Mathematics Commons^™

Generalized And Higher Dimensional Apollonian Packings, Daniel Lautzenheiser

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this thesis, we show that circle, sphere, and higher dimensional sphere packings may

be realized as subsets of the boundary of hyperbolic space, subject to certain symmetry

conditions based on a discrete group of motions of the hyperbolic space. This leads to

developing and applying counting methods which admit rigorous upper and lower bounds on

the Hausdorff (or Besikovitch) dimension of the residual set of several generalized Apollonian

circle packings. We find that this dimension (which also coincides with the critical exponent

of a zeta-type function) of each packing is strictly greater than that of the Apollonian

packing, supporting …

Weyl Groups And The Nil-Hecke Algebra, Arta Holaj

Mathematics and Statistics

We begin this paper with a short survey on finite reflection groups. First we establish what a reflection in Euclidean space is. Then we introduce a root system, which is then partitioned into two sets: one of positive roots and one with negative roots. Th is articulates our understanding of groups generated by simple reflections. Furthermore, we develop our insight to Weyl groups and crystallographic groups before exploring crystallographic root systems. The section section of this paper examines the twisted group algebra along with the Demazure element Xi and the Demazure-Lusztig element Ti. Lastly, the third section of this paper …

Phase-Only Digital Encryption, Jonathan Blackledge, Western Govere, Dumisani Sibanda

Articles

Abstract—We study then-dimensional deconvolution prob-lem associated with an impulse response function and an(additive) noise function that are both characterised by thesame phase-only stochastic spectrum. In this case, it is shownthat the deconvolution problem becomes well-posed and has ageneral solution that is both exact and unique, subject to are-normalisation condition relating to the scale of the solution.While the phase-only spectral model considered is of limitedvalue in general (in particular, problems arising in the fieldsof digital signal processing and communications engineering,specifically with regard to the retrieval of information fromnoise), its application to digital cryptography has potential.One of the reasons for this (as …

Finding The Symmetry Group Of An Lp With Equality Constraints And Its Application To Classifying Orthogonal Arrays, Andrew J. Geyer, Dursun A. Bulutoglu, Kenneth J. Ryan

Faculty Publications

Excerpt: For a given linear program (LP) a permutation of its variables that sends feasible points to feasible points and preserves the objective function value of each of its feasible points is a symmetry of the LP. The set of all symmetries of an LP, denoted by G^LP, is the symmetry group of the LP. Margot (2010) described a method for computing a subgroup of the symmetry group G^LP of an LP. This method computes G^LP when the LP has only non-redundant inequalities and its feasible set satisfies no equality constraints.

Integrated Representation And Discrimination Models For Functional Data Classification, Rana Haber

Theses and Dissertations

The modus operandi for machine learning is to map functional data to numerical summaries, filter the data, and/or subject it to global signature extractions with the objecive of building robust feature vectors that uniquely characterize each function and then proceed with training algorithms that seek to optimally partition the feature space S ⊂ Rn into labeled regions. This holds true even when the original data are functional in nature, i.e. curves or surfaces that inherently vary over a continuum such as time or space. Functional data are often reduced to summary statistics, locally-sensitive features, and global signatures with the objective …

Identification Of Parameters In Systems Biology, Roby Poteau

Theses and Dissertations

Systems Biology is an actively emerging interdisciplinary area between biology and applied mathematics, based on the idea of treating biological systems as a whole entity which is more than the sum of its interrelated components. One of the major goals of systems biology is to reveal, understand, and predict such properties through the development of mathematical models based on experimental data. In many cases, predictive models of systems biology are described by large systems of nonlinear differential equations. Quantitative identification of such systems requires the solution of inverse problems on the identification of parameters of the system. This dissertation explores …

Parametric Methods For Analysis Of Survival Times With Applications To Organ Transplantation, Farag Hamad

Theses and Dissertations

In this dissertation, we have two main objectives. First, we introduce a hybrid method to model hazard function. Different approaches have been used for modeling survival times including parametric, semi-parametric, and non-parametric models. Non-parametric and semi-parametric models are commonly used for survival time analysis due to their flexibility. However, the parametric models are in high demand because of their predictive power. A challenging task is to extend semi-parametric methods and design full parametric models for analysis of survival times by estimating a set of unknown parameters. In the proposed method, the nonparametric estimate of the survival function by Kaplan Meier …

A Differential Algebra Approach To Commuting Polynomial Vector Fields And To Parameter Identifiability In Ode Models, Peter Thompson

Dissertations, Theses, and Capstone Projects

In the first part, we study the problem of characterizing polynomial vector fields that commute with a given polynomial vector field. One motivating factor is that we can write down solution formulas for an ODE that corresponds to a planar vector field that possesses a linearly independent commuting vector field. This problem is also central to the question of linearizability of vector fields. We first show that a linear vector field admits a full complement of commuting vector fields. Then we study a type of planar vector field for which there exists an upper bound on the degree of a …

Analysis Of A Group Of Automorphisms Of A Free Group As A Platform For Conjugacy-Based Group Cryptography, Pavel Shostak

Dissertations, Theses, and Capstone Projects

Let F be a finitely generated free group and Aut(F) its group of automorphisms.

In this monograph we discuss potential uses of Aut(F) in group-based cryptography.

Our main focus is on using Aut(F) as a platform group for the Anshel-Anshel-Goldfeld protocol, Ko-Lee protocol, and other protocols based on different versions of the conjugacy search problem or decomposition problem, such as Shpilrain-Ushakov protocol.

We attack the Anshel-Anshel-Goldfeld and Ko-Lee protocols by adapting the existing types of the length-based attack to the specifics of Aut(F). We also present our own version of the length-based attack that significantly increases the attack' success …

Formation Of Radial Patterns Via Mixed Attractive And Repulsive Interactions For Schrödinger Systems, Jaeyoung Byeon, Youngae Lee, Zhi-Qiang Wang

Mathematics and Statistics Faculty Publications

This paper is concerned with the asymptotic behavior of least energy vector solutions for nonlinear Schrödinger systems with mixed couplings of attractive and repulsive forces. We focus here on the radially symmetric case while the general studies were already conducted in our earlier work [J. Byeon, Y. Sato, and Z.-Q. Wang, J. Math. Pures Appl. (9), 106 (2016), pp. 477--511], [J. Byeon, Y. Sato, and Z.-Q. Wang, J. Fixed Point Theory Appl., 19 (2017), pp. 559--583]. Though there is still the general phenomenon of component-wise pattern formation with co-existence of partial synchronization and segregation for positive least energy …

Mathematical Models: The Lanchester Equations And The Zombie Apocalypse, Hailey Bauer

Undergraduate Theses and Capstone Projects

This research study used mathematical models to analyze and depicted specific battle situations and the outcomes of the zombie apocalypse. The original models that predicted warfare were the Lanchester models, while the zombie apocalypse models were fictional expansions upon mathematical models used to examine infectious diseases. In this paper, I analyzed and compared different mathematical models by examining each model’s set of assumptions and the impact of the change in variables on the population classes. The purpose of this study was to understand the basics of the discrete dynamical systems and to determine the similarities between imaginary and realistic models. …

Orbit Spaces Of Unimodular Rows Over Smooth Real Affine Algebras., Soumi Tikader Dr.

Doctoral Theses

Let R be a commutative, Noetherian ring of (Krull) dimension d. It is well known that the set of isomorphism classes of (oriented, if d is even) stably free R-modules of rank d carries the structure of an abelian group. This group can be identified with the orbit space of unimodular rows namely, Umd+1(R)/SLd+1(R). The prime objective of this thesis is to provide the complete computation of this group, when X = Spec(R) be a smooth real affine variety of dimension d ≥ 2 (with the assumption that the set of real points of X is non-empty and orientable). In …

Improving Bertini 2.0: Classifying Singular Polynomials With Machine Learning, Riley Anderson

Departmental Honors & Graduate Capstone Projects

The purpose of this research is to decrease the run time of Bertini, a program that approximates solutions of polynomial systems. Bertini can be run more efficiently if it is known whether a polynomial is singular or non-singular. In this research, we focus on polynomials in one variable. We use a machine learning algorithm to classify polynomials into these two categories. To do so, we create and use a set of polynomials to train a neural network and create a model. Then, we create and use a test set to assess the accuracy of the model. By changing the hyper-parameters …

Analysis Of Deterministic And Stochastic Hiv Models, Emily Macindoe

Departmental Honors & Graduate Capstone Projects

In this research paper, I apply the Susceptible-Infected-Virus (SIV) model to the Human Immunodeficiency Virus (HIV). The SIV model is a compartmental model describing within-host dynamics of viral infections; I analyze it in both its deterministic (in which constants are assumed to be known exactly) and stochastic (in which the death rate of the healthy cells is represented by a random variable) forms. First, I give analytical solutions to two simplified versions of the deterministic model. Next, I apply numerical methods to the full deterministic and stochastic systems. The results give an illustrative picture of HIV in-host population dynamics in …

Knot Groups Of Torus Knots, Alexander H. Galindo

Undergraduate Research Symposium

For this project, an expository approach was taking insofar as examining knots groups goes, specifically for torus knots. As such, the first set of materials examined covered the concept of knot groups and the general tenets of knot theory itself. The rest of the time was spent gaining acquaintance with group theory, specifically relating to free groups, and the basics of abstract algebra. The last portion of work spent on the project was examining how all of these theories work together to define the knot groups of torus knots.

Series-Parallel Operations With Alpha-Graphs, Christian Barrientos, Sarah Minion

Theory & Applications of Graphs

Among difference vertex labelings of graphs, α-labelings are the most restrictive one. A graph is an α-graph if it admits an α-labeling. In this work, we study a new alternative to construct α-graphs using, the well-known, series-parallel operations on smaller α-graphs. As an application of the series operation, we show that all members of a subfamily of all trees with maximum degree 4, obtained using vertex amalgamation of copies of the path Ρ₁₁}, are α-graphs. We also show that the one-point union of up to four copies of Κ_n,n is an …

A Gene-Based Recessive Diplotype Exome Scan Discovers Fgf6, A Novel Hepcidin-Regulating Iron-Metabolism Gene, Shicheng Guo, Shuai Jiang, Narendranath Epperla, Yanyun Ma, Mehdi Maadooliat, Zhan Ye, Brent Olson, Minghua Wang, Terrie Kitchner, Jeffrey Joyce, Peng An, Fudi Wang, Robert Strenn, Joseph J. Mazza, Jennifer K. Meece, Wenyu Wu, Li Jin, Judith A. Smith, Jiucan Wang, Steven J. Schrodi

Mathematical and Statistical Science Faculty Research and Publications

Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor–encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. …

Predicting Parameters For Bertini Using Neural Networks, Makenzie Clower

Departmental Honors & Graduate Capstone Projects

The purpose of this research is to use machine learning algorithms to predict the fastest settings for a program called Bertini. Bertini is a computer program that approximates solutions to systems of polynomial equations. Settings for this program can be changed by the user, but the user may not necessarily know the best settings to use to optimize the run time for a particular system of polynomial equations. The settings that were focused on were the differential equation predictor methods when tracking the homotopy to the solution of the system. A neural network was used on a training set of …

2019 Petersheim Academic Exposition Schedule Of Events, Seton Hall University

Petersheim Academic Exposition

2019 Petersheim Academic Exposition

Using Neural Networks To Classify Pdes, Julia Balukonis, Sabrina Fuller, Haley Rosso

Mathematics & Computer Science Student Scholarship

Major: Mathematics
Minor: Computer Science and Film

Faculty Mentor: Dr. Lynette Boos, Mathematics and Computer Science

We designed two neural networks that can learn how to classify three different types of partial differential equations (PDEs). Our data consists of numerical solutions to three categories of PDEs: Burger’s, Diffusion, and Transport equations. Using TensorFlow and the Keras library, we performed two tasks – the first a binary classification of Burger’s and Diffusion equation data, and the second a multi-label classification incorporating the Transport Equations as well. Our binary classification network requires vector labels to perform efficiently. Furthermore, our tertiary classification network …