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819 full-text articles. Page 1 of 27.

Heads And Tails, Julie Simons 2017 The California Maritime Academy

Heads And Tails, Julie Simons

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


A Cellular Automaton Modeling Approach To Chestnut Blight Canker Development, Samuel Iselin 2017 Illinois State University

A Cellular Automaton Modeling Approach To Chestnut Blight Canker Development, Samuel Iselin

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


Distributed Evolution Of Spiking Neuron Models On Apache Mahout For Time Series Analysis, Andrew Palumbo 2017 Cylance, Inc.

Distributed Evolution Of Spiking Neuron Models On Apache Mahout For Time Series Analysis, Andrew Palumbo

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


A Method For Sensitivity Analysis And Parameter Estimation Applied To A Large Reaction-Diffusion Model Of Cell Polarization, Marissa Renardy, Tau-Mu Yi, Dongbin Xiu, Ching-Shan Chou 2017 The Ohio State University

A Method For Sensitivity Analysis And Parameter Estimation Applied To A Large Reaction-Diffusion Model Of Cell Polarization, Marissa Renardy, Tau-Mu Yi, Dongbin Xiu, Ching-Shan Chou

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


Filtered Subspace Iteration For Selfadjoint Operators, Jay Gopalakrishnan, Luka Grubišić, Jeffrey S. Ovall 2017 Portland State University

Filtered Subspace Iteration For Selfadjoint Operators, Jay Gopalakrishnan, Luka Grubišić, Jeffrey S. Ovall

Portland Institute for Computational Science Publications

We consider the problem of computing a cluster of eigenvalues (and its associated eigenspace) of a (possibly unbounded) selfadjoint operator in a Hilbert space. A rational function of the operator is constructed such that the eigenspace of interest is its dominant eigenspace, and a subspace iteration procedure is used to approximate this eigenspace. The computed space is then used to obtain approximations of the eigenvalues of interest. An eigenvalue and eigenspace convergence analysis that considers both iteration error and dis- cretization error is provided. A realization of the proposed approach for a model second-order elliptic operator is based on a ...


Low-Communication, Parallel Multigrid Algorithms For Elliptic Partial Differential Equations, Wayne Mitchell 2017 University of Colorado, Boulder

Low-Communication, Parallel Multigrid Algorithms For Elliptic Partial Differential Equations, Wayne Mitchell

Applied Mathematics Graduate Theses & Dissertations

When solving elliptic partial differential equations (PDE's) multigrid algorithms often provide optimal solvers and preconditioners capable of providing solutions with O(N) computational cost, where N is the number of unknowns. As parallelism of modern super computers continues to grow towards exascale, however, the cost of communication has overshadowed the cost of computation as the next major bottleneck for multigrid algorithms. Typically, multigrid algorithms require O((log P)^2) communication steps in order to solve a PDE problem to the level of discretization accuracy, where P is the number of processors. This has inspired the development of new algorithms ...


Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer 2017 Louisiana State University and Agricultural and Mechanical College

Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer

LSU Doctoral Dissertations

In many problems we are dealing with characterizing a behavior of a complex stochastic system or its response to a set of particular inputs. Such problems span over several topics such as machine learning, complex networks, e.g., social or communication networks; biology, etc. Probabilistic graphical models (PGMs) are powerful tools that offer a compact modeling of complex systems. They are designed to capture the random behavior, i.e., the joint distribution of the system to the best possible accuracy. Our goal is to study certain algebraic and topological properties of a special class of graphical models, known as Gaussian ...


On The Ramberg-Osgood Stress-Strain Model And Large Deformations Of Cantilever Beams, Ronald J. Giardina Jr 2017 University of New Orleans

On The Ramberg-Osgood Stress-Strain Model And Large Deformations Of Cantilever Beams, Ronald J. Giardina Jr

University of New Orleans Theses and Dissertations

In this thesis the Ramberg-Osgood nonlinear model for describing the behavior of many different materials is investigated. A brief overview of the model as it is currently used in the literature is undertaken and several misunderstandings and possible pitfalls in its application is pointed out, especially as it pertains to more recent approaches to finding solutions involving the model. There is an investigation of the displacement of a cantilever beam under a combined loading consisting of a distributed load across the entire length of the beam and a point load at its end and new solutions to this problem are ...


Euler-Richardson Method Preconditioned By Weakly Stochastic Matrix Algebras: A Potential Contribution To Pagerank Computation, Stefano Cipolla, Carmine Di Fiore, Francesco Tudisco 2017 University of Rome Tor Vergata

Euler-Richardson Method Preconditioned By Weakly Stochastic Matrix Algebras: A Potential Contribution To Pagerank Computation, Stefano Cipolla, Carmine Di Fiore, Francesco Tudisco

Electronic Journal of Linear Algebra

Let S be a column stochastic matrix with at least one full row. Then S describes a Pagerank-like random walk since the computation of the Perron vector x of S can be tackled by solving a suitable M-matrix linear system Mx = y, where M = I − τ A, A is a column stochastic matrix and τ is a positive coefficient smaller than one. The Pagerank centrality index on graphs is a relevant example where these two formulations appear. Previous investigations have shown that the Euler- Richardson (ER) method can be considered in order to approach the Pagerank computation problem by means ...


Joint Inversion Of Compact Operators, James Ford 2017 Boise State University

Joint Inversion Of Compact Operators, James Ford

Boise State University Theses and Dissertations

The first mention of joint inversion came in [22], where the authors used the singular value decomposition to determine the degree of ill-conditioning in inverse problems. The authors demonstrated in several examples that combining two models in a joint inversion, and effectively stacking discrete linear models, improved the conditioning of the problem. This thesis extends the notion of using the singular value decomposition to determine the conditioning of discrete joint inversion to using the singular value expansion to determine the well-posedness of joint linear operators. We focus on compact linear operators related to geophysical, electromagnetic subsurface imaging.

The operators are ...


Some Problems Arising From Mathematical Model Of Ductal Carcinoma In Situ., Heng Li 2017 University of Louisville

Some Problems Arising From Mathematical Model Of Ductal Carcinoma In Situ., Heng Li

Electronic Theses and Dissertations

Ductal carcinoma in situ (DCIS) is the earliest form of breast cancer. Three mathematical models in the one dimensional case arising from DCIS are proposed. The first two models are in the form of parabolic equation with initial and known moving boundaries. Direct and inverse problems are considered in model 1, existence and uniqueness are proved by using tool from heat potential theory and Volterra integral equations. Also, we discuss the direct problem and nonlocal problem of model 2, existence and uniqueness are proved. And approximation solution of these problems are implemented by Ritz-Galerkin method, which is the first attempt ...


Eignefunctions For Partial Differential Equations On Two-Dimensional Domains With Piecewise Constant Coefficients, Abdullah M. Aurko 2017 University of Southern Mississippi

Eignefunctions For Partial Differential Equations On Two-Dimensional Domains With Piecewise Constant Coefficients, Abdullah M. Aurko

Master's Theses

In this thesis, we develop a highly accurate and efficient algorithm for computing the solution of a partial differential equation defined on a two-dimensional domain with discontinuous coefficients. An example of such a problem is for modeling the diffusion of heat energy in two space dimensions, in the case where the spatial domain represents a medium consisting of two different but homogeneous materials, with periodic boundary conditions.

Since diffusivity changes based on the material, it will be represented using a piecewise constant function, and this results in the formation of a complicated mathematical model. Such a model is impossible to ...


Numerical Solution Of Partial Differential Equations Using Polynomial Particular Solutions, Thir R. Dangal 2017 University of Southern Mississippi

Numerical Solution Of Partial Differential Equations Using Polynomial Particular Solutions, Thir R. Dangal

Dissertations

Polynomial particular solutions have been obtained for certain types of partial differential operators without convection terms. In this dissertation, a closed-form particular solution for more general partial differential operators with constant coefficients has been derived for polynomial basis functions. The newly derived particular solutions are further coupled with the method of particular solutions (MPS) for numerically solving a large class of elliptic partial differential equations. In contrast to the use of Chebyshev polynomial basis functions, the proposed approach is more flexible in selecting the collocation points inside the domain. Polynomial basis functions are well-known for yielding ill-conditioned systems when their ...


An Investigation Of The Accuracy Of Parallel Analysis For Determining The Number Of Factors In A Factor Analysis, Mandy Matsumoto 2017 Western Kentucky University

An Investigation Of The Accuracy Of Parallel Analysis For Determining The Number Of Factors In A Factor Analysis, Mandy Matsumoto

Honors College Capstone Experience/Thesis Projects

Exploratory factor analysis is an analytic technique used to determine the number of factors in a set of data (usually items on a questionnaire) for which the factor structure has not been previously analyzed. Parallel analysis (PA) is a technique used to determine the number of factors in a factor analysis. There are a number of factors that affect the results of a PA: the choice of the eigenvalue percentile, the strength of the factor loadings, the number of variables, and the sample size of the study. Although PA is the most accurate method to date to determine which factors ...


Shrinkage Function And Its Applications In Matrix Approximation, Toby Boas, Aritra Dutta, Xin Li, Katie Mercier, Eric Niderman 2017 University of Florida, Gainesville

Shrinkage Function And Its Applications In Matrix Approximation, Toby Boas, Aritra Dutta, Xin Li, Katie Mercier, Eric Niderman

Electronic Journal of Linear Algebra

The shrinkage function is widely used in matrix low-rank approximation, compressive sensing, and statistical estimation. In this article, an elementary derivation of the shrinkage function is given. In addition, applications of the shrinkage function are demonstrated in solving several well-known problems, together with a new result in matrix approximation.


Full Wave Modeling Of Ultrasonic Scattering Using Nystrom Method For Nde Applications, Praveen Gurrala, Kun Chen, Jiming Song, Ron Roberts 2017 Iowa State University

Full Wave Modeling Of Ultrasonic Scattering Using Nystrom Method For Nde Applications, Praveen Gurrala, Kun Chen, Jiming Song, Ron Roberts

Jiming Song

Approximate methods for ultrasonic scattering like the Kirchhoff approximation and the geometrical theory of diffraction (GTD) can deliver fast solutions with relatively small computational resources compared to accurate numerical methods. However, these models are prone to inaccuracies in predicting scattered fields from defects that are not very large compared to wavelength. Furthermore, they do not take into account physical phenomena like multiple scattering and surface wave generation on defects. Numerical methods like the finite element method (FEM) and the boundary element method (BEM) can overcome these limitations of approximate models. Commercial softwares such as Abaqus FEA and PZFlex use FEM ...


Fast Algorithms On Random Matrices And Structured Matrices, Liang Zhao 2017 The City University of New York

Fast Algorithms On Random Matrices And Structured Matrices, Liang Zhao

All Graduate Works by Year: Dissertations, Theses, and Capstone Projects

Randomization of matrix computations has become a hot research area in the big data era. Sampling with randomly generated matrices has enabled fast algorithms to perform well for some most fundamental problems of numerical algebra with probability close to 1. The dissertation develops a set of algorithms with random and structured matrices for the following applications: 1) We prove that using random sparse and structured sampling enables rank-r approximation of the average input matrix having numerical rank r. 2) We prove that Gaussian elimination with no pivoting (GENP) is numerically safe for the average nonsingular and well-conditioned matrix preprocessed with ...


Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue WU, Steven C. H. HOI, Tao MEI, Nenghai Yu 2017 Singapore Management University

Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu

Research Collection School Of Information Systems

Feature selection (FS) is an important technique in machine learning and data mining, especially for largescale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may arrive sequentially and continuously, batch learning has to retrain the model for the new coming data, which is very computationally intensive. Online feature selection (OFS) is a promising new paradigm that is more efficient and scalable than batch learning algorithms. However, existing online algorithms usually fall short in their inferior efficacy. In this article, we ...


Revisiting Assert Use In Github Projects, Pavneet Singh KOCHHAR, David LO 2017 Singapore Management University

Revisiting Assert Use In Github Projects, Pavneet Singh Kochhar, David Lo

Research Collection School Of Information Systems

Assertions are often used to test the assumptions that developers have about a program. An assertion contains a boolean expression which developers believe to be true at a particular program point. It throws an error if the expression is not satisfied, which helps developers to detect and correct bugs. Since assertions make developer assumptions explicit, assertions are also believed to improve under-standability of code. Recently, Casalnuovo et al. analyse C and C++ programs to understand the relationship between assertion usage and defect occurrence. Their results show that asserts have a small effect on reducing the density of bugs and developers ...


High Performance Computation Of Cardiac Models In Real-Time Using Webgl, Abouzar Kaboudian, Flavio H. Fenton 2017 Georgia Institute of Technology

High Performance Computation Of Cardiac Models In Real-Time Using Webgl, Abouzar Kaboudian, Flavio H. Fenton

Biology and Medicine Through Mathematics Conference

No abstract provided.


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