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IEEE/ACM Symposium on Edge computing (SEC)

作者： Mozhgan Navardi Edward Humes Tinoosh Mohsenin Computer Science and Electrical Engineering Department Center for Real-time Distributed Sensing and Autonomy (CARDS) University of Maryland Baltimore County USA

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Artificial Intelligence (AI) and Deep Neural Networks (DNNs) have attracted attention as a solution within autonomous systems fields as they enable applications such as visual perception and navigation. Although cloud-based approaches have already been highly addressed, there is a growing interest in using both AI and DNNs on the edge as this allows for lower latency and avoids the potential security concerns of transmitting data to a remote server. However, deploying DNNs on edge devices is challenging due to the limited computational power available, as well as energy efficiency being of the utmost importance. In this work, we introduce an approach named E2EdgeAI for Energy-Efficient Edge computing that takes advantage of AI for autonomous tiny drones. This approach optimizes the energy efficiency of DNNs by considering the effects of memory access and core utilization on the energy consumption of tiny UAVs. To perform the experiment, we used a tiny drone named Crazyflie with the AI -deck expansion, which includes an octa-core RISC-V processor. The experimental results show the proposed approach reduces the model size by up to 14.4x, improves energy per inference by 78%, and increases energy efficiency by 5.6x. A recorded video for the proposed approach can be found here: Video.

关键词： Deep learning Navigation Computational modeling Neural networks Throughput Energy efficiency Artificial intelligence

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Modeling Multi-Threaded Aggregated I/O for Asynchronous Checkpointing on HPC Systems

Modeling Multi-Threaded Aggregated I/O for Asynchronous Chec...

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International Symposium on Parallel and Distributed computing

作者： Mikaila J. Gossman Bogdan Nicolae Jon C. Calhoun Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

HPC systems encompass more components with each new generation. As a result, the process of interacting with stable storage systems like parallel file systems (PFS) becomes increasingly difficult. Larger systems often result in more frequent failures, increasing the need and frequency to incorporate fault-tolerant mechanisms. One example is checkpoint-restart (C/R), where applications or systems save their data to non-volatile storage devices, such as a PFS. On failure, the system or application is restored to a saved state and computation continues. Today, asynchronous C/R is gaining traction for its ability to checkpoint data to permanent storage concurrently with the application. However, asynchronous C/R brings about many new challenges. For starters, asynchronous C/R introduces complex resource contention between the application and the C/R implementation. Additionally, some implementations adopt file-per-process writing strategies, which overwhelm PFS’ at high core counts. In this work, we explore how multi-threaded POSIX I/O impacts aggregated throughput. To this extent we characterize the influence of different I/O parameters, such as the number of writer threads and how they access storage devices, has on aggregated I/O. We use the information gathered in this study to identify best practices when performing aggregated I/O as a first step in designing an efficient I/O aggregation scheme for asynchronous C/R.

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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic Systems over a Lossy Communication Channel

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Journal of Systems science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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Optimizing Model-Driven Federated Learning for Rational and Data-Efficient in Social Mobile Network

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IEEE Network 2024年

作者： Lu, Jianfeng Zhang, Ying Cao, Shuqin Wang, Wei Tang, Changbing Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China

With the rise of data-intensive services in mobile networks, the demand for intelligent and efficient learning frameworks has grown exponentially. Federated Learning (FL), as an emerging paradigm, enables decentralized edge devices to collaboratively train large models without sharing raw data, ensuring privacy preservation. However, deploying FL in resource-constrained mobile environments faces significant challenges, such as free-riding and malicious behavior. These challenges hinder model performance and raise concerns about data integrity and inter-user cooperation. To address these issues, we propose a layered dynamic framework for social mobile networks, called FedSMN, which aims to enhance the generalization ability of collaboratively trained large models in FL environments. FedSMN leverages Bayesian inference to evaluate user behavior and employs evolutionary game theory to simulate dynamic interactions, thereby optimizing cooperation strategies and reducing communication overhead. Unlike traditional approaches that assume fully rational actors, FedSMN accounts for irrational behavior and fluctuating resources, designing an efficient incentive mechanism to improve data utilization. Extensive experiments on large-scale datasets demonstrate that FedSMN increases model accuracy by 21% and reduces communication overhead by 49%, paving the way for scalable, intelligent mobile networks powered by large models. © 1986-2012 IEEE.

关键词： Collaborative learning

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Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence

arXiv

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arXiv 2022年

作者： Madurasinghe, Dulip Venayagamoorthy, Ganesh K. Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition. © 2022, CC BY.

关键词： Network topology

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Cooperative and Competitive Multi-Agent Systems:From Optimization to Games

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 763-783页

作者： Jianrui Wang Yitian Hong Jiali Wang Jiapeng Xu Yang Tang Qing-Long Han Jürgen Kurths Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Potsdam Institute for Climate Impact Research 14473 Potsdam Institute of Physics Humboldt University of Berlin12489 BerlinGermany

Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation *** a multi-agent system,agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination,which is manifested as cooperative/competitive *** survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative *** from cooperative optimization,the studies on distributed optimization and federated optimization are *** survey mainly focuses on distributed online optimization and its application in privacy protection,and overviews federated optimization from the perspective of privacy protection ***,cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs,***-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects,according to whether each player can make decisions based on the information of other ***,future directions for cooperative optimization,cooperative/non-cooperative games,and their applications are discussed.

关键词： Cooperative games counterfactual regret minimization distributed optimization federated optimization fictitious

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Toward Generalized Multistage Clustering: Multiview Self-Distillation

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10311-10324页

作者： Jiatai Wang Zhiwei Xu Xin Wang Tao Li College of Computer Science Nankai University Tianjin China Haihe Laboratory of Information Technology Application Innovation (ITAI) Tianjin China Chinese Academy of Sciences Institute of Computing Technology Beijing China Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

Existing multistage clustering methods independently learn the salient features from multiple views and then perform the clustering task. Particularly, multiview clustering (MVC) has attracted a lot of attention in multiview or multimodal scenarios. MVC aims at exploring common semantics and pseudo-labels from multiple views and clustering in a self-supervised manner. However, limited by noisy data and inadequate feature learning, such a clustering paradigm generates overconfident pseudo-labels that misguide the model to produce inaccurate predictions. Therefore, it is desirable to have a method that can correct this pseudo-label mistraction in multistage clustering to avoid bias accumulation. To alleviate the effect of overconfident pseudo-labels and improve the generalization ability of the model, this article proposes a novel multistage deep MVC framework where multiview self-distillation (DistilMVC) is introduced to distill dark knowledge of label distribution. Specifically, in the feature subspace at different hierarchies, we explore the common semantics of multiple views through contrastive learning and obtain pseudo-labels by maximizing the mutual information between views. Additionally, a teacher network is responsible for distilling pseudo-labels into dark knowledge, supervising the student network and improving its predictive capabilities to enhance its robustness. Extensive experiments on real-world multiview datasets show that our method has better clustering performance than the state-of-the-art (SOTA) methods.

关键词： Semantics Feature extraction Contrastive learning Mutual information Clustering methods Representation learning Computational modeling Training Knowledge engineering Predictive models

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RECURSIVE DISENTANGLEMENT NETWORK 10

RECURSIVE DISENTANGLEMENT NETWORK

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10th International Conference on Learning Representations, ICLR 2022

作者： Chen, Yixuan Shi, Yubin Li, Dongsheng Wang, Yujiang Dong, Mingzhi Zhao, Yingying Dick, Robert Lv, Qin Yang, Fan Shang, Li China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Microsoft Research Asia Shanghai China Department of Computing Imperial College London London United Kingdom Department of Electrical Engineering and Computer Science University of Michigan Michigan United States Department of Computer Science University of Colorado Boulder Boulder United States School of Microelectronics Fudan University Shanghai China

Disentangled feature representation is essential for data-efficient learning. The feature space of deep models is inherently compositional. Existing β-VAE-based methods, which only apply disentanglement regularization to the resulting embedding space of deep models, cannot effectively regularize such compositional feature space, resulting in unsatisfactory disentangled results. In this paper, we formulate the compositional disentanglement learning problem from an information-theoretic perspective and propose a recursive disentanglement network (RecurD) that propagates regulatory inductive bias recursively across the compositional feature space during disentangled representation learning. Experimental studies demonstrate that RecurD outperforms β-VAE and several of its state-of-the-art variants on disentangled representation learning and enables more data-efficient downstream machine learning tasks. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Information theory

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Exoplanet Detection via Differentiable Rendering

arXiv

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arXiv 2025年

作者： Feng, Brandon Y. Ferrer-Chávez, Rodrigo Levis, Aviad Wang, Jason J. Bouman, Katherine L. Freeman, William T. The Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States The Department of Physics & Astronomy Northwestern University United States The Department of Computer Science The University of Toronto Canada The Departments of Computing and Mathematical Sciences Electrical Engineering and Astronomy California Institute of Technology United States

Direct imaging of exoplanets is crucial for advancing our understanding of planetary systems beyond our solar system, but it faces significant challenges due to the high contrast between host stars and their planets. Wavefront aberrations introduce speckles in the telescope science images, which are patterns of diffracted starlight that can mimic the appearance of planets, complicating the detection of faint exoplanet signals. Traditional post-processing methods, operating primarily in the image intensity domain, do not integrate wavefront sensing data. These data, measured mainly for adaptive optics corrections, have been overlooked as a potential resource for post-processing, partly due to the challenge of the evolving nature of wavefront aberrations. In this paper, we present a differentiable rendering approach that leverages these wavefront sensing data to improve exoplanet detection. Our differentiable renderer models wave-based light propagation through a coronagraphic telescope system, allowing gradient-based optimization to significantly improve starlight subtraction and increase sensitivity to faint exoplanets. Simulation experiments based on the James Webb Space Telescope configuration demonstrate the effectiveness of our approach, achieving substantial improvements in contrast and planet detection limits. Our results showcase how the computational advancements enabled by differentiable rendering can revitalize previously underexploited wavefront data, opening new avenues for enhancing exoplanet imaging and characterization. © 2025, CC BY.

关键词： Adaptive optics

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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