检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Law, Jeffrey N. Akers, Kyle Tasnina, Nure della Santina, Catherine M. deutsch, Shay Kshirsagar, Meghana Klein-Seetharaman, Judith Crovella, Mark Rajagopalan, Padmavathy Kasif, Simon Murali, T.M. Interdisciplinary Ph.D. Program in Genetics Bioinformatics and Computational Biology BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Department of Biomedical Engineering Boston University BostonMA United States Department of Mathematics University of California Los AngelesCA United States AI for Good Lab Microsoft RedmondWA United States Department of Chemistry Colorado School of Mines GoldenCO United States Department of Computer Science Boston University BostonMA United States Department of Chemical Engineering Virginia Tech BlacksburgVA United States

Background: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. Results: We design a network propagation framework with two novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. Conclusions: We examine how our provenance tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly-emerging viruses. © 2020, CC BY-NC-SA.

关键词： SARS

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OpenMM 8: Molecular dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, philip Wang, Yuanqing Zhang, Ivy Chodera, John d. de Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUdA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CdK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Fast Feasible and Unfeasible Matrix Multiplication

arXiv

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arXiv 2018年

作者： Pan, Victor Y. Department of Computer Science Lehman College City University of New York BronxNY10468 United States Ph.D. Programs in Mathematics and Computer Science Graduate Center City University of New York New YorkNY10036 United States

Matrix-by-matrix multiplication (hereafter referred to as MM) is a fundamental operation omnipresent in modern computations in Numerical and Symbolic Linear Algebra. Its acceleration makes major impact on various fields of Modern Computation and has been a highly recognized research subject for about five decades. The researchers introduced amazing novel techniques, found new insights into MM and numerous related computational problems, and devised advanced algorithms that performed n × n MM by using less than O(n2.38) scalar arithmetic operations versus 2n3 − n2 of the straightforward MM, that is, more than half-way to the information lower bound n2. The record upper bound 3 of 1968 on the exponent of the complexity MM decreased below 2.38 by 1987 and has been extended to various celebrated problems in many areas of computing and became most extensively cited constant of the Theory of Computing. The progress in decreasing the record exponent, however, has virtually stalled since 1987, while many scientists are still anxious to know its sharp bound, so far restricted to the range from 2 to about 2.3728639. Narrowing this range remains a celebrated challenge. Acceleration of MM in the Practice of Computing is a distinct challenge, much less popular, but also highly important. Since 1980 the progress towards meeting the two challenges has followed two distinct paths because of the curse of recursion – all the known algorithms supporting the exponents below 2.38 or even below 2.7733 involve long sequences of nested recursive steps, which blow up the size of an input matrix. As a result all these algorithms improve straightforward MM only for unfeasible MM of immense size, greatly exceeding the sizes of interest nowadays and in any foreseeable future. It is plausible and surely highly desirable that someone could eventually decrease the record MM exponent towards its sharp bound 2 even without ignoring the curse of recursion, but currently there are two distinct challen

关键词： Computational complexity

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SIAN: software for structural identifiability analysis of OdE models

arXiv

引用

arXiv 2018年

作者： Hong, Hoon Ovchinnikov, Alexey Pogudin, Gleb Yap, Chee Department of Mathematics North Carolina State University Raleigh United States Department of Mathematics CUNY Queens College Ph.D. Programs in Mathematics and Computer Science CUNY Graudate Center New York United States Courant Institute of Mathematical Sciences New York University

Biological processes are often modeled by ordinary differential equations with unknown parameters. The unknown parameters are usually estimated from experimental data. In some cases, due to the structure of the model, this estimation problem does not have a unique solution even in the case of continuous noise-free data. It is therefore desirable to check the uniqueness a priori before carrying out actual experiment. We present a new software SIAN (Structural Identifiability ANalyser) that does this. Our software can tackle problems that could not be tackled by previously developed packages. Copyright © 2018, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Commuting planar polynomial vector fields for conservative Newton systems

arXiv

引用

arXiv 2018年

作者： Nagloo, Joel Ovchinnikov, Alexey Thompson, Peter Cuny Bronx Community College Department of Mathematics and Computer Science 2155 University Avenue BronxNY10453 United States Cuny Queens College Department of Mathematics 65-30 Kissena Blvd QueensNY11367 United States Cuny Graduate Center PH.D. Programs in Mathematics and Computer Science 365 Fifth Avenue New YorkNY10016 United States Cuny Graduate Center PH.D. Program in Mathematics 365 Fifth Avenue New YorkNY10016 United States

We study the problem of characterizing polynomial vector fields that commute with a given polynomial vector field on a plane. It is a classical result that one can write down solution formulas for an OdE that corresponds to a planar vector field that possesses a linearly independent commuting vector field. This problemis also central to the question of linearizability of vector fields. Let f ϵ K[x], where K is a field of characteristic zero, and d the derivation that corresponds to the differential equation x = f (x) in a standard way. Let also H be the Hamiltonian polynomial for d, that is H = 1 2 y2-R f (x)dx. It is known that the set of all polynomial derivations that commute with d forms a K[H]-module Md . In this paper, we show that, for every such d, the module Md is of rank 1 if and only if deg f ≥ 2. For example, the classical elliptic equation x = 6x2+a, where a ϵ ℂ, falls into this category.37J30, 13N10, 70H07 Copyright © 2018, The Authors. All rights reserved.

关键词： Hamiltonians

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Polynomial Root-Finding and Algebraic Eigenvalue Problem

arXiv

引用

arXiv 2018年

作者： Pan, Victor Y. Department of Computer Science Lehman College of the City University of New York BronxNY10468 United States Ph.D. Program in Mathematics and Computer Science The Graduate Center of the City University of New York New YorkNY10036 United States

Univariate polynomial root-finding has been studied for four millennia and very intensively in the last decades. Our new near-optimal root-finders approximate all complex zeros of a polynomial almost as fast as one accesses its coefficients with the precision required for the solution within a prescribed error bound. The root-finders work even for a black box polynomial, defined by an oracle (black box subroutine) for its evaluation rather than by its coefficients. Hence they (i) support approximation of the eigenvalues of a matrix in a record Las Vegas expected bit operation time and (ii) are particularly fast for polynomials that can be evaluated fast such as the sum of a few shifted monomials of the form (x−c)d or a Mandelbrot-like polynomial defined by a recurrence. Our divide and conquer algorithm of ACM STOC 1995 is the only other known near-optimal polynomial root-finder, but it extensively uses the coefficients, is quite involved, and has never been implemented, while according to extensive numerical experiments with standard test polynomials already a slower initial implementation of our new root-finders competes with user’s choice package of root-finding subroutines MPSolve and supersedes it more and more significantly as the degree of a polynomial grows large. We elaborate upon design and analysis of our algorithms, comment on their potential heuristic acceleration, and briefly cover polynomial root-finding by means of functional iterations. Our techniques can be of independent interest. Copyright © 2018, The Authors. All rights reserved.

关键词： Subroutines

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derivative-free superiorization: Principle and algorithm

arXiv

引用

arXiv 2019年

作者： Censor, Yair Garduño, Edgar Helou, Elias S. Herman, Gabor T. Department of Mathematics University of Haifa Mt. Carmel Haifa3498838 Israel Departamento de Ciencias de la Computación Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Universidad Nacional Autónoma de México Cd. Universitaria Mexico CityC.P. 04510 Mexico Department of Applied Mathematics and Statistics University of São Paulo São Carlos São Paulo13566-590 Brazil Computer Science Ph.D. Program Graduate Center City University of New York New YorkNY10016 United States

The superiorization methodology is intended to work with input data of constrained minimization problems, that is, a target function and a set of constraints. However, it is based on an antipodal way of thinking to what leads to constrained minimization methods. Instead of adapting unconstrained minimization algorithms to handling constraints, it adapts feasibility-seeking algorithms to reduce (not necessarily minimize) target function values. This is done by inserting target-function-reducing perturbations into a feasibility-seeking algorithm while retaining its feasibility-seeking ability and without paying a high computational price. A superiorized algorithm that employs component-wise target function reduction steps is presented. This enables derivative-free superiorization (dFS), meaning that superiorization can be applied to target functions that have no calculable partial derivatives or subgradients. The numerical behavior of our derivative-free superiorization algorithm is illustrated on a data set generated by simulating a problem of image reconstruction from projections. The plots of proximity-target curves of our experiments demonstrate the advantage of the proposed derivative-free superiorization algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

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degree spectra of real closed fields

arXiv

引用

arXiv 2018年

作者： Miller, Russell González, Victor Ocasio Department of Mathematics Queens College - C.U.N.Y. 65-30 Kissena Blvd. FlushingNY11367 United States Ph.D. Programs in Mathematics and Computer Science C.U.N.Y. Graduate Center 365 Fifth Avenue New YorkNY10016 United States University of Puerto Rico at Mayaguez Department of Mathematical Sciences Call Box 9000 MayaguezPR00681-9018 United States

Several researchers have recently established that for every Turing de- gree c, the real closed field of all c-computable real numbers has spectrum {d : d′ ≥ c″}. We investigate the spectra of real closed fields further, focusing first on subfields of the field R0 of computable real numbers, then on archimedean real closed fields more generally, and finally on non- archimedean real closed fields. For each noncomputable, computably enu- merable set C, we produce a real closed C-computable subfield of R0 with no computable copy. Then we build an archimedean real closed field with no computable copy but with a computable enumeration of the dedekind cuts it realizes, and a computably presentable nonarchimedean real closed field whose residue field has no computable *** Codes 03d45 (Primary) 03C57 (Secondary) Copyright © 2018, The Authors. All rights reserved.

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BTEX exposure and its body burden pose differential risks for asthma and its phenotypic clusters

引用

Allergy 2023年第7期78卷 2036-2040页

作者： Yuan-Ting Hsu Chao-Chien Wu Chin-Chou Wang Wen-Yu Chung Chau-Chyun Sheu Yi-Hsin Yang Ming-Yen Cheng Ruay-Sheng Lai Sum-Yee Leung Chi-Cheng Lin Yu-Feng Wei Ching-Hsiung Lin Sheng-Hao Lin Jeng-Yuan Hsu Wei-Chang Huang Chia-Cheng Tseng Yung-Fa Lai Meng-Hsuan Cheng Huang-Chi Chen Chih-Jen Yang Chian-Heng Su Chien-Jen Wang Shih-Chang Hsu Chih-Hsing Hung Chon-Lin Lee Ming-Shyan Huang Shau-Ku Huang National Institute of Environmental Health Sciences National Health Research Institutes Miaoli Taiwan. Department of Internal Medicine Division of Pulmonary and Critical Care Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan. Department of Public Health Kaohsiung Medical University Kaohsiung Taiwan. Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan. Department of Internal Medicine Division of Pulmonary and Critical Care Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan. Department of Internal Medicine School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan. National Institute of Cancer Research National Health Research Institutes Tainan Taiwan. Department of Mathematics Hong Kong Baptist University Hong Kong China. Division of Chest Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan. Department of Internal Medicine Chest Division Antai Medical Care Cooperation Antai Tian-Sheng Memorial Hospital Pingtung Taiwan. Department of Internal Medicine E-Da Cancer Hospital I-Shou University Kaohsiung Taiwan. Department of Internal Medicine Division of Chest Medicine Changhua Christian Hospital Changhua Taiwan. Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan. Department of Recreation and Holistic Wellness MingDao University Changhua Taiwan. Department of Medical Research Division of Clinical Research Taichung Veterans General Hospital Taichung Taiwan. Department of Internal Medicine Division of Chest Medicine Taichung Veterans General Hospital Taichung Taiwan. Department of Medical Technology Jen-Teh Junior College of Medicine Nursing and Management Miaoli Taiwan. School of Medicine Chung Shan Medical University Taichung Taiwan. Ph.D. Program in Translational Medicine National Chung Hsing Universi

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STRING-AVERAGING ALGORITHMS FOR CONVEX FEASIBILITY WITH INFINITELY MANY SETS

arXiv

引用

arXiv 2018年

作者： Kong, T. Yung Pajoohesh, Homeira Herman, Gabor T. Computer Science Department Queens College City University of New York FlushingNY11367 United States Mathematics Department Medgar Evers College City University of New York BrooklynNY11225 United States Computer Science Ph.D. Program The Graduate Center City University of New York New YorkNY10016 United States

Algorithms for convex feasibility find or approximate a point in the intersection of given closed convex sets. Typically there are only finitely many convex sets, but the case of infinitely many convex sets also has some applications. In this context, a string is a finite sequence of points each of which is obtained from the previous point by considering one of the convex sets. In a string-averaging algorithm, an iterative step from a first point to a second point computes a number of strings, all starting with the first point, and then calculates a (weighted) average of those strings’ end-points: This average is that iterative step’s second point, which is used as the first point for the next iterative step. For string-averaging algorithms based on strings in which each point either is the projection of the previous point on one of the convex sets or is equal to the previous point, we present theorems that provide answers to the following question: "How can the iterative steps be specified so that the string-averaging algorithm generates a convergent sequence whose limit lies in the intersection of the sets of a given convex feasibility problem?" This paper focuses on the case where the given collection of convex sets is infinite, whereas prior work on the same question that we are aware of has assumed the collection of convex sets is finite (or has been applicable only to a small subset of the algorithms we consider). The string-averaging algorithms that are shown to generate a convergent sequence whose limit lies in the intersection are also shown to be perturbation resilient, so they can be superiorized to generate sequences that also converge to a point of the intersection but in a manner that is superior with respect to some application-dependent criterion.46N10 46N40 47H09 47H10 47J25 47N10 65F10 65J99 65K10 90C25 90C30 Copyright © 2018, The Authors. All rights reserved.

关键词： Iterative methods

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