Neste trabalho, provamos um teorema de imersões isométricas em variedades Lorentzianas homogêneas tridimensionais, usando a teoria de G- estruturas. Tais variedades são aquelas consideradas na classificação das 3- variedades Lorentzianas homogêneas de Dumitrescu e Zeghib. Provamos também um teorema de rigidez isométrica para hipersuperfícies em variedades semi-Riemannianas com G-estrutura infinitesimalmente homogêneas. No caso particular em que o ambiente são variedades semi-Riemannianas dadas por produto de uma forma espacial por R ou variedades Riemannianas homogêneas tridimensionais, provamos o mesmo teorema de rigidez isométrica, porém com hipóteses mais fracas. / In this work we prove an isometric embedding theorem in homogeneous Lorentzian manifolds of dimension 3, that were recently classified by Dumitrescu and Zeghib in [11]. We also prove a rigidity result of isometric embeddings of hypersurfaces in semi-Riemannian manifolds endowed with an infinitesimally homogeneous G-structure. In the special case that the semi-Riemannian manifolds are produtcs of the type Q^n_cxR, or Riemannian homogeneous 3-manifolds, the result is proven under wear assumptions.

Neste trabalho, provamos um teorema de imersões isométricas em variedades Lorentzianas homogêneas tridimensionais, usando a teoria de G- estruturas. Tais variedades são aquelas consideradas na classificação das 3- variedades Lorentzianas homogêneas de Dumitrescu e Zeghib. Provamos também um teorema de rigidez isométrica para hipersuperfícies em variedades semi-Riemannianas com G-estrutura infinitesimalmente homogêneas. No caso particular em que o ambiente são variedades semi-Riemannianas dadas por produto de uma forma espacial por R ou variedades Riemannianas homogêneas tridimensionais, provamos o mesmo teorema de rigidez isométrica, porém com hipóteses mais fracas. / In this work we prove an isometric embedding theorem in homogeneous Lorentzian manifolds of dimension 3, that were recently classified by Dumitrescu and Zeghib in [11]. We also prove a rigidity result of isometric embeddings of hypersurfaces in semi-Riemannian manifolds endowed with an infinitesimally homogeneous G-structure. In the special case that the semi-Riemannian manifolds are produtcs of the type Q^n_cxR, or Riemannian homogeneous 3-manifolds, the result is proven under wear assumptions.

Bio-ontologies and Linked Data have become integral part of biological and biomedical knowledge bases with over 500 of them and millions of triples. Such knowledge bases are primarily developed for information retrieval, query processing, data integration, standardization, and provision. Developing machine learning methods which can exploit the background knowledge in such resources for predictive analysis and novel discovery in the biomedical domain has become essential. In this dissertation, we present novel approaches which utilize the plethora of data sets made available as bio-ontologies and Linked Data in a single uni ed framework as knowledge graphs. We utilize representation learning with knowledge graphs and introduce generic models for addressing and tackling computational problems of major implications to human health, such as predicting disease-gene associations and drug repurposing. We also show that our methods can compensate for incomplete information in public databases and can smoothly facilitate integration with biomedical literature for similar prediction tasks. Furthermore, we demonstrate that our methods can learn and extract features that outperform relevant methods, which rely on manually crafted features and laborious features engineering and pre-processing. Finally, we present a systematic evaluation of knowledge graph representation learning methods and demonstrate their potential applications for data analytics in biomedicine.

Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors effectively condense the text message data into highly leverageable inputs to a logistic regression classifier in a matter of hours, as compared to the tedious and often quite lengthy task of manually coding data. Using our machine learning approach, we are able to train a logistic regression model to predict adolescents' engagement in substance abuse during distinct life phases with accuracy ranging from 76.5% to 88.1%. We show the effects of grade level and text message aggregation strategy on the efficacy of document embedding generation with doc2vec. Additional examination of the vectorizations for specific terms extracted from the text message data adds quantitative depth to this analysis. We demonstrate the ability of the method used herein to overcome traditional natural language processing concerns related to unconventional orthography. These results suggest that the approach described in this thesis is a competitive and efficient alternative to existing methodologies for predicting substance abuse behaviors. This work reveals the potential for the application of machine learning-based manipulation of text messaging data to development of automatic intervention strategies against substance abuse and other adolescent challenges.

This thesis is devoted to the study of different generalizations of the classical conception of a function of bounded variation. First, we study the functions of bounded p-variation introduced by Wiener in 1924. We obtain estimates of the total p-variation (1<p<∞) and other related functionals for a periodic function f in Lp([0,1]) in terms of its Lp-modulus of continuity ω(f;δ)p. These estimates are sharp for any rate of decay of ω(f;δ)p. Moreover, the constant coefficients in them depend on parameters in an optimal way. Inspired by these results, we consider the relationship between the Riesz type generalized variation vp,α(f) (1<p<∞, 0≤α≤1-1/p) and the modulus of p-continuity  ω1-1/p(f;δ). These functionals generate scales of spaces that connect the space of functions of bounded p-variation and the Sobolev space Wp1. We prove sharp estimates of vp,α(f) in terms of ω1-1/p(f;δ). In the same direction, we study relations between moduli of p-continuity and q-continuity for 1<p<q<∞. We prove an inequality that estimates ω1-1/p(f;δ) in terms of ω1-1/q(f;δ). The inequality is sharp for any order of decay of ω1-1/q(f;δ). Next, we study another generalization of bounded variation: the so-called bounded Λ-variation, introduced by Waterman in 1972. We investigate relations between the space ΛBV of functions of bounded Λ-variation, and classes of functions defined via integral smoothness properties. In particular, we obtain the necessary and sufficient condition for the embedding of the class Lip(α;p) into ΛBV. This solves a problem of Wang (2009). We consider also functions of two variables. Applying our one-dimensional result, we obtain sharp estimates of the Hardy-Vitali type p-variation of a bivariate function in terms of its mixed modulus of continuity in Lp([0,1]2). Further, we investigate Fubini-type properties of the space Hp(2) of functions of bounded Hardy-Vitali p-variation. This leads us to consider the symmetric mixed norm space Vp[Vp]sym of functions of bounded iterated p-variation. For p>1, we prove that Hp(2) is not embedded into Vp[Vp]sym, and that Vp[Vp]sym is not embedded into Hp(2). In other words, Fubini-type properties completely fail in the class of functions of bounded Hardy-Vitali type p-variation for p>1. / Baksidestext The classical concept of the total variation of a function has been extended in several directions. Such extensions find many applications in different areas of mathematics. Consequently, the study of notions of generalized bounded variation forms an important direction in the field of mathematical analysis. This thesis is devoted to the investigation of various properties of functions of generalized bounded variation.  In particular, we obtain the following results: sharp relations between spaces of generalized bounded variation and spaces of functions  defined by integral smoothness conditions  (e.g., Sobolev and Besov spaces); optimal properties of certain scales of function spaces of frac- tional smoothness generated by functionals of variational type; sharp embeddings within  the scale of spaces of functions of bounded p-variation; results concerning bivariate functions of bounded p-variation, in particular sharp estimates of total variation in terms of the mixed Lp-modulus of continuity, and Fubini-type properties.

This thesis is concerned with the Regular Inverse Galois Problem for p-groups over fields of characteristic unequal to p. Building upon results of Saltman, Dentzer characterized a class of finite groups that are automatically realized over every field, and proceeded to show that every group of order dividing p⁴ belongs to this class. We extend this result to include groups of order p⁵, provided that the base field k contains the p³-th roots of unity. The proof involves reducing to certain Brauer embedding problems defined over the rational function field k(x). Through explicit computation, we describe the cohomological obstructions to these embedding problems. Then by applying results about the Brauer group of a Dedekind domain, we show that they all possess solutions. / text

Spectral graph theory is the interplay between linear algebra and combinatorial graph theory. Laplace’s equation and its discrete form, the Laplacian matrix, appear ubiquitously in mathematical physics. Due to the recent discovery of very fast solvers for these equations, they are also becoming increasingly useful in combinatorial optimization, computer vision, computer graphics, and machine learning. In this thesis, we develop highly efficient and parallelizable algorithms for solving linear systems involving graph Laplacian matrices. These solvers can also be extended to symmetric diagonally dominant matrices and M-matrices, both of which are closely related to graph Laplacians. Our algorithms build upon two decades of progress on combinatorial preconditioning, which connects numerical and combinatorial algorithms through spectral graph theory. They in turn rely on tools from numerical analysis, metric embeddings, and random matrix theory. We give two solver algorithms that take diametrically opposite approaches. The first is motivated by combinatorial algorithms, and aims to gradually break the problem into several smaller ones. It represents major simplifications over previous solver constructions, and has theoretical running time comparable to sorting. The second is motivated by numerical analysis, and aims to rapidly improve the algebraic connectivity of the graph. It is the first highly efficient solver for Laplacian linear systems that parallelizes almost completely. Our results improve the performances of applications of fast linear system solvers ranging from scientific computing to algorithmic graph theory. We also show that these solvers can be used to address broad classes of image processing tasks, and give some preliminary experimental results.

This thesis is devoted to the study of mixed norm spaces that arise in connection with embeddings of Sobolev and Besov type spaces. We study different structural, integrability, and smoothness properties of functions satisfying certain mixed norm conditions. Conditions of this type are determined by the behaviour of linear sections of functions. The work in this direction originates in a paper due to Gagliardo (1958), and was further developed by Fournier (1988), by Blei and Fournier (1989), and by Kolyada (2005). Here we continue these studies. We obtain some refinements of known embeddings for certain mixed norm spaces introduced by Gagliardo, and we study general properties of these spaces. In connection with these results, we consider a scale of intermediate mixed norm spaces, and prove intrinsic embeddings in this scale. We also consider more general, fully anisotropic, mixed norm spaces. Our main theorem states an embedding of these spaces to Lorentz spaces. Applying this result, we obtain sharp embedding theorems for anisotropic Sobolev-Besov spaces, and anisotropic fractional Sobolev spaces. The methods used are based on non-increasing rearrangements, and on estimates of sections of functions and sections of sets. We also study limiting relations between embeddings of spaces of different type. More exactly, mixed norm estimates enable us to get embedding constants with sharp asymptotic behaviour. This gives an extension of the results obtained for isotropic Besov spaces by Bourgain, Brezis, and Mironescu, and for anisotropic Besov spaces by Kolyada. We study also some basic properties (in particular the approximation properties) of special weak type spaces that play an important role in the construction of mixed norm spaces, and in the description of Sobolev type embeddings. In the last chapter, we study mixed norm spaces consisting of functions that have smooth sections. We prove embeddings of these spaces to Lorentz spaces. From this result, known properties of Sobolev-Liouville spaces follow.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

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