Lau Chi Hin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 88-91). / Chapter 1 --- Introduction --- p.4 / Chapter 1.1 --- Rough Isometries --- p.4 / Chapter 1.2 --- Discrete approximation of Riemannian manifolds --- p.8 / Chapter 2 --- Basic Properties of Rough Isometries --- p.19 / Chapter 2.1 --- Volume growth rate --- p.19 / Chapter 2.2 --- Sobolev Inequalities --- p.25 / Chapter 2.3 --- Poincare Inequality --- p.32 / Chapter 3 --- Parabolic Harnack Inequality --- p.39 / Chapter 3.1 --- Parabolic Harnack Inequality --- p.39 / Chapter 4 --- Parabolicity and Liouville Dp-property --- p.58 / Chapter 4.1 --- Parabolicity --- p.58 / Chapter 4.2 --- Liouville Dp-property --- p.67

Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 53-54) Also available in print.

In this dissertation we study a special sub-collection of Polish metric spaces: complete separable ultrametric spaces. Polish metric spaces have been studied for quite a long while, and a lot of results have been obtained. Motivated by some of earlier research, we work on the following two main parts in this dissertation. In the first part, we show the existence of Urysohn Polish R-ultrametric spaces, for an arbitrary countable set R of non-negative numbers, including 0. Then we give point-by-point construction of a countable R-ultra-Urysohn space. We also obtain a complete characterization for the set R which corresponding to a R-Urysohn metric space. From this characterization we conclude that there exist R-Urysohn spaces for a wide family of countable R. Moreover, we determine the complexity of the classification of all Polish ultrametric spaces. In the second part, we investigate the isometry groups of Polish ultrametric spaces. We prove that isometry group of an Urysohn Polish R-ultrametric space is universal among isometry groups of Polish R-ultrametric spaces. We completely characterize the isometry groups of finite ultrametric spaces and the isometry groups of countable compact ultrametric spaces. Moreover, we give some necessary conditions for finite groups to be isomorphic to some isometry groups of finite ultrametric spaces.

Für die Konstruktion gebrauchstauglicher Anwendungssysteme ist eine exakte Evaluierung der Usability eine wertvolle Unterstützung. Zu diesem Zweck werden in der Praxis häufig Usability-Fragebögen herangezogen. Im deutschen Sprachraum sind die beiden Fragebögen Isonorm 9241/10 und Isometrics, die beide Software gemäß der EN ISO 9241-110 evaluieren, weit verbreitet. Die vorliegende Studie widmete sich einem Vergleich dieser beiden Fragebögen hinsichtlich testtheoretischer Gütekriterien. Im Rahmen eines experimentellen Designs wurden die beiden Fragebögen eingesetzt um die Usability von zwei Standard-Softwarepaketen zu bewerten. Hinsichtlich der inhaltlichen Validität der Fragebögen zeigten die Ergebnisse eine hohe Übereinstimmung der Usability-Messung der beiden Fragebögen. Auch weitere testtheoretische Analysen lieferten eine ähnliche Qualitätsbeurteilung beider Fragebögen, weshalb sie aus diesem Blickwinkel gleichermaßen für Forschung und Praxis empfohlen werden können.

The study of embeddings is applicable and signicant to higher dimensional theories of our universe, high-energy physics and classical general relativity. In this thesis we investigate local and global isometric embeddings of four-dimensional spherically symmetric spacetimes into five-dimensional Einstein manifolds. Theorems have been established that guarantee the existence of such embeddings. However, most known explicit results concern embedded spaces with relatively simple Ricci curvature. We consider the four-dimensional gravitational field of a global monopole, a simple non-vacuum space with a more complicated Ricci tensor, which is of theoretical interest in its own right, and occurs as a limit in Einstein-Gauss-Bonnet Kaluza-Klein black holes, and we obtain an exact solution for its embedding into Minkowski space. Our local embedding space can be used to construct global embedding spaces, including a globally at space and several types of cosmic strings. We present an analysis of the result and comment on its signicance in the context of induced matter theory and the Einstein-Gauss-Bonnet gravity scenario where it can be viewed as a local embedding into a Kaluza-Klein black hole. Difficulties in solving the five-dimensional equations for given four-dimensional spaces motivate us to investigate which embedded spaces admit bulks of a specific type. We show that the general Schwarzschild-de Sitter spacetime and the Einstein Universe are the only spherically symmetric spacetimes that can be embedded into an Einstein space with a particular metric form, and we discuss their five-dimensional solutions. Furthermore, we determine that the only spherically symmetric spacetime in retarded time coordinates that can be embedded into a particular Einstein bulk is the general Vaidya-de Sitter solution with constant mass. These analyses help to provide insight to the general embedding problem. We also consider the conformal Killing geometry of a five-dimensional Einstein space that embeds a static spherically symmetric spacetime, and we show how the Killing geometry of the embedded space is inherited by its bulk. The study of embedding properties such as these enables a deeper mathematical understanding of higher dimensional cosmological models and is also of physical interest as conformal symmetries encode conservation laws. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2012.

The size of data generated every year follows an exponential growth. The number of data points as well as the dimensions have increased dramatically the past 15 years. The gap between the demand from the industry in data processing and the solutions provided by the machine learning community is increasing. Despite the growth in memory and computational power, advanced statistical processing on the order of gigabytes is beyond any possibility. Most sophisticated Machine Learning algorithms require at least quadratic complexity. With the current computer model architecture, algorithms with higher complexity than linear O(N) or O(N logN) are not considered practical. Dimensionality reduction is a challenging problem in machine learning. Often data represented as multidimensional points happen to have high dimensionality. It turns out that the information they carry can be expressed with much less dimensions. Moreover the reduced dimensions of the data can have better interpretability than the original ones. There is a great variety of dimensionality reduction algorithms under the theory of Manifold Learning. Most of the methods such as Isomap, Local Linear Embedding, Local Tangent Space Alignment, Diffusion Maps etc. have been extensively studied under the framework of Kernel Principal Component Analysis (KPCA). In this dissertation we study two current state of the art dimensionality reduction methods, Maximum Variance Unfolding (MVU) and Non-Negative Matrix Factorization (NMF). These two dimensionality reduction methods do not fit under the umbrella of Kernel PCA. MVU is cast as a Semidefinite Program, a modern convex nonlinear optimization algorithm, that offers more flexibility and power compared to iv KPCA. Although MVU and NMF seem to be two disconnected problems, we show that there is a connection between them. Both are special cases of a general nonlinear factorization algorithm that we developed. Two aspects of the algorithms are of particular interest: computational complexity and interpretability. In other words computational complexity answers the question of how fast we can find the best solution of MVU/NMF for large data volumes. Since we are dealing with optimization programs, we need to find the global optimum. Global optimum is strongly connected with the convexity of the problem. Interpretability is strongly connected with local isometry1 that gives meaning in relationships between data points. Another aspect of interpretability is association of data with labeled information. The contributions of this thesis are the following: 1. MVU is modified so that it can scale more efficient. Results are shown on 1 million speech datasets. Limitations of the method are highlighted. 2. An algorithm for fast computations for the furthest neighbors is presented for the first time in the literature. 3. Construction of optimal kernels for Kernel Density Estimation with modern convex programming is presented. For the first time we show that the Leave One Cross Validation (LOOCV) function is quasi-concave. 4. For the first time NMF is formulated as a convex optimization problem 5. An algorithm for the problem of Completely Positive Matrix Factorization is presented. 6. A hybrid algorithm of MVU and NMF the isoNMF is presented combining advantages of both methods. 7. The Isometric Separation Maps (ISM) a variation of MVU that contains classification information is presented. 8. Large scale nonlinear dimensional analysis on the TIMIT speech database is performed. 9. A general nonlinear factorization algorithm is presented based on sequential convex programming. Despite the efforts to scale the proposed methods up to 1 million data points in reasonable time, the gap between the industrial demand and the current state of the art is still orders of magnitude wide.

Orientador: Claudina Izepe Rodrigues / Dissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-27T16:14:16Z (GMT). No. of bitstreams: 1 Franco_MarciaCristinaLemosGuimaraes_M.pdf: 20497304 bytes, checksum: c28b5c2e4a775ec0c3f67197069a584f (MD5) Previous issue date: 2015 / Resumo: Neste trabalho apresentamos um estudo sobre grupos, transformações geométricas e isometrias no plano. Apresentamos o teorema da classificação das isometrias no plano, o teorema de Leonardo que classifica os grupos de simetria de ornamentos limitados e o teorema da classificação dos grupos de frisos. Propomos sequências de atividades para a Educação Básica envolvendo as isometrias e a identificação do grupo de simetria de um ornamento limitado e de um friso. Além disso, as atividades sugeridas apresentam intuitivamente a ideia da estrutura algébrica de grupos. Finalizamos este trabalho relatando como ocorreu a aplicação de três das sequências sugeridas, os procedimentos adotados e os resultados obtidos / Abstract: We present a study of groups, geometric transformations and isometries in the plane. Introducing the classification theorem of isometries in the plane, the Leonardo theorem that classifies symmetry groups of limited ornaments and the classification theorem of friezes groups. We propose a sequence of activities for the basic education involving isometry and identification of symmetry group of limited ornaments and friezes. Moreover some of the suggested activities provide an intuitive idea of the algebraic structure of groups. We end this paper by reporting on the manner in which the application of three of the suggested sequences occurred, the procedures that were adopted and the results that were obtained / Mestrado / Matemática em Rede Nacional / Mestra

<p>Un code de permutations G(n,d) un sous-ensemble C de Sym(n) tel que la distance de Hamming D entre deux éléments de C est supérieure ou égale à d. Dans cette thèse, le groupe des isométries de (Sym(n),D) est déterminé et il est prouvé que ces isométries sont des automorphismes du schéma d'association induit sur Sym(n) par ses classes de conjugaison. Ceci mène, par programmation linéaire, à de nouveaux majorants de la taille maximale des G(n,d) pour n et d fixés et n compris entre 11 et 13. Des algorithmes de génération avec rejet d'objets isomorphes sont développés. Pour classer les G(n,d) non isométriques, des invariants ont été construits et leur efficacité étudiée. Tous les G(4,3) et les G(5,4) ont été engendrés à une isométrie près, il y en a respectivement 61 et 9445 (dont 139 sont maximaux et décrits explicitement). D’autres classes de G(n,d) sont étudiées.<p><p><p><p> <p><p><p><p>A permutation code G(n,d) is a subset C of Sym(n) such that the Hamming distance D between two elements of C is larger than or equal to d. In this thesis, we characterize the isometry group of the metric space (Sym(n),D) and we prove that these isometries are automorphisms of the association scheme induced on Sym(n) by the conjugacy classes. This leads, by linear programming, to new upper bounds for the maximal size of G(n,d) codes for n and d fixed and n between 11 and 13. We develop generating algorithms with rejection of isomorphic objects. In order to classify the G(n,d) codes up to isometry, we construct invariants and study their efficiency. We generate all G(4,3) and G(4,5)codes up to isometry; there are respectively 61 and 9445 of them. Precisely 139 out of the latter codes are maximal and explicitly described. We also study other classes of G(n,d)codes.<p><p><p><p> / Doctorat en sciences, Spécialisation mathématiques / info:eu-repo/semantics/nonPublished