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arXiv 2021年

作者： Hessenthaler, Andreas Falgout, Robert D. Schroder, Jacob B. de Vecchi, Adelaide Nordsletten, David Röhrle, Oliver Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Pfaffenwaldring 5a Stuttgart70569 Germany Stuttgart Center for Simulation Technology University of Stuttgart Pfaffenwaldring 5a Stuttgart70569 Germany Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94551 United States Department of Mathematics and Statistics University of New Mexico AlbuquerqueNM87131 United States School of Biomedical Engineering and Imaging Sciences King’s College London 4th FL Rayne Institute St. Thomas Hospital LondonSE1 7EH United Kingdom Department of Biomedical Engineering and Cardiac Surgery University of Michigan NCRC B20 2800 Plymouth Rd Ann Arbor48109 United States

In this paper, a time-periodic MGRIT algorithm is proposed as a means to reduce the time-to-solution of numerical algorithms by exploiting the time periodicity inherent to many applications in science and engineering. The time-periodic MGRIT algorithm is applied to a variety of linear and nonlinear single- and multiphysics problems that are periodic-in-time. It is demonstrated that the proposed parallel-in-time algorithm can obtain the same time-periodic steady-state solution as sequential time-stepping. It is shown that the required number of MGRIT iterations can be estimated a priori and that the new MGRIT variant can significantly and consistently reduce the time-to-solution compared to sequential time-stepping, irrespective of the number of dimensions, linear or nonlinear PDE models, single-physics or coupled problems and the employed computing resources. The numerical experiments demonstrate that the time-periodic MGRIT algorithm enables a greater level of parallelism yielding faster turnaround, and thus, facilitating more complex and more realistic problems to be solved. Copyright © 2021, The Authors. All rights reserved.

关键词： Fluid structure interaction

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Collective neutrino oscillations in three flavors on qubit and qutrit processors

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Physical Review D 2025年第10期111卷 103054-103054页

作者： Luca Spagnoli Noah Goss Alessandro Roggero Ermal Rrapaj Michael J. Cervia Amol V. Patwardhan Ravi K. Naik A. Baha Balantekin Ed Younis David I. Santiago Irfan Siddiqi Sheakha Aldaihan Physics Department University of Trento Via Sommarive 14 I-38123 Trento Italy INFN-TIFPA Trento Institute of Fundamental Physics and Applications Via Sommarive 14 I-38123 Trento Italy Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA INFN-TIFPA Trento Institute of Fundamental Physics and Applications Trento Italy National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics University of California Berkeley California 94720 USA RIKEN iTHEMS Wako Saitama 351-0198 Japan Department of Physics University of Washington Seattle Washington 98195 USA School of Physics and Astronomy University of Minnesota Minneapolis Minnesota 55455 USA Department of Physics New York Institute of Technology New York New York 10023 USA Department of Physics University of Wisconsin Madison Wisconsin 53706 USA Department of Physics and Astronomy College of Science King Saud University Riyadh 11451 Saudi Arabia

Collective neutrino flavor oscillations are of primary importance in understanding the dynamic evolution of core-collapse supernovae and subsequent terrestrial detection, but also among the most challenging aspects of numerical simulations. This situation is complicated by the quantum many-body nature of the problem due to neutrino-neutrino interactions, which demands a quantum treatment. An additional complication is the presence of three flavors, which often is approximated by the electron flavor and a heavy lepton flavor. In this work, we provide both qubit and qutrit encodings for all three flavors, and develop optimized quantum circuits for the time evolution and analyze the Trotter error. We conclude our study with a hardware experiment of a system of two neutrinos with superconducting hardware: the IBM Torino device for qubits and Advanced Quantum Testbed device at the Lawrence Berkeley National laboratory for qutrits. We find that error mitigation greatly helps in obtaining a signal consistent with simulations. While hardware results are comparable at this stage, we expect the qutrit setup to be more convenient for large-scale simulations since it does not suffer from probability leakage into nonphsycial qubit space, unlike the qubit setup.

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Reports of the AAAI 2019 spring symposium series

Reports of the AAAI 2019 spring symposium series

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作者： Baldini, Ioana Barrett, Clark Chella, Antonio Cinelli, Carlos Gamez, David Gilpin, Leilani H. Hinkelmann, Knut Holmes, Dylan Kido, Takashi Kocaoglu, Murat Lawless, William F. Lomuscio, Alessio Macbeth, Jamie C. Martin, Andreas Mittu, Ranjeev Patterson, Evan Sofge, Donald Tadepalli, Prasad Takadama, Keiki Wilson, Shomir IBM Research AI Department of Computer Science at Stanford University United States Department of Engineering University of Palermo Italy University of California Los Angeles United States Department of Computer Science Middlesex University London United Kingdom Massachusetts Institute of Technology United States FHNW University of Applied Sciences and Arts Northwestern Switzerland Switzerland Massachusetts Institute of Technology United States Visiting Researcher of Stanford University United States Researcher at MIT-IBM Watson AI Lab Paine College United States Department of Computing Imperial College London United Kingdom Smith College United States FHNW University of Applied Sciences and Arts Northwestern Switzerland Switzerland US Naval Research Laboratory Information Management and Decision Architectures Branch within Information Technology Division United States Department of Statistics at Stanford University United States Computer Scientist and Roboticist at US Naval Research Laboratory United States Computer Science at Oregon State University United States University of Electro Communications in Japan Japan College of Information Sciences and Technology at Pennsylvania State University United States

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data;self-driving cars have already been involved in fatalities, including a pedestrian;and yet machine learning is unable to explain the contexts within which it operates. Copyright © 2019, Association for the Advancement of Artificial Intelligence.

关键词： Machine learning

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Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies

arXiv

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arXiv 2020年

作者： Gan, Chee Kwan Liu, Yun Sum, Tze Chien Hippalgaonkar, Kedar Institute of High Performance Computing 1 Fusionopolis Way #16-16 Connexis 138632 Singapore Cavendish Laboratory University of Cambridge 19 JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore A*STAR Agency for Science Technology and Research 2 Fusionopolis Way #08-03 Innovis 138634 Singapore School of Materials Science and Engineering Nanyang Technological University 639798 Singapore

Small displacement methods have been successfully used to calculate the lattice dynamical properties of crystals. It involves displacing atoms by a small amount in order to calculate the induced forces on all atoms in a supercell for the computation of force constants. Even though these methods are widely in use, to our knowledge, there is no systematic discussion of optimal displacement directions from the crystal’s symmetry point of view nor a rigorous error analysis of such methods. Based on the group theory and point group symmetry of a crystal, we propose displacement directions, with an equivalent concept of the group of k, deduced directly in the Cartesian coordinates rather than the usual fractional coordinates, that maintain the theoretical maximum for the triple product V spanned by the three displacements to avoid possible severe roundoff errors. The proposed displacement directions are generated from a minimal set of irreducible atomic displacements that keep the required independent force calculations to a minimum. We find the error in the calculated force constant explicitly depends on the inverse of V and inaccuracy of the forces. Test systems such as Si, graphene, and orthorhombic Bi2S3 and Sb2S3 are used to illustrate the method. Our displacement method is shown to be very robust in treating low-symmetry cells with a large ‘aspect ratio’ due to huge differences in lattice parameters, use of a large vacuum height as in the test case of graphene, or a very oblique unit cell due to unconventional choice of primitive lattice vectors. It is expected that our displacement strategy can be used to address higher-order interatomic interactions to achieve good accuracy and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphene

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Crack propagation and study of fracture process in metallic glass reinforced mortars using holographic moiré 14

Crack propagation and study of fracture process in metallic ...

引用

14th International Conference on Fracture, ICF 2017

作者： Rastogi, P. Applied Computing and Mechanics Laboratory Swiss Federal Institute of Technology Lausanne1015 Switzerland

In materials such as mortar, concrete, or fiber-reinforced composites the current design sizes lead to a fracture process which cannot be described realistically by applying linear elastic fracture mechanics. The propagation of a crack is a complex process involving the development of a large damaged zone strongly dependent on the geometrical and mechanical properties of the different phases that represent the matrix, the aggregates and /or fibers, and the interface. Holographic moiré is used to investigate the behavior of fiber-reinforced mortar in bending before and after the peak load. Two different types of fibers in a same matrix have been used. The optically measured parameters are used to first describe qualitatively the fracture process before being used as input in a computerized analytical model. © 2017 Chinese Society of Theoretical and applied Mechanics. All Rights Reserved.

关键词： Cracks

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Blended learning and media centers: A bibliometric analysis

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Social Sciences and Humanities Open 2024年 10卷

作者： Challco, Geiser Chalco Silva, Wesley Vieira da Setton Gonçalves, Bruno de Almeida Levino, Natallya Bittencourt, Ibsen Mateus Kaczam, Fabíola de Oliveira, Elaine Harada Teixeira Martins, Flavio Pinheiro de Lima Filho, Walter Araujo de Araújo, Ronaldo Ferreira Bittencourt, Ig Ibert Paiva, Ranilson Oscar Araújo Department of Engineering and Technology (DETEC) Federal Rural University of Semi-Árido (UFERSA) RN Pau dos Ferros 59900-000 Brazil Scholarship CNPq Research Productivity (D Level) and Professor Faculty of Economics Administration and Accounting Federal University of Alagoas (UFAL) Maceió 57072-900 Brazil Faculty of Economics Administration and Accounting Federal University of Alagoas (UFAL) Maceió 57072-900 Brazil Leader of the Laboratory of Applied Public Administration (LAPA) of the Public Administration Course Campus Arapirca Federal University of Alagoas Arapiraca 57309-005 Brazil Faculty of Economics Business and Accounting Federal University of Alagoas Maceio 57072-900 Brazil Postgraduate Program in Administration Federal University Santa Maria (UFSM) Building 74C 2nd floor Room 4209 97105-900 Brazil Institute of Computing Federal University of Amazonas Amazonas Manaus Brazil The Bartlett School of Sustainable Construction University College London—UCL London WC1E 6BT United Kingdom Visiting Researcher at Leiden University ou Pesquisador Visitante na Universidade de Leiden Institute of Human Sciences Communication and Arts Federal University of Alagoas Alagoas Maceio 57072-900 Brazil Harvard Graduate School of Education Harvard University Cambridge MA United States Center of Excellence for Social Technologies Federal University of Alagoas Brazil Universidade Federal de Alagoas - Instituto de Computação Brazil

Blended learning can be considered an approach that combines face-to-face and online periods in education through the integration of some technological resources, such as media centers. The present research sought to investigate the panorama of scientific production on blended learning and identify evidence of how media centers are used to support this type of teaching. This is a bibliometric study carried out based on the principles of the Systematic Literature Review (SLR). Although the results indicate an increase in production, a high rate of authors with only one published work was identified, which can be interpreted as an expanding topic. The United States stood out as the country that contributed the most with research and the areas of Computer Science and Education among the fields of research with the highest number of articles. © 2024 The Author(s)

关键词： Blended learning Hybrid education Media centers Systematic review

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FAIR for AI: An interdisciplinary and international community building perspective

arXiv

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -XIV. Properties of resolved UC Hп regions

arXiv

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arXiv 2023年

作者： Zhang, C. Zhu, Feng-Yao Liu, Tie Ren, Z.-Y. Liu, H.-L. Wang, Ke Wu, J.-W. Zhang, Y. Zhou, J.-W. Tatematsu, K. Garay, Guido Tej, Anandmayee Li, Shanghuo Xu, W.F. Lee, Chang Won Bronfman, Leonardo Soam, Archana Li, D. Department of Physics Taiyuan Normal University Jinzhong030619 China Institute of Computational and Applied Physics Taiyuan Normal University Jinzhong030619 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Shanghai Astronomical Observatory Chinese Academy of Sciences 80 Nandan Road Shanghai200030 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Department of Astronomy Yunnan University Kunming650091 China Kavli Institute for Astronomy and Astrophysics Peking University 5 Yiheyuan Road Haidian District Beijing100871 China University of Chinese Academy of Sciences Beijing100049 China School of Physics and Astronomy Sun Yat-sen University 2 Daxue Road Guangdong Zhuhai519082 China Nobeyama Radio Observatory National Astronomical Observatory of Japan National Institutes of Natural Sciences Nobeyama Minamimaki Minamisaku Nagano 384-1 Japan Departamento de Astronomıa Universidad de Chile Las Condes Santiago Chile Indian Institute of Space Science and Technology Kerala Thiruvananthapuram695 547 India Korea Astronomy and Space Science Institute 776 Daedeokdaero Yuseong-gu Daejeon34055 Korea Republic of SOFIA Science Centre USRA NASA Ames Research Centre MS-12 N232 Moffett FieldCA94035 United States NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-Natal Durban4000 South Africa

Hydrogen recombination lines (RRLs) are one of the major diagnostics of the physical properties of HЦ regions. We use RRL H40U, He40U and 3 mm continuum emission to investigate the properties of a large sample of resolved UC Hп regions identified in the ATOMS survey. In total, we identify 94 UC Hп regions from H40U emission. The basic parameters for these UC Hп regions such as electron density, emission measure, electron temperature, ionic abundance ratio (nHe+/nH+), and line width are derived. The median electron density and the median nHe+/nH+ ratio of these UC Hп regions derived from RRLs are 9000 cm3 and 0.11, respectively. Within UC Hп regions, the nHe+/nH+ ratios derived from the intensity ratio of the He40U and H40U lines seems to be higher in the boundary region than in the center. The H40U line width is mainly broadened by thermal motion and microturbulence. The electron temperature of these UC Hп regions has a median value of ͂6700 K, and its dependence on galactocentric distance is weak. Copyright © 2023, The Authors. All rights reserved.

关键词： High frequency telecommunication lines

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Author Correction: Enhanced energy coupling for indirect-drive fast-ignition fusion targets

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Nature Physics 2020年第7期16卷 815-815页

作者： F. Zhang H. B. Cai W. M. Zhou Z. S. Dai L. Q. Shan H. Xu J. B. Chen F. J. Ge Q. Tang W. S. Zhang L. Wei D. X. Liu J. F. Gu H. B. Du B. Bi S. Z. Wu J. Li F. Lu H. Zhang B. Zhang M. Q. He M. H. Yu Z. H. Yang W. W. Wang H. S. Zhang B. Cui L. Yang J. F. Wu W. Qi L. H. Cao Z. Li H. J. Liu Y. M. Yang G. L. Ren C. Tian Z. Q. Yuan W. D. Zheng L. F. Cao C. T. Zhou S. Y. Zou Y. Q. Gu K. Du Y. K. Ding B. H. Zhang S. P. Zhu W. Y. Zhang X. T. He Science and Technology on Plasma Physics Laboratory Laser Fusion Research Center CAEP Mianyang China Institute of Applied Physics and Computational Mathematics Beijing China HEDPS Center for Applied Physics and Technology Peking University Beijing China College of Computing Science National University of Defense Technology Changsha China Center for Advanced Material Diagnostic Technology Shenzhen Technology University Shenzhen China Graduate School China Academy of Engineering Physics Beijing China

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