Direct Detection Of Relic Neutrino Background Remains Impossible: A Review Of More Recent Arguments, 2019 University of New Mexico
Direct Detection Of Relic Neutrino Background Remains Impossible: A Review Of More Recent Arguments, Victor Christianto, Florentin Smarandache
Branch Mathematics and Statistics Faculty and Staff Publications
The existence of big bang relic neutrinos—exact analogues of the big bang relic photons comprising the cosmic microwave background radiation—is a basic prediction of standard cosmology. The standard big bang theory predicts the existence of 1087 neutrinos per flavour in the visible universe. This is an enormous abundance unrivalled by any other known form of matter, falling second only to the cosmic microwave background (CMB) photon. Yet, unlike the CMB photon which boasts its first (serendipitous) detection in the 1960s and which has since been observed and its properties measured to a high degree of accuracy in a series of …
Unifications Of Pythagorean Triple Schema, 2019 East Tennessee State University
Unifications Of Pythagorean Triple Schema, Emily Hammes
Undergraduate Honors Theses
Euclid’s Method of finding Pythagorean triples is a commonly accepted and applied technique. This study focuses on a myriad of other methods behind finding such Pythagorean triples. Specifically, we discover whether or not other ways of finding triples are special cases of Euclid’s Method.
Ensemble Correlation Coefficient For Variable Association Detection, 2019 Florida Institute of Technology
Ensemble Correlation Coefficient For Variable Association Detection, Wejdan Deebani
Theses and Dissertations
Subjects in a population are represented by their characteristics, and the characteristics are represented by variables. Identifying the relationship between these variables is essential for prediction, hypothesis testing, and decision making. The relation between two variables is often quantified using a correlation factor. Once correlations between response and independent variables are known, they can be used to make predictions regarding response variables. That is, if two variables are correlated, by observing one, we can make predictions about the other one. A more accurate prediction can be made where there is a strong relationship between variables. Several correlation factors have been …
Parametric Methods For Analysis Of Survival Times With Applications To Organ Transplantation, 2019 Florida Institute of Technology
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 …
Formally Verifying Peano Arithmetic, 2019 Boise State University
Formally Verifying Peano Arithmetic, Morgan Sinclaire
Boise State University Theses and Dissertations
This work is concerned with implementing Gentzen’s consistency proof in the Coq theorem prover.
In Chapter 1, we summarize the basic philosophical, historical, and mathematical background behind this theorem. This includes the philosophical motivation for attempting to prove the consistency of Peano arithmetic, which traces itself from the first attempted axiomatizations of mathematics to the maturation of Hilbert’s program. We introduce many of the basic concepts in mathematical logic along the way: first-order logic (FOL), Peano arithmetic (PA), primitive recursive arithmetic (PRA), Gödel's 2nd Incompleteness theorem, and the ordinals below ε0.
In …
Computable Reducibility Of Equivalence Relations, 2019 Boise State University
Computable Reducibility Of Equivalence Relations, Marcello Gianni Krakoff
Boise State University Theses and Dissertations
Computable reducibility of equivalence relations is a tool to compare the complexity of equivalence relations on natural numbers. Its use is important to those doing Borel equivalence relation theory, computability theory, and computable structure theory. In this thesis, we compare many naturally occurring equivalence relations with respect to computable reducibility. We will then define a jump operator on equivalence relations and study proprieties of this operation and its iteration. We will then apply this new jump operation by studying its effect on the isomorphism relations of well-founded computable trees.
On The Fundamental Group Of Plane Curve Complements, 2019 Boise State University
On The Fundamental Group Of Plane Curve Complements, Mitchell Scofield
Boise State University Theses and Dissertations
Given a polynomial f(x,y) monic in y of degree d, we study the complement ℂ2-C, where C is the curve defined by the equation f(x,y)=0. The Zariski-Van Kampen theorem gives a presentation of the fundamental group of the complement ℂ2-C. Let NT be be the set of complex numbers x for which f(x,y) has multiple roots (as a polynomial in y). Let : ℂ − NT → ℂd − Δ be the map that …
Sampling Studies For Longitudinal Functional Data, 2019 Montclair State University
Sampling Studies For Longitudinal Functional Data, Toni Jassel
Theses, Dissertations and Culminating Projects
We study the data setting consisting of functional data sets repeatedly observed over time. The focus is on the dynamic prediction of the future trajectory for a subject. Regression methods based on dynamic functional models are used for dynamic prediction of individual trajectories. We propose strategies for the selection of the study sampling design in the context of longitudinal functional data. An application to simulated child growth data is presented. The height-for-age z-score (HAZ) was the response variable in the functional dynamic models for prediction. The intent was to recommend four months for removal in our initial historic data set. …
Pm2.5 Data Reliability And Air Quality Improvement Trends In Beijing, 2019 The University of Texas Rio Grande Valley
Pm2.5 Data Reliability And Air Quality Improvement Trends In Beijing, Huimin Li
Theses and Dissertations
PM2.5 has been a main environmental concern due to its adverse effects on human health and society. We used data from two sources: monitoring station of the U.S. Embassy in Beijing, and several nearby monitoring stations of the Chinese Ministry of Environmental Protection. This study includes investigating (1) PM2.5 historical data reliability, (2) PM2.5 real-time data reliability, and (3) air quality improvement trends in Beijing over the past decade. We used graphical methods, descriptive statistics, correlation analysis, and inferential analyses including paired samples t-test, ANOVA, and Kruskal-Wallis test. We reported effect sizes to aid study on practical significance. Inferential procedures' …
Numerical Analysis And Fluid Flow Modeling Of Incompressible Navier-Stokes Equations, 2019 University of Nevada, Las Vegas
Numerical Analysis And Fluid Flow Modeling Of Incompressible Navier-Stokes Equations, Tahj Hill
UNLV Theses, Dissertations, Professional Papers, and Capstones
The Navier-Stokes equations (NSE) are an essential set of partial differential equations for governing the motion of fluids. In this paper, we will study the NSE for an incompressible flow, one which density ρ = ρ0 is constant.
First, we will present the derivation of the NSE and discuss solutions and boundary conditions for the equations. We will then discuss the Reynolds number, a dimensionless number that is important in the observations of fluid flow patterns. We will study the NSE at various Reynolds numbers, and use the Reynolds number to write the NSE in a nondimensional form.
We will …
Unbounded Derivations Of C*-Algebras And The Heisenberg Commutation Relation, 2019 University of Nebraska-Lincoln
Unbounded Derivations Of C*-Algebras And The Heisenberg Commutation Relation, Lara M. Ismert
Department of Mathematics: Dissertations, Theses, and Student Research
This dissertation investigates the properties of unbounded derivations on C*-algebras, namely the density of their analytic vectors and a property we refer to as "kernel stabilization." We focus on a weakly-defined derivation δD which formalizes commutators involving unbounded self-adjoint operators on a Hilbert space. These commutators naturally arise in quantum mechanics, as we briefly describe in the introduction.
A first application of kernel stabilization for δD shows that a large class of abstract derivations on unbounded C*-algebras, defined by O. Bratteli and D. Robinson, also have kernel stabilization. A second application of kernel stabilization provides a sufficient condition …
Admissibility Of C*-Covers And Crossed Products Of Operator Algebras, 2019 University of Nebraska-Lincoln
Admissibility Of C*-Covers And Crossed Products Of Operator Algebras, Mitchell A. Hamidi
Department of Mathematics: Dissertations, Theses, and Student Research
In 2015, E. Katsoulis and C. Ramsey introduced the construction of a non-self-adjoint crossed product that encodes the action of a group of automorphisms on an operator algebra. They did so by realizing a non-self-adjoint crossed product as the subalgebra of a C*-crossed product when dynamics of a group acting on an operator algebra by completely isometric automorphisms can be extended to self-adjoint dynamics of the group acting on a C*-algebra by ∗-automorphisms. We show that this extension of dynamics is highly dependent on the representation of the given algebra and we define a lattice structure for an operator algebra's …
Model-Independent Estimation Of Optimal Hedging Strategies With Deep Neural Networks, 2019 University of Wisconsin-Milwaukee
Model-Independent Estimation Of Optimal Hedging Strategies With Deep Neural Networks, Tobias Michael Furtwaengler
Theses and Dissertations
Inspired by the recent paper Buehler et al. (2018), this thesis aims to investigate the optimal hedging and pricing of financial derivatives with neural networks. We utilize the concept of convex risk measures to define optimal hedging strategies without strong assumptions on the underlying market dynamics. Furthermore, the setting allows the incorporation of market frictions and thus the determination of optimal hedging strategies and prices even in incomplete markets. We then use the approximation capabilities of neural networks to find close-to optimal estimates for these strategies.
We will elaborate on the theoretical foundations of this approach and carry out implementations …
Large Scale Geometry Of Surfaces In 3-Manifolds, 2019 University of Wisconsin-Milwaukee
Large Scale Geometry Of Surfaces In 3-Manifolds, Hoang Thanh Nguyen
Theses and Dissertations
A compact, orientable, irreducible 3-manifold M with empty or toroidal boundary is
called geometric if its interior admits a geometric structure in the sense of Thurston. The
manifold M is called non-geometric if it is not geometric. Coarse geometry of an immersed
surface in a geometric 3-manifold is relatively well-understood by previous work of Hass,
Bonahon-Thurston. In this dissertation, we study the coarse geometry of an immersed
surface in a non-geometric 3- manifold.
The first chapter of this dissertation is a joint work with my advisor, Chris Hruska. We
answer a question of Daniel Wise about distortion of a horizontal …
Pricing Of Dependent Risks, 2019 University of Wisconsin-Milwaukee
Pricing Of Dependent Risks, Mark Benedikt Schultze
Theses and Dissertations
In some types of insurance businesses, such as cyber or homeowners insurance, the assumption that risks are independent is violated. Because of this, the commonly used expected value premium principle does not work. Therefore, we propose different premium principles for pricing dependent risks. We derive formulas for these principles when the risks are normally distributed, pareto distributed and each risk is an aggregate loss. Furthermore, we investigate the behavior of the different premium principles related to a change in the dependence of the risks. Additionally, we examine the impact that a parameter of one risk has on the premium for …
Hermite Interpolation In The Treecode Algorithm, 2019 University of Wisconsin-Milwaukee
Hermite Interpolation In The Treecode Algorithm, Benjamin St. Aubin
Theses and Dissertations
In this thesis, a treecode implementing Hermite interpolation is constructed to approximate a summation of pairwise interactions on large data sets. Points are divided into a hierarchical tree structure and the interactions between points and well-separated clusters are approximated by interpolating the kernel function over the cluster. Performing the direct summation takes O(N^2) time for system size N, and evidence is presented to show the method presented in this paper scales with O(N logN) time. Comparisons between this method and existing ones are made, highlighting the relative simplicity and adaptability of this process. Parallelization of the computational step is implemented …
An Analysis Of Momentum Flux Budgets And Profiles In A Large-Eddy Model, 2019 University of Wisconsin-Milwaukee
An Analysis Of Momentum Flux Budgets And Profiles In A Large-Eddy Model, Steffen Domke
Theses and Dissertations
Momentum fluxes and variances play an important role in the characterization and forecast of weather phenomena, but cannot be measured easily.
A subdivision of the flux changes into budget terms by the underlying physical processes, such as buoyancy transport, can assist in understanding their sources and influences.
Momentum flux and variance budgets for SAM, the System for Atmospheric Modeling, have been implemented and are compared to existing budgets from other simulations.
A tool for the visualization of these quantities from three-dimensional grid data has been developed to show and explain their distribution in conjunction with shallow cumulus and stratocumulus clouds. …
Model-Independent Estimation Of Optimal Hedging Strategies With Deep Neural Networks, 2019 University of Wisconsin-Milwaukee
Model-Independent Estimation Of Optimal Hedging Strategies With Deep Neural Networks, Tobias Michael Furtwaengler
Theses and Dissertations
Inspired by the recent paper Buehler et al. (2018), this thesis aims to investigate the optimal hedging and pricing of financial derivatives with neural networks. We utilize the concept of convex risk measures to define optimal hedging strategies without strong assumptions on the underlying market dynamics. Furthermore, the setting allows the incorporation of market frictions and thus the determination of optimal hedging strategies and prices even in incomplete markets. We then use the approximation capabilities of neural networks to find close-to optimal estimates for these strategies.
We will elaborate on the theoretical foundations of this approach and carry out implementations …
Large Scale Geometry Of Surfaces In 3-Manifolds, 2019 University of Wisconsin-Milwaukee
Large Scale Geometry Of Surfaces In 3-Manifolds, Hoang Thanh Nguyen
Theses and Dissertations
A compact, orientable, irreducible 3-manifold M with empty or toroidal boundary is
called geometric if its interior admits a geometric structure in the sense of Thurston. The
manifold M is called non-geometric if it is not geometric. Coarse geometry of an immersed
surface in a geometric 3-manifold is relatively well-understood by previous work of Hass,
Bonahon-Thurston. In this dissertation, we study the coarse geometry of an immersed
surface in a non-geometric 3- manifold.
The first chapter of this dissertation is a joint work with my advisor, Chris Hruska. We
answer a question of Daniel Wise about distortion of a horizontal …
Pricing Of Dependent Risks, 2019 University of Wisconsin-Milwaukee
Pricing Of Dependent Risks, Mark Benedikt Schultze
Theses and Dissertations
In some types of insurance businesses, such as cyber or homeowners insurance, the assumption that risks are independent is violated. Because of this, the commonly used expected value premium principle does not work. Therefore, we propose different premium principles for pricing dependent risks. We derive formulas for these principles when the risks are normally distributed, pareto distributed and each risk is an aggregate loss. Furthermore, we investigate the behavior of the different premium principles related to a change in the dependence of the risks. Additionally, we examine the impact that a parameter of one risk has on the premium for …