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arXiv 2023年

作者： Chen, Dingshuo Zhu, Yanqiao Zhang, Jieyu Du, Yuanqi Li, Zhixun Liu, Qiang Wu, Shu Wang, Liang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China Department of Computer Science University of California Los Angeles United States The Paul G. Allen School of Computer Science and Engineering University of Washington United States Department of Computer Science Cornell University United States Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis

Personalized Federated Learning with Collaborative Aggregati...

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Industrial automation, Robotics and Control engineering (IARCE), International Conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models Data centers Federated learning Service robots Collaboration Brain modeling Data models Servers Medical diagnostic imaging

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Efficient incremental model reduction approach for time-varying spatially distributed processes

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Neurocomputing 2025年 643卷

作者： Ling, Ai Ru, Hao Chen, Xueqin Teo, Kok Lay Department of Automation Harbin University of Science and Technology Harbin150086 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6845 Australia Research Center of Satellite Technology Harbin Institute of Technology Harbin150040 China School of Mathematical Sciences Sunway University Selangor Darul Ehsan 47500 Malaysia

The accurate and efficient modeling of complex spatially distributed processes (SDPs) represents a significant challenge for researchers in the field. A variety of modeling approaches have been developed with the objective of addressing issues related to spatiotemporal modeling. The conventional offline modeling approaches necessitate the availability of a spatiotemporal data set that encompasses the entirety of the system's dynamic features. It is not possible to fully satisfy this prerequisite in the context of real-world applications, particularly in the case of time-varying SDPs. Furthermore, the process of updating the model by repeatedly applying the offline algorithm is inherently time-consuming. In this paper, we put forth an efficient incremental model reduction approach for time-varying SDPs. Following the initialization phase, the modified Gram–Schmidt orthogonalization method is employed to extract the feature subspace in a sequential manner. The time-space synthesis is then utilized to reconstruct the spatiotemporal dynamics. The efficacy of the model is evaluated on a representative diffusion-reaction process, and the comparative experimental outcomes demonstrate that the presented algorithm is an efficient and effective approach for the online learning of time-varying SDPs. © 2025 Elsevier B.V.

关键词： Population distribution

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Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts

arXiv

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arXiv 2024年

作者： Zhao, Changyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin Letaief, Khaled B. CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational complexity and limited adaptability. Mixture of Experts (MoE), which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions. Firstly, we review GAI model’s applications in physical layer communication security, discuss limitations, and explore how MoE can help GAI overcome these limitations. Furthermore, we propose an MoE-enabled GAI framework for network optimization problems for communication security. To demonstrate the framework’s effectiveness, we provide a case study in a cooperative friendly jamming scenario. The experimental results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security. © 2024, CC BY.

关键词： Network security

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Distributed formation trajectory planning for multi-vehicle systems

Distributed formation trajectory planning for multi-vehicle ...

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American Control Conference (ACC)

作者： Binh Nguyen Truong Nghiem Linh Nguyen Tung Nguyen Hung La Mehdi Sookhak Thang Nguyen Department of Engineering Texas A&M University–Corpus Christi Corpus Christi TX USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA School of Engineering Information Technology and Physical Sciences Federation University Australia Churchill VIC Australia Department of Information Technology Uppsala University Uppsala Sweden Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA Department of Computing Sciences Texas A&M University–Corpus Christi Corpus Christi TX USA

This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system.

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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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Science China(Information Sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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Investigation of the Accuracy of Thermographic Inspections of Photovoltaic Modules

Investigation of the Accuracy of Thermographic Inspections o...

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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： Boris Evstatiev Nikolay Valov Katerina Gabrovska-Evstatieva Tsvetelina Mladenova Nicolay Mihailov Department of Electronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Automation and Mechatronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computer Science University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computing University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electrical Power Engineering University of Ruse “Angel Kanchev” Ruse Bulgaria

Remote inspections of photovoltaic installations are an important factor for taking timely measures to increase their efficiency and reduce degradation. This study deals with the accuracy and uncertainty of thermal measurements of PV cells. An experiment is organized in order to compare the measured temperatures with means of contact sensor and contactless infrared cameras from different distance. The obtained results indicate significant difference in the measured peak temperatures with the different means. Furthermore, the temperature gradient of an affected cell depends strongly on the soiling area and could surpass $40^{\circ}\mathrm{C}$, which is a precondition for significant errors in the measured temperatures, i.e. it creates a significant uncertainty. Another important observation is that for the investigated polycrystalline module no hotspot was created if 6% or less of a cell’s area is covered.

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Improved efficiency with adaptive front and rear axle independently driven powertrain and disconnect functionality

Transportation Engineering

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Transportation engineering 2023年 13卷

作者： Xu, Y. Kersten, A. Klacar, S. Ban, B. Hellsing, J. Sedarsky, D. AB Göteborg Sweden Chalmers University of Technology Division of Energy Conversion and Propulsion Systems Mechanics and Maritime Sciences Göteborg Sweden Bundeswehr University Munich Department of Electrical Engineering Neubiberg Germany Infimotion Technology Europe AB Göteborg Sweden Torquery Consulting Göteborg Sweden University of Zagreb Faculty of Electrical Engineering and Computing Department of Electric Machines and Automation Zagreb Croatia

Front and rear axle independently driven (FRID) powertrains are becoming a popular solution for electric vehicles (EVs) due to torque distribution capability which can enhance powertrain energy efficiency. Typically, permanent magnet synchronous machines (PMSMs) are used for FRID powertrains due to their high torque, and power density. However, the drive-cycle efficiency of FRID powertrains with PMSMs is typically reduced in comparison to single motor drives. This is due to the unwanted no-load losses of PMSMs in the field weakening region. To overcome this drawback of PMSM FRIDs, this paper proposes an adaptive front- and rear-axle independently driven (AFRID) powertrain, utilizing two dog clutches, so that the powertrain can be operated in different modes (rear, front, and all-wheel drive) by adaptively connecting and disconnecting the front and/or rear electric drive unit (EDU). A rule-based mode selection strategy is developed to utilize the flexibility of different powertrain operating modes of the powertrain for maximizing the energy efficiency of the EDU. The simulation results show that the suggested AFRID powertrain, in comparison to a common FRID powertrain, can improve the WLTC drive-cycle consumption from 22.17 kWhh to 20.50 kWhh per 100 km. Based on the route and road-load information, the energy-saving potential of the AFRID powertrain can be further improved to 20.37 kWhh per 100 km by a suggested predictive mode selection strategy, achieving an optimal mode selection. © 2023 The Author(s)

关键词： Energy efficiency

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Optimal AoI-based Block Propagation and Incentive Mechanism for Blockchain Networks in Web 3.0

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Wen, Jinbo Kang, Jiawen Xiong, Zehui Du, Hongyang Yang, Zhaohui Niyato, Dusit Shen, Meng Jiao, Yutao Zhang, Yang Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Guangdong University of Technology School of Automation China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Hong Kong University Department of Electrical and Electronic Engineering Hong Kong Zhejiang University College of Information Science and Electronic Engineering China Nanyang Technological University College of Computing and Data Science Singapore Beijing Institute of Technology School of Cyberspace Science and Technology China Army Engineering University of PLA College of Communications Engineering China

Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, blockchain-based Web 3.0 is still in its infancy, such as ensuring block freshness and optimizing block propagation to improve blockchain performance. In this paper, we develop a freshness-aware block propagation optimization framework for Web 3.0. We first propose a novel metric called Age of Block Information (AoBI) based on the concept of age of information to quantify block freshness. AoBI measures the time elapsed from the freshest transaction generation to the completion of block consensus. To make block propagation optimization tractable, we classify miners into five different states and propose a block propagation model for public blockchains inspired by epidemic models. Moreover, considering that the miners are bounded rational, we propose an incentive mechanism based on the evolutionary game for block propagation to improve block propagation efficiency. Numerical results demonstrate that compared with other block propagation mechanisms in public blockchains, the proposed scheme has a higher block forwarding probability, which improves block propagation efficiency and decreases the minimum value of average AoBI. © 2015 IEEE.

关键词： age of information block propagation evolutionary game Web 3.0 wireless networks

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Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

arXiv

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arXiv 2023年

作者： Xiao, Jiaren Dai, Quanyu Shen, Xiao Xie, Xiaochen Dai, Jing Lam, James Kwok, Ka-Wai Department of Mechanical Engineering The University of Hong Kong Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Hainan University Haikou China Department of Automation Harbin Institute of Technology Shenzhen China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. This paper proposes a novel method called SemiGCL to tackle the graph Semi-supervised domain adaptation with Graph Contrastive Learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph’s local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks. © 2023, CC BY-NC-ND.

关键词： Entropy

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