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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Elastic and Efficient Virtual Network Provisioning for cloud-Based Multi-tier applications

Elastic and Efficient Virtual Network Provisioning for Cloud...

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International Conference on Parallel Processing (ICPP)

作者： Meng Shen Ke Xu Fan Li Kun Yang Liehuang Zhu Lei Guan Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Department of Computer Science Tsinghua University P. R. China School of Computer Science & Electronic Engineering University of Essex UK The ISN National Key Lab XiDian University P. R. China School of Management & Economics Beijing Institute of Technology P. R. China

ISBN: (纸本)9781467375887

The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.

关键词： Bandwidth Elasticity cloud computing Algorithm design and analysis Load modeling Resource management

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面向微博的情感影响最大化模型

面向微博的情感影响最大化模型

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第29届中国数据库学术会议

作者： OU Gaoyan 欧高炎 CHEN Wei 陈薇 WANG Tengjiao 王腾蛟 LEI Kai 雷凯 YANG Dongqing 杨冬青 Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871 Chin 高可信软件技术教育部重点实验室北京100871 北京大学信息科学技术学院北京100871 The Shenzhen Key Lab for Cloud Computing Technology and Applications Shenzhen Graduate School Peki 北京大学深圳研究生院深圳市云计算关键技术与应用重点实验室广东深圳518055

社交网络中影响最大化问题是寻找具有最大影响范围的节点。影响最大化的大部分求解算法仅仅依赖社交网络图。基于微博的转发关系树和微博内容的情感倾向性,以及用户的社交网络图,提出了一个能够刻画用户情感影响的情感影响最大化模型—... 详细信息

社交网络中影响最大化问题是寻找具有最大影响范围的节点。影响最大化的大部分求解算法仅仅依赖社交网络图。基于微博的转发关系树和微博内容的情感倾向性,以及用户的社交网络图,提出了一个能够刻画用户情感影响的情感影响最大化模型——情感影响分配模型(SID),证明了SID模型下的情感影响最大化问题是一个NP难问题,给出了一个具有精度保证的贪心算法。在真实的微博数据上的实验结果表明,SID模型能够有效地找出情感影响最大化的节点集,同时具有很高的扩展性。

关键词：社交网络情感分析信息传播模型分配算法

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XML模式匹配:整体连接算法TurboStack

XML模式匹配:整体连接算法TurboStack

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第29届中国数据库学术会议

作者： Guo Shaosong 郭少松 Bao Xiaoyuan 包小源 Chen Wei 陈薇 Wang Tengjiao 王腾蛟 Lei Kai 雷凯 Yang Dongqing 杨冬青 Key Laboratory of High Confidence Software Technologies(Ministry of Education) School of EECS Peki 高可信软件技术教育部重点实验室北京大学信息科学技术学院北京 100871 School of EECS Peking University Beijing 100871 北京大学信息科学技术学院北京 100871 The Shenzhen Key Lab for Cloud Computing Technology and Applications(SPCCTA) Peking University Shen 深圳市云计算关键技术与应用重点实验室(SPCCTA) 北京大学深圳研究生院

含谓词的XPath查询为小枝查询(twig query),它是XQuery最基本的查询模式。小枝查询算法有两大类：小枝分解和整体小枝连接,其中TwigStack系列算法处理小枝查询的效率是最优的,但其缺点在于不支持Twig Query中含“OR”以及通配符“*”的... 详细信息

含谓词的XPath查询为小枝查询(twig query),它是XQuery最基本的查询模式。小枝查询算法有两大类：小枝分解和整体小枝连接,其中TwigStack系列算法处理小枝查询的效率是最优的,但其缺点在于不支持Twig Query中含“OR”以及通配符“*”的查询;在大型商业数据库管理系统中,TurboXPath是DB2用于处理小枝查询的基本算法,它对XPath的支持较全面,但由于TurboXPath是针对XML数据流查询处理设计的,因而对数据库中Native存储的XML数据的查询处理效率较低;针对已有方法的不足,设计了一种纯XML存储方式,以DDE编码为基础,提出了TurboStack算法,并通过大量实验验证了该方法的有效性。

关键词：可扩展标记语言数据模式匹配整体连接算法信息查询

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EVOC:一种基于链接和内容信息的动态社区挖掘方法

EVOC:一种基于链接和内容信息的动态社区挖掘方法

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第29届中国数据库学术会议

作者： Yan Qiuling 闫秋玲 Wu Liang 吴良 Chen Wei 陈薇 Wang Tengjiao 王腾蛟 Lei Kai 雷凯 Yang Dongqing 杨冬青 Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education B 高可信软件技术教育部重点实验室(北京大学) 北京 100871 北京大学信息科学技术学院北京 100871 The Shenzhen Key Laboratory for Cloud Computing Technology and Applications (SPCCTA) Peking Univers 深圳市云计算关键技术与应用重点实验室(SPCCTA)北京大学深圳研究生院广东深圳 518055

社会网络中，不仅包括链接结构，还包括内容信息，内容信息表达了社会网络中个体相关的话题、兴趣等语义层面的信息。只考虑链接结构的社区挖掘方法无疑忽视了内容信息的价值。提出了一种结合链接结构和内容信息进行动态社区挖掘的方法... 详细信息

社会网络中，不仅包括链接结构，还包括内容信息，内容信息表达了社会网络中个体相关的话题、兴趣等语义层面的信息。只考虑链接结构的社区挖掘方法无疑忽视了内容信息的价值。提出了一种结合链接结构和内容信息进行动态社区挖掘的方法，将链接结构和内容表达为矩阵形式，综合历史社区划分结果，使用非负矩阵分解方法进行社区划分，以使社区结果在反映网络的链接特征的同时，还反映话题、兴趣等语义层面的特征。在真实数据集上的实验结果表明，所得到的结果揭示了社区随时间的演化情况、社区在不同时刻所关联的内容信息，并且能应用于其他领域，如专家搜索。

关键词：社会网络数据挖掘搜索算法信息整合

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第Sup1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

Shaoqing Wang1, Xiancun Yang2, Meixia Su1, Qiang Liu1 1Department of MRI, Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China; 2Department of Interventional Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China Correspondence: Qiang Liu (2002md@***) Aims To evaluate the diagnostic value of three- dimensional rotational angiography (3D-RA) of intracranial micro-aneurysms (diameter ≤ 3 mm) and provide guidance on the value of endovascular treatment. Materials and methods 43 patients with intracranial micro-aneurysms were analyzed retrospectively, all patients had undergone angiography with both conventional 2D-DSA(Two-Dimensional Digital Subtraction Angiography) and rotational angiography with three-dimensional reconstruction; the frequency of detection of aneurysms, depiction of aneurysm neck, radiation dose, and the dosage of contrast agent were recorded respectively. Results 55 pieces of aneurysms were detected out from the 43 cases with intracranial micro-aneurysms by 3D-RA. But only 39 cases were detected out using 2D-DSA from the 55 samples, there were significant differences with regards to detection rate (P < 0.05). There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious advantages on displaying the shape of aneurysms, the aneurysm neck at the best angle, and the relationship with the parent artery, at the same time, the amount of contrast agent and radiation dose are reduced in 3D-RA compared to 2D-DSA.

关键词： Convolutional Neural Network High Resolution Compute Tomography Intensity Inhomogeneity Kernel Support Vector Machine Clonal Selection Algorithm

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Erratum: Towards a muon collider

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The European Physical Journal C 2024年第1期84卷 1-4页

作者： Accettura, Carlotta Adams, Dean Agarwal, Rohit Ahdida, Claudia Aimè, Chiara Amapane, Nicola Amorim, David Andreetto, Paolo Anulli, Fabio Appleby, Robert Apresyan, Artur Apyan, Aram Arsenyev, Sergey Asadi, Pouya Mahmoud, Mohammed Attia Azatov, Aleksandr Back, John Balconi, Lorenzo Bandiera, Laura Barlow, Roger Bartosik, Nazar Barzi, Emanuela Batsch, Fabian Bauce, Matteo Berg, J. Scott Bersani, Andrea Bertarelli, Alessandro Bertolin, Alessandro Black, Kevin Boattini, Fulvio Bogacz, Alex Bonesini, Maurizio Bordini, Bernardo Bottaro, Salvatore Bottura, Luca Braghieri, Alessandro Breschi, Marco Bruhwiler, Natalie Buffat, Xavier Buonincontri, Laura Burrows, Philip N. Burt, Graeme Buttazzo, Dario Caiffi, Barbara Calviani, Marco Calzaferri, Simone Calzolari, Daniele Capdevilla, Rodolfo Carli, Christian Casaburo, Fausto Casarsa, Massimo Castelli, Luca Catanesi, Maria Gabriella Cavallucci, Lorenzo Cavoto, Gianluca Celiberto, Francesco Giovanni Celona, Luigi Cerri, Alessandro Cesarini, Gianmario Cesarotti, Cari Chachamis, Grigorios Chance, Antoine Chen, Siyu Chien, Yang-Ting Chiesa, Mauro Colaleo, Anna Collamati, Francesco Collazuol, Gianmaria Costa, Marco Craig, Nathaniel Curatolo, Camilla Curtin, David Da Molin, Giacomo Dam, Magnus Damerau, Heiko Dasu, Sridhara de Blas, Jorge De Curtis, Stefania De Matteis, Ernesto De Rosa, Stefania Delahaye, Jean-Pierre Denisov, Dmitri Denizli, Haluk Densham, Christopher Dermisek, Radovan Di Luzio, Luca Di Meco, Elisa Di Micco, Biagio Dienes, Keith Diociaiuti, Eleonora Dorigo, Tommaso Dudarev, Alexey Edgecock, Robert Errico, Filippo Fabbrichesi, Marco Farinon, Stefania Ferrari, Anna Somoza, Jose Antonio Ferreira Filthaut, Frank Fiorina, Davide Fol, Elena Forslund, Matthew Franceschini, Roberto Ximenes, Rui Franqueira Gabrielli, Emidio Gallinaro, Michele Garosi, Francesco Giambastiani, Luca Gianelle, Alessio Gilardoni, Simone Giove, Dario Augusto Giraldin, Carlo Glioti, Alfredo Greco, Mario Greljo, Admir Groeber, Ramona Grojean, Christophe Grudiev, Alexej Gu, Jiayin Han, Chengcheng Han, T Organisation Européenne pour la Recherche Nucléaire (CERN) Geneva Switzerland STFC Rutherford Appleton Laboratory (RAL) Oxford UK Computer Science Department University of California (UC) Berkeley USA Dipartimento di Fisica Università di Pavia Pavia Italy INFN Sezione di Pavia Pavia Italy INFN Sezione di Torino Turin Italy Dipartimento di Fisica Università di Torino Turin Italy INFN Sezione di Padova Padua Italy INFN Sezione di Roma Rome Italy School of Physics and Astronomy University of Manchester Manchester UK Fermi National Accelerator Laboratory Batavia USA Department of Physics Brandeis University Waltham USA IRFU CEA University Paris-Saclay Gif-sur-Yvette France Center for Theoretical Physics Massachusetts Institute of Technology Cambridge USA Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt SISSA International School for Advanced Studies Trieste Italy INFN Sezione di Trieste Trieste Italy Department of Physics University of Warwick Coventry UK Dipartimento di Fisica Università di Milano Milan Italy INFN Laboratori Acceleratori e Superconduttività Applicata (LASA) Milan Italy INFN Sezione di Ferrara Ferrara Italy School of Computing and Engineering The University of Huddersfield Huddersfield UK Ohio State University Columbus USA Brookhaven National Laboratory Upton USA INFN Sezione di Genova Genoa Italy University of Wisconsin Wisconsin USA Center for Advanced Studies of Accelerators Jefferson Lab Newport News USA INFN Sezione di Milano Bicocca Milan Italy Dipartimento di Fisica Università di Milano Bicocca Milan Italy Scuola Normale Superiore Pisa Italy INFN Sezione di Pisa Pisa Italy Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione Università di Bologna Bologna Italy INFN Sezione di Bologna Bologna Italy Department of Physics University of California (UC) Berkeley USA Dipartimento di Fisica e Astronomia Università di Padova Padoa Italy John Adams Institute University of O

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