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arXiv 2019年

作者： Kitai, Koki Guo, Jiang Ju, Shenghong Tanaka, Shu Tsuda, Koji Shiomi, Junichiro Tamura, Ryo Graduate School of Frontier Sciences University of Tokyo Chiba277-8568 Japan Department of Mechanical Engineering University of Tokyo Tokyo113-8654 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki305-0047 Japan Green Computing Systems Research Organization Waseda University Tokyo162-0042 Japan JST PRESTO Saitama332-0012 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Ibaraki305-0044 Japan

Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed. Copyright © 2019, The Authors. All rights reserved.

关键词： Metamaterials

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Sysevr: A framework for using deep learning to detect software vulnerabilities

arXiv

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Jin, Hai Zhu, Yawei Chen, Zhaoxuan Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by many vulnerabilities reported on a daily basis. This calls for machine learning methods to automate vulnerability detection. Deep learning is attractive for this purpose because it does not require human experts to manually define features. Despite the tremendous success of deep learning in other domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the products. Copyright © 2018, The Authors. All rights reserved.

关键词： Syntactics

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Interference-free transceiver design and signal detection for ambient backscatter communication systems over frequency-selective channels

arXiv

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arXiv 2018年

作者： Zhang, Chong Wang, Gongpu Diamantoulakis, Panagiotis D. Gao, Feifei Karagiannidis, George K. Beijing Intelligent Logistics System Collaborative Innovation Center School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing100084 China Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece

In this letter, we study the ambient backscatter communication systems over frequency-selective channels. Specifically, we propose an interference-free transceiver design to facilitate signal detection at the reader. Our design utilizes the cyclic prefix (CP) of orthogonal frequency-division multiplexing (OFDM) source symbols, which can cancel the signal interference and thus enhance the detection accuracy at the reader. Meanwhile, our design results in no interference on the existing OFDM communication systems. We also suggest a maximum likelihood (ML) detector for the reader and derive two detection thresholds. Simulations are then provided to corroborate our proposed studies. Copyright © 2018, The Authors. All rights reserved.

关键词： Backscattering

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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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Host galaxy magnitude of OJ 287 from its colours at minimum light

arXiv

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arXiv 2022年

作者： Valtonen, Mauri J. Dey, Lankeswar Zola, S. Ciprini, S. Kidger, M. Pursimo, T. Gopakumar, A. Matsumoto, K. Sadakane, K. Caton, D.B. Nilsson, K. Komossa, S. Bagaglia, M. Baransky, A. Boumis, P. Boyd, D. Castro-Tirado, A.J. Debski, B. Drozdz, M. Escartin Pérez, A. Fiorucci, M. Garcia, F. Gazeas, K. Ghosh, S. Godunova, V. Gomez, J.L. Gredel, R. Grupe, D. Haislip, J.B. Henning, T. Hurst, G. Janík, J. Kouprianov, V.V. Lehto, H. Liakos, A. Mathur, S. Mugrauer, M. Naves Nogues, R. Nucciarelli, G. Ogloza, W. Ojha, D.K. Pajdosz-Śmierciak, U. Pascolini, S. Poyner, G. Reichart, D.E. Rizzi, N. Roncella, F. Sahu, D.K. Sillanpää, A. Simon, A. Siwak, M. Soldán Alfaro, F.C. Sonbas, E. Tosti, G. Vasylenko, V. Webb, J.R. Zielinski, P. Finnish Centre for Astronomy with ESO University of Turku Finland Tuorla Observatory Department of Physics and Astronomy University of Turku Finland Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Mumbai400005 India Astronomical Observatory Jagiellonian University ul. Orla 171 KrakowPL-30-244 Poland Mt Suhora Observatory Pedagogical University ul. Podchorazych 2 KrakowPL-30-084 Poland Space Science Data Center - Agenzia Spaziale Italiana via del Politecnico snc RomaI-00133 Italy Instituto Nazionale di Fisica Nucleare Sezione di Perugia PerugiaI-06123 Italy European Space Agency European Space Astronomy Centre Villanueva de la Cañada Madrid28691 Spain Nordic Optical Telescope Apartado 474 Santa Cruz de La PalmaE-38700 Spain Astronomical Institute Osaka Kyoiku University 4-698 Asahigaoka Kashiwara Osaka582-8582 Japan Dark Sky Observatory Department of Physics and Astronomy Appalachian State University BooneNC28608 United States Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Physics Department University of Perugia via A. Pascoli Perugia06123 Italy Astronomical Observatory of Taras Shevshenko National University of Kyiv Observatorna str. 3 Kyiv04053 Ukraine Institute for Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens Metaxa & Vas. Pavlou St. Penteli AthensGR-15236 Greece BAA Variable Star Section West Challow Observatory OX12 9TX United Kingdom P.O. Box 03004 GranadaE-18080 Spain Aritz Bidea No 8 4B Mungia Bizkaia 48100 Spain Asturias Valdés 33780 Spain Section of Astrophysics Astronomy and Mechanics Department of Physics National and Kapodistrian University of Athens Zografos AthensGR-15784 Greece ICAMER Observatory of NASU 27 Acad. Zabolotnoho str. Kyiv03143 Ukraine Max Planck Institute for Astronomy Koenigstuhl 17 Heidelberg D-69117 Germany Dept. of Physics Earth Science and Space System Engineering Morehe

OJ 287 is a BL Lacertae type quasar in which the active galactic nucleus (AGN) outshines the host galaxy by an order of magnitude. The only exception to this may be at minimum light when the AGN activity is so low that the host galaxy may make quite a considerable contribution to the photometric intensity of the source. Such a dip or a fade in the intensity of OJ 287 occurred in November 2017, when its brightness was about 1.75 magnitudes lower than the recent mean level. We compare the observations of this fade with similar fades in OJ 287 observed earlier in 1989, 1999, and 2010. It appears that there is a relatively strong reddening of the B−V colours of OJ 287 when its V-band brightness drops below magnitude 17. Similar changes are also seen V−R, V−I, and R−I colours during these deep fades. These data support the conclusion that the total magnitude of the host galaxy is V = 18.0 ± 0.3, corresponding to MK = −26.5 ± 0.3 in the K-band. This is in agreement with the results, obtained using the integrated surface brightness method, from recent surface photometry of the host. These results should encourage us to use the colour separation method also in other host galaxies with strongly variable AGN nuclei. In the case of OJ 287, both the host galaxy and its central black hole are among the biggest known, and its position in the black hole mass-galaxy mass diagram lies close to the mean correlation. © 2022, CC BY.

关键词： Color

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Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (vol 52, pg 1314, 2020)

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NATURE GENETICS 2021年第5期53卷 762-762页

作者： Surendran, Praveen Feofanova, Elena V. Lahrouchi, Najim Ntalla, Ioanna Karthikeyan, Savita Cook, James Chen, Lingyan Mifsud, Borbala Yao, Chen Kraja, Aldi T. Cartwright, James H. Hellwege, Jacklyn N. Giri, Ayush Tragante, Vinicius Thorleifsson, Gudmar Liu, Dajiang J. Prins, Bram P. Stewart, Isobel D. Cabrera, Claudia P. Eales, James M. Akbarov, Artur Auer, Paul L. Bielak, Lawrence F. Bis, Joshua C. Braithwaite, Vickie S. Brody, Jennifer A. Daw, E. Warwick Warren, Helen R. Drenos, Fotios Nielsen, Sune Fallgaard Faul, Jessica D. Fauman, Eric B. Fava, Cristiano Ferreira, Teresa Foley, Christopher N. Franceschini, Nora Gao, He Giannakopoulou, Olga Giulianini, Franco Gudbjartsson, Daniel F. Guo, Xiuqing Harris, Sarah E. Havulinna, Aki S. Helgadottir, Anna Huffman, Jennifer E. Hwang, Shih-Jen Kanoni, Stavroula Kontto, Jukka Larson, Martin G. Li-Gao, Ruifang Lindstrom, Jaana Lotta, Luca A. Lu, Yingchang Luan, Jian'an Mahajan, Anubha Malerba, Giovanni Masca, Nicholas G. D. Mei, Hao Menni, Cristina Mook-Kanamori, Dennis O. Mosen-Ansorena, David Muller-Nurasyid, Martina Pare, Guillaume Paul, Dirk S. Perola, Markus Poveda, Alaitz Rauramaa, Rainer Richard, Melissa Richardson, Tom G. Sepulveda, Nuno Sim, Xueling Smith, Albert V. Smith, Jennifer A. Staley, James R. Stanakova, Alena Sulem, Patrick Theriault, Sebastien Thorsteinsdottir, Unnur Trompet, Stella Varga, Tibor V. Velez Edwards, Digna R. Veronesi, Giovanni Weiss, Stefan Willems, Sara M. Yao, Jie Young, Robin Yu, Bing Zhang, Weihua Zhao, Jing-Hua Zhao, Wei Zhao, Wei Evangelou, Evangelos Aeschbacher, Stefanie Asllanaj, Eralda Blankenberg, Stefan Bonnycastle, Lori L. Bork-Jensen, Jette Brandslund, Ivan Braund, Peter S. Burgess, Stephen Cho, Kelly Christensen, Cramer Connell, John de Mutsert, Renee Dominiczak, Anna F. Dorr, Marcus Eiriksdottir, Gudny Farmaki, Aliki-Eleni Gaziano, J. Michael Grarup, Niels Grove, Megan L. Hallmans, Goran Hansen, Torben Have, Christian T. Heiss, Gerardo Jorgensen, Marit E. Jousilahti, Pekka Kajantie, Eero Kamat, Mihir Karajamaki, AnneMari Kar British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge UK Rutherford Fund Fellow Department of Public Health and Primary Care University of Cambridge Cambridge UK MRC Biostatistics Unit University of Cambridge Cambridge UK National Institute for Health Research Cambridge Biomedical Research Centre University of Cambridge and Cambridge University Hospitals Cambridge UK MRC Integrative Epidemiology Unit (IEU) Population Health Sciences Bristol Medical School University of Bristol Bristol UK Robertson Centre for Biostatistics University of Glasgow Glasgow UK MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK Department of Immunology and Inflammation Imperial College London London UK Centre for Non-communicable Disease Research (CNCR) Dhaka Bangladesh National Institute for Health Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics at the University of Cambridge Cambridge UK Department of Human Genetics Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge UK Department of Biostatistics and Epidemiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Non-Communicable Diseases Karachi Pakistan Department of Genetics Novo Nordisk Research Centre Oxford Oxford UK Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX USA Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Cardiovascular Research Center Center for Genomic Medicine

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

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International Conference on Applied Mathematics & Computer Science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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High-depth African genomes inform human migration and health (vol 586, pg 741, 2020)

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NATURE 2021年第7856期592卷 E26-E26页

作者： Choudhury, Ananyo Aron, Shaun Botigue, Laura R. Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Muzny, Donna Metcalf, Ginger Gibbs, Richard A. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Rotimi, Charles Ramsay, Michele Choudhury, Ananyo Choudhury, Ananyo Aron, Shaun Botigue, Laura Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Rotimi, Charles Ramsay, Michele Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Adebamowo, Clement Agongo, Godfred Boua, Romuald P. Oduro, Abraham Sorgho, Hermann Landoure, Guida Cisse, Lassana Diarra, Salimata Samassekou, Oumar Anabwani, Gabriel Matshaba, Mogomotsi Joloba, Moses Kekitiinwa, Adeodata Mardon, Graeme Mpoloka, Sununguko W. Kyobe, Samuel Mlotshwa, Busisiwe Mwesigwa, Savannah Retshabile, Gaone Williams, Lesedi Wonkam, Ambroise Moussa, Ahmed Adu, Dwomoa Ojo, Akinlolu Burke, David Salako, Babatunde O. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Awadalla, Philip Bruat, Vanessa Gbeha, Elias Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Division of Human Genetics National Health Laboratory Service and School of Pathology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Navrongo Health Research Centre Navrongo Ghana Clinical Research Unit of Nanoro Institut de Recherche en Sciences de la Sante Bobo-Dioulasso Burkina Faso Center for Research in Agricultural Genomics (CRAG) Plant and Animal Genomics Program CSIC-IRTA-UAB-UB Barcelona Spain Computational Biology Division and H3ABioNet Department of Integrative Biomedical Sciences IDM University of Cape Town Cape Town South Africa System and Data Engineering Team Abdelmalek Essaadi University ENSA Tangier Morocco Centre for Proteomic and Genomic Research (CPGR) Cape Town South Africa South African National Bioinformatics Institute University of the Western Cape Bellville South Africa Africa Health Research Institute Durban South Africa Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA Department of Agriculture and Food Science Faculty of Agriculture University of Mauritius Reduit Mauritius Department of Digital Technologies Faculty of Information Communication & Digital Technologies University of Mauritius Reduit Mauritius Department of Public Health and Primary Care University of Cambridge Cambridge UK Institute of Human Virology Nigeria Abuja Nigeria Department of Epidemiology and Public Health University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA University of Maryland Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA Institute of Human Virology and Greeneba

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Efficient construction method for phase diagrams using uncertainty sampling

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Physical Review Materials 2019年第3期3卷 033802-033802页

作者： Kei Terayama Ryo Tamura Yoshitaro Nose Hidenori Hiramatsu Hideo Hosono Yasushi Okuno Koji Tsuda RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan Medical Sciences Innovation Hub Program RIKEN Cluster for Science Technology and Innovation Hub Kanagawa 230-0045 Japan Graduate School of Medicine Kyoto University Kyoto 606-8507 Japan International Center for Materials Nanoarchitectonics (WPI-MANA) National Institute for Materials Science Ibaraki 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki 305-0047 Japan Graduate School of Frontier Sciences University of Tokyo Chiba 277-8568 Japan Department of Materials Science and Engineering Kyoto University Kyoto 606-8501 Japan Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology Yokohama 226-8503 Japan Materials Research Center for Element Strategy Tokyo Institute of Technology Yokohama 226-8503 Japan

We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phases can be rapidly found, and smaller numbers of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science.

关键词： Phase diagrams

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A nonparametric adaptive sampling strategy for online monitoring of big data streams

A nonparametric adaptive sampling strategy for online monito...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Xiaochen Xian Andi Wang Kaibo Liu Department of Industrial and Systems Engineering University of Wisconsin-Madison Madison WI USA Department of Industrial Engineering and Logistics Management Data Analytics for System & Service Improvement and Automation Hong Kong

With the rapid development of sensor techniques, we often face the challenges of monitoring big data streams in modern quality control, which consist of massive series of real-time, continuously and sequentially ordered observations. For example, in manufacturing industries, hundreds or thousands of variables are observed during online production for quality insurance. Also, smart grid infrastructure needs to simultaneously monitor massive access points for intrusion and threat detection. As another example, an image sensing device continuously collects high-resolution images at high frequency for video surveillance and object movement tracking. Ideally, in those applications, it is preferable to detect assignable causes as early as possible, while maintaining a prespecified in-control Average Run Length (ARL).

关键词： Monitoring Big data Process control Automation Computer aided software engineering Real-time systems Production

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