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A Network Model of Local Field Potential Activity in Essential Tremor and the Impact of Deep Brain Stimulation

PLoS Computational Biology 2017年第1期13卷 e1005326-e1005326页

作者： Yousif, Nada Mace, Michael Pavese, Nicola Borisyuk, Roman Nandi, Dipankar Bain, Peter Division of Brain Sciences Imperial College London London United Kingdom School of Engineering and Technology University of Hertfordshire Hatfield United Kingdom Department of Bioengineering Imperial College London London United Kingdom School of Computing and Mathematics University of Plymouth Plymouth United Kingdom Institute of Mathematical Problems of Biology of RAS The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences Moscow Russia

Essential tremor (ET), a movement disorder characterised by an uncontrollable shaking of the affected body part, is often professed to be the most common movement disorder, affecting up to one percent of adults over 40 years of age. The precise cause of ET is unknown, however pathological oscillations of a network of a number of brain regions are implicated in leading to the disorder. Deep brain stimulation (DBS) is a clinical therapy used to alleviate the symptoms of a number of movement disorders. DBS involves the surgical implantation of electrodes into specific nuclei in the brain. For ET the targeted region is the ventralis intermedius (Vim) nucleus of the thalamus. Though DBS is effective for treating ET, the mechanism through which the therapeutic effect is obtained is not understood. To elucidate the mechanism underlying the pathological network activity and the effect of DBS on such activity, we take a computational modelling approach combined with electrophysiological data. The pathological brain activity was recorded intra-operatively via implanted DBS electrodes, whilst simultaneously recording muscle activity of the affected limbs. We modelled the network hypothesised to underlie ET using the Wilson-Cowan approach. The modelled network exhibited oscillatory behaviour within the tremor frequency range, as did our electrophysiological data. By applying a DBS-like input we suppressed these oscillations. This study shows that the dynamics of the ET network support oscillations at the tremor frequency and the application of a DBS-like input disrupts this activity, which could be one mechanism underlying the therapeutic benefit. © 2017 Yousif et al.

关键词： Brain

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An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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A multigrid method for nonlocal problems: Non-diagonally dominant toeplitz-plus-tridiagonal systems

arXiv

引用

arXiv 2018年

作者： Chen, Minghua Ekström, Sven-Erik Serra-Capizzano, Stefano School of Mathematics and Statistics Gansu Key Laboratory of Applied Mathematics and Complex Systems Lanzhou University Lanzhou730000 China Department of Information Technology Division of Scientific Computing Uppsala University - ITC L gerhyddsv. 2 hus 2 P.O. Box 337 UppsalaSE-751 05 Sweden Department of Science and High Technology University of Insubria Via Valleggio 11 22100 Como Italy

The nonlocal problems have been used to model very different applied scientific phenomena, which involve the fractional Laplacian when one looks at the Lévy processes and stochastic interfaces. This paper deals with the nonlocal problems on a bounded domain, where the stiffness matrices of the resulting systems are Toeplitz-plus-tridiagonal and far from being diagonally dominant, as it occurs when dealing with linear finite element approximations. By exploiting a weakly diagonally dominant Toeplitz property of the stiffness matrices, the optimal convergence of the two-grid method is well established [Fiorentino and Serra-Capizzano, SIAM J. Sci. Comput., 17 (1996), pp. 1068-1081;Chen and Deng, SIAM J. Matrix Anal. Appl., 38 (2017), pp. 869-890];and there are still questions about best ways to define coarsening and interpolation operator when the stiffness matrix is far from being weakly diagonally dominant [Stüben, J. Comput. Appl. Math., 128 (2001), pp. 281-309]. In this work, using spectral indications from our analysis of the involved matrices, the simple (traditional) restriction operator and prolongation operator are employed in order to handle general algebraic systems which are neither Toeplitz nor weakly diagonally dominant corresponding to the fractional Laplacian kernel and the constant kernel, respectively. We focus our efforts on providing the detailed proof of the convergence of the two-grid method for such situations. Moreover, the convergence of the full multigrid is also discussed with the constant kernel. The numerical experiments are performed to verify the convergence with only O(NlogN) complexity by the fast Fourier transform, where N is the number of the grid points.26A33, 65M55, 65T50 Copyright © 2018, The Authors. All rights reserved.

关键词： Stiffness matrix

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Association of leisure time physical activity with gut microbiota composition in early adulthood

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Scientific reports 2025年第1期15卷 19697页

作者： Hanna-Mari Boelius Anna-Katariina Aatsinki Marja A Heiskanen Eero A Haapala Eveliina Munukka Juha Mykkänen Noora Kartiosuo Leo Lahti Anniina Keskitalo Pentti Huovinen Harri Niinikoski Jorma Viikari Tapani Rönnemaa Hanna Lagström Antti Jula Suvi P Rovio Olli T Raitakari Katja Pahkala Centre for Population Health Research University of Turku and Turku University Hospital Turku Finland. hanna-mari.boelius@utu.fi. Research Centre of Applied and Preventive Cardiovascular Medicine University of Turku Turku Finland. hanna-mari.boelius@utu.fi. Centre for Population Health Research University of Turku and Turku University Hospital Turku Finland. anna.a.aatsinki@utu.fi. FinnBrain Birth Cohort Study Turku Brain and Mind Center Department of Clinical Medicine University of Turku and Turku University Hospital Turku Finland. anna.a.aatsinki@utu.fi. Centre for Population Health Research University of Turku and Turku University Hospital Turku Finland. marja.heiskanen@utu.fi. Research Centre of Applied and Preventive Cardiovascular Medicine University of Turku Turku Finland. marja.heiskanen@utu.fi. Faculty of Sport and Health Sciences University of Jyväskylä Jyväskylä Finland. Institute of Biomedicine School of Medicine University of Eastern Finland Kuopio Finland. Microbiome Biobank Institute of Biomedicine University of Turku Turku Finland. Department of Clinical Microbiology Turku University Hospital Turku Finland. Centre for Population Health Research University of Turku and Turku University Hospital Turku Finland. Research Centre of Applied and Preventive Cardiovascular Medicine University of Turku Turku Finland. Department of Mathematics and Statistics University of Turku Turku Finland. Department of Computing Faculty of Technology University of Turku Turku Finland. Department of Pediatrics and Adolescent Medicine Turku University Hospital University of Turku Turku Finland. Department of Medicine University of Turku Turku Finland. Division of Medicine Turku University Hospital Turku Finland. Department of Public Health University of Turku Turku University Hospital Turku Finland. Nutrition and Food Research Center Faculty of Medicine University of Turku Turku Finland. Department of Chronic Disease Prevention Institute for Health and Welfa

Physical activity is associated with compositional changes in gut microbiota. Studies applying data from general populations exhibiting varying physical activity levels are scarce. This study investigated the associations between leisure time physical activity and gut microbiota composition in a general population of young adults. Faecal samples were analyzed with 16 S rRNA sequencing and leisure time physical activity were assessed in N = 302 (176 females, 126 males) 26-year-old individuals of European descent. Leisure time physical activity was assessed using a self-administrated questionnaire and expressed as metabolic equivalent (MET) h/wk. Participants were defined as inactive (N = 83) and active (N = 219) based on estimation to meet the physical activity guidelines. Diet was assessed using food diaries. First, associations of physical activity and gut microbiota were investigated and then the models were further adjusted for diet (N = 280). While gut microbiota diversity was similar, community composition differed between the active and inactive participants. The association diluted after adjustment for diet. Moreover, 16 genera, e.g., Lactobacillus, Veillonella, and Prevotella were more abundant in the active compared with the inactive participants. After adjustment for diet, these associations, except Barnesiella, were no longer detected, but other genera were found. In conclusion, physical activity in young adults is associated with gut microbiota community composition and an increase in the abundance of genera considered beneficial for overall health. The associations are partly confounded by diet.

关键词： Cohort study Diet Gut microbiota Health Physical activity

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Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

arXiv

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arXiv 2023年

作者： Alexeev, Yuri Amsler, Maximilian Barroca, Marco Antonio Bassini, Sanzio Battelle, Torey Camps, Daan Casanova, David Choi, Young Jai Chong, Frederic T. Chung, Charles Codella, Christopher Córcoles, Antonio D. Cruise, James Di Meglio, Alberto Duran, Ivan Eckl, Thomas Economou, Sophia Eidenbenz, Stephan Elmegreen, Bruce Fare, Clyde Faro, Ismael Fernández, Cristina Sanz Ferreira, Rodrigo Neumann Barros Fuji, Keisuke Fuller, Bryce Gagliardi, Laura Galli, Giulia Glick, Jennifer R. Gobbi, Isacco Gokhale, Pranav de la Puente Gonzalez, Salvador Greiner, Johannes Gropp, Bill Grossi, Michele Gull, Emanuel Healy, Burns Hermes, Matthew R. Huang, Benchen Humble, Travis S. Ito, Nobuyasu Izmaylov, Artur F. Javadi-Abhari, Ali Jennewein, Douglas Jha, Shantenu Jiang, Liang Jones, Barbara de Jong, Wibe Albert Jurcevic, Petar Kirby, William Kister, Stefan Kitagawa, Masahiro Klassen, Joel Klymko, Katherine Koh, Kwangwon Kondo, Masaaki Kürkçüoglu, Doga Murat Kurowski, Krzysztof Laino, Teodoro Landfield, Ryan Leininger, Matt Leyton-Ortega, Vicente Li, Ang Lin, Meifeng Liu, Junyu Lorente, Nicolas Luckow, Andre Martiel, Simon Martin-Fernandez, Francisco Martonosi, Margaret Marvinney, Claire Medina, Arcesio Castaneda Merten, Dirk Mezzacapo, Antonio Michielsen, Kristel Mitra, Abhishek Mittal, Tushar Moon, Kyungsun Moore, Joel Mostame, Sarah Motta, Mario Na, Young-Hye Nam, Yunseong Narang, Prineha Ohnishi, Yu-Ya Ottaviani, Daniele Otten, Matthew Pakin, Scott Pascuzzi, Vincent R. Pednault, Edwin Piontek, Tomasz Pitera, Jed Rall, Patrick Ravi, Gokul Subramanian Robertson, Niall Rossi, Matteo A.C. Rydlichowski, Piotr Ryu, Hoon Samsonidze, Georgy Sato, Mitsuhisa Saurabh, Nishant Sharma, Vidushi Sharma, Kunal Shin, Soyoung Slessman, George Steiner, Mathias Sitdikov, Iskandar Suh, In-Saeng Switzer, Eric D. Tang, Wei Thompson, Joel Todo, Synge Tran, Minh C. Trenev, Dimitar Trott, Christian Tseng, Huan-Hsin Tubman, Norm M. Tureci, Esin Valiñas, David García Vallecorsa, Sofia Wever, Christopher Wojciechowski, Konrad Wu, Xiaodi Yoo, Shinjae Yoshioka, Argonne National Laboratory LemontIL60439 United States Corporate Sector Research and Advance Engineering Robert Bosch GmbH Robert-Bosch-Campus 1 RenningenD-71272 Germany IBM Research RJ Rio de Janeiro20031-170 Brazil Centro Brasileiro de Pesquisas Físicas RJ Rio de Janeiro22290-180 Brazil CINECA via Magnanelli 6/3 BO Casalecchio di Reno40033 Italy Arizona State University TempeAZ United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Euskadi Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Physics Yonsei University Seoul03722 Korea Republic of University of Chicago ChicagoIL United States IBM Quantum IBM T.J. Watson Research Center Yorktown Heights NY10598 United States Cambridge Consultants part of Capgemini Invent Cambridge United Kingdom Geneva1211 Switzerland Virginia Tech BlacksburgVA24061 United States Los Alamos National Laboratory Los AlamosNM87545 United States Osaka University Osaka560-8531 Japan Department of Chemistry Chicago Center for Theoretical Chemistry University of Chicago ChicagoIL United States Fraunhofer ITWM Rheinland-Pfalz Kaiserslautern67663 Germany Infleqtion ChicagoIL60622 United States University of Illinois Urbana-Champaign United States University of Michigan Ann ArborMI48109 United States Research Office Oak Ridge National Laboratory One Bethel Valley Road Oak RidgeTN37831 United States Hyogo Kobe650-0047 Japan Chemical Physics Theory Group Department of Chemistry University of Toronto TorontoONM5S 3H6 Canada Department of Physical and Environmental Sciences University of Toronto Scarborough TorontoONM1C 1A4 Canada Brookhaven National Laboratory UptonNY United States Rutgers University New BrunswickNJ United States IBM Quantum Almaden Research Center San JoseCA95120 United States Applied Mathematics and Computational Research Division

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Characterization (materials science)

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Lattice QCD and Particle Physics

arXiv

引用

arXiv 2022年

作者： Kronfeld, Andreas S. Bhattacharya, Tanmoy Blum, Thomas Christ, Norman H. DeTar, Carleton Detmold, William Edwards, Robert Hasenfratz, Anna Lin, Huey-Wen Mukherjee, Swagato Orginos, Konstantinos Brower, Richard Cirigliano, Vincenzo Davoudi, Zohreh Jóo, Bálint Jung, Chulwoo Lehner, Christoph Meinel, Stefan Neil, Ethan T. Petreczky, Peter Richards, David G. Bazavov, Alexei Catterall, Simon Dudek, Jozef J. El-Khadra, Aida X. Engelhardt, Michael Fleming, George T. Giedt, Joel Gupta, Rajan Hansen, Maxwell T. Izubuchi, Taku Karsch, Frithjof Laiho, Jack Liu, Keh-Fei Meyer, Aaron S. Rinaldi, Enrico Savage, Martin Schaich, David Shanahan, Phiala E. Sharpe, Stephen R. Sufian, Raza Syritsyn, Sergey Van de Water, Ruth S. Wagman, Michael L. Weinberg, Evan Witzel, Oliver Aubin, Christopher Boyle, Peter Chandrasekharan, Shailesh Cloët, Ian C. Constantinou, Martha Cushman, Kimmy DeGrand, Thomas Fodor, Zoltan Foreman, Sam Gottlieb, Steven Hoying, Daniel Jang, Yong-Chull Jay, William I. Jin, Xiao-Yong Kelly, Christopher Kuti, Julius Lamm, Henry Lin, Meifeng Lin, Yin Lytle, Andrew T. Mackenzie, Paul Mandula, Jeffrey Meurice, Yannick Monahan, Christopher Morningstar, Colin Osborn, James C. Park, Sungwoo Simone, James N. Strelchenko, Alexei Tomii, Masaaki Vaquero, Alejandro Vranas, Pavlos Wang, Bigeng Wilcox, Walter Yoon, Boram Zhao, Yong Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Group T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics University of Connecticut StorrsCT06269 United States RIKEN BNL Research Center Brookhaven National Laboratory UptonNY11973 United States Department of Physics Columbia University New YorkNY10027 United States Department of Physics and Astronomy University of Utah Salt Lake CityUT84112 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF Institute for Artificial Intelligence and Fundamental Interactions Theory Center Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Physics Department Brookhaven National Laboratory UptonNY11973 United States Department of Physics College of William & Mary WilliamsburgVA23187 United States Department of Physics Center for Computational Science Boston University BostonMA02215 United States Institute of Nuclear Theory University of Washington SeattleWA98195 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN37831 United States Fakultät für Physik Universität Regensburg RegensburgD-93040 Germany Department of Physics University of Arizona TucsonAZ85721 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics Syracuse University SyracuseNY13244 United States Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics New Mexic

Lattice field theory provides a mathematically rigorous definition of quantum field theory, including gauge theories. The rigor provides a platform for computation of strongly-coupled gauge theories, not only quantum chromodynamics (QCD) at long-distances but also strongly-coupled sectors that might lie beyond the Standard Model (BSM) of particle physics. Indeed, the interpretation of many experiments in particle physics, nuclear physics, and astrophysics relies, often crucially, on results from lattice QCD or BSM. In quark-flavor physics, lattice QCD is essential for grounding theoretical predictions. Together with experimental measurements, lattice QCD is used to determine many of the fundamental parameters of the Standard Model's flavor sector: five out of six quark masses and the three mixing angles and CP-violating phase of the Cabibbo-Kobayashi-Maskawa (CKM) matrix. Further Standard-Model predictions requiring lattice QCD are for processes that could reveal signatures of new physics. This area of lattice QCD has become a precision science, as has the determination of the strong coupling, αs, which is key in many areas, for example for understanding Higgs-boson decay to gluons. Lattice-QCD calculations of the hadronic contributions to the anomalous magnetic moment of the muon are another area for which precision is both mandatory and achievable. While these contributions can be estimated via a combination of certain measurements and general theoretical concepts, a direct ab initio calculation from QCD is desirable to confirm robustly the tantalizingly large discrepancy between experiment and the Standard Model. Other indirect searches for new physics are complementary to quark-flavor physics and the muon anomalous magnetic moment. Lattice QCD is an essential tool for understanding newly discovered hadrons with quark content different from the usual baryons and mesons. Lattice-QCD calculations are needed to interpret bounds on electric dipole moments, proton dec

关键词： Calculations

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The NANOGrav 15 year Data Set: Removing pulsars one by one from the pulsar timing array

arXiv

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arXiv 2024年

作者： Agazie, Gabriella Anumarlapudi, Akash Archibald, Anne M. Arzoumanian, Zaven Baier, Jeremy G. Baker, Paul T. Bécsy, Bence Blecha, Laura Brazier, Adam Brook, Paul R. Burke-Spolaor, Sarah Casey-Clyde, J. Andrew Charisi, Maria Chatterjee, Shami Cohen, Tyler Cordes, James M. Cornish, Neil J. Crawford, Fronefield Cromartie, H. Thankful Crowter, Kathryn DeCesar, Megan E. Demorest, Paul B. Deng, Heling Dey, Lankeswar Dolch, Timothy Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Freedman, Gabriel E. Gardiner, Emiko C. Garver-Daniels, Nate Gentile, Peter A. Gersbach, Kyle A. Glaser, Joseph Good, Deborah C. Guertin, Lydia Gültekin, Kayhan Hazboun, Jeffrey S. Jennings, Ross J. Johnson, Aaron D. Jones, Megan L. Kaiser, Andrew R. Kaplan, David L. Kelley, Luke Zoltan Kerr, Matthew Key, Joey S. Laal, Nima Lam, Michael T. Lamb, William G. Larsen, Bjorn Lazio, T.W. Joseph Lewandowska, Natalia Liu, Tingting Lorimer, Duncan R. Luo, Jing Lynch, Ryan S. Ma, Chung-Pei Madison, Dustin R. McEwen, Alexander McKee, James W. McLaughlin, Maura A. McMann, Natasha Meyers, Bradley W. Meyers, Patrick M. Middleton, Hannah Mingarelli, Chiara M.F. Mitridate, Andrea Moore, Christopher J. Ng, Cherry Nice, David J. Ocker, Stella Koch Olum, Ken D. Pennucci, Timothy T. Perera, Benetge B.P. Pol, Nihan S. Radovan, Henri A. Ransom, Scott M. Ray, Paul S. Romano, Joseph D. Runnoe, Jessie C. Saffer, Alexander Sardesai, Shashwat C. Schmiedekamp, Ann Schmiedekamp, Carl Schmitz, Kai Shapiro-Albert, Brent J. Siemens, Xavier Simon, Joseph Siwek, Magdalena S. Sosa Fiscella, Sophia V. Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Susobhanan, Abhimanyu Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Unal, Caner Vallisneri, Michele Vecchio, Alberto Vigeland, Sarah J. Wahl, Haley M. Witt, Caitlin A. Wright, David Young, Olivia Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Newcastle University NE1 7RU United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics Oregon State University CorvallisOR97331 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Physics Department University of Florida GainesvilleFL32611 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Department of Physics New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States Department of Physics Montana State University BozemanMT59717 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States National Research Council Research Associate National Academy of Sciences WashingtonDC20001 United States Naval Research Laboratory WashingtonDC20375 United States Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Cana

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data. © 2024, CC BY.

关键词： Gravitational effects

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CP2K: An electronic structure and molecular dynamics software package quickstep: Efficient and accurate electronic structure calculations

arXiv

引用

arXiv 2020年

作者： Kühne, Thomas D. Iannuzzi, Marcella Ben, Mauro Del Rybkin, Vladimir V. Seewald, Patrick Stein, Frederick Laino, Teodoro Khaliullin, Rustam Z. Schütt, Ole Schiffmann, Florian Golze, Dorothea Wilhelm, Jan Chulkov, Sergey Bani-Hashemian, Mohammad Hossein Weber, Valéry Borštnik, Urban Taillefumier, Mathieu Jakobovits, Alice Shoshana Lazzaro, Alfio Pabst, Hans Müller, Tiziano Schade, Robert Guidon, Manuel Andermatt, Samuel Holmberg, Nico Schenter, Gregory K. Hehn, Anna Bussy, Augustin Belleflamme, Fabian Tabacchi, Gloria Glöß, Andreas Lass, Michael Bethune, Iain Mundy, Christopher J. Plessl, Christian Watkins, Matt VandeVondele, Joost Krack, Matthias Hutter, Jürg Dynamics of Condensed Matter and Center for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University Warburger Str. 100 PaderbornD-33098 Germany Department of Chemistry University of Zurich Winterthurerstrasse 190 ZürichCH-8057 Switzerland Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States IBM Research - Zurich Säumerstrasse 4 RüschlikonCH-8803 Switzerland Department of Chemistry McGill University CH-801 Sherbrooke St. West MontrealQCH3A 0B8 Canada Department of Materials ETH Zürich ZürichCH-8092 Switzerland Centre of Policy Studies Victoria University Melbourne Australia Department of Applied Physics Aalto University Otakaari 1 EspooFI-02150 Finland Institute of Theoretical Physics University of Regensburg Universitätsstraße 31 RegensburgD-93053 Germany School of Mathematics and Physics University of Lincoln Brayford Pool Lincoln United Kingdom Integrated Systems Laboratory ETH Zürich ZürichCH-8092 Switzerland Scientific IT Services ETH Zürich Zürich Switzerland ETH Zürich HPE Switzerland GmbH Basel Switzerland Intel Extreme Computing Software and Systems Zürich Switzerland Department of Computer Science and Paderborn Center for Parallel Computing Paderborn University Warburger Str. 100 PaderbornD-33098 Germany Department of Chemistry and Materials Science Aalto University P.O. Box 16100 Aalto00076 Finland Physical Science Division Pacific Northwest National Laboratory P. O. Box 999 RichlandWA99352 United States Department of Science and High Technology University of Insubria INSTM via Valleggio 9 ComoI-22100 Italy BASF SE Carl-Bosch-Straße 38 Ludwigshafen am RheinD-67056 Germany Hartree Centre Science and Technology Facilities Council United Kingdom Laboratory for Scientific Computing and Modelling Paul Scherrer Institute Villigen PSICH-5232 Switzerland

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is especially aimed at massively-parallel and linear-scaling electronic structure methods and state-of-the-art ab-initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension. Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic structure

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

arXiv

引用

arXiv 2021年

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

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

关键词： Learning systems

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Gaussian variant of freivalds' algorithm for efficient and reliable matrix product verification

arXiv

引用

arXiv 2017年

作者： Ji, Hao Mascagni, Michael Li, Yaohang Department of Computer Science Old Dominion University Departments of Computer Science Mathematics and Scientific Computing Florida State University Applied and Computational Mathematics Division National Institute of Standards and Technology

In this article, we consider the general problem of checking the correctness of matrix multiplication. Given three n × n matrices A, B, and C, the goal is to verify that A × B = C without carrying out the computationally costly operations of matrix multiplication and comparing the product A×B with C, term by term. This is especially important when some or all of these matrices are very large, and when the computing environment is prone to soft errors. Here we extend Freivalds' algorithm to a Gaussian Variant of Freivalds' Algorithm (GVFA) by projecting the product A × B as well as C onto a Gaussian random vector and then comparing the resulting vectors. The computational complexity of GVFA is consistent with that of Freivalds' algorithm, which is O(n2). However, unlike Freivalds' algorithm, whose probability of a false positive is 2-k, where k is the number of iterations. Our theoretical analysis shows that when A × B 6= C, GVFA produces a false positive on set of inputs of measure zero with exact arithmetic. When we introduce round-off error and floating point arithmetic into our analysis, we can show that the larger this error, the higher the probability that GVFA avoids false positives. Moreover, by iterating GVFA k times, the probability of a false positive decreases as pk, where p is a very small value depending on the nature of the fault on the result matrix and the arithmetic system's floating-point precision. Unlike deterministic algorithms, there do not exist any fault patterns that are completely undetectable with GVFA. Thus GVFA can be used to provide efficient fault tolerance in numerical linear algebra, and it can be efficiently implemented on modern computing architectures. In particular, GVFA can be very efficiently implemented on architectures with hardware support for fused multiply-add operations. Copyright © 2017, The Authors. All rights reserved.

关键词： Matrix algebra

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