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Chinese Control Conference (CCC)

作者： Zhuo Wang Qi Yuan Sixun Liu Research Institute for Frontier Science Beihang University Beijing People's Republic of China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing People's Republic of China School of Instrumentation and Opto-electronics Engineering Beihang University Beijing People's Republic of China

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The state transition matrix $e^{At}$ and its norm $\Vert e^{At}\Vert_{2}$ play an indispensable role in the analysis and design of control systems. Existing literature has provided two kinds of estimation results of $\Vert e^{At}\Vert_{2}$ . However, because of rather low precision, they can only be applied for stability, local controllability, observability analysis, and it is incapable for them to estimate the dynamic performance indexes. Main contributions of our work are summarized as two aspects: 1) the accurate expressions of $\Vert e^{At}\Vert_{2}$ while $A$ is normal and 2) the high-precision estimation results of $\Vert e^{At}\Vert_{2}$ while $A$ is nonnormal. Furthermore, these results above not only effectively reduce a lot of computation load but also significantly improve estimation precision, and they can be used to estimate the dynamic performance index of control systems.

关键词： Stability criteria Estimation Controllability Performance analysis Observability Computational complexity

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Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation

Heterogeneous Graph Neural Network for Privacy-Preserving Re...

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IEEE International Conference on data Mining (ICDM)

作者： Yuecen Wei Xingcheng Fu Qingyun Sun Hao Peng Jia Wu Jinyan Wang Xianxian Li Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computing Macquarie University Sydney Australia

Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspects of information about individuals in the training stage. That means more information has been covered in the learning result, especially sensitive information. However, the privacy-preserving methods on homogeneous graphs only preserve the same type of node attributes or relationships, which cannot effectively work on heterogeneous graphs due to the complexity. To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology. In particular, we first define a new attack scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we design a two-stage pipeline framework, which includes the privacy-preserving feature encoder and the heterogeneous link reconstructor with gradients perturbation based on differential privacy to tolerate data diversity and against the attack. To better control the noise and promote model performance, we utilize a bi-level optimization pattern to allocate a suitable privacy budget for the above two modules. Our experiments on four public benchmarks show that the HeteDP method is equipped to resist heterogeneous graph privacy leakage with admirable model generalization.

关键词： Training Privacy Differential privacy Social networking (online) Graph neural networks Topology Resource management

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Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation

arXiv

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arXiv 2022年

作者： Wei, Yuecen Fu, Xingcheng Sun, Qingyun Peng, Hao Wu, Jia Wang, Jinyan Li, Xianxian Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computing Macquarie University Sydney Australia

关键词： Budget control

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Interplays of liver fibrosis-associated microRNAs:Molecular mechanisms and implications in diagnosis and therapy

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Genes & Diseases 2023年第4期10卷 1457-1469页

作者： Hong Li Tingli Liu Yongchun Yang William C.Cho Robin J.Flynn Majid Fasihi Harandi Houhui Song Xuenong Luo Yadong Zheng State Key Laboratory of Veterinary Etiological Biology Key Laboratory of Veterinary Parasitology of Gansu ProvinceLanzhou Veterinary Research InstituteChinese Academy of Agricultural SciencesLanzhouGansu 730046China Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Provincial Engineering Laboratory for Animal Health Inspection&Internet TechnologyZhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health ManagementChina-Australia Joint Laboratory for Animal Health Big Data AnalyticsCollege of Animal Science and Technology&College of Veterinary Medicine of Zhejiang A&F UniversityHangzhouZhejiang 311300China Department of Clinical Oncology Queen Elizabeth HospitalHong Kong SAR 999077China Department of Infection Biology Institute of Infection and Global HealthUniversity of LiverpoolLiverpool L35RFUK Graduate Studies Office Department of ResearchInnovation and Graduate StudiesWaterford Institute of TechnologyX91 K0EKIreland Research Center for Hydatid Disease in Iran Kerman University of Medical SciencesKerman ***Iran

microRNAs(miRNAs)are a class of non-coding functional small RNA composed of 21e23 nucleotides,having multiple associations with liver ***-associated miRNAs are roughly classified into pro-fibrosis or anti-fibrosis *** former is capable of activating hepatic stellate cells(HSCs)by modulating pro-fibrotic signaling pathways,mainly including TGF-b/SMAD,WNT/b-catenin,and Hedgehog;the latter is responsible for maintenance of the quiescent phenotype of normal HSCs,phenotypic reversion of activated HSCs(aHSCs),inhibition of HSCs proliferation and suppression of the extracellular matrix-associated gene ***,several miRNAs are involved in regulation of liver fibrosis via alternative mechanisms,such as interacting between hepatocytes and other liver cells via exosomes and increasing autophagy of ***,understanding the role of these miRNAs may provide new avenues for the development of novel interventions against hepatic fibrosis.

关键词： Diagnosis Hepatic stellate cell Liver fibrosis miRNA Therapy

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Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data

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Computers in Biology and Medicine 2024年 178卷 108704-108704页

作者： Taberna, Gaia Amaranta Samogin, Jessica Zhao, Mingqi Marino, Marco Guarnieri, Roberto Cuartas Morales, Ernesto Ganzetti, Marco Liu, Quanying Mantini, Dante Movement Control and Neuroplasticity Research Group KU Leuven Leuven3001 Belgium Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou730000 China Department of General Psychology University of Padova Padova35131 Italy Dirección Académica Universidad Nacional de Colombia Sede de La Paz La Paz202017 Colombia pRED Data & Analytics Roche Innovation Center Basel F. Hoffmann–La Roche Ltd Basel4070 Switzerland Department of Biomedical Engineering Southern University of Science and Technology Shenzhen518055 China KU Leuven Brain Institute KU Leuven Leuven3000 Belgium

Introduction: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets. Findings: Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. Conclusions: NET is freely available (https://***/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows. © 2024

关键词： Electroencephalography

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Exploratory study of highly susceptible to SARS-CoV-2 populations

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Life Research 2022年第3期5卷 10-14页

作者： Xiaodong Jia Chengliang Yin Shanshan Lu Yan Chen Qingyan Liu Junfan Bai Lei Zhi Yinying Lu Comprehensive Liver Cancer Center The Fifth Medical Center of PLA General HospitalBeijingChina.0000-0001-8262-5749 National Engineering Laboratory for Medical Big Data Application Technology Chinese PLA General HospitalBeijingChina Faculty of Medicine Macao University of Science and TechnologyMacaoChina Medical Big Data Research Center Medical Innovation Research Division of PLA General HospitalBeijingChina Center for Synthetic and Systems Biology(CSSB) Tsinghua UniversityBeijingChina Peking University BeijingChina Department of Chronic Disease Luzhou Center for Disease Control and PreventionLuzhouChina

The coronavirus disease 2019(COVID-19),caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is threatening public *** human angiotensin-converting enzyme 2(ACE2)has a remarkably high affinity binding to *** study explored whether certain populations,including obese and cancer patients,are susceptible to *** expression pattern of ACE2 in normal and tumor tissues of cancer patients was compared by the search for network database and re-analysis of pubic *** level of ACE2 expression in normal adipose tissue was higher than that in normal lung tissue,which indicated the adipose tissue might be vulnerable to SARS-CoV-2;the levels of ACE2 expressed by adipocytes and adipose progenitor cells were similar between non-obese individuals and obese individuals(BMI>29),but obese individuals have more adiposes so as to increase the number of ACE2-expressing cells;the expression of ACE2 in tumor tissues posed by five different types of cancers increased significantly compared with that in adjacent ***,we proposed the following hypothesis:the obese individuals and five types of cancer patients might have a higher risk of SARS-CoV-2 infection,which might become the target population of SARS-CoV-2 infection in the future.

关键词： COVID-19 adipose tissue obesity cancer ACE2

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中国碳收支:1980~2021年

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science Bulletin 2024年第1期69卷 114-124页

作者：夏晓圣任培阳王旭辉刘丹陈修治丹利何斌何洪林居为民梁敏琪陆星劼彭静覃章才夏江周郑博魏静乐旭于贵瑞朴世龙袁文平 School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon CycleSun Yat-sen UniversityZhuhai 510245China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100091China State Key Laboratory of Tibetan Plateau Earth System Resources and EnvironmentInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System ScienceBeijing Normal UniversityBeijing 100875China Synthesis Research Center of China’s Ecosystem Research Network Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China International Institute for Earth System Science Nanjing UniversityNanjing 210023China Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal UniversityTianjin 300387China Institute of Environment and Ecology Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044China

As one of the world's largest emitters of greenhouse gases,China has set itself the ambitious goal of achieving carbon peaking and carbon ***,it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide(CO_(2)),and to monitor China's progress toward these *** state-of-the-art datasets and models,this study comprehensively estimated the anthropogenic CO_(2)emissions from energy,industrial processes and product use,and waste along with natural sources and sinks of CO_(2)for all of China during *** recognize the differences among various methods of estimating greenhouse emissions,the estimates are compared with China's National Greenhouse Gas Inventories(NGHGIs)for 1994,2005,2010,2012,and *** CO_(2)emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO_(2)a^(-1)in *** benefiting from ecological projects(e.g.,Three Norths Shelter Forest System Project),the land carbon sink in China has reached 1.65 Gt CO_(2)a^(-1)averaged through 2010-2021,which is almost 15.81 times that of the carbon sink in the *** average,China's terrestrial ecosystems offset 14.69%±2.49%of anthropogenic CO_(2)emissions through *** provincial-level administrative regions of China,Xizang and Qinghai,have achieved carbon neutrality according to our estimates,but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10%of anthropogenic CO_(2)*** study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO_(2)emissions,but found notable differences for land carbon *** estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

关键词： CO_(2)emission Land carbon sink China Carbon neutrality Carbon budget

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The Power Law Statistics of the Spiking Timing in a Neuronal Network

SSRN

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SSRN 2023年

作者： Yao, Chenggui Sun, JianQiang Jin, Jun Shuai, Jianwei Li, Xiang Yao, Yuangen Xu, Xunfan College of Data science Jiaxing University Jiaxing314000 China Wenzhou Key Laboratory of Biophysics Wenzhou Institute University of Chinese Academy of Sciences Wenzhou325000 China School of Automation and Electrical Engineering Linyi University Linyi276000 China Department of Physics State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling Network Xiamen University Xiamen361102 China School of science Huazhong Agricultural University Wuhan430000 China College of material and textile engineering Jiaxing University Jiaxing314000 China

How information is encoded and decoded by neurons is a fundamental and significant question of neuroscience. Here we present a statistical investigation on the spiking timing that arises from the neuronal population in the small world network with Hodgkin-Huxley model after a neuron has been received a stimulus current, including a transient or continuous stimulus. No matter if the interaction between the neurons inside is implemented via electrical coupling or chemical synapses, we find the same power law statistics for the spiking timing, which are independent of the topological structure of the neuronal network. We further suggest that such power law statistics are universal features of spiking timing in the neuronal networks. Our findings provide new insights into the coding mechanism for the spiking timing and improve the understanding of power-low behavior in the nature. © 2023, The Authors. All rights reserved.

关键词： Neurons

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High voltage-controlled magnetic anisotropy and interface magnetoelectric effect in sputtered multilayers annealed at high temperatures

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science China(Physics,Mechanics & Astronomy) 2020年第7期63卷 114-119页

作者： LeZhi Wang Xiang Li Taisuke Sasaki Kin Wong GuoQiang Yu ShouZhong Peng Chao Zhao Tadakatsu Ohkubo Kazuhiro Hono WeiSheng Zhao KangLong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Computer Science&Engineering Beihang UniversityBejing 100191China Department of Electrical Engineering University of CaliforniaLos AngelesLos Angeles 90095USA School of Microeletronics Beihang UniversityBeijing 100191China Inston IncLos Angeles 90095USA National Institute for Materials Science(NIMS) Tsukuba 305-0047Japan Institute of Microelectronics Chinese Academy of SciencesBejing 100029China

Voltage control of magnetism promises great energy efficiency in writing magnetic memory. Here, using Cr/Mo/CoFeB/MgO multilayers stable under high annealing temperatures up to 590°C, we significantly enhance the interfacial crystallinity, thereby the interface-originated perpendicular magnetic anisotropy(PMA), voltage-controlled magnetic anisotropy(VCMA), and interface magnetoelectric(ME) effect. High interfacial PMA of 1.35 mJ/m^2, VCMA coefficient of-138 fJ/(V m), and interface ME coefficient, which is 2-3 orders of magnitude larger than ab initio calculation results are simultaneously achieved after annealing at 500°C. These promising results enabled by the industry-applicable sputtering process will pave the way for highdensity voltage-controlled spintronic devices.

关键词： voltage-controlled magnetic anisotropy(VCMA) magnetoelectric(ME)effect perpendicular magnetic anisotropy

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Roof greening in major Chinese cities possibly afford a large potential carbon sink

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science Bulletin 2024年第20期69卷 3216-3220页

作者： Chao Yang Yinghui Zhang Min Chen Song Zhu Yuzhi Tang Zhixin Zhang Wei Ma Huizeng Liu Junyi Chen Bohui Tang Dejin Zhang Zhengdong Huang Xuqing Wang Wei Tu Cuiling Liu Tiezhu Shi Haiying Xu Aihong Cui Fanyi Meng Tianhong Zhao Kai Guo Wenhao Guo Bi Fan Quande Qin Wuyang Hong Haishan Wu Bing Wang Jinrong Zeng Ka Wai Wu Qingquan Li Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Ministry of Natural Resources&Guangdong Key Laboratory of Urban InformaticsShenzhen UniversityShenzhen 518060China School of Architecture and Urban Planning Shenzhen UniversityShenzhen 518060China Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Shenzhen Zhiyuan Space Innovation Technology Co. LtdShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China School of Geography and Ocean Science Nanjing UniversityNanjing 210008China School of Geography Nanjing Normal UniversityNanjing 210023China School of Civil Engineering Chongqing Jiaotong UniversityChongqing 400074China Institute for Advanced Study&Shenzhen University Shenzhen 518060China Tiandu-Shenzhen University Deep Space Joint Laboratory Shenzhen UniversityShenzhen 518060China Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China Center for Hydrogeology and Environmental Geology China Geological SurveyBaoding 071051China Department of Landscape Architecture and Urban Planning Texas A&M UniversityTX 77840USA Department of Geography Hong Kong Baptist UniversityHong Kong 999077China College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518060China College of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China College of Management Shenzhen UniversityShenzhen 518060China WeBank(Shenzhen)Co. LtdShenzhen 518060China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong Kong 100872China Esri C

China is experiencing accelerated urbanisation,with a large number of people moving from rural to urban areas[1].It has resulted in large losses in the net primary production(NPP),biodiversity and carbon stocks and an increase in environmental pollution and CO_(2)emissions[2–4].In 2015,196 countries signed the Paris Agreement and committed to setting long-term goals to jointly manage climate change and reduce their individual emissions,aiming to control the increase in global average temperature from the pre-industrial level to below 2℃and to curtail the temperature rise within 1.5℃till the end of the 21st century[5].China is bolstering its efforts to achieve the climate change mitigation goals and has announced a plan for achieving carbon neutrality by 2060[6].The carbon neutrality goal poses a challenge to the current policies promoting rapid urbanisation across China.

关键词： jointly carbon goals

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