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Electron spin mediated distortion in metallic systems

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arXiv 2019年

作者： Anand, G. Eisenbach, Markus Goodall, Russell Freeman, Colin L. Department of Metallurgy and Materials Engineering Indian Institute of Engineering Science and Technology-Shibpur Howrah WB India Department of Materials Science and Engineering University of Sheffield United Kingdom Warwick Centre for Predictive Modelling University of Warwick United Kingdom Scientific Computing Group Centre for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States

The deviation of positions of atoms from their ideal lattice sites in crystalline solid state systems causes distortion and can lead to variation in structural [1] and functional properties [2]. Distortion in molecular systems has been traditionally understood in the term of Jahn-Teller distortion [3], while for the one-dimensional chain of metals, the Peierls distortion mechanism has been proposed [4]. In real three dimensional metallic systems, a fundamental description of the distortion is missing, which we need to design alloys with attractive structural properties. The present investigation presents the evolution of distortion in metallic systems in terms of magnetovolume effects arising due to magnetic ground-state of the system. Particularly the significant distortion in the case of Cr due to presence of other transition metals is seen. Cr with transition metals as a candidate system to study the correlation between charge disproportion, spin-flip, magnetovolume effects and Fermi surface nesting. This study provides a novel explanation of strengthening effect of Cr observed in alloys due to its unique magnetic properties. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic moments

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

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arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Sadeghi, Delaram Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Hussain, Sadiq Zare, Assef Sani, Zahra Alizadeh Khozeimeh, Fahime Nahavandi, Saeid Rajendra Acharya, U. Gorriz, Juan M. The Data Science and Computational Intelligence Institute University of Granada Spain The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Deakin University VIC3217 Australia The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran System Administrator at Dibrugarh University Assam786004 India Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran The School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Dept. of Psychiatry University of Cambridge United Kingdom

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed. © 2020, CC BY-NC-ND.

关键词： COVID-19

A lowest-order mixed finite element method for the elastic transmission eigenvalue problem

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arXiv 2018年

作者： Xi, Yingxia Ji, Xia School of Science Nanjing University of Science and Technology Nanjing210094 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China

The goal of this paper is to develop numerical methods computing a few smallest elastic interior transmission eigenvalues, which are of practical importance in inverse elastic scattering theory. The problem is challenging since it is nonlinear, non-self-adjoint, and of fourth order. In this paper, we construct a lowest-order mixed finite element method which is close to the Ciarlet-Raviart mixed finite element method. This scheme is based on Lagrange finite elements and is one of the less expensive methods in terms of the amount of degrees of freedom. Due to the non-self-adjointness, the discretization of elastic transmission eigenvalue problem leads to a non-classical mixed method which does not fit into the framework of classical theoretical analysis. In stead, we obtain the convergence analysis based on the spectral approximation theory of compact operators. Numerical examples are presented to verify the theory. Both real and complex eigenvalues can be *** Codes 65N25, 65N30, 47B07 Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

A foundation model for atomistic materials chemistry

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arXiv 2023年

作者： Batatia, Ilyes Benner, Philipp Chiang, Yuan Elena, Alin M. Kovács, Dávid P. Riebesell, Janosh Advincula, Xavier R. Asta, Mark Avaylon, Matthew Baldwin, William J. Berger, Fabian Bernstein, Noam Bhowmik, Arghya Blau, Samuel M. Cărare, Vlad Darby, James P. De, Sandip Pia, Flaviano Della Deringer, Volker L. Elijošius, Rokas El-Machachi, Zakariya Falcioni, Fabio Fako, Edvin Ferrari, Andrea C. Genreith-Schriever, Annalena George, Janine Goodall, Rhys E.A. Grey, Clare P. Grigorev, Petr Han, Shuang Handley, Will Heenen, Hendrik H. Hermansson, Kersti Holm, Christian Hofmann, Stephan Jaafar, Jad Jakob, Konstantin S. Jung, Hyunwook Kapil, Venkat Kaplan, Aaron D. Karimitari, Nima Kermode, James R. Kroupa, Namu Kullgren, Jolla Kuner, Matthew C. Kuryla, Domantas Liepuoniute, Guoda Margraf, Johannes T. Magdău, Ioan-Bogdan Michaelides, Angelos Harry Moore, J. Naik, Aakash A. Niblett, Samuel P. Norwood, Sam Walton O’Neill, Niamh Ortner, Christoph Persson, Kristin A. Reuter, Karsten Rosen, Andrew S. Schaaf, Lars L. Schran, Christoph Shi, Benjamin X. Sivonxay, Eric Stenczel, Tamás K. Svahn, Viktor Sutton, Christopher Swinburne, Thomas D. Tilly, Jules van der Oord, Cas Vargas, Santiago Varga-Umbrich, Eszter Vegge, Tejs Vondrák, Martin Wang, Yangshuai Witt, William C. Zills, Fabian Csányi, Gábor Engineering Laboratory University of Cambridge Trumpington St and JJ Thomson Ave Cambridge United Kingdom Berlin Germany Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Mathematics Department University of British Columbia 1984 Mathematics Rd VancouverBCV6T 1Z2 Canada Institute of Condensed Matter Theory and Solid State Optics Friedrich Schiller University Jena Germany Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Bayreuth Germany Fritz-Haber-Institute of the Max-Planck-Society Berlin Germany Energy Technologies Area Lawrence Berkeley National Laboratory BerkeleyCA94720 United States U. S. Naval Research Laboratory WashingtonDC20375 United States Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Road Cambridge United Kingdom Cavendish Laboratory University of Cambridge J. J. Thomson Ave Cambridge United Kingdom Department of Materials Science and Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom Chemix Inc. SunnyvaleCA94085 United States Inorganic Chemistry Laboratory Department of Chemistry University of Oxford OxfordOX1 3QR United Kingdom Scientific Computing Department Science and Technology Facilities Council Daresbury Laboratory Keckwick Lane DaresburyWA4 4AD United Kingdom BASF SE Carl-Bosch-Straße 38 Ludwigshafen67056 Germany Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Department of Chemistry and Biochemistry University of South Carolina South Carolina29208 United States Lennard-Jones Centre University of Cambridge Trinity Ln CambridgeCB2 1TN United Kingdom Institute for Computational Physics University of Stuttgart Stuttgart70569 Germany Department of Chemistry–Ångström Uppsala University Box 538 Uppsala

Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest;and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials. We demonstrate the power of the MACE-MP-0 model — and its qualitative and at times quantitative accuracy — on a diverse set problems in the physical sciences, including the properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box and as a starting or "foundation model" for any atomistic system of interest and is thus a step towards democratising the revolution of ML force fields by lowering the barriers to entry. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximation

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arXiv 2019年

作者： Tang, Shanshan Li, Bo Yu, Haijun Software Development Center Industrial and Commercial Bank of China No. 16 Building of ZhongGuanCun Software Park Haidian District Beijing100193 China Huawei Technologies Co. Ltd Bai Ruida Apartment Bantian Street Longgang District Shenzhen518129 China NCMIS & LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

In a previous study [B. Li, S. Tang and H. Yu, Commun. Comput. Phy. 27(2):379-411, 2020], it is shown that deep neural networks built with rectified power units (RePU) as activation functions can give better approximation for sufficient smooth functions than those built with rectified linear units, by converting polynomial approximations using power series into deep neural networks with optimal complexity and no approximation error. However, in practice, power series approximations are not easy to obtain due to the associated stability issue. In this paper, we propose a new and more stable way to construct RePU deep neural networks based on Chebyshev polynomial approximations. By using a hierarchical structure of Chebyshev polynomial approximation in frequency domain, we obtain efficient and stable deep neural network construction, which we call ChebNet. The approximation of smooth functions by ChebNets is no worse than the approximation by deep RePU nets using power series. On the same time, ChebNets are much more stable. Numerical results show that the constructed ChebNets can be further fine-tuned to obtain much better results than those obtained by tuning deep RePU nets constructed by power series approach. As spectral accuracy is hard to obtain by direct training of deep neural networks, ChebNets provide a practical way to obtain spectral accuracy, it is expected to be useful in real applications that require efficient approximations of smooth functions. © 2019, CC BY-NC-ND.

关键词： Chebyshev polynomials

Metrics Reloaded: Recommendations for image analysis validation

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette 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 Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab 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 Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

One-Bit precoding and constellation range design for massive MIMO with QAM Signaling

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arXiv 2018年

作者： Sohrabi, Foad Liu, Ya-Feng Yu, Wei Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S3G4 Canada State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics Systems Science Chinese Academy of Sciences Beijing100190 China

The use of low-resolution digital-to-analog converters (DACs) for transmit precoding provides crucial energy efficiency advantage for massive multiple-input multiple-output (MIMO) implementation. This paper formulates a quadrature amplitude modulation (QAM) constellation range and one-bit symbol-level precoding design problem for minimizing the average symbol error rate (SER) in downlink massive MIMO transmission. A tight upper-bound for SER with low-resolution DAC precoding is first derived. The derived expression suggests that the performance degradation of one-bit precoding can be interpreted as a decrease in the effective minimum distance of the QAM constellation. Using the obtained SER expression, we propose a QAM constellation range design for the singleuser case. It is shown that in the massive MIMO limit, a reasonable choice for constellation range with one-bit precoding is that of the infinite-resolution precoding with per-symbol power constraint, but reduced by a factor of p 2/π or about 0:8. The corresponding minimum distance reduction translates to about 2dB gap between the performance of one-bit precoding and infinite-resolution precoding. This paper further proposes a low-complexity heuristic algorithm for one-bit precoder design. Finally, the proposed QAM constellation range and precoder design are generalized to the multi-user downlink. We propose to scale the constellation range for infinite-resolution zero-forcing (ZF) precoding with per-symbol power constraint by the same factor of p 2/π for one-bit precoding. The proposed one-bit precoding scheme is shown to be within 2dB of infinite-resolution ZF. In term of number of antennas, one-bit precoding requires about 50% more antennas to achieve the same performance as infinite-resolution precoding. Copyright © 2018, The Authors. All rights reserved.

关键词： Quadrature amplitude modulation

High-performance dune modules for solving large-scale, strongly anisotropic elliptic problems with applications to aerospace composites

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arXiv 2019年

作者： Butler, R. Dodwell, T. Sandhu, A. Scheichl, R. Seelinger, L. Reinarz, Anne Department of Mechanical Engineering University of Bath United Kingdom College of Engineering Mathematics and Physical Sciences University of Exeter United Kingdom Alan Turing Institute LondonNW1 2DB United Kingdom Institute of Informatics Technical University of Munich Germany Institute for Scientific Computing University of Heidelberg Germany Department of Mathematical Sciences University of Bath United Kingdom

The key innovation in this paper is an open-source, high-performance iterative solver for high contrast, strongly anisotropic elliptic partial differential equations implemented within dune-pdelab. The iterative solver exploits a robust, scalable two-level additive Schwarz preconditioner, GenEO (Spillane et al. 2014). The development of this solver has been motivated by the need to overcome the limitations of commercially available modeling tools for solving structural analysis simulations in aerospace composite applications. Our software toolbox dune-composites encapsulates the mathematical complexities of the underlying packages within an efficient C++ framework, providing an application interface to our new high-performance solver. We illustrate its use on a range of industrially motivated examples, which should enable other scientists to build on and extend dune-composites and the GenEO preconditioner for use in their own applications. We demonstrate the scalability of the solver on more than 15,000 cores of the UK national supercomputer Archer, solving an aerospace composite problem with over 200 million degrees of freedom in a few minutes. This scale of computation brings composites problems that would otherwise be unthinkable into the feasible range. To demonstrate the wider applicability of the new solver, we also confirm the robustness and scalability of the solver on SPE10, a challenging benchmark in subsurface flow/reservoir simulation. Copyright © 2019, The Authors. All rights reserved.

关键词： Domain decomposition methods

Data-driven polynomial chaos expansions: A weighted least-square approximation

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arXiv 2018年

作者： Guo, Ling Liu, Yongle Zhou, Tao Department of Mathematics Shanghai Normal University Shanghai China Department of Mathematics Southern University of Science and Technology Shenzhen China LSEC Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this work, we combine the idea of data-driven polynomial chaos expansions with the weighted least-square approach to solve uncertainty quantification (UQ) problems. The idea of data-driven polynomial chaos is to use statistical moments of the input random variables to develop an arbitrary polynomial chaos expansion, and then use such data-driven bases to perform UQ computations. Here we adopt the bases construction procedure by following [1], where the bases are computed by using matrix operations on the Hankel matrix of moments. Different from previous works, in the postprocessing part, we propose a weighted least-squares approach to solve UQ problems. This approach includes a sampling strategy and a least-squares solver. The main features of our approach are two folds: On one hand, our sampling strategy is independent of the random input. More precisely, we propose to sampling with the equilibrium measure, and this measure is also independent of the data-driven bases. Thus, this procedure can be done in prior (or in a off- line manner). On the other hand, we propose to solve a Christoffel function weighted least-square problem, and this strategy is quasi-linearly stable - the required number of PDE solvers depends linearly (up to a logarithmic factor) on the number of (data-driven) bases. This new approach is thus promising in dealing with a class of problems with epistemic uncertainties. Several numerical tests are presented to show the effectiveness of our approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Uncertainty analysis