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npj Complexity 2024年第1期1卷 1-8页

作者： Cristian Axenie Oliver López-Corona Michail A. Makridis Meisam Akbarzadeh Matteo Saveriano Alexandru Stancu Jeffrey West Department of Computer Science and Center for Artificial Intelligence Nuremberg Institute of Technology Georg Simon Ohm Nuremberg Germany Investigadores por México (IxM) at Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) Universidad Nacional Autónoma de México (UNAM) Ciudad Universitaria CDMX Mexico Institute for Transport Planning and Systems (IVT) ETH Zurich Switzerland Department of Transportation Engineering Isfahan University of Technology Isfahan Iran Department of Industrial Engineering University of Trento Trento Italy Department of Electrical and Electronic Engineering The University of Manchester Manchester UK Department of Integrated Mathematical Oncology H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA

Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system’s output response to input variability. systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems’ antifragility. We frame this review within three scales common to technical systems: intrinsic (input–output nonlinearity), inherited (extrinsic environmental signals), and induced (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility–adaptiveness–resilience–robustness–antifragility, the principles behind it, and its practical implications.

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NECESSARY AND SUFFICIENT CONDITIONS FOR DEADLOCKS IN FLEXIBLE MANUFACTURING systems BASED ON A DIGRAPH MODEL

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Asian Journal of Control 2008年第2期6卷

作者： Wenle Zhang Robert P. Judd Paul Deering School of Electrical Engineering and Computer Science Ohio University Athens OH 45701. Dr. Judd has over twenty years of experience as an educator researcher and consultant in system simulation discrete event controls and systems integration. Dr. Judd has received over thirty research grants totaling over $3500000. His funding has come from both industrial and governmental sources. He is the author or co-author of over a hundred published papers in journals and conferences. He also wrote a book chapter devoted to integrating manufacturing design tools and edited a special issue of the International Journal of Computer Integrated Manufacturing dedicated to intelligent manufacturing systems. Dr. Judd holds a patent on simulation software integration. Russ College of Engineering and Technology Ohio University Athens OH 45701.

As typical discrete event systems, flexible manufacturing systems have been extensively studied in such aspects as modeling, control and performance analysis. One important topic in the study of such systems is the deadlock detection, prevention and avoidance. In the past decade, two major modeling formalisms, i.e., Petri nets and digraphs, have been adopted for developing deadlock control policies for flexible manufacturing systems. In this paper, the concepts of slack, knot, order and effective free space of circuits in the digraph are established and used to concisely and precisely quantify the sufficient conditions for a system state to be live. Necessary conditions for this liveness is quantified for a special class of system states - called evaluation states. The significance of the result is that the conditions are true for avoiding both primary deadlocks and impending deadlocks that are arbitrary steps away from a primary one, whereas only second level deadlocks have been studied in the literature. Examples are provided to illustrate the method.

关键词： Flexible manufacturing systems deadlock avoidance deadlock prevention petri net digraph

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Construction of KAGRA: An underground gravitational wave observatory

arXiv

引用

arXiv 2017年

作者： Akutsu, T. Ando, M. Araki, S. Araya, A. Arima, T. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Baiotti, L. Barton, M.A. Chen, D. Cho, K. Craig, K. DeSalvo, R. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujibayashi, S. Fujii, Y. Fujimoto, M.-K. Fukushima, M. Furuhata, T. Hagiwara, A. Haino, S. Harita, S. Hasegawa, K. Hasegawa, M. Hashino, K. Hayama, K. Hirata, N. Hirose, E. Ikenoue, B. Inoue, Y. Ioka, K. Ishizaki, H. Itoh, Y. Jia, D. Kagawa, T. Kaji, T. Kajita, T. Kakizaki, M. Kakuhata, H. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kasuya, J. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawamura, S. Kawazoe, F. Kim, C. Kim, J. Kim, J.C. Kim, W. Kimura, N. Kitaoka, Y. Kobayashi, K. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kubo, K. Kumar, R. Kume, T. Kuroda, K. Kuwahara, Y. Lee, H.-K. Lee, H.-W. Lin, C.-Y. Liu, Y. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsumoto, N. Matsushima, F. Michimura, Y. Mio, N. Miyakawa, O. Miyake, K. Miyamoto, A. Miyamoto, T. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Muraki, Y. Murakoshi, M. Musha, M. Nagano, K. Nagano, S. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakano, M. Nakao, H. Nakao, K. Narikawa, T. Ni, W.-T. Nonomura, T. Obuchi, Y. Oh, J.J. Oh, S.-H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohmae, N. Okino, K. Okutomi, K. Ono, K. Ono, Y. Oohara, K. Ota, S. Park, J. Peña Arellano, F.E. Pinto, I.M. Principe, M. Sago, N. Saijo, M. Saito, T. Saito, Y. Saitou, S. Sakai, K. Sakakibara, Y. Sasaki, Y. Sato, S. Sato, T. Sato, Y. Sekiguchi, T. Sekiguchi, Y. Shibata, M. Shiga, K. Shikano, Y. Shimoda, T. Shinkai, H. Shoda, A. Someya, N. Somiya, K. Son, E.J. Starecki, T. Suemasa, A. Sugimoto, Y. Susa, Y. Suwabe, H. Suzuki, T. Tachibana, Y. Tagoshi, H. Takada, S. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. National Astronomical Observatory of Japan Tokyo181-8588 Japan Graduate School of Science University of Tokyo Tokyo113-0033 Japan Department of Physics Graduate School of Science University of Tokyo Tokyo113-0033 Japan Ibaraki305-0801 Japan Earthquake Research Institute University of Tokyo Tokyo113-0032 Japan Department of Physics Graduate School of Science Osaka City University Osaka558-8585 Japan Graduate School of Science and Technology Hirosaki University Aomori036-8561 Japan Department of Physics Graduate School of Science and Engineering Tokyo Institute of Technology Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Chiba277-8582 Japan Institute for Cosmic Ray Research University of Tokyo Gifu506-1205 Japan Graduate School of Science Osaka University Osaka560-0043 Japan Department of Physics Sogang University Seoul121-742 Korea Republic of University of Sannio at Benevento BeneventoI-82100 Italy Sezione di Napoli NapoliI-80126 Italy University of Toyama Toyama930-8555 Japan Department of Physics Graduate School of Science Kyoto University Kyoto606-8502 Japan Institute of Physics Academia Sinica Taipei11529 Taiwan University of Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan Albert-Einstein-Institut Max-Planck-Institut für Gravitationsphysik HannoverD-30167 Germany Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of Daejeon34055 Korea Republic of Department of Physics Myongji University Yongin449-728 Korea Republic of Department of Computer Simulation Inje University Gimhae-shi50834 Korea Republic of Daejeon34047 Korea Republic of Department of Physical Science Graduate School of Science Hiroshima University Hiroshima903-0213 Japan Department of Applied Physics Graduate School of Science Fukuoka University Fukuoka814-0180 Japan Graduate School of Science and Engineer

Major construction and initial-phase operation of a second-generation gravitational-wave detector KAGRA has been completed. The entire 3-km detector is installed underground in a mine in order to be isolated from background seismic vibrations on the surface. This allows us to achieve a good sensitivity at low frequencies and high stability of the detector. Bare-bones equipment for the interferometer operation has been installed and the first test run was accomplished in March and April of 2016 with a rather simple configuration. The initial configuration of KAGRA is named iKAGRA. In this paper, we summarize the construction of KAGRA, including the study of the advantages and challenges of building an underground detector and the operation of the iKAGRA interferometer together with the geophysics interferometer that has been constructed in the same tunnel. Copyright © 2017, The Authors. All rights reserved.

关键词： Interferometers

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The Mimo Predictive Pid Controller Design

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Asian Journal of Control 2008年第4期4卷

作者： M.H. Moradi M.R. Katebi M.A. Johnson Department of Electronic and Electrical Engineering University of Strath-clyde Glasgow GI 1QE UK. Dr. Moradi is a Lecturer in the Department of Electronic and Electrical Engineering University of Bu-Ali Sina in Iran. He obtained the BSc and MSc in 1991 and 1993 from the Sharif University of Technology and Tarbiat Modarres University respectively. Dr. Moradi joined the Bu-Ali Sina University in 1993. In 2002 he obtained his PhD degree from the University of Strath-clyde Glasgow Scotland. His theoretical research interests include predictive PID control advanced classical control generalised predictive control system identification and fault monitoring robust control design computer networks and more recently control through networks. His industrial interests are in the areas of large-scale systems especially power systems and power plant modelling and control supervisory control and process control. Dr. Moradi has published a number of Journal and Conference papers. Dr. Katebi is a senior lecturer in the Department of Electronic and Electrical Engineering University of Strathclyde. His theoretical research interests are currently focused on Non-linear Control and Filtering for Complex Systems Autonomous Control Design System integration and Fault monitoring and Reconfiguration Plant Monitoring through the Internet Discrete Event Simulation Process Optimisation and Robust Control Design. The industrial research interests are in the area of Hot and Rolling for Steel and Aluminium Power Plant Modelling and Control Marine Control Systems Process Control and Wastewater Treatment Control. Dr Katebi is the author/co-author of four books and more than 150 papers and industrial reports. Professor Johnson's academic education began at Coventry University (1969) and continued under the supervision of Professor Greyham Bryant in the field of Control Systems at Imperial College London. He obtained the DIC MSc and PhD from Imperial College leaving for industry in 1978. Subsequent experience was in the

This paper is concerned with the design of Multi-Inputs and Multi-Outputs (MIMO) predictive PID controllers, which have similar performance to that obtainable from model-based predictive controllers. A new PID control structure is defined which incorporates the prediction of future outputs and uses future set point. A method is proposed to calculate the optimal values of the PID gains from generalised predictive control results. A decentralized version of the predictive PID controllers is presented and the stability of the closed loop system is studied. Simulation studies demonstrate the superior performance of the proposed controller compared with a conventional PID controller. The results are also compared with generalised predictive control solutions.

关键词： PID control design predictive PID MIMO controller optimisation

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Citizen science and the United Nations Sustainable Development Goals (vol 2, pg 922, 2019)

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NATURE SUSTAINABILITY 2019年第11期2卷 1063-1063页

作者： Fritz, Steffen See, Linda Carlson, Tyler Haklay, Mordechai (Muki) Oliver, Jessie L. Fraisl, Dilek Mondardini, Rosy Brocklehurst, Martin Shanley, Lea A. Schade, Sven Wehn, Uta Abrate, Tommaso Anstee, Janet Arnold, Stephan Billot, Matthew Campbell, Jillian Espey, Jessica Gold, Margaret Hager, Gerid He, Shan Hepburn, Libby Hsu, Angel Long, Deborah Maso, Joan McCallum, Ian Muniafu, Maina Moorthy, Inian Obersteiner, Michael Parker, Alison J. Weisspflug, Maike West, Sarah Ecosystem Services and Management Program International Institute for Applied Systems Analysis Laxenburg Austria School of Resource and Environmental Management Simon Fraser University Burnaby Canada University College London London UK School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia Australian Citizen Science Association c/o Faculty of Science Partner Engagement and Outreach University of Sydney Sydney Australia University of Natural Resources and Life Sciences Vienna Austria Competence Center Citizen Science ETH Zurich and University of Zurich Zurich Switzerland Citizen Science Global Partnership Washington DC USA European Citizen Science Association Museum für Naturkunde Berlin Berlin Germany Nelson Institute University of Wisconsin at Madison Madison USA European Commission Joint Research Center Ispra Italy IHE Delft Institute for Water Education Delft The Netherlands World Meteorological Organization Geneva Switzerland Commonwealth Scientific and Industrial Research Organisation Canberra Australia Federal Statistical Office – Destatis Wiesbaden Germany UN Environment Programme Nairobi Kenya United Nations Sustainable Development Solutions Network New York USA CitizenScience.Asia Hong Kong China Yale-NUS College Singapore Singapore University of Dundee Dundee UK Scottish Environment LINK Perth UK Grumets Research Group CREAF Universitat Autònoma de Barcelona Barcelona Spain African Citizen Science Association Nairobi Kenya Woodrow Wilson Center Washington DC USA Museum für Naturkunde Berlin Berlin Germany Stockholm Environment Institute University of York York UK

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution.

关键词： Planning

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MARKOV DECISION PROCESSES WITH UNCERTAIN TRANSITION RATES: SENSITIVITY AND MAX HYPHEN MIN CONTROL

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Asian Journal of Control 2008年第2期6卷

作者： Suresh Kalyanasundaram Edwin K. P. Chong Ness B. Shroff Motorola India Electronics Limited No. 66/1 Plot 5 Bagmane Techpark C. V. Raman Nagar Post Bangalore 560 093 India. Department of Electrical and Computer Engineering Colorado State University Fort Collins CO 80523-1373 USA. Professor Edwin K. P. Chong received the B.E.(Hons.) degree with First Class Honors from the University of Adelaide South Australia in 1987 graduating top of his class and the M.A. and Ph.D. degrees in 1989 and 1991 respectively both from Princeton University where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991 where he was named a University Faculty Scholar in 1999 and promoted to Full Professor in 2001. Since August 2001 he has been a Professor of Electrical and Computer Engineering and Professor of Mathematics at Colorado State University. His current interests are in communication networks and optimization methods. He coauthored the best-selling book An Introduction to Optimization 2nd Edition Wiley-Interscience 2001. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He coauthored a paper that was awarded Best Paper in the journal Computer Networks 2003. Professor Chong is a Fellow of the IEEE. He was founding chairman of the IEEE Control Systems Society Technical Committee on Discrete Event Systems and until recently served as an IEEE Control Systems Society Distinguished Lecturer. He has been on the editorial board of the IEEE Transactions on Automatic Control. He is currently on the editorial board of the journal Computer Networks. He has also served on the organizing committees of several international conferences. He has been on the program committees for the IEEE Conference on Decision and Control the American Control Conference the IEEE International Symposium on Intelligent Control IEEE Symposium on Computers and Communications and the IEEE Global Telecommunications Conference. He has also served in the executive committees for the IEEE Co

Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain transition rates represented as compact sets. We first consider the problem of sensitivity analysis where the aim is to quantify the range of uncertainty of the average per-unit-time reward given the range of uncertainty of the transition rates. We then develop solution techniques for the problem of obtaining the max-min optimal policy, which maximizes the worst-case average per-unit-time reward. In each of these problems, we distinguish between systems that can have their transition rates chosen independently and those where the transition rates depend on each other. Our solution techniques are applicable to Markov decision processes with fixed but unknown transition rates and to those with time-varying transition rates.

关键词： Markov decision processes sensitivity max-min control call admission control policy iteration

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

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BMC NEUROSCIENCE 2017年第Sup1期18卷 1-79页

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

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International Society for Therapeutic Ultrasound Conference 2016

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Journal of Therapeutic Ultrasound 2017年第1期5卷 1-114页

作者： Brian Fowlkes Pejman Ghanouni Narendra Sanghvi Constantin Coussios Paul C. Lyon Michael Gray Christophoros Mannaris Marie de Saint Victor Eleanor Stride Robin Cleveland Robert Carlisle Feng Wu Mark Middleton Fergus Gleeson Jean-Franҫois Aubry Kim Butts Pauly Chrit Moonen Jacob Vortman Shirley Sharabi Dianne Daniels David Last David Guez Yoav Levy Alexander Volovick Javier Grinfeld Itay Rachmilevich Talia Amar Zion Zibly Yael Mardor Sagi Harnof Michael Plaksin Yoni Weissler Shy Shoham Eitan Kimmel Omer Naor Nairouz Farah Dong-Guk Paeng Zhiyuan Xu John Snell Anders H. Quigg Matthew Eames Changzhu Jin Ashli C. Everstine Jason P. Sheehan Beatriz S. Lopes Neal Kassell Thomas Looi Vera Khokhlova Charles Mougenot Kullervo Hynynen James Drake Michael Slayton Richard C. Amodei Keegan Compton Ashley McNelly Daniel Latt John Kearney David Melodelima Aurelien Dupre Yao Chen David Perol Jeremy Vincenot Jean-Yves Chapelon Michel Rivoire Wei Guo Guoxin Ren Guofeng Shen Michael Neidrauer Leonid Zubkov Michael S. Weingarten David J. Margolis Peter A. Lewin Nathan McDannold Jonathan Sutton Natalia Vykhodtseva Margaret Livingstone Thiele Kobus Yong-Zhi Zhang Michael Schwartz Yuexi Huang Nir Lipsman Jennifer Jain Martin Chapman Tejas Sankar Andres Lozano Robert Yeung Christakis Damianou Nikolaos Papadopoulos Omer Brokman Eyal Zadicario Ori Brenner David Castel Shih-Ying Wu Julien Grondin Wenlan Zheng Marc Heidmann Maria Eleni Karakatsani Carlos J. Sierra Sánchez Vincent Ferrera Elisa E. Konofagou Marinos Yiannakou HongSeok Cho Hwayoun Lee Mun Han Jong-Ryul Choi Taekwan Lee Sanghyun Ahn Yongmin Chang Juyoung Park Nicholas Ellens Ari Partanen Keyvan Farahani Raag Airan Alexandre Carpentier Michael Canney Alexandre Vignot Cyril Lafon Jean-yves Delattre Ahmed Idbaih Henrik Odéen Bradley Bolster Eun Kee Jeong Dennis L. Parker Pooja Gaur Xue Feng Samuel Fielden Craig Meyer Beat Werner William Grissom Michael Marx Hans Weber Valentina Taviani Brian Hargreaves Jun Tanaka Kentaro Kikuchi Ayumu Ishijima Takashi Azuma Kosuke Minamihata Satoshi Basic Radiological Sciences Division Department of Radiology University of Michigan Ann Arbor USA Stanford School of Medicine Stanford USA SonaCare Medical Indianapolis USA Institute of Biomedical Engineering University of Oxford Oxford UK HIFU Unit Churchill Hospital Oxford UK Department of Oncology University of Oxford Oxford UK Department of Radiology Churchill Hospital Oxford UK Institut Langevin Paris France Center for Imaging Sciences Imaging Division University Medical Center Utrecht The Netherlands InSighTec Haifa Israel Sheba Medical Center Ramat-Gan Israel Tel-Aviv University Tel-Aviv Israel Faculty of Biomedical Engineering & Russell Berrie Nanotechnology Institute Technion – Israel Institute of Technology Haifa Israel ELSC Center for Brain Sciences Hebrew University Jerusalem Israel Biomedical Engineering Technion- Israel Institute of Technology Haifa Israel Faculty of Life Sciences Bar Ilan University Ramat Gan Israel Focused Ultrasound Foundation Charlottesville USA Ocean System Engineering Jeju National University Jeju Republic of Korea Neurosurgery University of Virginia Charlottesville USA Biology University of Virginia Charlottesville USA Pathology University of Virginia Charlottesville USA Hospital for Sick Children Toronto Canada University of Toronto Toronto Canada University of Washington Seattle USA Philips Healthcare Markham Canada Sunnybrook Research Institute Toronto Canada Guided Therapy Systems LLC Mesa USA School of Medicine University of Arizona Tucson USA The CORE Institute Phoenix USA LabTAU - U1032 INSERM Lyon France Centre Leon Berard Lyon France Department of Oral Maxillofacial and Head Neck Oncology Shanghai 9th People’s Hospital Shanghai China The School of Biomedical Engineering Science and Health Systems Drexel University Philadelphia USA Department of Surgery College of Medicine Drexel University Philadelphia USA Biostatistics and Epidemiology University of Pennsylvania Philadelphi

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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Forecasting: theory and practice

arXiv

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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

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