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15th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016

作者： Melton, Niklas Wunsch, Donald C. Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9781509038466

A method for hybridizing supervised learning with adaptive dynamic programming was developed to increase the speed, quality, and robustness of on-line neural network learning from an imperfect teacher. Reinforcement learning is used to modify and enhance the original supervisory signal before learning occurs. This paper describes the method of hybridization and presents a model problem in which a human supervisor teaches a simulated car to drive around a race track. Simulation results show successful learning and improvements in convergence time, error rate, and stability over either component method alone. © 2016 IEEE.

关键词： Reinforcement learning

Simulation study of the influence of experimental variations on the structure and quality of plasma liners

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arXiv 2018年

作者： Shih, Wen Samulyak, Roman Hsu, Scott C. Langendorf, Samuel J. Yates, Kevin C. Thio, Y.C. Francis Department of Applied Mathematics and Statistics Stony Brook University Stony BrookNY11794 United States Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Physics Division Los Alamos National Laboratory Los AlamosNM87545 United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States HyperJet Fusion Corporation ChantillyVA20151 United States

Simulation studies of a section of a spherically imploding plasma liner, formed by the merger of six hypersonic plasma jets, have been performed at conditions relevant to the Plasma Liner Experiment (PLX) [S. C. Hsu et al., IEEE Trans. Plasma Sci. 46, 1951 (2018)]. The main aim of simulations was the sensitivity study of the detailed structure of plasma liners and their global properties to experimental mass variations and timing jitter across the six plasma jets. Experimentally observable synthetic quantities have been computed using simulation data and compared with the available experimental data. Simulations predicted that the primary oblique shock wave structure is preserved at small experimental variations. At later phases of the liner implosion, primary shocks and, especially, secondary shocks are more sensitive to experimental variations. These conclusions follow from the simulation data as well as comparisons between synthetic and experimental interferometry and visible images. Small displacements of the shock wave structures may cause significant changes in the synthetic interferometer data at early time. Our studies also showed that the global properties of the plasma liners (averaged Mach number and averaged ram pressure along leading edges of plasma liners) are not very sensitive to experimental variations. Simulation data of the liner structure were largely confirmed by the PLX experimental data. Copyright © 2018, The Authors. All rights reserved.

关键词： Plasma jets

Improvement of a multi-stage model for the modeling of a functionalized nursing bed as support for the sensor-assisted functionalization of furniture in the hospital and care sector

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Improvement of a multi-stage model for the modeling of a fun...

作者： Kitzig, Andreas Schröter, Stefan Naroska, Edwin Stockmanns, Gudrun Viga, Reinhard Grabmaier, Anton Niederrhein University of Applied Sciences Faculty of Electrical Engineering and Computer Science Ambient Intelligence Laboratory Reinarzstr. 49 Krefeld47805 Germany University of Duisburg-Essen Department of Electronic Components and Circuits Bismarkstr. 81 Duisburg47057 Germany 2 Finkenstraße 61 Duisburg47057 Germany

Development of preparation-free functionalized furniture based patient monitoring systems for use in the area of home- or stationary- care is often empirically driven. In particular, functionalization of furniture by means of different sensors is strongly affected by this development methodology. As a result, the systems are often not extensive-ly extendable or cannot be optimized because basic mechanisms are not comprehensible. In order to support development or optimization, a modelling approach is often useful. Thus, using a more comprehensive approach the required sensitivity of the sensors as well as their position in the system can be derived from a simulation model. In order to solve this problem, a multi-stage model was introduced at the BMT conference in 2014 by the authors, which allows the designer to model the entire system. The model has been extended and improved in the meantime and the achieved progress is presented in this work. The presented modelling approach can be divided into three main components. These are the person under supervision, the furniture (in our case a nursing bed) and the sensors (force measuring cells) which are modelled separately. In this work the main focus will be on improving the modelling of the human movement process and its implementation. Furthermore, the modelling of the sensor behavior in the nursing bed is described in detail with regard to their oscillation behavior and the influence on the model. © 2017 Andreas Kitzig et al. published by De Gruyter.

关键词： Nursing

Approximately groups in proximal relator spaces: An algebraic view of digital images

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arXiv 2017年

作者： Inan, Ebubekir Department of Mathematics Faculty of Arts and Sciences Adıyaman University Adıyaman Turkey Computational Intelligence Laboratory Department of Electrical & Computer Engineering University of Manitoba WinnipegMB Canada

The focus of this article is to define the descriptively approximations in proximal relator spaces. Afterwards, descriptive approximately algebraic structures such as groupoids, semigroups and groups in digital images endowed with descriptive Efremovic̆ proximity relations were *** Codes 08A05, 68Q32, 54E05 Copyright © 2017, The Authors. All rights reserved.

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Quantum simulators: Architectures and opportunities

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arXiv 2019年

作者： Altman, Ehud Brown, Kenneth R. Carleo, Giuseppe Carr, Lincoln D. Demler, Eugene Chin, Cheng DeMarco, Brian Economou, Sophia E. Eriksson, Mark A. Fu, Kai-Mei C. Greiner, Markus Hazzard, Kaden R.A. Hulet, Randall G. Kollár, Alicia J. Lev, Benjamin L. Lukin, Mikhail D. Ma, Ruichao Mi, Xiao Misra, Shashank Monroe, Christopher Murch, Kater Nazario, Zaira Ni, Kang-Kuen Potter, Andrew C. Roushan, Pedram Saffman, Mark Schleier-Smith, Monika Siddiqi, Irfan Simmonds, Raymond Singh, Meenakshi Spielman, I.B. Temme, Kristan Weiss, David S. Vučković, Jelena Vuletić, Vladan Ye, Jun Zwierlein, Martin Department of Physics University of California BerkeleyCA94720 United States Department of Electrical and Computer Engineering Department of Physics Department of Chemistry Duke University DurhamNC27708 United States Center for Computational Quantum Physics Flatiron Institute New YorkNY10010 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Physics Chicago University ChicagoIL60637 United States Department of Physics and IQUIST University of Illinois Urbana-ChampaignIL61801 United States Department of Physics Virgina Tech BlacksburgVA24061 United States Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Department of Electrical and Computer Engineering Department of Physics University of Washington SeattleWA98195 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Joint Quantum Institute Department of Physics University of Maryland College ParkMD20742 United States IonQ Inc. College ParkMD20740 United States Department of Physics Department of Applied Physics Stanford University StanfordCA94305 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Google Inc. Santa BarbaraCA93101 United States Sandia National Laboratory AlbuquerqueNM87123 United States Department of Physics Washington University St. LouisMO63130 United States IBM T.J. Watson Research Center Yorktown HeightsNY10598 United States Department of Chemistry and Chemical Biology Harvard University CambridgeMA02138 United States Department of Physics The University of Texas at Austin AustinTX78712 United States National Institute of Standards and Technology BoulderCO80305 United States Department of Physics Pennsylvania State University University ParkPA16802 United States Department of Electri

Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize entanglement and many-particle behaviors to explore and solve hard scientific, engineering, and computational problems. Rapid development over the last two decades has produced more than 300 quantum simulators in operation worldwide using a wide variety of experimental platforms. Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices. These developments have enabled a convergence of ideas drawn from fundamental physics, computer science, and device engineering. They have strong potential to address problems of societal importance, ranging from understanding vital chemical processes, to enabling the design of new materials with enhanced performance, to solving complex computational problems. It is the position of the community, as represented by participants of the NSF workshop on "Programmable Quantum Simulators," that investment in a national quantum simulator program is a high priority in order to accelerate the progress in this field and to result in the first practical applications of quantum machines. Such a program should address two areas of emphasis: (1) support for creating quantum simulator prototypes usable by the broader scientific community, complementary to the present universal quantum computer effort in industry;and (2) support for fundamental research carried out by a blend of multi-investigator, multi-disciplinary collaborations with resources for quantum simulator software, hardware, and education. Copyright © 2019, The Authors. All rights reserved.

关键词： Blending

Forecasting: theory and practice

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

Neuromatch academy: Teaching computational neuroscience with global accessibility

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arXiv 2020年

作者： van Viegen, Tara Akrami, Athena Bonnen, Kate DeWitt, Eric Hyafil, Alexandre Ledmyr, Helena Lindsay, Grace W. Mineault, Patrick Murray, John D. Pitkow, Xaq Puce, Aina Sedigh-Sarvestani, Madineh Stringer, Carsen Achakulvisut, Titipat Alikarami, Elnaz Atay, Melvin Selim Batty, Eleanor Erlich, Jeffrey C. Galbraith, Byron V. Guo, Yueqi Juavinett, Ashley L. Krause, Matthew R. Li, Songting Pachitariu, Marius Straley, Elizabeth Valeriani, Davide Vaughan, Emma Vaziri-Pashkam, Maryam Waskom, Michael L. Blohm, Gunnar Kording, Konrad Schrater, Paul Wyble, Brad Escola, Sean Peters, Megan A.K. Princeton Neuroscience Institute Princeton University PrincetonNJ United States Sainsbury Wellcome Centre University College London London United Kingdom Center for Neural Science New York University New York CityNY United States Champalimaud Research Champalimaud Foundation Lisbon Portugal Centre de Recerca Matemàtica Bellaterra Universitat Autònoma de Barcelona Barcelona Spain International Neuroinformatics Coordinating Facility Sweden Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre University College London London United Kingdom Department of Psychiatry Yale University New HavenCT United States Baylor College of Medicine Dept. of Neuroscience HoustonTX United States Baylor College of Medicine Center for Neuroscience and Artificial Intelligence HoustonTX United States Rice University Dept of Electrical and Computer Engineering HoustonTX United States Psychological & Brain Sciences Indiana University BloomingtonIN United States Max Planck Florida Institute for Neuroscience JupiterFL United States HHMI Janelia Research Campus AshburnVA United States University of Pennsylvania Dept of Neuroscience and Bioengineering PhiladelphiaPA United States Faculty of dentistry McGill University MontrealQB Canada Middle East Technical University Ankara Turkey Department of Neurobiology Harvard University CambridgeMA United States New York University Shanghai Shanghai China Talla Inc BostonMA United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD United States Division of Biological Sciences UC San Diego San diegoCA United States Montreal Neurological Institute McGill University MontrealQB Canada School of Mathematical Sciences MOE-LSC Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Department of Otolaryngology - Head & Neck Surgery Massachusetts Eye and Ear BostonMA United States Department of Otolaryngology - Head & Neck Surgery Harvard Medical School BostonM

Neuromatch Academy designed and ran a fully online 3-week computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility. © 2020, CC BY.

关键词： Python

An information theoretic ART for robust unsupervised learning

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An information theoretic ART for robust unsupervised learnin...

2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Da Silva, Leonardo Enzo Brito Wunsch, Donald C. Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States CAPES Foundation Ministry of Education of Brazil Brasília DF Brazil

ISBN: (纸本)9781509006199

In this paper, an information-theoretic-based adaptive resonance theory (IT-ART) neural network architecture is presented. Each IT-ART category is defined by the first and second order statistics (mean and covariance matrix) of the cluster or class it represents. This information is used to estimate probability density functions (multivariate Gaussians) and compute the activation functions. The match function of the vigilance check is based on Renyi's quadratic cross-entropy: it is the cross information potential. Experiments involving several real world and synthetic data sets were carried out to assess the performance of IT-ART, which was measured in terms of external validity indices. IT-ART expanded the range of successful vigilance parameter values in these tests. © 2016 IEEE.

关键词： Probability density function

Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

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NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria