in contemporary science with rich data, which can uncover the mysterious patterns and mechanisms of the formation and evolution of complex phenomena in various fields and assist in decision-making. In this work, we develop a universa... | {
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} |
2411.06835 | HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of
Quantization on Model Alignment | [
"cs.CL",
"cs.CR"
] | With the introduction of the transformers architecture, LLMs have revolutionized the NLP field with ever more powerful models. Nevertheless, their development came up with several challenges. The exponential growth in computational power and reasoning capabilities of language models has heightened concerns about their ... | {
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} |
2411.06836 | Spatially Constrained Transformer with Efficient Global Relation
Modelling for Spatio-Temporal Prediction | [
"cs.LG"
] | Accurate spatio-temporal prediction is crucial for the sustainable development of smart cities. However, current approaches often struggle to capture important spatio-temporal relationships, particularly overlooking global relations among distant city regions. Most existing techniques predominantly rely on Convolutiona... | {
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} |
2411.06837 | Persuasion with Large Language Models: a Survey | [
"cs.CL"
] | The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, by enabling fully-automated personalized and interactive content generation at an unprecedented scale. In this paper, we survey the research field of LLM-based persuasion that has emerged as a result. W... | {
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} |
2411.06839 | LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language
Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In this paper, we propose a novel LLM-Neo framework that efficiently transfers knowledge from a large language model (LLM) teacher to a compact student. Initially, we revisit the knowledge distillation (KD) and low-rank adaption (LoRA), and argue that they share the same paradigm. Inspired by this observation, we explo... | {
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} |
2411.06842 | Maximizing domain generalization in fetal brain tissue segmentation: the
role of synthetic data generation, intensity clustering and real image
fine-tuning | [
"eess.IV",
"cs.CV"
] | Fetal brain tissue segmentation in magnetic resonance imaging (MRI) is a crucial tool that supports the understanding of neurodevelopment, yet it faces challenges due to the heterogeneity of data coming from different scanners and settings, and due to data scarcity. Recent approaches based on domain randomization, like... | {
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} |
2411.06848 | Generative Feature Training of Thin 2-Layer Networks | [
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] | We consider the approximation of functions by 2-layer neural networks with a small number of hidden weights based on the squared loss and small datasets. Due to the highly non-convex energy landscape, gradient-based training often suffers from local minima. As a remedy, we initialize the hidden weights with samples fro... | {
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} |
2411.06850 | 1-800-SHARED-TASKS @ NLU of Devanagari Script Languages: Detection of
Language, Hate Speech, and Targets using LLMs | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This paper presents a detailed system description of our entry for the CHiPSAL 2025 shared task, focusing on language detection, hate speech identification, and target detection in Devanagari script languages. We experimented with a combination of large language models and their ensembles, including MuRIL, IndicBERT, a... | {
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} |
2411.06851 | Fast and Efficient Transformer-based Method for Bird's Eye View Instance
Prediction | [
"cs.CV",
"cs.LG"
] | Accurate object detection and prediction are critical to ensure the safety and efficiency of self-driving architectures. Predicting object trajectories and occupancy enables autonomous vehicles to anticipate movements and make decisions with future information, increasing their adaptability and reducing the risk of acc... | {
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} |
2411.06852 | Evaluating Large Language Models on Financial Report Summarization: An
Empirical Study | [
"cs.CL",
"cs.AI"
] | In recent years, Large Language Models (LLMs) have demonstrated remarkable versatility across various applications, including natural language understanding, domain-specific knowledge tasks, etc. However, applying LLMs to complex, high-stakes domains like finance requires rigorous evaluation to ensure reliability, accu... | {
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} |
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