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arXiv 2022年

作者： Al Maruf, Abdullah Niu, Luyao Clark, Andrew Mertoguno, J. Sukarno Poovendran, Radha Network Security Lab. Department of Electrical and Computer Engineering University of Washington P.O. Box 1212 SeattleWA98195-2500 United States Network Security Lab. Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States Electrical and Systems Engineering Department McKelvey School of Engineering Washington University St. LouisMO63130 United States School of Cybersecurity and Privacy Georgia Institute of Technology AtlantaGA30332 United States

Cyber-physical systems (CPS) are required to satisfy safety constraints in various application domains such as robotics, industrial manufacturing systems, and power systems. Faults and cyber attacks have been shown to cause safety violations, which can damage the system and endanger human lives. Resilient architectures have been proposed to ensure safety of CPS under such faults and attacks via methodologies including redundancy and restarting from safe operating conditions. The existing resilient architectures for CPS utilize different mechanisms to guarantee safety, and currently there is no approach to compare them. Moreover, the analysis and design undertaken for CPS employing one architecture is not readily extendable to another. In this paper, we propose a timing-based framework for CPS employing various resilient architectures and develop a common methodology for safety analysis and computation of control policies and design parameters. Using the insight that the cyber subsystem operates in one out of a finite number of statuses, we first develop a hybrid system model that captures CPS adopting any of these architectures. Based on the hybrid system, we formulate the problem of joint computation of control policies and associated timing parameters for CPS to satisfy a given safety constraint and derive sufficient conditions for the solution. Utilizing the derived conditions, we provide an algorithm to compute control policies and timing parameters relevant to the employed architecture. We also note that our solution can be applied to a wide class of CPS with polynomial dynamics and also allows incorporation of new architectures. We verify our proposed framework by performing a case study on adaptive cruise control of vehicles. © 2022, CC BY.

关键词： Cyber Physical System

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Reliability-Latency-Rate Tradeoff in Low-Latency Communications with Finite-Blocklength Coding

arXiv

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arXiv 2023年

作者： Li, Lintao Chen, Wei Popovski, Petar Letaief, Khaled B. Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Space Network and Communications The Beijing National Research Center for Information Science and Technology Beijing100084 China Department of Electronic Systems Aalborg University Aalborg9220 Denmark Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

Low-latency communication plays an increasingly important role in delay-sensitive applications by ensuring the real-time information exchange. However, due to the constraint on the maximum instantaneous power, guaranteeing bounded latency is challenging. In this paper, we investigate the reliability-latency-rate tradeoff in low-latency communication systems with finite-blocklength coding (FBC). Specifically, we are interested in the fundamental tradeoff between error probability, delay-violation probability (DVP), and service rate. Based on the effective capacity (EC), we present the gain-conservation equations to characterize the reliability-latency-rate tradeoffs in low-latency communication systems. In particular, we investigate the low-latency transmissions over an additive white Gaussian noise (AWGN) channel and a Nakagami-m fading channel. By defining the service rate gain, reliability gain, and real-time gain, we conduct an asymptotic analysis to reveal the fundamental reliability-latency-rate tradeoff of ultra-reliable and low-latency communications in the high signal-to-noise-ratio (SNR) regime. To analytically evaluate and optimize the quality-of-service-constrained throughput of low-latency communication systems adopting FBC, an EC-approximation method is conceived to derive the closed-form expression of that throughput. Our results may offer some insights into the efficient scheduling of low-latency wireless communications, in which statistical latency and reliability metrics are crucial. © 2023, CC BY.

关键词： Asymptotic analysis

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Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking

arXiv

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arXiv 2024年

作者： Chen, Xi Qin, Chuan Fang, Chuyu Wang, Chao Zhu, Chen Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China Baidu Inc China Institute of Artificial Intelligence Beihang University China SKLSDE School of Computer Science Beihang University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark. © 2024, CC BY-NC-SA.

关键词： Prediction models

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Few-Shot Class-Incremental Learning with Prior Knowledge

arXiv

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arXiv 2024年

作者： Jiang, Wenhao Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Kargobot of DiDi Shanghai201210 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequently underestimated in these studies. Therefore, to enhance the generalization ability of the pre-trained model, we propose Learning with Prior Knowledge (LwPK) by introducing nearly free prior knowledge from a few unlabeled data of subsequent incremental classes. We cluster unlabeled incremental class samples to produce pseudo-labels, then jointly train these with labeled base class samples, effectively allocating embedding space for both old and new class data. Experimental results indicate that LwPK effectively enhances the model resilience against catastrophic forgetting, with theoretical analysis based on empirical risk minimization and class distance measurement corroborating its operational principles. The source code of LwPK is publicly available at: https://***/StevenJ308/LwPK. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Effects of spatially dense adrenergic stimulation to rotor behaviour in simulated atrial sheets

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computers in Biology and Medicine 2024年 182卷 109195-109195页

作者： Magtibay, Karl Massé, Stéphane Nanthakumar, Kumaraswamy Umapathy, Karthikeyan Biomedical Signal and Image Processing Laboratory Department of Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science Toronto Metropolitan University 350 Victoria St TorontoONM5B 2K3 Canada Toby Hull Cardiac Fibrillation Management Toronto General Hospital University Health Network 200 Elizabeth Street TorontoONM5G 2C4 Canada

Sympathetic hyperactivity via spatially dense adrenergic stimulation may create pro-arrhythmic substrates even without structural remodelling. However, the effect of sympathetic hyperactivity on arrhythmic activity, such as rotors, is unknown. Using simulations, we examined the effects of gradually increasing the spatial density of adrenergic stimulation (AS) in atrial sheets on rotors. We compared their characteristics against rotors hosted in atrial sheets with increasing spatial density of minimally conductive (MC) elements to simulate structural remodelling due to injury or disease. We generated rotors using an S1-S2 stimulation protocol. Then, we created phase maps to identify phase singularities and map their trajectory over time. We measured each rotor's duration (s), angular speed (rad/s), and spatiotemporal organization. We demonstrated that atrial sheets with increased AS spatial densities could maintain rotors longer than with MC elements (2.6 ± 0.1 s vs. 1.5 ± 0.2 s, p © 2024 The Authors

关键词： Digital elevation model

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xuefei Li Mingzhe Chen Danpu Liu Zhilong Zhang Tony Q. S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

关键词： Base stations Correlation network topology Simulation Collaboration Reinforcement learning Vectors Sensors Global communication Optimization

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

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arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

arXiv

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arXiv 2024年

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Collaborative Innovation Center of Novel Software Technology and Industrialization Suzhou China School of Computer Science and Technology Soochow University Suzhou China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University Auburn United States Department of Electrical and Computer Engineering North Carolina State University NC United States BEDI Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this paper, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent trade-off between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024, CC BY.

关键词： Federated learning

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Study of Whisker Growth from SAC0307-SiC Composite Solder Alloy

Study of Whisker Growth from SAC0307-SiC Composite Solder Al...

引用

International Spring Seminar on Electronics Technology

作者： Halim Choi Agata Skwarek Tamás Hurtony Jaeduk Byun Balázs Illés Department of Electronics Technology Faculty of Electrical Engineering and Computer Science Budapest University of Technology and Economics Budapest Hungary Department of Microelectronics Łukasiewicz Research Network—Institute of Microelectronics and Photonics Kraków Poland Department of Physics College of Science & Technology Dankook University Cheonan Republic of Korea

In the current study, tin (Sn) whisker growth was observed on the surface of the SAC0307-SiC composite solder joint. A commercial SAC0307 solder alloys were reinforced with SiC nanophases to produce composite solder joints. SiC nanoparticles and nanowires were mixed with SAC0307 solder paste using the ball milling method. The solder paste was deposited by stencil printing on the test PCBs, and 0805 chip resistors were soldered by IR reflow method. The solder joints facilitated the growth of the Sn whisker through accelerated lifetime tests. The samples were observed by scanning electron microscopy (SEM) every 1000 hours of the test. On the SiC composite solder joints, Sn whiskers and large corrosion spots were observed after 1000 hours of the THB test. In the case of SAC0307 reference samples, only minor corrosion signs were found after 1000 hours of the THB test. The main finding was that the addition of SiC nanophases promoted Sn whisker growth and decreased the corrosion resistance of the composite solder.

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VDN: Variant-depth network for motion deblurring

VDN: Variant-depth network for motion deblurring

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作者： Guo, Cai Wang, Qian Dai, Hong-Ning Li, Ping School of Computer Science and Engineering Macau University of Science and Technology Taipa China Network and Educational Technology Center Hanshan Normal University Chaozhou China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong Department of Computing and Decision Sciences Lingnan University Tuen Mun Hong Kong School of Design The Hong Kong Polytechnic University Kowloon Hong Kong

Motion deblurring is a challenging task in vision and graphics. Recent researches aim to deblur by using multiple sub-networks with multi-scale or multi-patch inputs. However, scaling or splitting operations on input images inevitably loses the spatial details of the images. Meanwhile, their models are usually complex and computationally expensive. To address these problems, we propose a novel variant-depth scheme. In particular, we utilize the multiple variant-depth sub-networks with scale-invariant inputs to combine into a variant-depth network (VDN). In our design, different levels of sub-networks accomplish progressive deblurring effects without transforming the inputs, thereby effectively reducing the computational complexity of the model. Extensive experiments have shown that our VDN outperforms the state-of-the-art motion deblurring methods while maintaining a lower computational cost. The source code is publicly available at: https://***/CaiGuoHS/VDN. © 2022 John Wiley & Sons, Ltd.

关键词： Complex networks

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