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arXiv 2018年

作者： Fontan, Angela Shi, Guodong Hu, Xiaoming Altafini, Claudio Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden Research School of Engineering Australian National University CanberraACT0200 Australia Department of Optimization and Systems Theory Royal Institute of Technology StockholmSE-100 44 Sweden Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

The constrained consensus problem considered in this paper, denoted interval consensus, is characterized by the fact that each agent can impose a lower and upper bound on the achievable consensus value. Such constraints can be encoded in the consensus dynamics by saturating the values that an agent transmits to its neighboring nodes. We show in the paper that when the intersection of the intervals imposed by the agents is nonempty, the resulting constrained consensus problem must converge to a common value inside that intersection. In our algorithm, convergence happens in a fully distributed manner, and without need of sharing any information on the individual constraining intervals. When the intersection of the intervals is an empty set, the intrinsic nonlinearity of the network dynamics raises new challenges in understanding the node state evolution. Using Brouwer fixed-point theorem we prove that in that case there exists at least one equilibrium, and in fact the possible equilibria are locally stable if the constraints are satisfied or dissatisfied at the same time among all nodes. For graphs with sufficient sparsity it is further proven that there is a unique equilibrium that is globally attractive if the constraint intervals are pairwise disjoint. Copyright © 2018, The Authors. All rights reserved.

关键词： Fixed point arithmetic

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Stability and Neimark-Sacker bifurcation analysis for single gene discrete system with delay

Stability and Neimark-Sacker bifurcation analysis for single...

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Chinese control Conference

作者： Feng Liu Qicheng Mei Fenglan Sun Xinmei Wang Hua O. Wang Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Automation China University of Geosciences Wuhan China Research Center of System Theory and Application Chongqing University of Posts and Telecommunications Chongqing China Department of Mechanical Engineering Boston University Boston MA USA

The network structure and functional properties of gene regulatory system and their relationships arc an important research field in systems biology. In this paper, a new improved model is proposed based on the study of gene expression process. The model takes into account the effect of protein concentration on the gene expression, so as to obtain a new bifurcation point and improve the performance of the system. The validity of the model is verified by theoretical analysis and data simulation.

关键词： Mathematical model Proteins Stability analysis Bifurcation Biological system modeling Delays RNA

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Bifurcation analysis and control of quorum sensing network model 5

Bifurcation analysis and control of quorum sensing network m...

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5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017

作者： Mei, Qicheng Liu, Feng Ren, Jie Sun, Fenglan Wang, Hua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Research Center of System Theory and Application Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Mechanical Engineering Boston University BostonMA02215 United States

In this paper, a model is constructed to investigate the mechanism induced quorum sensing (QS) by coexisting of small RNA and signal molecule. The QS network model regulated by small RNAs is studied. The condition of the stability of the QS network is considered by using Routh-Hurwitz stability criterion. The conditions for the existence of Hopf bifurcation are obtained. Then, a linear feedback control is designed to make the system asymptotically stable. The numerical simulation is used to verify the results of theoretical analysis.

关键词： Hopf bifurcation

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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control on a molecular scale: A perspective

Control on a molecular scale: A perspective

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American control Conference

作者： Rolf Findeisen Martha A. Grover Christian Wagner Michael Maiworm Ruslan Temirov F. Stefan Tautz Murti V. Salapaka Srinivasa Salapaka Richard D. Braatz S.O. Reza Moheimani Otto-von-Guericke-Universit?t Magdeburg Laboratory for Systems Theory and Automatic Control Germany School of Chemical & Biomolecular Engineering Georgia Institute of Technology USA Peter Gruenberg Institute (PGI-3) Juelich Research Center Germany Electrical and Computer Engineering Department University of Minnesota USA Massachusetts Institute of Technology USA Department of Mechanical Engineering The University of Texas at Dalla USA

ISBN: (纸本)9781467386838

Imaging and manipulating objects down to the molecular level plays a crucial role in many disciplines. It allows to unravel molecular phenomena, to form materials with new chemical and physical properties, or to build objects on an atomic scale. Science on the nanoscale is inherently interdisciplinary. It requires knowledge and insight into many fields, spanning from modeling, measuring, imaging, actuation to control. Often feedback control facilitates operation with molecular precision, despite the fact that many physical phenomena at the molecular level are still not well understood, and that stochastic and nonlinear effects are inherently present. This work provides an insight into some of the current control related research activities on a molecular scale. This is done considering examples from different fields: control related to scanning probe microscopy such as atomic force microscopy, controlled self assembly on a molecular scale, control aspects of molecular transport, and the use of control for manipulation of single molecules using macroscopic probe tips. The hope is that control related researchers, who are not experts in these fields, become aware of the opportunities present, which could also drive new theoretical developments.

关键词： Computational modeling Mathematical model Force Atomic force microscopy Numerical models Computational modeling Atomic Force Microscopy numerical models Mathematical Model Force Standing Orders Scale formation Insight Transport Process Physical Phenomena Scanning Probe Microscopy Hopefulness research Activities chemical and physical properties feedback control

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Pose synchronization of rigid body networks with switching topologies

Pose synchronization of rigid body networks with switching t...

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第三十四届中国控制会议

作者： Juan Deng Wenjun Song Zhixin Liu John S.Baras Key Laboratory of Systems and Control Academy of Mathematics and Systems Science CAS Division of Optimization and Systems Theory KTH Royal Institute of Technology Electrical Engineering Department and the Institute for Systems Research University of Maryland College Park

ISBN: (纸本)9781467374439

Coordination control of multiple rigid bodies attracts much attention of researchers due to its wide applications. This paper presents the pose synchronization problem of the moving rigid bodies whose dynamics is described by the group SE(3).The case of bidirectional neighbor graphs with switching interconnection topologies is considered. We design a distributed control law based on relative rotation matrices and relative positions between the neighboring rigid bodies, and show that the SE(3)reaches pose synchronization if and only if the neighbor graphs are infinitely jointly connected, which relaxes the theoretical results in the existing literature.

关键词： SE(3),pose synchronization,coordination control,infinitely joint connectivity

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Fast-slow analysis for parametrically and externally excited systems with two slow rationally related excitation frequencies

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Physical Review E 2015年第1期92卷 012911-012911页

作者： Xiujing Han Qinsheng Bi Peng Ji Jürgen Kurths Faculty of Civil Engineering and Mechanics Jiangsu University Zhenjiang Jiangsu 212013 People's Republic of China Potsdam Institute for Climate Impact Research Potsdam 14473 Germany Department of Physics Humboldt University Berlin 12489 Germany Institute for Complex Systems and Mathematical Biology University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Control Theory Nizhny Novgorod State University Gagarin Avenue 23 606950 Nizhny Novgorod Russia

We present a general method for analyzing mixed-mode oscillations (MMOs) in parametrically and externally excited systems with two low excitation frequencies (PEESTLEFs) for the case of arbitrary m:n relation between the slow frequencies of excitations. The validity of the approach has been demonstrated using the equations of Duffing and van der Pol, separately. Our study shows that, by introducing a slow variable and finding the relation between the slow variable and the slow excitations, PEESTLEFs can be transformed into a fast-slow form with a single slow variable and therefore MMOs observed in PEESTLEFs can be understood by the classical machinery of fast subsystem analysis of the transformed fast-slow system.

关键词： OSCILLATIONS research DYNAMICAL systems HOPF bifurcations FREQUENCIES of oscillating systems VAN der Pol oscillators (Physics) DUFFING oscillators EXCITATION spectrum

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Emergence of amplitude and oscillation death in identical coupled oscillators

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Physical Review E 2014年第3期90卷 032906-032906页

作者： Wei Zou D. V. Senthilkumar Jinqiao Duan Jürgen Kurths School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Center for Mathematical Sciences Huazhong University of Science and Technology Wuhan 430074 China Center for Nonlinear Science and Engineering School of Electrical and Electronics Engineering SASTRA University Thanjavur 613401 India Potsdam Institute for Climate Impact Research Telegraphenberg Potsdam D-14415 Germany Institute of Physics Humboldt University Berlin Berlin D-12489 Germany Institute for Complex Systems and Mathematical Biology University of Aberdeen Aberdeen AB24 3FX United Kingdom Department of Control Theory Nizhny Novgorod State University Gagarin Avenue 23 606950 Nizhny Novgorod Russia

We deduce rigorous conditions for the onset of amplitude death (AD) and oscillation death (OD) in a system of identical coupled paradigmatic Stuart-Landau oscillators. A nonscalar coupling and high frequency are beneficial for the onset of AD. In strong contrast, scalar diffusive coupling and low intrinsic frequency are in favor of the emergence of OD. Our finding contributes to clearly distinguish intrinsic geneses for AD and OD, and further substantially corroborates that AD and OD are indeed two dynamically distinct oscillation quenching phenomena due to distinctly different mechanisms.

关键词： NONLINEAR oscillators GENES CELLULAR signal transduction CELL differentiation BROKEN symmetry (Physics)

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