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Switching plasticity in compensated ferrimagnetic multilayers for neuromorphic computing

Chinese Physics B 2022年第11期31卷 143-148页

作者： Weihao Li Xiukai Lan Xionghua Liu Enze Zhang Yongcheng Deng Kaiyou Wang State Key Laboratory for Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049 China Beijing Academy of Quantum Information Sciences Beijing 100083 China Center for Excellence in Topological Quantum Computation University of Chinese Academy of SciencesBeijing 100049 China

Current-induced multilevel magnetization switching in ferrimagnetic spintronic devices is highly pursued for the application in neuromorphic *** this work,we demonstrate the switching plasticity in Co/Gd ferrimagnetic multilayers where the binary states magnetization switching induced by spin–orbit toque can be tuned into a multistate one as decreasing the domain nucleation ***,the switching plasticity can be tuned by the perpendicular magnetic anisotropy of the multilayers and the in-plane magnetic ***,we used the switching plasticity of Co/Gd multilayers for demonstrating spike timing-dependent plasticity and sigmoid-like activation *** work gives useful guidance to design multilevel spintronic devices which could be applied in high-performance neuromorphic computing.

关键词： switching plasticity compensated ferrimagnet spin-orbit torque spike timing-dependent plasticity sigmoidal neuron handwritten digits recognition neuromorphic computing

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Normalization in R2-Based Hypervolume and Hypervolume Contribution Approximation

Normalization in R2-Based Hypervolume and Hypervolume Contri...

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IEEE Symposium Series on computational Intelligence (SSCI)

作者： Guotong Wu Tianye Shu Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

In this paper, we examine the effect of normalization in R2-based hypervolume and hypervolume contribution approximation. The fact is that the region with different scales on objective space brings approximation bias. The basic idea of normalization is to perform a coordinate transformation to make the shape of the approximated region more regular, and then transform it to obtain the final value according to the property of hypervolume and hypervolume contribution. The performance of normalization is evaluated on different datasets by comparing it with the original R2-based method. We use two different metrics to evaluate hypervolume and hypervolume contribution separately, and the results indicate that normalization does exactly improve the approximation accuracy and outperforms the original R2-based method.

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Few-Shot Data Completion for New Tasks in Sparse Crowdsensing

Few-Shot Data Completion for New Tasks in Sparse Crowdsensin...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： En Wang Mijia Zhang Bo Yang Yang Xu Zixuan Song Yongjian Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Electronic Engineering Hunan University China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Mobile Crowdsensing is a type of technology that utilizes mobile devices and volunteers to gather data about specific topics at large scales in real-time. However, in practice, limited participation leads to missing data, i.e., the collected data may be sparse, which makes it difficult to perform accurate analysis. A possible technique called sparse crowdsensing incorporates the sparse case with data completion, where unsensed data could be estimated through inference. However, sparse crowdsensing typically suffers from poor performance during the data completion stage due to various challenges: the sparsity of the sensed data, reliance on numerous timeslots, and uncertain spatiotemporal connections. To resolve such few-shot issues, the proposed solution uses the Correlated Data Fusion for Matrix Completion (CDFMC) approach, which leverages a small amount of objective data to retrain an auxiliary dataset-based pre-trained model that can estimate unsensed data efficiently. CDFMC is trained using a combination of the traditional Deep Matrix Factorization and the Kalman Filtering, which not only enables the efficient representation and comparison of data samples but also fuses the objective data and auxiliary data effectively. Evaluation results show that the proposed CDFMC outperforms baseline techniques, achieving high accuracy in completing unsensed data with minimal training data.

关键词： Performance evaluation Accuracy Crowdsensing Filtering Training data Data models Real-time systems

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A Multi-objective Perspective Towards Improving Meta-Generalization

A Multi-objective Perspective Towards Improving Meta-General...

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International Joint Conference on Neural Networks (IJCNN)

作者： Weiduo Liao Ying Wei Qirui Sun Qingfu Zhang Hisao Ishibuchi Department of Computer Science City University of Hong Kong Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology School of Computer Science and Engineering Nanyang Technological University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

To improve meta-generalization, i.e., accommodating out-of-domain meta-testing tasks beyond meta-training ones, is of significance to extending the success of meta-learning beyond standard benchmarks. Previous heterogeneous meta-learning algorithms have shown that tailoring the global meta-knowledge by the learned clusters during meta-training promotes better meta-generalization to novel meta-testing tasks. Inspired by this, we propose a novel multi-objective perspective to sharpen the compositionality of the meta-trained clusters, through which we have empirically validated that the meta-generalization further improves. Grounded on the hierarchically structured meta-learning framework, we formulate a hypervolume loss to evaluate the degree of conflict between multiple cluster-conditioned parameters in the two-dimensional loss space over two randomly chosen tasks belonging to two clusters and two mixed tasks imitating out-of-domain tasks. Experimental results on more than 16 few-shot image classification datasets show not only improved performance on out-of-domain meta-testing datasets but also better clusters in visualization.

关键词： Metalearning Visualization Neural networks Clustering algorithms Benchmark testing Standards Image classification

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Efficient Sharing of Energy Consumption Data: A Privacy-Preserving Threshold Aggregation Approach

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IEEE Internet of Things Journal 2025年

作者： Li, Guohao Zhou, Lu Lian, Jiale Liu, Siyi Yang, Li Zhong, Yantao Li, Qiang Xidian University School of Computer Science and Technology Xi’an China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Co. Ltd Guangdong Shenzhen China

Energy consumption data collected by smart meters is increasingly used by various subscribers in the smart grid for load management, energy monitoring, and policy planning. To protect user privacy, edge-assisted privacy-preserving data aggregation (PPDA) techniques are commonly employed. However, existing methods face several challenges: i) limited scalability, ii) strict trust requirements, and iii) the risk of revealing unique consumption patterns to data collectors. To address these challenges, we propose a privacy-preserving threshold aggregation method that is easily scalable and facilitates efficient energy data sharing under limited trust assumptions. Specifically, we design VFPNTRU, a quantum-resistant homomorphic proxy re-encryption scheme with fault tolerance and re-encryption verification. In VFP-NTRU, smart meters can encrypt data with a public key without the need for prior negotiation of decryption keys with multiple subscribers. Additionally, we develop a privacy threshold collection protocol that uses a verifiable oblivious pseudorandom function to provide privacy guarantees similar to k-anonymity for SM data collection. We further introduce an energy consumption model to determine optimal collection strategies, improving system responsiveness. We provide correctness analysis and prove the security of our scheme. Experimental results demonstrate that our approach outperforms existing PPDA methods, making it particularly suitable for resource-constrained smart meters and central servers managing large-scale energy data. © 2014 IEEE.

关键词： Differential privacy

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Unraveling climate change-induced compound low-solar-low-wind extremes in China

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国家科学评论（英文版） 2025年第1期12卷 92-105页

作者： Licheng Wang Yawen Liu Lei Zhao Xi Lu Liangdian Huang Yana Jin Steven J.Davis Amir Aghakouchak Xin Huang Tong Zhu Yue Qin College of Environmental Sciences and Engineering Peking UniversityBeijing 100871China Institute of Carbon Neutrality Peking UniversityBeijing 100871China School of Atmospheric Sciences Nanjing UniversityNanjing 210023China Department of Civil and Environmental Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA National Center for Supercomputing Applications University of Illinois at Urbana-ChampaignUrbanaIL 61801USA School of Environment State Key Joint Laboratory of Environment Simulation and Pollution ControlTsinghua UniversityBeijing 100084China Department of Earth System Science University of CaliforniaIrvineIrvineCA 92697USA Department of Earth System Science University of CaliforniaIrvineIrvineCA 92697USA Department of Civil and Environmental Engineering University of CaliforniaIrvineIrvineCA 92697USA

China's pursuit of carbon neutrality targets hinges on a profound shift towards low-carbon energy,primarily reliant on intermittent and variable,yet crucial,solar and wind power *** particular,low-solar-low-wind(LSLW)compound extremes present a critical yet largely ignored threat to the reliability of renewable electricity *** existing studies have largely evaluated the impacts of average climate-induced changes in renewable energy resources,comprehensive analyses of the compound extremes and,particularly,the underpinning dynamic mechanisms remain *** we show the dynamic evolution of compound LSLW extremes and their underlying mechanisms across China via coupling multi-model simulations with diagnostic *** results unveil a strong topographic dependence in the frequency of compound LSLW extremes,with a national average frequency of 16.4(10th-90th percentile interval ranges from 5.3 to 32.6)days/yr,when renewable energy resources in eastern China are particularly compromised(～80％lower than that under an average climate).We reveal a striking increase in the frequency of LSLW extremes,ranging from 12.4％under SSP126 to 60.2％under SSP370,primarily driven by both renewable energy resource declines and increasingly heavily-tailed distributions,resulting from weakened meridional temperature(pressure)gradient,increased frequency of extremely dense cloud cover and additional distinctive influence of increased aerosols under *** study underscores the urgency of preparing for significantly heightened occurrences of LSLW events in a warmer future,emphasizing that such climate-induced compound LSLW extreme changes are not simply by chance,but rather projectable,thereby underscoring the need for proactive adaptation *** insights are crucial for countries navigating a similar transition towards renewable energy.

关键词： wind energy solar energy renewable energy climate change compound energy droughts

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An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

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Fundamental Research 2024年第3期4卷 430-441页

作者： Jianping Huang Danfeng Wang Yongguan Zhu Zifeng Yang Maosheng Yao Xiaoming Shi Taicheng An Qiang Zhang Cunrui Huang Xinhui Bi Jiang Li Zifa Wang Yongqin Liu Guibing Zhu Siyu Chen Jian Hang Xinghua Qiu Weiwei Deng Huaiyu Tian Tengfei Zhang Tianmu Chen Sijin Liu Xinbo Lian Bin Chen Beidou Zhang Yingjie Zhao Rui Wang Han Li Collaborative Innovation Center for Western Ecological Safety College of Atmospheric SciencesLanzhou UniversityLanzhou 730000China Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China National Clinical Research Center for Respiratory Disease Guangzhou Institute of Respiratory HealthFirst Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease(Guangzhou Medical University)Guangzhou 510230China College of Environmental Sciences and Engineering Peking UniversityBeijing 100871China China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing 100021China Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Vanke School of Public Health Tsinghua UniversityBeijing 100084China State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization Guangzhou Institute of GeochemistryChinese Academy of SciencesGuangzhou 510640China National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Center for Pan-third Pole Environment Lanzhou UniversityLanzhou 730000China College of Atmospheric Sciences Lanzhou UniversityLanzhou 730000China School of Atmospheric Sciences Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 510640

Corona virus disease 2019(COVID-19)has exerted a profound adverse impact on human *** have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among *** monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic *** of pathogenic microorganisms in the air,especially in densely populated areas,may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier *** multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation,allocate health resources,and formulate epidemic response *** review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission,which lays a theoretical foundation for the monitoring and prediction of epidemic ***,the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and ***,this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology,atmospheric sciences,environmental sciences,sociology,demography,*** summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere,this review proposes suggestions for epidemic response,namely,the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

关键词： Aerosol transmission Epidemic monitoring methods Epidemic prediction modelsEpidemic response Multidisciplinary cooperation

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Influences of Element Types on Nonlinear Finite Element Analysis of a Concrete Column Under Near-Field Blast Loading

Influences of Element Types on Nonlinear Finite Element Anal...

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Recent Advances in Applied Mechanics and Mechanical engineering - Select Proceedings of ICAMME 2022

作者： Xu, Jie Hendriks, Max A. N. Rots, Jan G. Tsouvalas, Apostolos College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions Hunan Changsha410073 China Faculty of Civil Engineering and Geosciences Delft University of Technology Zuid Holland Delft2628 CN Netherlands

ISBN: (纸本)9789819923748

Due to the accompanying severe consequences of explosions, the blast puts a great threat to public security. Nonlinear finite element analysis is a possible method for civil engineers to check the integrity of the structures under blast loading without underestimating the limit of the structures. However, different choices of element types would generally put a great influence on the analytical results and the corresponding computational expenses. Therefore, how should civil engineers simplify their physical model into finite element models to gain relatively accurate numerical results with acceptable computational expenses is of great interest. In this article, 6 different types of elements are discussed with different orders and shapes for a certain physical situation, and the corresponding experimental results and the numerical results for a very detailed finite element model are used as the baseline for judgement, which could be helpful for civil engineers to make proper simplifications in the set-up of finite element models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Engineers

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Enhancing the Energy Density and Specific Capacitance of High-Entropy Oxides for Supercapacitor Applications Through Electrodeposition

SSRN

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SSRN 2024年

作者： Duan, Yuzheng Meng, Zeshuo Kang, Ziqian Wang, Bo Qiao, Liang Liu, Shujie Hu, Xiaoying Tian, Hongwei College of Science Laboratory of Materials Design and Quantum Simulation Changchun University Changchun130022 China Key Laboratory of Automobile Materials MOE School of Materials Science and Engineering Jilin University Changchun130012 China

High-entropy oxides (HEOs) have shown promise as electrode materials in supercapacitors due to their unique four high-entropy effects. However, achieving high energy density remains a challenge. In this study, we increased the specific capacitance and energy density of HEOs by constructing a favorable electrode material structure and expanding the potential window. Transition metal elements Cr, Mn, Ni, Fe, and Co were chosen in this work, as they demonstrated high electrochemical activity in previous studies. Through electrodeposition, we achieved uniform growth of HEO nanoparticles on carbon cloth (CC), with good electrical conductivity. The resulting composite electrode exhibited enhanced redox reactions and an improved potential window of −0.9 to 0.4 V. Notably, the electrode material had a high specific capacitance of 474.2 F/g at 0.5 A/g, due to the broader potential window. The electrode also exhibited excellent charge/discharge reversibility, with a capacitor retention rate of 64.58%, under a current density of 10 A/g. An asymmetric supercapacitor (ASC) was also synthesized using the HEO@CC electrode via electrodeposition, achieving an energy density of 42.3 Wh/kg and demonstrating excellent cyclic stability. © 2024, The Authors. All rights reserved.

关键词： Entropy

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Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

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science China(Information sciences) 2024年第2期67卷 20-40页

作者： Peng YANG Laoming ZHANG Haifeng LIU Guiying LI Department of Statistics and Data Science Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Guangdong OPPO Mobile Telecommunications Corp. Ltd Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.

关键词： evolutionary reinforcement learning evolutionary multi-objective optimization load balance cloud computing

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