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Optomechanical dissipative solitons (vol 600, pg 75, 2021)

NATURE 2022年第7896期602卷 E24-E24页

作者： Zhang, Jing Peng, Bo Kim, Seunghwi Monifi, Faraz Jiang, Xuefeng Li, Yihang Yu, Peng Liu, Lianqing Liu, Yu-xi Alu, Andrea Yang, Lan Department of Electrical and Systems Engineering Washington University St. Louis MO USA Department of Automation Tsinghua University Beijing P. R. China Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Physics Program Graduate Center City University of New York New York NY USA State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang P. R. China Institute of Microelectronics Tsinghua University Beijing P. R. China

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Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

Open X-Embodiment: Robotic Learning Datasets and RT-X Models...

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2024 IEEE International Conference on robotics and automation, ICRA 2024

作者： O'Neill, Abby Rehman, Abdul Maddukuri, Abhiram Gupta, Abhishek Padalkar, Abhishek Lee, Abraham Pooley, Acorn Gupta, Agrim Mandlekar, Ajay Jain, Ajinkya Tung, Albert Bewley, Alex Herzog, Alex Irpan, Alex Khazatsky, Alexander Rai, Anant Gupta, Anchit Wang, Andrew Singh, Anikait Garg, Animesh Kembhavi, Aniruddha Xie, Annie Brohan, Anthony Raffin, Antonin Sharma, Archit Yavary, Arefeh Jain, Arhan Balakrishna, Ashwin Wahid, Ayzaan Burgess-Limerick, Ben Kim, Beomjoon Scholkopf, Bernhard Wulfe, Blake Ichter, Brian Lu, Cewu Xu, Charles Le, Charlotte Finn, Chelsea Wang, Chen Xu, Chenfeng Chi, Cheng Huang, Chenguang Chan, Christine Agia, Christopher Pan, Chuer Fu, Chuyuan Devin, Coline Xu, Danfei Morton, Daniel Driess, Danny Chen, Daphne Pathak, Deepak Shah, Dhruv Buchler, Dieter Jayaraman, Dinesh Kalashnikov, Dmitry Sadigh, Dorsa Johns, Edward Foster, Ethan Liu, Fangchen Ceola, Federico Xia, Fei Zhao, Feiyu Stulp, Freek Zhou, Gaoyue Sukhatme, Gaurav S. Salhotra, Gautam Yan, Ge Feng, Gilbert Schiavi, Giulio Berseth, Glen Kahn, Gregory Wang, Guanzhi Su, Hao Fang, Hao-Shu Shi, Haochen Bao, Henghui Ben Amor, Heni Christensen, Henrik I Furuta, Hiroki Walke, Homer Fang, Hongjie Ha, Huy Mordatch, Igor Radosavovic, Ilija Leal, Isabel Liang, Jacky Abou-Chakra, Jad Kim, Jaehyung Drake, Jaimyn Peters, Jan Schneider, Jan Hsu, Jasmine Bohg, Jeannette Bingham, Jeffrey Wu, Jeffrey Gao, Jensen Hu, Jiaheng Wu, Jiajun Wu, Jialin Sun, Jiankai Luo, Jianlan Gu, Jiayuan Tan, Jie Oh, Jihoon Wu, Jimmy Lu, Jingpei Yang, Jingyun Malik, Jitendra Silverio, Joao Hejna, Joey Booher, Jonathan Tompson, Jonathan Yang, Jonathan Salvador, Jordi Lim, Joseph J. Han, Junhyek Wang, Kaiyuan Rao, Kanishka Pertsch, Karl Hausman, Karol Go, Keegan Gopalakrishnan, Keerthana Goldberg, Ken Byrne, Kendra Oslund, Kenneth Kawaharazuka, Kento Black, Kevin Lin, Kevin Zhang, Kevin Ehsani, Kiana Lekkala, Kiran Ellis, Kirsty Rana, Krishan Srinivasan, Krishnan Fang, Kuan Singh, Kunal Pratap Zeng, Kuo-Hao Hatch, Kyle Hsu, Kyle Itti, Laurent Chen, Lawrence Yunliang Pinto, Lerrel Allen Institute for AI United States Arizona State University United States California Institute of Technology United States Carnegie Mellon University United States Columbia University United States EPFL Switzerland ETH Zürich Switzerland Flexiv Robotics United States Georgia Institute of Technology United States German Aerospace Center Germany Google DeepMind United Kingdom Google Research United States IO-AI Tech Imperial College London United Kingdom Intrinsic Llc United States Istituto Italiano di Tecnologia Italy Korea Advanced Institute of Science & Technology Korea Republic of Max Planck Institute Germany Meta AI United States Mila Quebec Canada NVIDIA United States New York University United States Princeton University United States Queensland University of Technology Australia Riken Japan Shanghai Jiao Tong University China Stanford University United States Technische Universität Darmstadt Germany University of Texas at Austin United States University of Tokyo Japan Toyota Research Institute United States University of California Berkeley United States University of California Davis United States University of California San Diego United States University of Edinburgh United Kingdom University of Freiburg Germany University of Illinois Urbana-Champaign United States University of Michigan United States University of Montreal Canada University of Pennsylvania United States University of Southern California United States University of Technology Nuremberg Germany University of Washington United States

ISBN: (纸本)9798350384574

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist"X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is ***. © 2024 IEEE.

关键词： Robot learning

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Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

Open X-Embodiment: Robotic Learning Datasets and RT-X Models...

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IEEE International Conference on robotics and automation (ICRA)

作者： Abby O’Neill Abdul Rehman Abhiram Maddukuri Abhishek Gupta Abhishek Padalkar Abraham Lee Acorn Pooley Agrim Gupta Ajay Mandlekar Ajinkya Jain Albert Tung Alex Bewley Alex Herzog Alex Irpan Alexander Khazatsky Anant Rai Anchit Gupta Andrew Wang Anikait Singh Animesh Garg Aniruddha Kembhavi Annie Xie Anthony Brohan Antonin Raffin Archit Sharma Arefeh Yavary Arhan Jain Ashwin Balakrishna Ayzaan Wahid Ben Burgess-Limerick Beomjoon Kim Bernhard Schölkopf Blake Wulfe Brian Ichter Cewu Lu Charles Xu Charlotte Le Chelsea Finn Chen Wang Chenfeng Xu Cheng Chi Chenguang Huang Christine Chan Christopher Agia Chuer Pan Chuyuan Fu Coline Devin Danfei Xu Daniel Morton Danny Driess Daphne Chen Deepak Pathak Dhruv Shah Dieter Büchler Dinesh Jayaraman Dmitry Kalashnikov Dorsa Sadigh Edward Johns Ethan Foster Fangchen Liu Federico Ceola Fei Xia Feiyu Zhao Freek Stulp Gaoyue Zhou Gaurav S. Sukhatme Gautam Salhotra Ge Yan Gilbert Feng Giulio Schiavi Glen Berseth Gregory Kahn Guanzhi Wang Hao Su Hao-Shu Fang Haochen Shi Henghui Bao Heni Ben Amor Henrik I Christensen Hiroki Furuta Homer Walke Hongjie Fang Huy Ha Igor Mordatch Ilija Radosavovic Isabel Leal Jacky Liang Jad Abou-Chakra Jaehyung Kim Jaimyn Drake Jan Peters Jan Schneider Jasmine Hsu Jeannette Bohg Jeffrey Bingham Jeffrey Wu Jensen Gao Jiaheng Hu Jiajun Wu Jialin Wu Jiankai Sun Jianlan Luo Jiayuan Gu Jie Tan Jihoon Oh Jimmy Wu Jingpei Lu Jingyun Yang Jitendra Malik João Silvério Joey Hejna Jonathan Booher Jonathan Tompson Jonathan Yang Jordi Salvador Joseph J. Lim Junhyek Han Kaiyuan Wang Kanishka Rao Karl Pertsch Karol Hausman Keegan Go Keerthana Gopalakrishnan Ken Goldberg Kendra Byrne Kenneth Oslund Kento Kawaharazuka Kevin Black Kevin Lin Kevin Zhang Kiana Ehsani Kiran Lekkala Kirsty Ellis Krishan Rana Krishnan Srinivasan Kuan Fang Kunal Pratap Singh Kuo-Hao Zeng Kyle Hatch Kyle Hsu Laurent Itti Lawrence Yunliang Chen Lerrel Pinto Li Fei-Fei Liam Tan Linxi Jim Fan Lionel Ott Lisa Lee Luca Weihs Magnum Chen Marion Lepert Marius Memmel Masayoshi Tomizuka Masha Itkina Mateo University of California Berkeley University of Edinburgh University of Texas at Austin University of Washington German Aerospace Center Google DeepMind Stanford University NVIDIA Intrinsic LLC New York University Meta AI Georgia Institute of Technology Allen Institute for AI University of California Davis Toyota Research Institute Queensland University of Technology Korea Advanced Institute of Science & Technology Max Planck Institute Shanghai Jiao Tong University Flexiv Robotics Columbia University University of Freiburg Carnegie Mellon University University of Pennsylvania Imperial College London Istituto Italiano di Tecnologia IO-AI TECH University of Southern California University of California San Diego ETH Zürich University of Montreal Mila Quebec California Institute of Technology Arizona State University The University of Tokyo Technische Universität Darmstadt The University of Texas at Austin Google Research Princeton University EPFL University of Illinois Urbana-Champaign University of Michigan RIKEN University of Technology Nuremberg

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is ***.

关键词： Learning systems Adaptation models Computer vision Computational modeling Collaboration Data models Task analysis

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Preface——Special Issue on new Directions in Nonlinear and Distributed Control

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Journal of Systems Science & Complexity 2018年第1期31卷 1-3页

作者： LIU Tengfei XIN Bin JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing 100081 China Tandon School of Engineering New York University Brooklyn NY 11201 USA

Nonlinearity is ubiquitous in engineering and natural *** development of nonlinear control can be traced back to decades *** date,the research has reached the stage that emphasizes developing methodologies that can ha... 详细信息

关键词：控制问题非线性分布式工程应用程序电子物理自然系统生产线相互作用

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Open X-Embodiment: Robotic Learning Datasets and RT-X Models

arXiv

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arXiv 2023年

作者： O’Neill, Abby Rehman, Abdul Gupta, Abhinav Maddukuri, Abhiram Gupta, Abhishek Padalkar, Abhishek Lee, Abraham Pooley, Acorn Gupta, Agrim Mandlekar, Ajay Jain, Ajinkya Tung, Albert Bewley, Alex Herzog, Alex Irpan, Alex Khazatsky, Alexander Rai, Anant Gupta, Anchit Wang, Andrew Kolobov, Andrey Singh, Anikait Garg, Animesh Kembhavi, Aniruddha Xie, Annie Brohan, Anthony Raffin, Antonin Sharma, Archit Yavary, Arefeh Jain, Arhan Balakrishna, Ashwin Wahid, Ayzaan Burgess-Limerick, Ben Kim, Beomjoon Schölkopf, Bernhard Wulfe, Blake Ichter, Brian Lu, Cewu Xu, Charles Le, Charlotte Finn, Chelsea Wang, Chen Xu, Chenfeng Chi, Cheng Huang, Chenguang Chan, Christine Agia, Christopher Pan, Chuer Fu, Chuyuan Devin, Coline Xu, Danfei Morton, Daniel Driess, Danny Chen, Daphne Pathak, Deepak Shah, Dhruv Büchler, Dieter Jayaraman, Dinesh Kalashnikov, Dmitry Sadigh, Dorsa Johns, Edward Foster, Ethan Liu, Fangchen Ceola, Federico Xia, Fei Zhao, Feiyu Frujeri, Felipe Vieira Stulp, Freek Zhou, Gaoyue Sukhatme, Gaurav S. Salhotra, Gautam Yan, Ge Feng, Gilbert Schiavi, Giulio Berseth, Glen Kahn, Gregory Yang, Guangwen Wang, Guanzhi Su, Hao Fang, Hao-Shu Shi, Haochen Bao, Henghui Amor, Heni Ben Christensen, Henrik I. Furuta, Hiroki Bharadhwaj, Homanga Walke, Homer Fang, Hongjie Ha, Huy Mordatch, Igor Radosavovic, Ilija Leal, Isabel Liang, Jacky Abou-Chakra, Jad Kim, Jaehyung Drake, Jaimyn Peters, Jan Schneider, Jan Hsu, Jasmine Vakil, Jay Bohg, Jeannette Bingham, Jeffrey Wu, Jeffrey Gao, Jensen Hu, Jiaheng Wu, Jiajun Wu, Jialin Sun, Jiankai Luo, Jianlan Gu, Jiayuan Tan, Jie Oh, Jihoon Wu, Jimmy Lu, Jingpei Yang, Jingyun Malik, Jitendra Silvério, João Hejna, Joey Booher, Jonathan Tompson, Jonathan Yang, Jonathan Salvador, Jordi Lim, Joseph J. Han, Junhyek Wang, Kaiyuan Rao, Kanishka Pertsch, Karl Hausman, Karol Go, Keegan Gopalakrishnan, Keerthana Goldberg, Ken Byrne, Kendra Oslund, Kenneth Kawaharazuka, Kento Black, Kevin Lin, Kevin Zhang, Kevin Ehsani, Kiana Lekkala, Kiran Ellis, Kirsty Rana, Krishan Srinivasan, Krishnan Fang, Kuan Singh, Allen Institute for AI Arizona State University United States California Institute of Technology United States Carnegie Mellon University United States Columbia University United States EPFL Switzerland ETH Zürich Switzerland Flexiv Robotics United States Georgia Institute of Technology United States German Aerospace Center Germany Google DeepMind United Kingdom Google Research United States IO-AI TECH Imperial College London United Kingdom Intrinsic LLC United States Istituto Italiano di Tecnologia Italy Korea Advanced Institute of Science & Technology Korea Republic of Max Planck Institute Germany Meta AI Microsoft Research United States Mila QC Canada NVIDIA United States New York University United States Princeton University United States Queensland University of Technology Australia RIKEN Japan Shanghai Jiao Tong University China Stanford University United States Technische Universität Darmstadt Germany The University of Texas Austin United States The University of Tokyo Japan Toyota Research Institute United States Tsinghua University China University of California Berkeley United States University of California Davis United States University of California San Diego United States University of Edinburgh United Kingdom University of Freiburg Germany University of Illinois Urbana-Champaign United States University of Michigan United States University of Montreal Canada University of Pennsylvania United States University of Southern California United States University of Technology Nuremberg Germany University of Washington United States

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is ***. © 2023, CC BY.

关键词： Robots

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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Author Correction: A consensus protocol for functional connectivity analysis in the rat brain

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Nature neuroscience 2023年 2023 Apr 18页

作者： Joanes Grandjean Gabriel Desrosiers-Gregoire Cynthia Anckaerts Diego Angeles-Valdez Fadi Ayad David A Barrière Ines Blockx Aleksandra Bortel Margaret Broadwater Beatriz M Cardoso Marina Célestine Jorge E Chavez-Negrete Sangcheon Choi Emma Christiaen Perrin Clavijo Luis Colon-Perez Samuel Cramer Tolomeo Daniele Elaine Dempsey Yujian Diao Arno Doelemeyer David Dopfel Lenka Dvořáková Claudia Falfán-Melgoza Francisca F Fernandes Caitlin F Fowler Antonio Fuentes-Ibañez Clément M Garin Eveline Gelderman Carla E M Golden Chao C G Guo Marloes J A G Henckens Lauren A Hennessy Peter Herman Nita Hofwijks Corey Horien Tudor M Ionescu Jolyon Jones Johannes Kaesser Eugene Kim Henriette Lambers Alberto Lazari Sung-Ho Lee Amanda Lillywhite Yikang Liu Yanyan Y Liu Alejandra López-Castro Xavier López-Gil Zilu Ma Eilidh MacNicol Dan Madularu Francesca Mandino Sabina Marciano Matthew J McAuslan Patrick McCunn Alison McIntosh Xianzong Meng Lisa Meyer-Baese Stephan Missault Federico Moro Daphne M P Naessens Laura J Nava-Gomez Hiroi Nonaka Juan J Ortiz Jaakko Paasonen Lore M Peeters Mickaël Pereira Pablo D Perez Marjory Pompilus Malcolm Prior Rustam Rakhmatullin Henning M Reimann Jonathan Reinwald Rodrigo Triana Del Rio Alejandro Rivera-Olvera Daniel Ruiz-Pérez Gabriele Russo Tobias J Rutten Rie Ryoke Markus Sack Piergiorgio Salvan Basavaraju G Sanganahalli Aileen Schroeter Bhedita J Seewoo Erwan Selingue Aline Seuwen Bowen Shi Nikoloz Sirmpilatze Joanna A B Smith Corrie Smith Filip Sobczak Petteri J Stenroos Milou Straathof Sandra Strobelt Akira Sumiyoshi Kengo Takahashi Maria E Torres-García Raul Tudela Monica van den Berg Kajo van der Marel Aran T B van Hout Roberta Vertullo Benjamin Vidal Roël M Vrooman Victora X Wang Isabel Wank David J G Watson Ting Yin Yongzhi Zhang Stefan Zurbruegg Sophie Achard Sarael Alcauter Dorothee P Auer Emmanuel L Barbier Jürgen Baudewig Christian F Beckmann Nicolau Beckmann Guillaume J P C Becq Erwin L A Blezer Radu Bolbos Susann Boretius Sandrine Bouvard Eike Budinger Joseph D Buxbaum Diana Cash Victor Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada. Integrated Program in Neuroscience McGill University Montreal QC Canada. Bio-imaging Lab University of Antwerp Antwerp Belgium. µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium. Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico. Biological and Biomedical Engineering McGill University Montreal QC Canada. McConnell Brain Imaging Centre McGill University Montreal QC Canada. Montreal Neurological Institute McGill University Montreal QC Canada. UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France. Department of Neurology and Neurosurgery McGill University Montreal QC Canada. Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA. Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal. Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France. Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México. Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cyberneti

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UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on advanced Traffic Monitoring

UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Adva...

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IEEE Conference on advanced Video and Signal Based Surveillance (AVSS)

作者： Siwei Lyu Ming-Ching Chang Dawei Du Longyin Wen Honggang Qi Yuezun Li Yi Wei Lipeng Ke Tao Hu Marco Del Coco Pierluigi Carcagnì Dmitriy Anisimov Erik Bochinski Fabio Galasso Filiz Bunyak Guang Han Hao Ye Hong Wang Kannappan Palaniappan Koray Ozcan Li Wang Liang Wang Martin Lauer Nattachai Watcharapinchai Nenghui Song Noor M. Al-Shakarji Shuo Wang Sikandar Amin Sitapa Rujikietgumjorn Tatiana Khanova Thomas Sikora Tino Kutschbach Volker Eiselein Wei Tian Xiangyang Xue Xiaoyi Yu Yao Lu Yingbin Zheng Yongzhen Huang Yuqi Zhang University at Albany State University of New York Albany NY US University at Albany State University of New York USA University of the Chinese Academy of Sciences Beijing Beijing CN GE Global Research Niskayuna NY US National Research Council Italy Intel Nizhny Novgorod Russia Technische Universität Berlin Germany OSRAM GmbH Germany University of Missouri Columbia USA Nanjing University of Posts and Telecommunications Nanjing CN Shanghai Advanced Research Institute Shanghai Shanghai CN Shanghai Advanced Research Institute Chinese Academy of Sciences China Iowa State University USA Fudan University China Institute of Automation Chinese Academy of Sciences Beijing Beijing CN Karlsruhe Institute of Technology Germany National Electronics and Computer Technology Center Thailand OSRAM Licht AG Munchen Bayern DE Fudan University Shanghai Shanghai CN Nanjing University of Posts and Telecommunications China University of Washington Seattle WA US Institute of Automation Chinese Academy of Sciences China

The rapid advances of transportation infrastructure have led to a dramatic increase in the demand for smart systems capable of monitoring traffic and street safety. Fundamental to these applications are a community-based evaluation platform and benchmark for object detection and multi-object tracking. To this end, we organize the AVSS2017 Challenge on advanced Traffic Monitoring, in conjunction with the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S), to evaluate the state-of-the-art object detection and multi-object tracking algorithms in the relevance of traffic surveillance. Submitted algorithms are evaluated using the large-scale UA-DETRAC benchmark and evaluation protocol. The benchmark, the evaluation toolkit and the algorithm performance are publicly available from the website http://***.

关键词： Benchmark testing Surveillance Measurement Detectors Protocols

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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

引用

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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