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Correction to: Multivariate statistical and trend analyses of surface water quality in the central Indian River Lagoon area, Florida

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Environmental Earth sciences 2018年第5期77卷 1-1页

作者： Sayem Zaman Ming Ye Fan Zhang Mingming Zhu Department of Scientific Computing Florida State University Tallahassee USA Draper Aden Associates Richmond USA School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China Department of Earth Ocean and Atmospheric Science Florida State University Tallahassee USA Key Laboratory of Tibetan Environmental Changes and Land Surface Processes Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Center of Hydraulic Engineering Regulation and Management Jinzhou Waterwork Office Jinzhou China

Unfortunately, the online version of the original article has an error in authors’ names.

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The medical segmentation decathlon

arXiv

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal 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 Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

arXiv

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arXiv 2023年

作者： Jha, Debesh Sharma, Vanshali Banik, Debapriya Bhattacharya, Debayan Roy, Kaushiki Hicks, Steven A. Tomar, Nikhil Kumar Thambawita, Vajira Krenzer, Adrian Ji, Ge-Peng Poudel, Sahadev Batchkala, George Alam, Saruar Ahmed, Awadelrahman M.A. Trinh, Quoc-Huy Khan, Zeshan Nguyen, Tien-Phat Shrestha, Shruti Nathan, Sabari Gwak, Jeonghwan Jha, Ritika K. Zhang, Zheyuan Schlaefer, Alexander Bhattacharjee, Debotosh Bhuyan, M.K. Das, Pradip K. Fan, Deng-Ping Parasa, Sravanthi Ali, Sharib Riegler, Michael A. Halvorsen, Pål de Lange, Thomas Bagci, Ulas Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States SimulaMet Oslo Norway Oslo Metropolitan University Oslo Norway Jadavpur University Kolkata India Indian Institute of Technology Guwahati India Institute of Medical Technology and Intelligent Systems Technische Universität Hamburg Germany University of Oslo Norway Julius-Maximilian University of Würzburg Germany Faculty of Information Technology University of Science VNU-HCM Viet Nam Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of School of Computing University of Leeds LeedsLS2 9JT United Kingdom College of Engineering Australian National University Canberra Australia Department of Engineering Science University of Oxford Oxford United Kingdom National University of Computer and Emerging Sciences Karachi Campus Pakistan Swedish Medical Center Seattle United States Kathmandu Nepal Couger Inc Tokyo Japan Department of Software Korea National University of Transportation Chungju Korea Republic of University of Bergen Bergen Norway Department of Medicine and Emergencies Mölndal Sahlgrenska University Hospital Region Västra Götaland Sweden Department of Molecular and Clinical Medicin Sahlgrenska Academy University of Gothenburg Sweden ETH Zurich Zurich Switzerland

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a

关键词： computer aided diagnosis

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AIM 2020 challenge on efficient super-resolution: Methods and results

arXiv

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arXiv 2020年

作者： Zhang, Kai Danelljan, Martin Li, Yawei Timofte, Radu Liu, Jie Tang, Jie Wu, Gangshan Zhu, Yu He, Xiangyu Xu, Wenjie Li, Chenghua Leng, Cong Cheng, Jian Wu, Guangyang Wang, Wenyi Liu, Xiaohong Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao Luo, Xiaotong Chen, Liang Zhang, Jiangtao Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Li, Xiaochuan Lang, Zhiqiang Nie, Jiangtao Wei, Wei Zhang, Lei Muqeet, Abdul Hwang, Jiwon Yang, Subin Kang, JungHeum Bae, Sungho Kim, Yongwoo Chen, Liang Zhang, Jiangtao Luo, Xiaotong Qu, Yanyun Jeon, Geun-Woo Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Marty, Steven Marty, Eric Xiong, Dongliang Chen, Siang Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Wang, Haicheng Bhaskara, Vineeth Levinshtein, Alex Tsogkas, Stavros Jepson, Allan Kong, Xiangzhen Zhao, Tongtong Zhao, Shanshan Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Nan, Nan Liu, Shuai Cai, Jie Meng, Zibo Ding, Jiaming Ho, Chiu Man Wang, Xuehui Yan, Qiong Zhao, Yuzhi Chen, Long Zhang, Jiangtao Luo, Xiaotong Chen, Liang Qu, Yanyun Sun, Long Wang, Wenhao Liu, Zhenbing Lan, Rushi Umer, Rao Muhammad Micheloni, Christian Computer Vision Lab ETH Zurich Switzerland State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China MAICRO China University of Electronic Science and Technology of China China McMaster University Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Xiamen University China Indian Institute of Technology Madras India Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science Northwestern Polytechnical University China Kyung Hee University Korea Republic of Sang Myung University Korea Republic of Xianmen University China Yonsei University Korea Republic of ETH Zurich Switzerland Zhejiang University China Qingdao Hi-image Technologies Co. Ltd [Hisense Visual Technology Co.Ltd. China Xidian University China Samsung AI Centre Toronto Canada Dalian Maritime University China China Everbright Bank Co. Ltd China College of Engineering Trivandrum India InnoPeak Technology Inc. United States SenseTime Research Hong Kong Sun Yat-sen University China City University of Hong Kong Hong Kong Guilin University of Electronic Technology Guilin541004 China University of Udine Italy

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Environmentally safe layered crystal produced from hazardous chemical elements: TlPb2BrI4, a new promising detector material

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Journal of Alloys and Compounds 2022年 924卷 1页

作者： Khyzhun, O.Y. Vu, Tuan V. Parasyuk, O.V. Fedorchuk, A.O. Fochuk, P.M. Lavrentyev, A.A. Gabrelian, B.V. Levchuk, Ievgen Matt, Gebhard J. Tedde, Sandro F. Schmidt, Oliver Shrestha, Shreetu Brabec, Christoph J. Kityk, I.V. Piasecki, M. Frantsevych Institute for Problems of Materials Science National Academy of Sciences of Ukraine 3 Krzhyzhanovsky Street KyivUA-03142 Ukraine Faculty of Mechanical - Electrical and Computer Engineering School of Engineering and Technology Van Lang University Ho Chi Minh City Viet Nam Department of Inorganic and Physical Chemistry Lesya Ukrainka Volyn National University 13 Voli Avenue LutskUA-43025 Ukraine Department of Inorganic and Organic Chemistry Lviv National University of Veterinary Medicine and Biotechnologies 50 Pekarska Street Lviv79010 Ukraine Yuriy Fed'kovych Chernivtsi National University 2 Kotziubynskoho Street Chernivtsi58012 Ukraine Department of Electrical Engineering and Electronics Don State Technical University 1 Gagarin Square Rostov-on-Don344010 Russia Department of Computational Technique and Automated System Software Don State Technical University 1 Gagarin Square Rostov-on-Don344010 Russia Martensstrasse 7 Erlangen91058 Germany Siemens Healthineers Technology Center Guenther-Scharowsky-Str. 1 Erlangen91058 Germany Electrical Engineering Department Czestochowa University Technology Armii Krajowej 17 Częstochowa42217 Poland Departmentof Experimental Physics and Information-measuring Technology Lesya Ukrainka Volyn National University Voli Avenue 13 Lutsk43025 Ukraine J.Dlugosz University Częstochowa Armii Krajowej 13/15 Częstochowa42200 Poland

Exploring better Gamma, X-Ray and IR-Mid-R detectors, adequately sensitive to ambient temperatures and environmental conditions, is highly desirable. Therefore, following this path, we present detecting opportunities, based on data of experimental and theoretical investigation of the electronic and optical properties, photocurrent behavior at different bias voltages as well as the reflection for the He-Cd cw laser after irradiation by three different IR laser wavelengths, for recently grown TlPb2BrI4 single crystal with strong anisotropic, two-dimensional (2D) layered structure. The X-ray photoelectron spectroscopy brings data on binding energy values of core levels of TlPb2BrI4 for both as-grown and Ar+-ion-treated crystal surfaces as well as for elucidation of the energy distribution of the valence electronic states. In opposite to well-known IR-detector materials, TlPb2BrI4 crystal surface shows high chemical stability concerning Ar+-ion bombardment. Further, we carry out ab initio band-structure calculations to gain curves of total and partial densities of states and to elucidate the principal optical constants of TlPb2BrI4. The present studies indicate that, in spite of the fact that the TlPb2BrI4 crystal contains two very hazardous/toxic chemical elements, thallium and lead;however, it is chemically stable and reveals very low hygroscopic behavior when being exposed to environmental conditions. © 2022 Elsevier B.V.

关键词： X ray photoelectron spectroscopy

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Correction to: Geometric correction method for Tibetan woodcut document images

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Multimedia Tools and Applications 2022年第11期81卷 15633-15633页

作者： Hu, Jian Xiawu, Lijia Qiao, Shaojie Tan, Wenrong Yin, Feng Liu, Tao Han, Nan Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest MinZu University Chengdu China Minzu Language’s Information Processing Laboratory Southwest MinZu University Chengdu China School of Computer Science and Technology Southwest MinZu University Chengdu China Southwest Nationalities Research Academy Southwest MinZu University Chengdu China School of Software Engineering Chengdu University of Information Technology Chengdu China Automatic Software Generation and Intelligence Service Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China Library Southwest MinZu University Chengdu China School of Management Chengdu University of Information Technology Chengdu China

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在线同步输入纸质与电子表单的方法

在线同步输入纸质与电子表单的方法

引用

第十二届中国虚拟现实大会

作者： Xiong Jinquan 熊金泉 Fan Yinting 樊银亭 Teng Dongxing 滕东兴 Ma Cuixia 马翠霞 Dai Guozhong 戴国忠 Wang Hongan 王宏安 Department of Computer Jiangxi Institute of Education Nanchang 330029 江西教育学院数学与计算机科学系南昌 330029 School of Computer Science Zhong yuan University of Technology Zhengzhou 450007 Intelligence Engi 中原工学院计算机学院郑州 450007 中国科学院软件研究所人机交互技术与智能信息处理实验室北京 100190 Intelligence Engineering Laboratory Institute of Software Chinese Academy of Science Beijing 10 中国科学院软件研究所人机交互技术与智能信息处理实验室北京 100190

针对用数字笔输入纸质表单时相对应的电子表单目标定位不准确、识别率不高，且用户在纸和显示器上频繁切换，降低了用户的交互体验的问题，提出一种纸质表单和电子表单的在线同步输入方法。首先对笔迹信息进行误差补偿，并结合上下文和... 详细信息

针对用数字笔输入纸质表单时相对应的电子表单目标定位不准确、识别率不高，且用户在纸和显示器上频繁切换，降低了用户的交互体验的问题，提出一种纸质表单和电子表单的在线同步输入方法。首先对笔迹信息进行误差补偿，并结合上下文和知识库，对目标单元格进行智能性定位;然后用基于统计语言模型和词库的识别算法改进识别器，提高输入识别率，并能把识别结果反馈到词库及误差校正模块中，以提高系统的自适应性。该方法是通过建立基于纸质和电子表单的在线同步输入框架--FSIF而实现的，其中框架包括笔信息校正转换器、智能定位器、笔迹分割器和自适应识别器4大部分，并按照一定方法和流程分别对笔信息进行采集、校正、转换、定位、分割、分类和识别等;最后通过实验对文中方法进行评估。实验结果表明，文中方法对笔式同步输入纸质和电子表单有一定的改进作用。

关键词：信息处理电子表单纸质表单数据同步

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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Sup1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 Kings County Hospital Center Brooklyn NY 11203 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

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THE U.S. COAST GUARD MULTI‐MISSION CUTTER: COMMAND, DISPLAY, & CONTROL (COMDAC)

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Naval Engineers Journal 1980年第5期92卷 59-70页

作者： HALL, JON W. ANDERSON, MICHAEL D. USCG LCdr. Jon W. Hall USCGenlisted in the U.S. Coast Guard in 1960 and subsequently served in a number of High Endurance Cutters as an Electronics Technician. In 1969. LCdr. Hall was commissioned an Ensign. U.S. Coast Guard. upon graduation from the Coast Guard Officer Candidate School Yorktown. Va.His first assignment as a commissioned officer was in theUSCGC Hamiltonas a Deck Watch Officer and Electronics Material Officer. He then served as the High Endurance Cutter Representative for Electronics in the First Coast Guard District. Boston Mass. and later at Coast Guard Headquarters in Washington D.C. In June 1978. he was assigned to the New Construction Section of Electronics Engineering. located in Coast Guard Headquarters. as a 270-foot WMEC Project Officer. Currently he is the Chief. New Cosntruction Section responsible for overseeing the development and installation of the 270-foot WMEC electronics package. LCdr. Hall is a graduate of Wentworth Institute Boston. Mass. and has an associate degree in Electronics Technology. Lt.(j. g.) Michael D. Anderson USCGgraduated from the U.S. Coast Guard Academy. New London Conn. in June 1976. with a Bachelor's degree in Computer Science and Electrical Engineering. He served in theUSCGC Madronaas Deck Watch Officer. Communications Officer and Electronics Officer. In August 1977 he was assigned to the Electronics Engineering Division U.S. Coast Guard Headquarters. and until October 1978 held the Type Desk for Electronics Installations in Coast Guard Buoy Tenders Tugs and River Tenders. He is presently assigned to the Software Section. Electronics Engineering Division which is responsible for the procurement of the operational software for the 270-foot WMEC. Lt.(j. g.) Anderson currently is pursuing his Master's degree in Computer Science at The George Washington University.

The U.S. Coast Guard has undertaken a ship construction program to replace Cutters which are nearing 30 to 40 years of service. These replacement ships, classed as the 270‐foot Medium Endurance Cutters (MEC), are intended for multi mission roles to satisfy both Coast Guard and Navy requirements. To achieve the multi mission posture, and to realize a minimal manning level, an automated approach to employ the principles of a command, communication, and control system was Selected. Since the design limitations (size and weight) for the 270‐foot WMEC were stringent, the Coast Guard designed a Command, Display, and Control System (COMDAC) which incorporated the features of previous automation efforts by the U.S. Coast Guard and Navy. COMDAC's uniqueness lies in its ability to receive data, to process and format it in accordance with predetermined doctrine (Navy and Coast Guard), and then to display it at any of several locations. The use of COMDAC provides for the reduction of Plotters and Status Board Keepers and also allows for the NTDS interface to accomplish the various Navy missions. This paper will address COMDAC's utilization as a command, communication, and control system as applied to the Coast Guard and Navy missions. © 1980 American Society of Naval Engineers

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Correction to: Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach

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BMC neuroscience 2022年第1期23卷 14页

作者： Kang-Han Oh Il-Seok Oh Uyanga Tsogt Jie Shen Woo-Sung Kim Congcong Liu Nam-In Kang Keon-Hak Lee Jing Sui Sung-Wan Kim Young-Chul Chung Department of Computer and Software Engineering Wonkwang University Iksan 54538 Korea. Department of Computer Science and Engineering Jeonbuk National University Jeonju Korea. Department of Psychiatry Jeonbuk National University Medical School Geonjiro 20 Jeonju Korea. Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeon- buk National University Hospital Jeonju Korea. Department of Psychiatry Maeumsarang Hospital Wanju Jeollabuk-do Korea. Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China. University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing 100049 China. Department of Psychiatry Chonnam National University Medical School Gwangju Republic of Korea. Department of Psychiatry Jeonbuk National University Medical School Geonjiro 20 Jeonju Korea. chungyc@jbnu.ac.kr. Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeon- buk National University Hospital Jeonju Korea. chungyc@jbnu.ac.kr.

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