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中国科学：信息科学 2023年第8期53卷 1575-1592页

作者：杨赟余辉赵进张宇廖小飞姜新宇金海刘海坤毛伏兵张吉王彪华中科技大学大数据技术与系统国家地方联合工程研究中心武汉430074 华中科技大学服务计算技术与系统教育部重点实验室武汉430074 华中科技大学集群与网格计算湖北省重点实验室武汉430074 华中科技大学计算机科学与技术学院武汉430074 School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba 4350Australia 之江实验室杭州311121

随着现实世界中动态图计算需求的快速增长,现有的研究工作已经提出了多种方法来有效支持单调图算法在动态有向图中的处理.然而,由于动态有向图的图结构频繁发生变化,其相邻图顶点之间的状态更新存在复杂的依赖关系,这使得现有的软硬件... 详细信息

随着现实世界中动态图计算需求的快速增长,现有的研究工作已经提出了多种方法来有效支持单调图算法在动态有向图中的处理.然而,由于动态有向图的图结构频繁发生变化,其相邻图顶点之间的状态更新存在复杂的依赖关系,这使得现有的软硬件方法在处理单调图算法时依然面临着数据访问成本高和收敛速度慢的问题.为此,本文提出了一种面向动态有向图的单调图算法加速器DSGraph,它能够充分利用图顶点之间的依赖关系来加快单调图算法在动态有向图处理中的收敛速度,并有效降低数据访问成本.具体来说,DSGraph通过实时提取动态有向图中图顶点的局部拓扑依赖顺序来执行异步迭代处理,从而显著减少冗余的图顶点状态更新.同时,DSGraph设计了一种异步迭代流水线架构,其按照依赖顺序对图顶点状态进行异步迭代处理,从而加速图顶点状态传播速度并减少数据访问开销.最后,DSGraph提出了一种无阻塞数据同步机制,通过并行执行本地图顶点的状态更新和外部图顶点的数据同步来减少系统同步开销.实验显示,与目前最先进的面向单调图算法的动态图处理系统KickStarter相比,DSGraph将动态有向图处理速度平均提升了11.2倍.

关键词：动态有向图单调图算法增量计算依赖感知图加速器

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论南宋道学之党的政党特质

孔学堂 2021年第3期8卷 90-99,I0107-I0115页

作者：汪洋 Zhu Yuan 贵州师范大学贵州阳明文化研究院贵州贵阳550025 the School of Foreign Languages at Renmin University of china

南宋孝宗乾道、淳熙之际,道学兴盛,在学派之间长期的学术交流、辩论以及与佞幸的斗争过程中,道学士大夫及其同道形成了“道学之党”。“道学之党”是宋代士大夫结党的巅峰,已非传统意义之朋党,而具有政党特质,具体表现在:具有共同的政... 详细信息

南宋孝宗乾道、淳熙之际,道学兴盛,在学派之间长期的学术交流、辩论以及与佞幸的斗争过程中,道学士大夫及其同道形成了“道学之党”。“道学之党”是宋代士大夫结党的巅峰,已非传统意义之朋党,而具有政党特质,具体表现在:具有共同的政治纲领、明显的集团性、一定的组织性、取得政权的目的性。正因其政党特质,“道学之党”的发展已经威胁到了皇权,故而被“非道学之党”利用皇权进行镇压,结果便是“庆元党禁”。

关键词：道学之党朋党政党特质朱熹庆元党禁

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一种高效的面向高并发图分析任务的存储系统

中国科学：信息科学 2022年第1期52卷 111-128页

作者：赵进姜新宇张宇廖小飞金海刘海坤杨赟张吉王彪余婷华中科技大学大数据技术与系统国家地方联合工程研究中心武汉430074 华中科技大学服务计算技术与系统教育部重点实验室武汉430074 华中科技大学集群与网格计算湖北省重点实验室武汉430074 华中科技大学计算机科学与技术学院武汉430074 School of Sciences University of southern Queensland Toowoomba 4350Australia 之江实验室杭州311121

随着现实世界中图计算需求的快速增长,同一平台上往往并发运行着大量迭代图分析任务.然而,现有的图计算系统主要是为了高效执行单个图分析任务而设计的.因此,当多个并发图分析任务同时在同一个底层图上并行执行时,现有图计算系统会面临... 详细信息

随着现实世界中图计算需求的快速增长,同一平台上往往并发运行着大量迭代图分析任务.然而,现有的图计算系统主要是为了高效执行单个图分析任务而设计的.因此,当多个并发图分析任务同时在同一个底层图上并行执行时,现有图计算系统会面临巨大的数据访问开销.为了提高并发图分析任务的吞吐量,现有的核外并发图处理方案通过共享图数据减少并发任务的数据存储与访问开销.但是,由于现实世界中图的图顶点度数幂律分布特性以及图分析任务之间的差异性,现有方案在访问数据时依旧存在着大量的不必要的冗余I/O开销.这是因为即使静态图分区中绝大部分顶点处于非活跃状态或者只被少数图分析任务共享,现有方法也依旧会将整个分区加载入内存供并发图分析任务处理.为解决上述问题,本文提出了一个面向并发图分析任务的高效存储系统GraphDP.它能够插入到现有核外图计算系统中来透明有效地减少现有图计算系统执行并发图分析任务时的存储消耗与数据访问开销,从而提高并发图分析任务的吞吐量.具体来说,GraphDP使用一种新颖的动态I/O调度策略,能够使系统以最优的I/O访问方式完成图数据的加载,并有效地减少加载到内存和cache的数据.同时,GraphDP通过高效的缓存机制在内存中优先缓存被频繁访问的图数据,从而进一步减少数据访问开销.为证明GraphDP的有效性,我们将GraphDP插入到目前流行的核外图计算系统中,包括gridGraph,GraphChi和X-Stream.实验结果表明,GraphDP分别将gridGraph,GraphChi和X-Stream的吞吐量提高了1.57~2.19倍,1.86~2.37倍和1.62~2.21倍.

关键词：迭代图处理并发任务存储系统 I/O开销吞吐量

CEPC Technical Design Report

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Radiation Detection Technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi Yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-Yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui University HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu University Institute of ScienceVaranasi Bandirma Onyedi Eylul University Bandirma Beihang University Beijing Beijing Computational Science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal University Beijing Brown University ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad University Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo University Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central china Normal University WuhanHubei Central South University ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan china academy of engineering physics MianyangSichuan china building materials academy Beijing china Center of Advanced Science and Technology Beijing china Institute of Atomic Energy Beijing china Jiliang University HangzhouZhejiang china Nuclear Instrument CO. LTDBeijing china University of Geosciences Beijing china University of Geosciences WuhanHubei china University of Mining and Technology XuzhouJiangsu china West Normal University NanchongSichuan Chongqing University Department of PhysicsChongqing Chulalongkorn University Bangkok City University of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell University IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu University DalianLiaoning Dalian University of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East china University Of Science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by china,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

关键词： initiated exceeding precise

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电网故障智能诊断技术研究综述

南京师大学报（自然科学版） 2019年第3期42卷 138-144页

作者：刘仲民呼彦喆张鑫兰州理工大学电气工程与信息工程学院甘肃兰州730050 Electrical Engineering&Information Engineering School Gansu 730050China 国网甘肃省电力公司检修公司甘肃兰州730050

电网故障诊断技术在国内外应用已十分广泛,随着人工智能的快速发展,基于智能方法的电网故障诊断得到前所未有的发展.本文对结合专家系统、贝叶斯网络、Petri网、多源信息融合技术、人工神经网络的电网故障诊断原理及框架进行了综述.根... 详细信息

电网故障诊断技术在国内外应用已十分广泛,随着人工智能的快速发展,基于智能方法的电网故障诊断得到前所未有的发展.本文对结合专家系统、贝叶斯网络、Petri网、多源信息融合技术、人工神经网络的电网故障诊断原理及框架进行了综述.根据实际工程的应用情况,对各种智能诊断方法的长处和不足以及各自未来的发展方向进行了详细阐释.最后以智能电网建设为背景,大数据为依托,利用智能电网故障诊断技术解决所面临的实际问题,并对电网故障诊断技术的未来发展趋势进行了展望.

关键词：电网故障诊断智能技术发展趋势

ZTE Communications Special Issue on Recent Advances in Smart grid

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ZTE Communications 2015年第1期13卷 49-49页

作者： Kun Yang Yingfei Dong Zhisheng Niu School of Computer Science & ElectronicEngineering University of Essex United Kingdom Dept. of Electrical and Computer Engineering University of Hawaii USA Department of Electronic Engineering Tsinghua University China

The smart grid is the next generatiou electric： grid that enables effi- cient, intelligent, and economical power generation, transmission, and distribution. It has attracted significant attentions and become a global trend due to the immense potential benefits including en- hanced reliability and resilience, higher operational efficiency, more efficient energy consumption, and better power quality. This special issue expects to address smart grid issues related to data sensing, data communications and data networking, including high-level ideology/methodology, concrete smart grid inspired data communications and networking technolngies, smart grid system ar- chitecture, QoS, energy-efficiency, and fault tolerance in smart grid systems, management of smart grid systems, and real-world deploy- ment experiences.

关键词： ZTE Communications Special Issue on Recent Advances in Smart grid grid data

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基于磁电复合材料的开合式大电流传感器研究

自动化与仪器仪表 2022年第1期 226-229页

作者：辛明勇徐长宝祝健杨王宇欧正宇鲁彩江贵州电网有限责任公司电力科学研究院贵阳550002 四川率维智能科技有限公司成都610001 School of Mechanical Engineering Southwest Jiaotong UniversityChengdu 610031China

通过间接测量由电流所激发的磁场,基于磁电材料的磁场传感器件可以被用作电流传感器。但目前的闭环磁电电流传感器在测量时,由于聚磁环无法开合,需要穿芯才能测量,不利于实际应用。基于Terfenol-D/PZT/Terfenol-D磁电敏感元件制备了一... 详细信息

通过间接测量由电流所激发的磁场,基于磁电材料的磁场传感器件可以被用作电流传感器。但目前的闭环磁电电流传感器在测量时,由于聚磁环无法开合,需要穿芯才能测量,不利于实际应用。基于Terfenol-D/PZT/Terfenol-D磁电敏感元件制备了一种可以开合的磁电大电流传感器,并测试了其相关性能。实验结果表明,在50 A-1000 A测量范围内,传感器的灵敏度为52.79 mV/A,其R^(2)值即拟合直线的相关系数到达0.9988;同时,具有较小的测量角差。由此,提出的传感器可以用于实际的大量流测量。

关键词：磁电材料电流传感器开合磁芯有限元仿真

A multi-proxy reconstruction of spatial and temporal variations in Asian summer temperatures over the last millennium

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A multi-proxy reconstruction of spatial and temporal variati...

众脑之力:通过开放神经影像数据和大规模协作催化神经精神疾病的创新发现（英文）

中国科学院地质与地球物理研究所2015年度（第15届）学术年会

作者： Feng Shi Quansheng Ge Bao Yang Jianping Li Fengmei Yang Fredrik Charpentier Ljungqvist Olga Solomina Takeshi Nakatsuka Ninglian Wang Sen Zhao Chenxi Xu Keyan Fang Masaki Sano Guoqiang Chu Zexin Fan Narayan P.Gaire Muhammad Usama Zafar Key Laboratory of Cenozoic Geology and Environment Institute of Geology and GeophysicsChinese Academy of Sciences Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences College of Global Change and Earth System Science Beijing Normal University Joint Center for Global Change Studies china Meteorological Administration Training Centre Department of History Stockholm University Bolin Centre for Climate Research Stockholm University Institute of Geography Russian Academy of Sciences Tomsk State University Central Department of Environmental Science Tribhuvan University

To investigate climate variability in Asia during the last millennium, the spatial and temporal evolution of summer(June–July–August;JJA) temperature in eastern and south-central Asia is reconstructed using multi-proxy records and the regularized expectation maximization(RegE M) algorithm with truncated total least squares(TTLS), under a point-by-point regression(PPR) framework. The temperature index reconstructions show that the late 20 th century was thewarmest period in Asia over the past millennium. The temperature field reconstructions illustrate that temperatures in central, eastern, and southern china during the 11 th and 13 th centuries, and in western Asia during the 12 th century, were significantly higher than those in other regions, and comparable to levels in the 20 th century. Except for the most recent warming, all identified warm events showed distinct regional expressions and none were uniform over the entire reconstruction area. The main finding of the study is that spatial temperature patterns have, on centennial timescales, varied greatly over the last millennium. Moreover, seven climate model simulations, from the Coupled Model Intercomparison Project Phase 5(CMIP5), over the same region of Asia, are all consistent with the temperature index reconstruction at the 99 % confidence level. Only spatial temperature patterns extracted as the first empirical orthogonal function(EOF) from the GISS-E2-R and MPI-ESM-P model simulations are significant and consistent with the temperature field reconstruction over the past millennium in Asia at the 90 % confidence level. This indicates that both the reconstruction and the simulations depict the temporal climate variability well over the past millennium. However, the spatial simulation or reconstruction capability of climate variability over the past millennium could be still limited. For reconstruction, some grid points do not pass validation tests and reveal the need for more proxies with high temporal res

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Science Bulletin 2024年第10期 1536-1555页

作者：鲁彬陈骁 Francisco Xavier Castellanos Paul M.Thompson 左西年臧玉峰严超赣 CAS Key Laboratory of Behavioral Science Institute of Psychology Department of Psychology University of Chinese Academy of Sciences Department of Child and Adolescent Psychiatry NYU Grossman School of Medicine Nathan Kline Institute for Psychiatric Research Imaging Genetics Center Mark&Mary Stevens Institute for Neuroimaging&InformaticsKeck School of MedicineUniversity of Southern California Developmental Population Neuroscience Research Center IDG/McGovern Institute for Brain ResearchBeijing Normal University National Basic Science Data Center Centre for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Institute of Psychological Science Hangzhou Normal University Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment International Big-Data Center for Depression Research Institute of PsychologyChinese Academy of Sciences Magnetic Resonance Imaging Research Center Institute of PsychologyChinese Academy of Sciences

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric *** also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction,contributing to optimal brain health.

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