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arXiv 2017年

作者： Wang, Han Zhang, Linfeng Han, Jiequn Weinan, E. Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Center for Data Science Beijing International Center for Mathematical Research Peking University Beijing Institute of Big Data Research Beijing100871 China

Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-performance classical molecular dynamics and quantum (path-integral) molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus, upon training, the potential energy and force field models can be used to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model. Copyright © 2017, The Authors. All rights reserved.

关键词： Potential energy

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Geomechanic application for hydraulic fracturing in lower Pematang formation, Central Sumatra Basin

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AIP Conference Proceedings 2018年第1期2023卷

作者： N. N. Fitri F. Fennita M. Adrian S. Supriyanto R. Ramadian 1Reservoir Geophysics Graduate Program Department of Physics Faculty of Mathematics and Natural Sciences (FMIPA) Universitas Indonesia Jakarta 10430 Indonesia 2Department of Physics Faculty of Mathematics and Natural Sciences (FMIPA) Universitas Indonesia Depok 16424 Indonesia 3Geology and Geophysics Program Faculty of Mathematics and Natural Sciences (FMIPA) Universitas Indonesia Depok 16424 Indonesia 4Geoscience Division PT Energi Mega Persada Jalan H.R. Rasuna Said Jakarta 12940 Indonesia

NN field is located onshore within the Malacca Strait Block. The field was discovered in 1990 by drilling N-01 exploration well which proven oil in the Manggala and Pematang Formations. In 1998, 3D Seismic was acquired and successfully identified three compartments in this field which separated by N-S faults. This study is focusing on Lower Pematang Formation which belongs to Pematang Group and deposited in braided-fluvial system. The Lower Pematang reservoir is tight sandstone with blocky log-type model. In this study Eaton method is used to calculate pore pressure prediction and to make transformation log Vp, Vs and density into Poisson’s Ratio and to calculate fracture pressure. In order to optimize the oil production, hydraulic fracturing stimulation was chosen and became proven technique in this reservoir. Further geomechanic study is required to support hydraulic fracturing jobs by providing a 3D model of pore pressure and fracture pressure. Several geo-mechanics rock properties such as pore pressure, Poisson’s ratio, Young’s Modulus and Fracture Pressure were calculated in wells and then propagate troughout NN fields. With 3D model, a robust recommendation on field deveopment via hydraulic fracturing can be achieved and oil recovery will optimum. Moreover, from 3D model, we could propose how much pressure can be applied in injection without damaging formation target.

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Erratum: “Stabilization of orthorhombic phase in single-crystal ZnSnN2 films” [AIP Advances 6, 075019 (2016)]

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AIP Advances 2018年第12期8卷

作者： Nancy Senabulya Nathaniel Feldberg Robert. A. Makin Yongsoo Yang Guangsha Shi Christina M. Jones Emmanouil Kioupakis James Mathis Roy Clarke Steven M. Durbin 1Applied Physics Program University of Michigan Ann Arbor Michigan 48109 USA 2Department of Physics University at Buffalo Buffalo New York 14260 USA 3Department of Electrical and Computer Engineering Western Michigan University Kalamazoo Michigan 49008-5329 USA 4Department of Physics University of Michigan Ann Arbor Michigan 48109 USA 5Department of Materials Science and Engineering University of Michigan Ann Arbor Michigan 48109 USA

Both these corrections are typographical errors that do not affect the validity of results in the original article.

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Enhancing the quality of nata de coco starter by channeling the oxygen into the bioreactor through agitation method

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AIP Conference Proceedings 2018年第1期2049卷

作者： Saharman Gea Khatarina Meldawati Pasaribu Kerista Sebayang Elisa Julianti Suci Aisyah Amaturahim Siti Utari Rahayu Yasir Arafat Hutapea 1Department of Chemistry Faculty of Mathematics and Natural Science University of Sumatera Utara Jl. Bioteknologi No. 1 Medan 20155 Indonesia 2Department of Physics Faculty of Mathematics and Natural Science University of Sumatera Utara Jl. Bioteknologi No. 1 Medan 20155 Indonesia 3Food Science and Biotechnology Study Program Faculty of Agriculture University of Sumatera Utara Jl. Prof. A. Sofyan No. 3 Medan 20155 Indonesia

The provision of a proper quality starter to produce nata de coco through agitation method has been done. The main concern of this method was its effect after the oxygen flow into the bioreactor containing glucose-based medium which was inoculated by Acetobacterxylinum. The 10-liter bioreactor was operated with a stirring speed of 200 rpm at room temperature. After 10 days, the broth within the bioreactor was harvested and analyzed by using an optical microscope, and the results were compared with an oxygen-free broth. Based on the observation of bacterial clusters placed on the watch glass, the oxygenated broth was discovered to remain compact with each other. In contrast, the oxygen-free broth of the bacterial cluster was found to be cloistered. When the bacterial cluster was used to inoculate the media, the 15-day observation performed different results.

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SciPy 1.0—Fundamental algorithms for scientific computing in python

arXiv

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arXiv 2019年

作者： Virtanen, Pauli Gommers, Ralf Oliphant, Travis E. Haberland, Matt Reddy, Tyler Cournapeau, David Burovski, Evgeni Peterson, Pearu Weckesser, Warren Bright, Jonathan van der Walt, Stéfan J. Brett, Matthew Wilson, Joshua Millman, K. Jarrod Mayorov, Nikolay Nelson, Andrew R.J. Jones, Eric Kern, Robert Larson, Eric Carey, C.J. Polat, Ilhan Feng, Yu Moore, Eric W. VanderPlas, Jake Laxalde, Denis Perktold, Josef Cimrman, Robert Henriksen, Ian Quintero, E.A. Harris, Charles R. Archibald, Anne M. Ribeiro, Antônio H. Pedregosa, Fabian van Mulbregt, Paul University of Jyväskylä University of Jyväskylä FI-40014 Finland Quansight LLC AustinTX United States Ultrasound Imaging Mayo Clinic RochesterMN55902 United States Electrical Engineering Brigham Young University ProvoUT84604 United States Enthought Inc. AustinTX78701 United States Anaconda Inc AustinTX78701 United States BioResource and Agricultural Engineering Department California Polytechnic State University San Luis ObispoCA93407 United States Department of Mathematics University of California Los AngelesCA90025 United States Los Alamos National Laboratory Los AlamosNM87544 United States Independent Researcher Higher School of Economics Myasnitskaya 20 Moscow101000 Russia Independent Researcher Saue Estonia Department of Mechanics and Applied Mathematics Institute of Cybernetics Tallinn Technical University Akadeemia Rd 21 Tallinn Estonia Independent Researcher NY United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States School of Psychology University of Birmingham Edgbaston BirmighamB15 2TT United Kingdom Division of Biostatistics University of California BerkeleyCA94720 United States WayRay LLC Skolkovo Innovation Center Moscow143026 Russia Australian Nuclear Science and Technology Organisation Locked Bag 2001 Kirrawee DCNSW2232 Australia Institute for Learning and Brain Sciences University of Washington SeattleWA98195 United States College of Information and Computing Sciences University of Massachusetts Amherst AmherstMA01002 United States Berkeley Center for Cosmological Physics University of California BerkeleyCA94720 United States Bruker Biospin Corp. 15 Fortune Drive BillericaMA01821 United States University of Washington SeattleWA98195 United States New Technologies - Research Centre University of West Bohemia Univerzitní 8 Plzeň306 14 Czech Republic Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX7

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments. Copyright © 2019, The Authors. All rights reserved.

关键词： Open source software

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EEG channels reduction using PCA to increase XGBoost’s accuracy for stroke detection

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AIP Conference Proceedings 2017年第1期1862卷

作者： N. Fitriah S. K. Wijaya M. I. Fanany C. Badri M. Rezal 1Biomedical Engineering Study Program Universitas Indonesia Jl. Salemba Raya No. 4 Jakarta 10430 DKI Jakarta Indonesia 2Department of Physics Faculty of Mathematics and Natural Sciences (FMIPA) Universitas Indonesia Depok 16424 Indonesia 3Faculty of Computer Science Universitas Indonesia Depok 16424 Indonesia

In Indonesia, based on the result of Basic Health Research 2013, the number of stroke patients had increased from 8.3 ‰ (2007) to 12.1 ‰ (2013). These days, some researchers are using electroencephalography (EEG) result as another option to detect the stroke disease besides CT Scan image as the gold standard. A previous study on the data of stroke and healthy patients in National Brain Center Hospital (RS PON) used Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), and Delta-Theta-Alpha-Beta Ratio (DTABR) as the features for classification by an Extreme Learning Machine (ELM). The study got 85% accuracy with sensitivity above 86 % for acute ischemic stroke detection. Using EEG data means dealing with many data dimensions, and it can reduce the accuracy of classifier (the curse of dimensionality). Principal Component Analysis (PCA) could reduce dimensionality and computation cost without decreasing classification accuracy. XGBoost, as the scalable tree boosting classifier, can solve real-world scale problems (Higgs Boson and Allstate dataset) with using a minimal amount of resources. This paper reuses the same data from RS PON and features from previous research, preprocessed with PCA and classified with XGBoost, to increase the accuracy with fewer electrodes. The specific fewer electrodes improved the accuracy of stroke detection. Our future work will examine the other algorithm besides PCA to get higher accuracy with less number of channels.

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Elemental detection of arabica and robusta green bean coffee using laser-induced plasma spectroscopy

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AIP Conference Proceedings 2017年第1期1801卷

作者： Syahrun Nur Abdulmadjid Hesti Meilina Rinda Hedwig Koo Hendrik Kurniawan 1Department of Physics Faculty of Mathematics and Natural Sciences Syiah Kuala University Nangroe Aceh Darussalam 23111 Indonesia 2Department of Chemical Engineering Graduate Program of Syiah Kuala University Darussalam Nangroe Aceh Darussalam 23111 Indonesia 3Department of Computer Engineering Bina Nusantara University Jakarta 14810 Indonesia 4Research Center of Maju Makmur Mandiri Foundation Jakarta 11630 Indonesia

The elemental detection of green bean of arabica and robusta coffee from Gayo Highland, Aceh-Indonesia, has been identified by using fundamental Nd-YAG Laser at 10 Torr of surrounding air gas pressure for distinguishing the characteristics of both coffees. As the preliminary study, we have detected the elements of K 766.49 nm, Na 588.9 nm, Ca 393.3 nm, CN band at 388.3 nm, N 337.13 nm and C 247.8 nm of both coffees. It is noticed that the order of elements concentration from highest to lowest are Ca>K>CN> Na>N> C for arabica and K>Ca>CN >Na>C>N for robusta. The emission intensity of K 766.49 nm is almost same for both of coffee. However, the emission intensity of Na 588.9 nm is lower in Arabica coffee. To distinguish the Arabica coffee and Robusta Coffee, we take the ratio intensity of K/C, Na/C, CN/C, and Ca/C. It is found that the ratio intensities of CN/C and Ca/C in arabica bean are significantly different with robusta bean. That ratio intensities can be used as a marker to discriminate kind of coffee. We also noted that the arabica green bean is 1.3 harder than robusta green bean. These findings prove that the technique of laser-induced plasma spectroscopy can be used to make rapid identification of elements in coffee and can potentially be applied to measure the concentration of blended coffee for the purpose of authentication.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Journeys from quantum optics to quantum technology

arXiv

引用

arXiv 2017年

作者： Barnett, Stephen M. Beige, Almut Ekert, Artur Garraway, Barry M. Keitel, Christoph H. Kendon, Viv Lein, Manfred Milburn, Gerard J. Moya-Cessa, Héctor M. Murao, Mio Pachos, Jiannis K. Palma, G. Massimo Paspalakis, Emmanuel Phoenix, Simon J.D. Piraux, Bernard Plenio, Martin B. Sanders, Barry C. Twamley, Jason Vidiella-Barranco, A. Kim, M.S. School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom School of Physics and Astronomy University of Leeds LeedsLS2 9JT United Kingdom Mathematical Institute University of Oxford Centre for Quantum Technologies National University of Singapore Department of Physics and Astronomy University of Sussex Falmer BrightonBN1 9QH United Kingdom Max-Planck-Institut für Kernphysik Saupfercheckweg 1 Heidelberg69117 Germany Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom Institute for Theoretical Physics Leibniz Universität Hannover Hannover30167 Germany Centre for Engineered Quantum Systems School of Mathematics and Physics University of Queensland 4072 Australia Instituto Nacional de Astrofísica Óptica y Electrónica Calle Luis Enrique Erro No. 1 Sta. Ma. Tonantzintla Pue.CP 72840 Mexico Department of Physics Graduate School of Science University of Tokyo Japan NEST Istituto Nanoscienze CNR Dipartimento di Fisica e Chimica Università degli Studi di Palermo via Archirafi 36 PalermoI-90123 Italy Materials Science Department School of Natural Sciences University of Patras Patras265 04 Greece Department of Applied Mathematics and Sciences Khalifa University P.O. Box: 127788 Abu Dhabi United Arab Emirates Universite Catholique de Louvain Louvain-la-NeuveB-1348 Belgium Universität Ulm Albert-Einstein-Allee 11 Ulm89069 Germany Institute for Quantum Science and Technology University of Calgary ABT2N 1N4 Canada Hefei National Laboratory for Physical Sciences at Microscale University of Science and Technology of China Hefei Anhui230026 China Shanghai Branch CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Program in Quantum Information Science Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada Centre for Engineered Quantum Systems Department of Physics and

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Reduced modeling of transitional exact coherent states in shear flow 15

Reduced modeling of transitional exact coherent states in sh...

引用

15th European Turbulence Conference, ETC 2015

作者： Beaume, Cédric Chini, Greg P. Julien, Keith Knobloch, Edgar Department of Aeronautics Imperial College London LondonSW7 2AZ United Kingdom Department of Mechanical Engineering and Program in Integrated Applied Mathematics University of New Hampshire DurhamNH03824 United States Department of Applied Mathematics University of Colorado at Boulder BoulderCO80309 United States Department of Physics University of California BerkeleyCA94720 United States

In parallel shear flows, the lower branch solution follows simple streamwise dynamics. A decomposition of this solution into Fourier modes in this direction yields modes whose amplitudes scale with inverse powers of the Reynolds number. We use this scaling to derive a reduced model for exact coherent structures in general parallel shear flows. The reduced model is regularized by retaining higher order viscous terms. Both lower branch and upper branch solutions are captured and studied. © TU Delft.

关键词： Shear flow

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