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arXiv 2017年

作者： Bartók, Albert P. De, Sandip Poelking, Carl Bernstein, Noam Kermode, James Csányi, Gábor Ceriotti, Michele Scientific Computing Department Science and Technology Facilities Council Rutherford Appleton Laboratory OxfordshireOX11 0QX United Kingdom Laboratory of Computational Science and Modelling Institute of Materials EPFL Lausanne Switzerland Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Center for Materials Physics and Technology U.S. Naval Research Laboratory WashingtonDC20375 United States Warwick Centre for Predictive Modelling School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Engineering Laboratory University of Cambridge

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and transformations. Here we show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provides new insight into the potential energy surface of materials and molecules. Copyright © 2017, The Authors. All rights reserved.

关键词： Molecules

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Batched Generation of Incomplete Sparse Approximate Inverses on GPUs

Batched Generation of Incomplete Sparse Approximate Inverses...

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Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)

作者： Hartwig Anzt Edmond Chow Thomas Huckle Jack Dongarra Innovative Computing Laboratory University of Tennessee School of Computational Science and Engineering Georgia Institute of Technology Department of Informatics Technical University Munich University of Manchester Oak Ridge National Laboratory

ISBN: (纸本)9781509052233

Incomplete Sparse Approximate Inverses (ISAI) have recently been shown to be an attractive alternative to exact sparse triangular solves in the context of incomplete factorization preconditioning. In this paper we propose a batched GPU-kernel for the efficient generation of ISAI matrices. Utilizing only thread-local memory allows for computing the ISAI matrix with very small memory footprint. We demonstrate that this strategy is faster than the existing strategy for generating ISAI matrices, and use a large number of test matrices to assess the algorithm's efficiency in an iterative solver setting.

关键词： Sparse matrices Kernel Instruction sets Linear systems Jacobian matrices Approximation algorithms Context

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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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Track 1-A Study of Nanodroplet Diffusion on Smooth Surfaces

Track 1-A Study of Nanodroplet Diffusion on Smooth Surfaces

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2016年大连国际微纳流体和微流控芯片大会(2016 International Conference of Microfluidics, Nanofluidics and Lab-On-A-Chip

作者： Chu Li Jizu Huang Zhigang Li Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

In this study, we investigate how nanodroplets diffuse on smooth surfaces through molecular dynamics (MD) simulations and theoretical *** dynamics simulations show that nanodroplet surface diffusion is different from that of single molecules and solid *** dependence of nanodroplet diffusion coefficient on temperature undergoes a transition from linear to nonlinear as the surface wettability is weakened due to the coupling of temperature and surface *** also develop a simple relation for the diffusion coefficient by using the contact angle and contact radius of the *** works well for a wide range of surface wettabilities and different sized nanodroplets, as confirmed by MD simulations.

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Sustainable building design: A challenge at the intersection of machine learning and design optimization 29

Sustainable building design: A challenge at the intersection...

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29th AAAI Conference on Artificial Intelligence, AAAI 2015

作者： Gilan, Siamak Safarzadegan Dilkina, Bistra Georgia Institute of Technology School of Computational Science and Engineering College of Computing AtlantaGA United States

ISBN: (纸本)9781577357179

Residential and commercial buildings are responsible for about 40% of primary energy consumption in the United States, hence improving their energy efficiency could have important sustainability benefits. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model that is queried during the optimization. We propose a method that more tightly integrates the machine learning and optimization components, by employing active learning during optimization. In particular, we use a Gaussian Process (GP) model for the prediction and active learning and multi- objective genetic algorithm NSGA-II for the optimization. We develop a comprehensive and publicly available benchmark for building design optimization. We evaluate 5 machine learning approaches on our dataset, and show that the GP model is competitive, in addition to being well-suited for the active learning setting. We compare our optimization approach against the 2-stage approach and simulation optimization. Our results show that our approach produces solutions at the Pareto frontier compared to the other two approaches, while using only a fraction of the simulations and time. © Copyright 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Energy efficiency

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Coarse models for bird migrations using clustering and non-stationary markov chains 29

Coarse models for bird migrations using clustering and non-s...

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29th AAAI Conference on Artificial Intelligence, AAAI 2015

作者： Jain, Nitin Dilkina, Bistra Georgia Institute of Technology School of Computational Science and Engineering College of Computing AtlantaGA United States

ISBN: (纸本)9781577357179

While great strides have been made in collecting presence data and developing accurate species distribution models, much less is known about the migratory process that guides the spatio-temporal changes in distributions for migrating species, especially birds. In this work, we address a challenging inference task, where given only aggregate and noisy data of the volume of birds for each spatial pixel and time window, we predict the likely transition links with their associated probabil-ities. We propose a framework to build such migration networks for different bird species and present a real world example of constructing a network using our approach. © Copyright 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Birds

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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SUPPL 1期18卷 59-59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 Brain Sciences Saxony Lei

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Parkes Pulsar Timing Array constraints on ultralight scalar-field dark matter

arXiv

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arXiv 2018年

作者： Porayko, Nataliya K. Zhu, Xingjiang Levin, Yuri Hui, Lam Hobbs, George Grudskaya, Aleksandra Postnov, Konstantin Bailes, Matthew Bhat, N.D. Ramesh Coles, William Dai, Shi Dempsey, James Keith, Michael J. Kerr, Matthew Kramer, Michael Lasky, Paul D. Manchester, Richard N. Oslowski, Stefan Parthasarathy, Aditya Ravi, Vikram Reardon, Daniel J. Rosado, Pablo A. Russell, Christopher J. Shannon, Ryan M. Spiewak, Renée van Straten, Willem Toomey, Lawrence Wang, Jingbo Wen, Linqing You, Xiaopeng Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany School of Physics and Astronomy Monash University ClaytonVIC3800 Australia School of Physics University of Western Australia CrawleyWA6009 Australia Australian Research Council Centre of Excellence for Gravitational Wave Discovery Center for Theoretical Physics Department of Physics Columbia University New YorkNY10027 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States CSIRO Astronomy and Space Science P.O. Box 76 EppingNSW1710 Australia Sternberg Astronomical Institute Lomonosov Moscow State University Universitetskii pr. 13 Moscow119234 Russia Kazan Federal University Kremlevskaya 18 Kazan420008 Russia Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Department of Electrical and Computer Engineering University of California at San Diego La JollaCA92093 United States CSIRO Information Management and Technology PO Box 225 DicksonACT2602 Australia Jodrell Bank Centre for Astrophysics University of Manchester ManchesterM13 9PL United Kingdom Space Science Division Naval Research Laboratory WashingtonDC20375 United States Cahill Center for Astronomy and Astrophysics California Institute of Technology MC 249-17 PasadenaCA91125 United States CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi Xinjiang830011 China School of Physical Science and Technology Southwest University Chongqing400715 China

It is widely accepted that dark matter contributes about a quarter of the critical mass-energy density in our Universe. The nature of dark matter is currently unknown, with the mass of possible constituents spanning nearly one hundred orders of magnitude. The ultralight scalar field dark matter, consisting of extremely light bosons with m ∼ 10−22 eV and often called "fuzzy" dark matter, provides intriguing solutions to some challenges at sub-Galactic scales for the standard cold dark matter model. As shown by Khmelnitsky and Rubakov, such a scalar field in the Galaxy would produce an oscillating gravitational potential with nanohertz frequencies, resulting in periodic variations in the times of arrival of radio pulses from pulsars. The Parkes Pulsar Timing Array (PPTA) has been monitoring 20 millisecond pulsars at two to three weeks intervals for more than a decade. In addition to the detection of nanohertz gravitational waves, PPTA offers the opportunity for direct searches for fuzzy dark matter in an astrophysically feasible range of masses. We analyze the latest PPTA data set which includes timing observations for 26 pulsars made between 2004 and 2016. We perform a search in this data set for evidence of ultralight dark matter in the Galaxy using Bayesian and Frequentist methods. No statistically significant detection has been made. We, therefore, place upper limits on the local dark matter density. Our limits, improving on previous searches by a factor of 2 to 5, constrain the dark matter density of ultralight bosons with m ≤ 10−23 eV to be below 6 GeV cm−3 with 95% confidence in the Earth neighborhood. Finally, we discuss the prospect of probing the astrophysically favored mass range m & 10−22 eV with next-generation pulsar timing facilities. Copyright © 2018, The Authors. All rights reserved.

关键词： Timing circuits

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Bounds on the dynamics of periodic quantum walks and emergence of the gapless and gapped Dirac equation

arXiv

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arXiv 2017年

作者： Pradeep Kumar, N. Balu, Radhakrishna Laflamme, Raymond Chandrashekar, C.M. Institute of Mathematical Sciences C. I. T Campus Taramani Chennai600113 India U.S. Army Research Laboratory Computational and Information Sciences Directorate AdelphiMD20783 United States Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai400094 India

We study the dynamics of discrete-time quantum walk using quantum coin operations, Ĉ(θ1) and Ĉ(θ2) in time-dependent periodic sequence. For the two-period quantum walk with the parameters θ1 and θ2 in the coin operations we show that the standard deviation [σθ1,θ2(t)] is the same as the minimum of standard deviation obtained from one of the one-period quantum walks with coin operations θ1 or θ2, σθ1,θ2(t) = min{σθ1(t), σθ2(t)}. Our numerical result is analytically corroborated using the dispersion relation obtained from the continuum limit of the dynamics. Using the dispersion relation for one- and two-period quantum walks, we present the bounds on the dynamics of three- and higher period quantum walks. We also show that the bounds for the two-period quantum walk will hold good for the split-step quantum walk which is also defined using two coin operators using θ1 and θ2. Unlike the previous known connection of discrete-time quantum walks with the massless Dirac equation where coin parameter θ = 0, here we show the recovery of the massless Dirac equation with non-zero θ parameters contributing to the intriguing interference in the dynamics in a totally non-relativistic situation. We also present the effect of periodic sequence on the entanglement between coin and position space. Copyright © 2017, The Authors. All rights reserved.

关键词： Coin operated equipment

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