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arXiv 2018年

作者： Chen, Tianyi Zhang, Kaiqing Giannakis, Georgios B. Başar, Tamer Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering Digital Technology Center University of Minnesota MinneapolisMN55455 United States

This paper deals with distributed policy optimization in reinforcement learning, which involves a central controller and a group of learners. In particular, two typical settings encountered in several applications are considered: multi-agent reinforcement learning (RL) and parallel RL, where frequent information exchanges between the learners and the controller are required. For many practical distributed systems, however, the overhead caused by these frequent communication exchanges is considerable, and becomes the bottleneck of the overall performance. To address this challenge, a novel policy gradient approach is developed for solving distributed RL. The novel approach adaptively skips the policy gradient communication during iterations, and can reduce the communication overhead without degrading learning performance. It is established analytically that: i) the novel algorithm has convergence rate identical to that of the plain-vanilla policy gradient;while ii) if the distributed learners are heterogeneous in terms of their reward functions, the number of communication rounds needed to achieve a desirable learning accuracy is markedly reduced. Numerical experiments corroborate the communication reduction attained by the novel algorithm compared to alternatives. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Roadmap on spin-wave computing

arXiv

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arXiv 2021年

作者： Chumak, Andrii V. Kabos, P. Wu, M. Abert, C. Adelmann, C. Adeyeye, A. Åkerman, J. Aliev, F.G. Anane, A. Awad, A. Back, C.H. Barman, A. Bauer, G.E.W. Becherer, M. Beginin, E.N. Bittencourt, V.A.S.V. Blanter, Y.M. Bortolotti, P. Boventer, I. Bozhko, D.A. Bunyaev, S.A. Carmiggelt, J.J. Cheenikundil, R.R. Ciubotaru, F. Cotofana, S. Csaba, G. Dobrovolskiy, O.V. Dubs, C. Elyasi, M. Fripp, K.G. Fulara, H. Golovchanskiy, I.A. Gonzalez-Ballestero, C. Graczyk, P. Grundler, D. Gruszecki, P. Gubbiotti, G. Guslienko, K. Haldar, A. Hamdioui, S. Hertel, R. Hillebrands, B. Hioki, T. Houshang, A. Hu, C.-M. Huebl, H. Huth, M. Iacocca, E. Jungfleisch, M.B. Kakazei, G.N. Khitun, A. Khymyn, R. Kikkawa, T. Kläui, M. Klein, O. Klos, J.W. Knauer, S. Koraltan, S. Kostylev, M. Krawczyk, M. Krivorotov, I.N. Kruglyak, V.V. Lachance-Quirion, D. Ladak, S. Lebrun, R. Li, Y. Lindner, M. Macêdo, R. Mayr, S. Melkov, G.A. Mieszczak, S. Nakamura, Y. Nembach, H.T. Nikitin, A.A. Nikitov, S.A. Novosad, V. Otalora, J.A. Otani, Y. Papp, A. Pigeau, B. Pirro, P. Porod, W. Porrati, F. Qin, H. Rana, B. Reimann, T. Riente, F. Romero-Isart, O. Ross, A. Sadovnikov, A.V. Safin, A.R. Saitoh, E. Schmidt, G. Schultheiss, H. Schultheiss, K. Serga, A.A. Sharma, S. Shaw, J.M. Suess, D. Surzhenko, O. Szulc, K. Taniguchi, T. Urbánek, M. Usami, K. Ustinov, A.B. van der Sar, T. van Dijken, S. Vasyuchka, V.I. Verba, R. Viola Kusminskiy, S. Wang, Q. Weides, M. Weiler, M. Wintz, S. Wolski, S.P. Zhang, X. Faculty of Physics University of Vienna Boltzmanngasse 5 ViennaA-1090 Austria National Institute of Standards and Technology BoulderCO80305 United States Department of Physics Colorado State University Fort CollinsCO80523 United States Research Platform MMM Mathematics-Magnetism-Materials University of Vienna Oskar-Morgenstern-Platz 1 Vienna1090 Austria Imec Leuven3001 Belgium Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom Department of Physics University of Gothenburg Gothenburg412 96 Sweden Universidad Autónoma de Madrid Madrid28049 Spain Unité Mixte de Physique CNRS Thales Université Paris Saclay Palaiseau91767 France Department of Physics Technical University of Munich Garching85748 Germany Department of Condensed Matter Physics and Material Sciences S N Bose National Centre for Basic Sciences Salt Lake Kolkata700106 India Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Zernike Institute for Advanced Materials University of Groningen Netherlands Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Laboratory of Magnetic Metamaterials Saratov State University Saratov410012 Russia Max Planck Institute for the Science of Light Erlangen91058 Germany Department of Quantum Nanoscience Kavli Institute of Nanoscience Delft University of Technology Delft2628 CJ Netherlands Department of Physics and Energy Science University of Colorado Colorado Springs Colorado SpringsCO80918 United States Departamento de Física e Astronomia Universidade do Porto Porto4169-007 Portugal Institut de Physique et Chimie des Matériaux de Strasbourg CNRS Université de Strasbourg StrasbourgF-67000 France Department of Quantum and Computer Engineering Delft University of Technology Delft2628 CD Netherlands Faculty for Information Technology and Bionics Pazmany Peter Catholic University Prater u. 50/A BudapestH-1083 Hungary INNOVENT e.V.

Magnonics is a field of science that addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operations in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors that covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with Boolean digital data, unconventional approaches like neuromorphic computing, and the progress towards magnon-based quantum computing. The article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of the current challenges and the outlook of the further development of the research directions. © 2021, CC BY.

关键词： Spin waves

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Reinforcement learning meets minority game: Toward optimal resource allocation

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Physical Review E 2019年第3期99卷 032302-032302页

作者： Si-Ping Zhang Jia-Qi Dong Li Liu Zi-Gang Huang Liang Huang Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou 730000 China Institute of Theoretical Physics Key Laboratory of Theoretical Physics Chinese Academy of Sciences P.O. Box 2735 Beijing 100190 China School of Software Engineering Chongqing University Chongqing 400044 People's Republic of China School of Electrical Computer and Energy Engineering Department of Physics Arizona State University Tempe Arizona 85287 USA

The main point of this paper is to provide an affirmative answer through exploiting reinforcement learning (RL) in artificial intelligence (AI) for eliminating herding without any external control in complex resource allocation systems. In particular, we demonstrate that when agents are empowered with RL (e.g., the popular Q-learning algorithm in AI) in that they get familiar with the unknown game environment gradually and attempt to deliver the optimal actions to maximize the payoff, herding can effectively be eliminated. Furthermore, computations reveal the striking phenomenon that, regardless of the initial state, the system evolves persistently and relentlessly toward the optimal state in which all resources are used efficiently. However, the evolution process is not without interruptions: there are large fluctuations that occur but only intermittently in time. The statistical distribution of the time between two successive fluctuating events is found to depend on the parity of the evolution, i.e., whether the number of time steps in between is odd or even. We develop a physical analysis and derive mean-field equations to gain an understanding of these phenomena. Since AI is becoming increasingly widespread, we expect our RL empowered minority game system to have broad applications.

关键词： Collective dynamics Competition Self-organized systems Mean field theory

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年 1-12页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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Optical data transmission at 44 Terabits/s with a 49GHz Kerr soliton crystal microcomb

TechRxiv

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TechRxiv 2020年

作者： Tan, Mengxi Corcoran, Bill Xu, Xingyuan Wu, Jiayang Boes, Andreas Nguyen, Thach G. Chu, Sai T. Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi’an Institute of Optics and Precision Mechanics Precision Mechanics of CAS Xi’an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada

We report world record high data transmission over standard optical fiber from a single optical source. We achieve a line rate of 44.2 Terabits per second (Tb/s) employing only the C-band at 1550nm, resulting in a spectral efficiency of 10.4 bits/s/Hz. We use a new and powerful class of micro-comb called soliton crystals that exhibit robust operation and stable generation as well as a high intrinsic efficiency that, together with an extremely low spacing of 48.9 GHz enables a very high coherent data modulation format of 64 QAM. We achieve error free transmission across 75 km of standard optical fiber in the lab and over a field trial with a metropolitan optical fiber network. This work demonstrates the ability of optical micro-combs to exceed other approaches in performance for the most demanding practical optical communications applications. © 2020, CC BY.

关键词： Optical fibers

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Photonic RF and microwave channelizer based on a Kerr soliton crystal micro-comb

TechRxiv

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TechRxiv 2020年

作者： Tan, Mengxi Xu, Xingyuan Wu, Jiayang Boes, Andreas Corcoran, Bill Nguyen, Thach G. Chu, Sai T. Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Hong Kong Xi’an Institute of Optics and Precision Mechanics Precision Mechanics of CAS Xi’an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada

We report a 92 channel RF channelizer based on a 48.9 GHz integrated micro-comb that operates via soliton crystals, together with a passive high-Q ring resonator that acts as a periodic filter with an optical 3dB bandwidth of 121.4 MHz. We obtain an instant RF bandwidth of 8.08 GHz and 17.55 GHz achieved through temperature tuning. These results represent a major advance to achieving fully integrated photonic RF spectrum channelizers with reduced low complexity, size, and high performance for digital-compatible signal detection and broadband analog signal processing. © 2020, CC BY.

关键词： Microwave photonics

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Quantum metamaterial for nondestructive microwave photon counting

arXiv

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arXiv 2020年

作者： Grimsmo, Arne Løhre Royer, Baptiste Kreikebaum, John Mark Ye, Yufeng O'Brien, Kevin Siddiqi, Irfan Blais, Alexandre Institut quantique Départment de Physique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Centre for Engineered Quantum Systems School of Physics The University of Sydney SydneyNSW2006 Australia Department of Physics Yale University New HavenCT06520 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Quantum Nanoelectronics Laboratory Department of Physics University of California BerkeleyCA94720 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Canadian Institute for Advanced Research Toronto Canada

Detecting traveling photons is an essential primitive for many quantum information processing tasks. We introduce a single-photon detector design operating in the microwave domain, based on a weakly nonlinear metamaterial where the nonlinearity is provided by a large number of Josephson junctions. The combination of weak nonlinearity and large spatial extent circumvents well-known obstacles limiting approaches based on a localized Kerr medium. Using numerical many-body simulations we show that the single-photon detection fidelity increases with the length of the metamaterial to approach one at experimentally realistic lengths. A remarkable feature of the detector is that the metamaterial approach allows for a large detection bandwidth. In stark contrast to conventional photon detectors operating in the optical domain, the photon is not destroyed by the detection and the photon wavepacket is minimally disturbed. The detector design we introduce offers new possibilities for quantum information processing, quantum optics and metrology in the microwave frequency domain. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

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A flexible method for time-of-flight camera calibration using random forest 1st

A flexible method for time-of-flight camera calibration usin...

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1st International Conference on Smart Multimedia, ICSM 2018

作者： Xu, Chi Li, Cheng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Bioinformatics Institute A*STAR Singapore138671 Singapore Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (纸本)9783030043742

A learning based approach is proposed to calibrate the geometric distortion of a Time-of-Flight (ToF) camera. Our method is flexible as it requires only a ToF camera and a standard camera calibration chessboard. We treat the noise model of a ToF camera as a black box, and employ random forest to automatically learn the underlying unique noise model. The geometric property of the point-cloud can be effectively restored by the learned distortion model. The method can be used in a range of computer vision applications including e.g. hand pose estimation. © 2018, Springer Nature Switzerland AG.

关键词： Calibration

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Machine learning–based detection model of early Alzheimer's disease using wearable device and gait assessment

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Alzheimer's & Dementia 2021年第S5期17卷

作者： YoungHoon Jeon Thi Kieu Khanh Ho Jaeyong Kang Byeong C. Kim Kun Ho Lee Jong-In Song Jeonghwan Gwak School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju South Korea Department of Software Korea National University of Transportation Chungju South Korea Department of Neurology Chonnam National University Medical School Gwangju South Korea Department of Biomedical Science Chosun University Gwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Daegu South Korea Gwangju Alzheimer’s Disease and Related Dementia Cohort Research Center Chosun University Gwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Chungju South Korea Department of AI Robotics Engineering Korea National University of Transportation Chungju South Korea Department of Biomedical Engineering Korea National University of Transportation Chungju South Korea

Background A method for diagnosing early stages of Alzheimer's disease (AD) is developed by analyzing human gait and training a machine learning classifier using signals obtained from inertial measurement unit. This patient-friendly and cost-effective method enables periodic monitoring, contributing to savings in treatment costs and effort. Method 145 subjects (77 healthy controls, 68 Mild Cognitive Impairment) were employed to perform seven types of gait paradigms, including normal and complex walking. The gait of the employed subjects was measured by 7 shimmer devices, which consist of a three-dimensional accelerometer and gyroscope. The acquired signal data is converted into 35 features including gait cycle. The extracted features are used to train an ensemble classifier based on machine learning with an oversampling method. In order to find the optimal combination in the ensemble model, an elimination technique was used to compare performance by removing single classifiers one by one. For performance comparison, the dataset is divided into training set/test set by leave-one-patient-out cross-validation, and several metrics such as accuracy and F1 score are used. Result The classifier for MCI detection classified severity to an accuracy of about 0.7 for 145 subjects. The combination of oversampling techniques and different single classifiers contributes to improved performance. In the seven walking experimental paradigms, the accuracy of the classification of normal walking without subtasks is about 0.67. O the other hand, the classification of walking with associative word-speaking or counting numbers shows significant results with accuracy of 0.7413 and 0.7338, respectively. Conclusion Our proposed method predicts severity through efficient walking with less space-time constraints. Subtasks that stress the brain in walking contribute to the performance improvement of the ensemble model with oversampling. These results demonstrate that our proposed method with g

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