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arXiv 2017年

作者： Wu, Mincheng He, Shibo Zhang, Yongtao Chen, Jiming Sun, Youxian Liu, Yang-Yu Zhang, Junshan Vincent Poor, H. State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou Zhejiang310027 China Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Center for Cancer Systems Biology Dana-Farber Cancer Institute BostonMA02115 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Invisible Secure Keypad Solution Resilient against Shoulder Surfing Attacks

Invisible Secure Keypad Solution Resilient against Shoulder ...

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International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)

作者： Dongmin Choi Chang Choi Xin Su Division of Undeclared Majors Chosun University Gwangju South Korea Dept. of Computer Engineering Chosun University Gwangju South Korea College of IoT Engineering Changzhou Key Laboratory of Robotics and Intelligent Technology Hohai University Changzhou China

ISBN: (纸本)9781509009855

In recent years, both of financial technologies and its services for mobile devices have been increased sharply. However, according to the case studies of mobile security, security threats have been increased too. Thus, for sustaining their services safe, security department of companies or bank institutes continuously research and develop to upgrade their service solutions. However, recent researches of security threats reported that various new types of social engineering attack were frequently observed in mobile service. In this paper, we compare several user authentication schemes against social engineering attacks, especially shoulder surfing attack. In the experimental result, each one of existing schemes have a slightly different weaknesses. According to the result of comparative analysis, we suggest some of considerations for developing anti-shoulder surfing security solution.

关键词： Mobile communication Keyboards Layout Authentication Mobile handsets Sensors

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A Continuous Second-Order Sliding Mode Guidance against Highly Maneuverable Targets

A Continuous Second-Order Sliding Mode Guidance against High...

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The Society of Instrument and control Engineers Annual Conference 2015

作者： WANG Yongkun LIU Zhongxin SUN Mingwei CHEN Zengqiang Department of Automation Nankai University Tianjin Key Laboratory of Intelligent Robotics College of Computer and Control Engineering Nankai University

ISBN: (纸本)9784907764487

Conventional proportional navigation or augmented proportional navigation guidance laws are widely used for intercepting maneuverable targets;however, they are effective only in intercepting a target with a constant velocity or a known acceleration. In this paper, a continuous second-order sliding mode guidance law is designed to intercept a highly maneuverable target in connection with the super-twisting algorithm. Meanwhile, the target maneuver information, regarded as unpredictable disturbance, is coped with by an extended state observer(ESO) and then is compensated in the guidance law. In the proposed guidance scheme, no information about target motion is needed. The closed-loop error can be guaranteed to be bounded by using Lyapunov method. The simulation results demonstrate the effectiveness and robustness of the proposed guidance method against a highly maneuverable target.

关键词： guidance law second-order sliding mode super-twisting algorithm extended state observer target maneuver

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Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

arXiv

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Distributed subgradient algorithm for multi-agent optimization with directed communication topology

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IFAC-PapersOnLine 2015年第28期48卷 863-868页

作者： YIN, Yanhui LIU, Zhongxin CHEN, Zengqiang College of Computer and Control Engineering Nankai University Tianjin300071 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300071 China College of Science Civil Aviation University of China Tianjin300300 China

This paper studies a distributed subgradient algorithm in directed graphs. In contrast to previous work the problem is considered when the weighted adjacency matrices are not doubly stochastic. First the paper shows that an agreement can be reached in general directed graphs, but the global optimal function may not be minimized. Then some knowledge about homogeneous Markov chains is used to analyze the transition matrices and a new update rule is proposed to ensure that the a lgorithm converges to the optimal set for the case when the topology of graphs is fixed and known to all agents. For switching topology the paper establishes the relationship between the optimal results and the limit vector sequence. The paper provides explicit proof for the results and stimulation research validates the effectiveness. © 2015

关键词： Topology Chains Directed graphs Graphic methods Markov processes Multi agent systems Stochastic systems Adjacency matrices Communication topologies Distributed optimization Doubly stochastic Homogeneous markov chains Sub gradient algorithm Switching topology Transition matrices

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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Gene Selection for Cancer Classification Using Improved Group Lasso

Gene Selection for Cancer Classification Using Improved Grou...

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第28届中国控制与决策会议

作者： Juntao Li Wenpeng Dong Deyuan Meng Huimin Xiao Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control School of Mathematics and Information ScienceHenan Normal University The Seventh Research Division Beihang University(BUAA) College of Computer and Information Engineering Henan University of Economics and Law

ISBN: (纸本)9781467397155

An improved group lasso is proposed for simultaneous cancer classification and gene selection.A new criterion is firstly proposed to evaluate the individual gene importance by using the conditional mutual *** the weights with biological explanation are constructed and the improved group lasso is presented.A blockwise descent algorithm for solving the proposed model is also *** experimental results on lung cancer and prostate cancer data sets demonstrate that the proposed method can effectively perform classification and gene selection.

关键词： Cancer classification Gene selection Conditional mutual information Group lasso

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control laws development for a free-flying unmanned robotic system to support interplanetary bodies prospecting and characterization missions

Control laws development for a free-flying unmanned robotic ...

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AIAA Guidance, Navigation, and control Conference, 2016

作者： Perez, Andres E. Moncayo, Hever Prazenica, Richard J. Zacny, Kris Mueller, Robert P. Ebert, Tom Dupuis, Michael Flight Dynamics and Control Research Laboratory Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Aerospace Engineering Department Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Exploration Technology Group Honneybee Robotics Company CA United States Exploration Research and Technology Programs Science and Technology Projects Division NASA Kennedy Space Center FL United States Autonomous Flight Systems Lead Testing and Design Branch NASA Kennedy Space Center FL United States Robotics and Autonomous Systems Testing and Design Branch NASA Kennedy Space Center FL United States

ISBN: (数字)9781624103896

ISBN: (纸本)9781624103896

In situ Resource Utilization (ISRU) facilitates planetary exploration by drawing needed resources, such as water, from the local environment. However, the extreme nature of these environments require the development of advanced unmanned space systems integrated with sample-capture devices to achieve the ultimate goal of prospecting these resources. This paper presents the design, development and Hardware-in-the-Loop (HIL) simulation testing of guidance and tracking control laws for an autonomous small marsupial free-flyer prospector system. The control laws are based on an extended non-linear dynamic inversion (NLDI) approach and its implementation is illustrated through HIL simulation using a mathematical model of an autonomous vehicle research platform developed by NASA Kennedy Space Center. This vehicle has been designed to support the development, testing and validation of algorithms for safe, reliable, and scalable control space missions with minimal need for human intervention in complex, unstructured environments. The main objective of the control laws is to minimize 3-axis distances with respect to a desired trajectory and maintain stability and adequate performance in the presence of uncertainties. The performance of the control laws is evaluated during autonomous flight in terms of trajectory tracking errors, real-time execution on board the flight computer, and control activity at nominal and dynamically-changing conditions. The results show that for all mission cases investigated the control laws approach has desirable capabilities and is reliable for in-flight testing operation as a next step towards the validation and verification of this configuration. © 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hardware-in-the-loop simulation

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Robustness of controlling edge dynamics in complex networks against node failure

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Physical Review E 2016年第5期94卷 052310-052310页

作者： Shao-Peng Pang Fei Hao Wen-Xu Wang The Seventh Research Division School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China School of Systems Science Beijing Normal University Beijing 100875 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA

The robustness of controlling complex networks is significant in network science. In this paper, we focus on evaluating and analyzing the robustness of controlling edge dynamics in complex networks against node failure. Using three categories of all nodes to quantify the robustness, we find that the percentages of the three types of nodes are mainly related to the degree distribution of networks. The simulation results of model networks and analytic calculations show that the sparse inhomogeneous networks, which emerge in many real complex networks, have strong control robustness from the point of the number of ordinary nodes, but the strong positive correlation between in and out degrees reduces the control robustness. Evaluation of real-world networks indicates that most of them have few or no critical nodes, that is, they do not need to increase driver nodes to maintain control for most of node failures. Then an adding circuit-link strategy is proposed to optimize the robustness of edge controllability.

关键词： Degree correlations Degree distributions Network optimization Robustness Directed networks Real world networks

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