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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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S-Cluster++: a fast program for solving the cluster containment problem for phylogenetic networks

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Bioinformatics (Oxford, England) 2018年第17期34卷 i680-i686页

作者： Hongwei Yan Andreas D M Gunawan Louxin Zhang Department of Mathematics National University of Singapore Singapore. Computational Biology Program National University of Singapore Singapore.

Motivation:Comparative genomic studies indicate that extant genomes are more properly considered to be a fusion product of random mutations over generations (vertical evolution) and genomic material transfers between individuals of different lineages (reticulate transfer). This has motivated biologists to use phylogenetic networks and other general models to study genome evolution. Two fundamental algorithmic problems arising from verification of phylogenetic networks and from computing Robinson-Foulds distance in the space of phylogenetic networks are the tree and cluster containment problems. The former asks how to decide whether or not a phylogenetic tree is displayed in a phylogenetic network. The latter is to decide whether a subset of taxa appears as a cluster in some tree displayed in a phylogenetic network. The cluster containment problem (CCP) is also closely related to testing the infinite site model on a recombination network. Both the tree containment and CCP are NP-complete. Although the CCP was introduced a decade ago, there has been little progress in developing fast algorithms for it on arbitrary phylogenetic networks. Results:In this work, we present a fast computer program for the CCP. This program is developed on the basis of a linear-time transformation from the small version of the CCP to the SAT problem. Availability and implementation:The program package is available for download on http://***/∼matzlx/ccp.

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High order discretization methods for spatial-dependent epidemic models

arXiv

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arXiv 2019年

作者： Takács, Bálint Hadjimichael, Yiannis Applied Analysis and Computational Mathematics MTA-ELTE Numerical Analysis and Large Networks Research Group Eötvös Loránd University Pázmány Péter sétány 1/C Budapest1117 Hungary Department of Differential Equations Budapest University of Technology and Economics Egry József u. 1 Budapest1111 Hungary MTA-ELTE Numerical Analysis and Large Networks Research Group Eötvös Loránd University Pázmány Péter sétány 1/C Budapest1117 Hungary Mohrenstraße 39 Berlin10117 Germany

In this paper, an epidemic model with spatial dependence is studied and results regarding its stability and numerical approximation are presented. We consider a generalization of the original Kermack and McKendrick model in which the size of the populations differs in space. The use of local spatial dependence yields a system of partial-differential equations with integral terms. The uniqueness and qualitative properties of the continuous model are analyzed. Furthermore, different spatial and temporal discretizations are employed, and step-size restrictions for the discrete model's positivity, monotonicity preservation, and population conservation are investigated. We provide sufficient conditions under which high-order numerical schemes preserve the stability of the computational process and provide sufficiently accurate numerical approximations. computational experiments verify the convergence and accuracy of the numerical *** Codes 65M12, 65L07, 65L06, 91D25 Copyright © 2019, The Authors. All rights reserved.

关键词： Discrete event simulation

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Mapping Multiple Wild Pig Species’ Population Dynamics in Southeast Asia During the African Swine Fever Outbreak (2018–2024)

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Conservation Letters 2025年第3期18卷

作者： Zoë E. Lieb Erik Meijaard Jedediah F. Brodie Adi Shabrani Jayasilan Mohd-Azlan Jatna Supriatna Matthew John Struebig Nicolas J. Deere Katie L. Spencer Suipeng Heon Lok-Jinn Wong Suzika Juiling Andrew Hearn Camille N. Z. Coudrat Agus Sudibyo Jati Matthew Linkie Dusit Ngoprasert Dhritiman Das Oliver R. Wearn Russell J. Gray Al John C. Cabanas Andaman Chankhao Apinya Saisamorn Badrul Azhar Benjamin P. Y.-H. Lee Benoit Goossens Carl Traeholt David W. MacDonald Emilia A. Lastica-Ternura Fernando Garcia-Gil Freddy Pattiselanno Gabriella Fredriksson Glyn Davies Harry Hilser Jamiee Wheelhouse Jan van der Ploeg John Carlo Redeña-Santos Jonathan M. Moore Karmila Parakkasi Laura Marie Berman Samuel Xin Tham Lee Liam J. Hughes Lukemann Haqeem Alen Marc Ancrenaz Marcus A. H. Chua Markus Handschuh Matthew Ward Mohamad Arif Rifqi Mohammad Aliyuddin Bin Jaini Muhammad Syazwan Bin Omar Nantachai Pongpattananurak NayMyo Shwe Olivia Z. Daniel Pablo Sinovas Parag Deka Radinal Ret Thaung Robert M. Ewers Romain Legrand Ronglarp Sukmasuang Sally Soo Kaicheen Salwa Khalid Saw Soe Aung Sheherazade Sheherazade Stuart J. Davies Thiemo Braasch Thomas N. E. Gray Tim Redford Ulmar Grafe Xiaoyang Song Matthew Scott Luskin School of the Environment University of Queensland Brisbane QLD Australia Borneo Futures Bandar Seri Begawan Brunei Durrell Institute of Conservation and Ecology (DICE) University of Kent Canterbury UK Division of Biological Sciences and Wildlife Biology Program University of Montana Missoula Montana USA World Wildlife Fund Malaysia Sabah Office Kota Kinabalu Malaysia Institute of Biodiversity and Environmental Conservation Universiti Malaysia Sarawak Kota Samarahan Malaysia Department of Biology Faculty of Mathematics and Natural Sciences University of Indonesia University of Indonesia Depok Indonesia Southeast Asia Rainforest Research Partnership (SEARPP) Kota Kinabalu Malaysia Wildlife Conservation Research Unit Department of Biology University of Oxford Oxford UK Association Anoulak Luang Prabang Lao PDR SSC Wild Pig Specialist Group IUCN Heidelberg Germany Wildlife Conservation Society Indonesia Program Jakarta Indonesia Conservation Ecology Program School of Bioresources and Technology King Mongkut's University of Technology Thonburi Bangkok Thailand Pygmy Hog Conservation Programme Durrell Wildlife Conservation Trust Jersey UK Fauna & Flora International Vietnam Program Hanoi Vietnam Save Vietnam's Wildlife Hoa Lư Vietnam Global Initiative Against Transnational Organized Crime (GI-TOC) Geneva Switzerland Mathematics and Natural Sciences Southern Luzon State University Lucban Philippines Department of Forest Biology Kasetsart University Bangkok Thailand Universiti Putra Malaysia Serdang Malaysia Wildlife & Natural Heritage Royal Commission for AlUla Riyadh Saudi Arabia Danau Girang Field Centre Kota Kinabalu Malaysia Organisms and Environment Division Cardiff School of Biosciences Cardiff University Cardiff UK Research and Conservation Division Copenhagen Zoo Copenhagen Denmark University of the Philippines Los Baños Philippines D'ABOVILLE Foundation and Demo Farm Inc. San Jose Philippines Universitas Papua Manokwari West Papua In

The 2018 arrival of African swine fever (ASF) in China was followed by reports of wild pig deaths across most countries in Southeast Asia. However, the magnitude and duration of population-level impacts of ASF on wild pig species remain unclear. To elucidate the spatiotemporal spread of ASF in the region for native pig species, we gathered qualitative information on wild pig population dynamics in Southeast Asia between 2018 and 2024 from 88 expert elicitation questionnaires representing sites in 11 countries. Peak reported population declines occurred in 2021 and 2022, with more than half of respondents reporting declining wild pig populations, far higher than in earlier years. The reported declines waned to 44.23% in 2024, whereas simultaneously, the number of populations reported to be “increasing” increased from 11.3%–13.2% in 2019–2022 to 28.9% in 2024. These reports suggest that the ASF outbreak may have peaked for wild boars and bearded pigs in mainland Southeast Asia, Borneo, and Sumatra, with some subsequent recovery. However, the disease is still expanding into the ranges of island endemic species, such as new reports for the Sulawesi warty pig ( Sus celebensis ) in September of 2024. Island endemics remain particularly vulnerable to extinction from ASF and require urgent monitoring and conservation action.

关键词： bearded pig (Sus barbatus) biodiversity monitoring expert elicitation survey predator-prey depletion Suidaeisland endemics threatened and endangered species wildlife disease ecology wild boar (Sus scrofa) zoonotic virus

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Evolution of electronic structure and electron-phonon coupling in ultrathin tetragonal CoSe films

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Physical Review Materials 2018年第11期2卷 114005-114005页

作者： L. Shen C. Liu F. W. Zheng X. Xu Y. J. Chen S. C. Sun L. Kang Z. K. Liu Q. K. Xue L. L. Wang Y. L. Chen L. X. Yang State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China School of Physical Science and Technology CAS-Shanghai Science Research Center ShanghaiTech University Shanghai 201210 China Collaborative Innovation Center of Quantum Matter Beijing 100084 China Department of Physics Clarendon Laboratory University of Oxford Parks Road Oxford OX1 3PU United Kingdom

Two-dimensional materials have attracted considerable research attention recently due to their extraordinary physical and chemical properties. In this paper, we systematically investigate the electronic structure of ultrathin tetragonal CoSe films with different thicknesses that are isostructural to two-dimensional high-temperature superconductor FeSe using high-resolution angle-resolved photoemission spectroscopy, scanning tunneling microscopy, and ab initio calculation. Interestingly, the nonsymmorphic symmetry of the CoSe monolayer protects both exotic saddle band degeneracy and Dirac points, realizing two-dimensional Dirac fermions. Our temperature and film-thickness-dependent measurements reveal momentum-dependent electron-phonon coupling in CoSe films, which is enhanced with reduced film thickness. Moreover, the electronic states in the CoSe monolayer strongly couple to high-frequency phonons in the SrTiO3 substrate, similar to the situation in the FeSe monolayer. Our results not only present a platform to investigate the electronic properties in close vicinity of the high-temperature superconductor FeSe monolayer, but also shed light on the understanding of the coupling between two-dimensional materials and substrates beneath.

关键词： Electronic structure Superconductivity Surface & interfacial phenomena Interfaces Thin films Ultrathin films Angle-resolved photoemission spectroscopy Density functional theory Molecular beam epitaxy Scanning tunneling microscopy Scanning tunneling spectroscopy

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Solving High-Dimensional Partial Differential Equations Using Deep Learning

arXiv

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arXiv 2017年

作者： Han, Jiequn Jentzen, Arnulf Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics ETH Zürich Rämistrasse 101 Zürich8092 Switzerland Department of Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the "curse of dimensionality". This paper introduces a deep learning-based approach that can handle general high-dimensional parabolic PDEs. To this end, the PDEs are reformulated using backward stochastic differential equations and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function. Numerical results on examples including the nonlinear Black-Scholes equation, the Hamilton-Jacobi-Bellman equation, and the Allen-Cahn equation suggest that the proposed algorithm is quite effective in high dimensions, in terms of both accuracy and cost. This opens up new possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their inter-relationships. Copyright © 2017, The Authors. All rights reserved.

关键词： Partial differential equations

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Author Correction: The new nanophysiology: regulation of ionic flow in neuronal subcompartments

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Nature reviews. Neuroscience 2019年第8期20卷 509页

作者： David Holcman Rafael Yuste David Holcman is at the Computational Biology and Applied Mathematics (IBENS) Ecole Normale Superieure 75005 Paris France. david.holcman@ens.fr. Rafael Yuste is at the Neurotechnology Center Department of Biological Sciences and Neuroscience Columbia University New York New York 10027 USA.

In this article, the origin of some of the presented modelling information has been stated and more details have been provided to understand how the curve in Box 2 and the electrodiffusion-related curves in Figure 3 w...

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Phonon Quasiparticles from First-Principles:A Case Study of Beryllium under Extreme Conditions

Phonon Quasiparticles from First-Principles:A Case Study of ...

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第26届国际高压科学技术大会、第8届亚洲高压科技学术会议暨第19届中国高压科学技术会议(The 26th International Conference on High Pressure Science & Technology)(AIRAPT 26)(ACHPR 8)(CHPC 19)

作者： Dong-Bo Zhang Yong Lu Tao Sun Ping Zhang Peihong Zhang Renata M.Wentzcovitch Beijing Computational Science Research Center Beijing 100193China Key Laboratory of Computational Geodynamics UCASBeijing 100049China Institute of Applied Physics and Computational Mathematics Beijing 100088China Department of Physics University at BuffaloState University of New YorkNew York 14260USA Department of Applied Physics and Applied Mathematics Columbia UniversityNew York10027USA

We developed a hybrid approach to quantitatively characterize phonon quasiparticles by combing first-principles molecular dynamics and lattice dynamics [1].

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Randomized local model order reduction

arXiv

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arXiv 2017年

作者： Buhr, Andreas Smetana, Kathrin Institute for Computational and Applied Mathematics University of Münster Einsteinstraße 62 Münster48149 Germany Department of Applied Mathematics University of Twente P.O. Box 217 Enschede7500 AE Netherlands

In this paper we propose local approximation spaces for localized model order reduction procedures such as domain decomposition and multiscale methods. Those spaces are constructed from local solutions of the partial differential equation (PDE) with random boundary conditions, yield an approximation that converges provably at a nearly optimal rate, and can be generated at close to optimal computational complexity. In many localized model order reduction approaches like the generalized finite element method, static condensation procedures, and the multiscale finite element method local approximation spaces can be constructed by approximating the range of a suitably defined transfer operator that acts on the space of local solutions of the PDE. Optimal local approximation spaces that yield in general an exponentially convergent approximation are given by the left singular vectors of this transfer operator [I. Babu_ska and R. Lipton 2011, K. Smetana and A. T. Patera 2016]. However, the direct calculation of these singular vectors is computationally very expensive. In this paper, we propose an adaptive randomized algorithm based on methods from randomized linear algebra [N. Halko et al. 2011], which constructs a local reduced space approximating the range of the transfer operator and thus the optimal local approximation spaces. Moreover, the adaptive algorithm relies on a probabilistic a posteriori error estimator for which we prove that it is both efficient and reliable with high probability. Several numerical experiments confirm the theoretical *** Codes 65N15, 65N12, 65N55, 65N30, 65C20, 65N25 Copyright © 2017, The Authors. All rights reserved.

关键词： Domain decomposition methods

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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