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arXiv 2020年

作者： Soda, Paolo D’Amico, Natascha Claudia Tessadori, Jacopo Valbusa, Giovanni Guarrasi, Valerio Bortolotto, Chandra Akbar, Muhammad Usman Sicilia, Rosa Cordelli, Ermanno Fazzini, Deborah Cellina, Michaela Oliva, Giancarlo Callea, Giovanni Panella, Silvia Cariati, Maurizio Cozzi, Diletta Miele, Vittorio Stellato, Elvira Carrafiello, Gian Paolo Castorani, Giulia Simeone, Annalisa Preda, Lorenzo Iannello, Giulio Bue, Alessio Del Tedoldi, Fabio Alì, Marco Sona, Diego Papa, Sergio Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome00128 Italy Department of Diagnostic Imaging and Stereotactic Radiosurgey Centro Diagnostico Italiano S.p.A. Via S. Saint Bon 20 Milan20147 Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Via Morego 30 Genoa16163 Italy Bracco Imaging S.p.A. Via Caduti di Marcinelle 13 Milan20134 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Radiology Institute Fondazione IRCCS Policlinico San Matteo Viale Golgi 19 Pavia27100 Italy Department of Naval Electrical Electronic and Telecommunications Engineering - University of Genova Via All’Opera Pia 11 A Genoa16145 Italy Radiology Department ASST Fatebenefratelli Sacco Piazza Principessa Clotilde 3 Milan20121 Italy Diagnostic and interventional radiology unit ASST Santi Paolo e Carlo - San Paolo Hospital Via Antonio di Rudinì 8 Milan20142 Italy Department of Advanced Diagnostic Technologies - Therapeutic Diagnostic and Radiology Units ASST Santi Paolo e Carlo - San Paolo Hospital Via Antonio di Rudinì 8 Milan20142 Italy Department of Emergency Radiology Careggi University Hospital Largo Piero Palagi 1 Florence50139 Italy Operative Unit of Radiology Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan Via della Commenda 10 Milan20122 Italy Department of Health Sciences Univeristy of Milan Via Festa del Perdono 7 Milan20122 Italy Diagnostic Imaging Postgraduate Medical School University of Foggia Via Antonio Gramsci 89 Foggia71122 Italy Department of Diagnostic Imaging IRCCS Ospedale Casa Sollievo della Sofferenza Viale Cappuccini 1 San Giovanni Rotondo71013 Italy Radiology Unit Department of Clinical Surgical Diagnostic and Pediatric Sciences University of Pavia Corso Str. Nuova 65 Pavia27100 Italy Neuroinformatics Laboratory Fondazione Bru

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources. © 2020, CC BY-NC-ND.

关键词： Deep learning

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Modeling and analysis of secondary controlled virtual synchronous generator with dynamic droop for microgrid

Modeling and analysis of secondary controlled virtual synchr...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Weihang Yan David Wenzhong Gao Fang Zhang Lin Cheng Shijie Yan Department of Electrical and Computer Engineering University of Denver Denver USA State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China College of Information Science and Technology Northeastern University Shenyang China

ISBN: (纸本)9781538614280

In this paper, a dynamic droop mechanism is proposed to damp the synchronizing oscillation that exists in a virtual synchronous generator based distributed generator system without varying the steady-state active-power-loop droop coefficient. In order to eliminate the steady-state error introduced due to load changes or generator trips, a distributed secondary controller based on extremum seeking is employed so that microgrid frequency can be restored to nominal value. Besides, the small-signal model of virtual synchronous generator system is presented under d-q reference frame where virtual synchronous generator is deployed as a PV node. Moreover, the linearized system model of virtual synchronous generator with dynamic droop mechanism is derived and its advantage is discussed. Finally, a modified IEEE 14 bus microgrid system is established in Matlab/Simulink platform. Simulation results demonstrate the validity and feasibility of the proposed method.

关键词： Mathematical model Microgrids Synchronous generators Frequency control Inverters Power system stability Power system dynamics

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2018年 620卷 v-vi页

作者： Omatu, Sigeru Rodríguez, Sara Villarrubia, Gabriel Faria, Pedro Sitek, Pawel Prieto, Javier Faculty of Engineering Department of Electronics Information and Communication Engineering Osaka Institute of Technology OsakaOsaka Japan Faculty of Science Department of Computer Science and Automation Control University of Salamanca Salamanca Spain GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Instituto Superior de Engenharia do Porto Porto Portugal Faculty of Electrical Engineering and Computer Science Department of Control and Management Systems Department of Information Systems Kielce University of Technology Kielce Poland

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Fault diagnosis for multi-energy flows of energy internet: Framework and prospects

Fault diagnosis for multi-energy flows of energy internet: F...

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2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017

作者： Wang, Tao Wang, Jun Fan, Zhennan Wei, Xiaoguang Zang, Tianlei Pérez-Jiménez, Mario J. Huang, Tao School of Electrical Engineering and Electronic Information Sichuan Province Key Laboratory of Power Electronics Energy-saving Technologies and Equipment Key Laboratory of Fluid and Power Machinery Ministry of Education Xihua University Chengdu China School of Electrical Engineering Southwest Jiaotong University Chengdu China State Key Laboratory of Security Control and Simulation of Power Systems and Large Scale Generation Department of Electrical Engineering Tsinghua University Beijing China Research Groupon Natural Computing Department of Computer Science and Artificial Intelligence University of Sevilla Sevilla Spain Department of Energy Politecnico di Torino Torino Italy

ISBN: (纸本)9781538614273

Energy Internet (EI) is an inevitable development trend of energy systems under the background of technology development, environmental pressure and energy transition. Multi-energy flow coupling is one of the key characteristics of the EI, which enhances the interoperability of different types of energy flows while consequently increases the probability of cascading failures. Therefore it is of great significance to study the multi-energy flow fault diagnosis of the EI to ensure its safe and stable operation as well as the continuous energy supply. This paper introduces the concept of multi-energy flow cascading fault of the EI for the first time. The energy internet framework for multi-energy flow cascading fault diagnosis is firstly proposed, and then characteristics of various energy networks in the EI are analyzed from the perspective of fault diagnosis. Finally, future research prospects are discussed. © 2017 IEEE.

关键词： Failure analysis

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Estimation and measurement of closed-loop delays in the actual WACS of Guizhou Power Grid

Estimation and measurement of closed-loop delays in the actu...

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2016 IEEE Power and Energy Society General Meeting, PESGM 2016

作者： Zhang, Fang Cheng, Lin Li, Xiong Min, Zeng Sun, Yuanzhang Chow, Joe H. State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China School of Electrical Engineering Wuhan University China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

ISBN: (纸本)9781509041688

The characterization of the closed-loop delays in a wide-area control system (WACS) is an important foundation for further research. The method to estimate and measure the closed-loop delay in actual WACSs were proposed in this paper. Then, the closed-loop delays in the actual WACS in Guizhou Power Grid in China were measured and analyzed. Also, the measured delays were used to verify the normal distribution estimation model proposed in previous research. The results indicate that the closed-loop delay in the WACS followed a normal distribution. © 2016 IEEE.

关键词： Normal distribution

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Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

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Journal of Ambient Intelligence and Humanized Computing 2017年 1-11页

作者： Wang, Shui-Hua Muhammad, Khan Phillips, Preetha Dong, Zhengchao Zhang, Yu-Dong Jiangsu Key Laboratory of Advanced Manufacturing Technology HuaiyinJiangsu223003 China School of Computer Science and Technology Henan Polytechnic University JiaozuoHenan454000 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China College of Software Convergence Sejong University Seoul Korea Republic of Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Translational Imaging Division and MRI Unit Columbia University and New York State Psychiatric Institute New YorkNY10032 United States

Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches. © 2017 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Fractal dimension

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Event-triggered active-passive dynamic consensus filters

Event-triggered active-passive dynamic consensus filters

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American control Conference

作者： J. Daniel Peterson Tansel Yucelen S. Jagannathan Eduardo Pasiliao the Mechanical and Aerospace Engineering Department and a member of the Advanced Systems Research Laboratory at the Missouri University of Science and Technology Rolla MO 65409 USA the Mechanical Engineering Department and the Director of the Laboratory for Autonomy Control Information and Systems at the University of South Florida Tampa FL 33620 USA the William A. Rutledge Distinguished Professor of the Electrical and Computer Engineering Department and Director of the Embedded Control Systems and Networking Laboratory at the Missouri University of Science and Technology Rolla MO 65409 USA the Air Force Research Laboratory Munitions Directorate Eglin Air Force Base FL 32542 USA

ISBN: (纸本)9781509045839

Multiagent systems consist of groups of agents that exchange local information to achieve given system-level goals. Traditionally, these systems assumed that all agents have some knowledge (e.g., observations of a process of interest) to contribute in order to achieve a common goal of interest, which may not be the case in many scenarios. Motivated from this standpoint, a new class of multiagent systems, active-passive multiagent systems, were recently proposed, which consists of agents subject to observations of a process of interest (i.e., active agents) and agents without any observations (i.e., passive agents). However, in this class of multiagent systems, agents need to continuously exchange information among themselves, incurring a high cost of interagent information exchange, and requiring that agents synchronize their update times. To address these shortcomings, this paper presents a new class of event-triggered active-passive dynamic consensus filters, in which agents schedule information exchange dependent on errors exceeding user-defined thresholds, significantly reducing the overall cost of interagent information exchange, and allowing agents to determine when to broadcast their information to their neighbors thus eliminating the need to synchronize their states. An illustrative numerical example is presented to demonstrate the efficacy of our approach.

关键词： consist groups agents

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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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Clustering in block Markov chains

arXiv

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arXiv 2017年

作者： Sanders, Jaron Proutière, Alexandre Yun, Se-Young Kth Royal Institute of Technology School of Electrical Engineering Dept. of Automatic Control Osquldasväg 10 Stockholm10044 Sweden Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Dept. of Quantum and Computer Engineering Mekelweg 4 Delft2628CD Netherlands Korea Advanced Institute of Science and Technology Dept. of Industrial and Systems Engineering 291 Daehak-ro Eoeun-dong Yuseong-gu Korea Republic of

This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the n possible states are divided into a finite number of K groups or clusters, such that states in the same cluster exhibit the same transition rates to other states. One observes a trajectory of the Markov chain, and the objective is to recover, from this observation only, the (initially unknown) clusters. In this paper we devise a clustering procedure that accurately, efficiently, and provably detects the clusters. We first derive a fundamental information-theoretical lower bound on the detection error rate satisfied under any clustering algorithm. This bound identifies the parameters of the BMC, and trajectory lengths, for which it is possible to accurately detect the clusters. We next develop two clustering algorithms that can together accurately recover the cluster structure from the shortest possible trajectories, whenever the parameters allow detection. These algorithms thus reach the fundamental detectability limit, and are optimal in that sense. Copyright © 2017, The Authors. All rights reserved.

关键词： Asymptotic analysis

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