Orientadores: Aurelio Ribeiro Leite de Oliveira, Christiano Lyra Filho / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-14T10:57:39Z (GMT). No. of bitstreams: 1 Cantane_DanielaRenata.pdf: 1253133 bytes, checksum: 870b16a2b9360f77ebd88f50491d181c (MD5) Previous issue date: 2009 / Resumo: A solução eficiente de sistemas lineares é fundamental em problemas de otimização linear e o primeiro método a obter êxito nesta classe de problemas foi o método Simplex. Com o objetivo de desenvolver alternativas eficientes para sua implementação, são apresentadas nesta tese técnicas de atualização da decomposição LU da base para aperfeiçoar a solução dos sistemas lineares oriundos do método Simplex, utilizando um reordenamento estático nas colunas da matriz. Uma simulação do método Simplex é implementada, realizando troca de bases obtidas pelo MINOS e verificando sua esparsidade. Somente os elementos afetados pela mudança de base são considerados para obter uma atualização da decomposição LU eficaz. As colunas da matriz são reordenadas de acordo com três estratégias: mínimo grau; forma bloco triangular e estratégia de Björck. Assim, obtém-se uma decomposição esparsa para qualquer base sem esforço computacional para obter a ordem das colunas, pois o reordenamento da matriz é estático e as colunas da base obedecem esta ordem. A forma bloco triangular obteve os melhores resultados, para os maiores problemas testados, em relação ao mínimo grau e a estratégia de Björck. Resultados computacionais para problemas da Netlib mostram a robustez e um bom desempenho computacional do método de atualização da decomposição LU proposto, pois não são necessárias refatorações periódicas da base como nos métodos de atualização tradicionais. O método proposto obteve uma redução do número de elementos não nulos da base em relação ao MINOS. Esta abordagem foi aplicada em problemas de corte de estoque e a atualização da decomposição LU proposta obteve uma redução do tempo computacional na solução destes problemas em relação ao GPLK. / Abstract: Finding efficient solution of linear systems is fundamental in the linear programming problems and the first method to obtain success for this class of problems was the Simplex method. With the objective to develop efficient alternatives to its implementation, techniques of the simplex basis LU factorization update are developed in this thesis to improve the solution of the Simplex method linear systems towards a matrix columns static reordering. A simulation of the Simplex method is implemented, carrying through the change of basis obtained from MINOS and verifying its sparsity. Only the factored columns actually modified by the change of the base are carried through to obtain an efficient LU factorization update. The matrix columns are reordered according to three strategies: minimum degree; block triangular form and the Björck strategy. Thus, sparse factorizations are obtained for any base without computational effort to obtain the order of columns, since the reordering of the matrix is static and base columns follow this ordering. The application of the block triangular form achieved the best results, for larger scale problems tested, in comparison to minimum degree method and the Björck strategy. Computational results for Netlib problems show the robustness of this approach and good computational performance, since there is no need of periodical factorizations as used in traditional updating methods. The proposed method obtained a reduction of the nonzero entries of the basis with respect to MINOS. This approach was applied in the cutting stock problems and the proposed method achieved a reduction of the computational time in the solution of such problems with respect to the GLPK. / Universidade Estadual de Campi / Automação / Doutor em Engenharia Elétrica

Clustering is actively used in several research fields, such as pattern recognition, machine learning and data mining. This dissertation focuses on clustering algorithms in the data mining area. Clustering algorithms can be applied to solve the unsupervised learning problem, which deals with finding clusters in unlabeled data. Most clustering algorithms require the number of cluster centers be known in advance. However, this is often not suitable for real world applications, since we do not know this information in most cases. Another question becomes, once clusters are found by the algorithms, do we believe the clusters are exactly the right ones or do there exist better ones? In this dissertation, we present two new Swarm Intelligence based approaches for data clustering to solve the above issues. Swarm based approaches to clustering have been shown to be able to skip local extrema by doing a form of global search, our two newly proposed ant clustering algorithms take advantage of this. The first algorithm is a kernel-based fuzzy ant clustering algorithm using the Xie-Beni partition validity metric, it is a two stage algorithm, in the first stage of the algorithm ants move the cluster centers in feature space, the cluster centers found by the ants are evaluated using a reformulated kernel-based Xie-Beni cluster validity metric. We found when provided with more clusters than exist in the data our new ant-based approach produces a partition with empty clusters and/or very lightly populated clusters. Then the second stage of this algorithm was applied to automatically detect the number of clusters for a data set by using threshold solutions. The second ant clustering algorithm, using chemical recognition of nestmates is a combination of an ant based algorithm and a consensus clustering algorithm. It is a two-stage algorithm without initial knowledge of the number of clusters. The main contributions of this work are to use the ability of an ant based clustering algorithm to determine the number of cluster centers and refine the cluster centers, then apply a consensus clustering algorithm to get a better quality final solution. We also introduced an ensemble ant clustering algorithm which is able to find a consistent number of clusters with appropriate parameters. We proposed a modified online ant clustering algorithm to handle clustering large data sets. To our knowledge, we are the first to use consensus to combine multiple ant partitions to obtain robust clustering solutions. Experiments were done with twelve data sets, some of which were benchmark data sets, two artificially generated data sets and two magnetic resonance image brain volumes. The results show how the ant clustering algorithms play an important role in finding the number of clusters and providing useful information for consensus clustering to locate the optimal clustering solutions. We conducted a wide range of comparative experiments that demonstrate the effectiveness of the new approaches.

京都大学 / 0048 / 新制・課程博士 / 博士(薬科学) / 甲第22651号 / 薬科博第123号 / 新制||薬科||13(附属図書館) / 京都大学大学院薬学研究科薬科学専攻 / (主査)教授 石濱 泰, 教授 緒方 博之, 教授 馬見塚 拓, 教授 山下 富義 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23314号 / 情博第750号 / 新制||情||128(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 山本 章博, 教授 鹿島 久嗣, 教授 河原 達也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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<div><div><div><p>It is well-known that the sum of reciprocals of twin primes converges or is a finite sum.</p><p>In the same spirit, Samuel Wagstaff proved in 2021 that the sum of reciprocals of primes p</p><p>such that ap + b is prime also converges or is a finite sum for any a, b where gcd(a, b) = 1</p><p>and 2 | ab. Wagstaff gave upper and lower bounds in the case that ab is a power of 2. Here,</p><p>we expand on his work and allow any a, b satisfying gcd(a, b) = 1 and 2 | ab. Let Πa,b be the</p><p>product of p−1 over the odd primes p dividing ab. We show that the upper bound of these p−2</p><p>sums is Πa,b times the upper bound found by Wagstaff and provide evidence as to why we cannot hope to do better than this. We also give several examples for specific pairs (a, b).</p><p><br></p><p>Next, we turn our attention to elliptic Carmichael numbers. In 1987, Dan Gordon defined the notion of an elliptic Carmichael number as a composite integer n which satisfies a Fermat- like criterion on elliptic curves with complex multiplication. More recently, in 2018, Thomas Wright showed that there are infinitely such numbers. We build off the work of Wright to prove that there are infinitely many elliptic Carmichael numbers of the form a (mod M) for a certain M, using an improved lower bound due to Carl Pomerance. We then apply this result to comment on the infinitude of strong pseudoprimes and strong Lucas pseudoprimes.</p><p><br></p><p>Finally, we consider the problem of classifying for which k does one have Φk(x) | Φn(x)−1, where Φn(x) is the nth cyclotomic polynomial. We provide a motivating example as to how this can be applied to primality proving. Then, we complete the case k = 8 and give a partial characterization for the case k = 16. This leads us to conjecture necessary and sufficient conditions for when Φk(x) | Φn(x) − 1 whenever k is a power of 2.</p></div></div></div>

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The elections in South Africa are contested by multiple political parties appealing to a diverse population that comes from a variety of socioeconomic backgrounds. As a result, a rich source of discourse is created to inform voters about election-related content. Two common sources of information to help voters with their decision are news articles and tweets, this study aims to understand the discourse in these two sources using natural language processing. Topic modelling techniques, Latent Dirichlet Allocation and Non- negative Matrix Factorization, are applied to digest the breadth of information collected about the elections into topics. The topics produced are subjected to further analysis that uncovers similarities between topics, links topics to dates and events and provides a summary of the discourse that existed prior to the South African general elections. The primary focus is on the 2019 elections, however election-related articles from 2014 and 2019 were also compared to understand how the discourse has changed. / Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2019. / Computer Science / MIT (Big Data Science) / Unrestricted

This thesis deals with distributed applications and WCF framework. The first part is based on theoretical information about distributed systems and we also concentrate on models of distributed systems. Next part describes WCF framework and key elements of WCF application. The following chapter is designated to introduce information about prime factorization. Then the knowledge from previous parts is used to create examples of service-oriented applications. In conclusion we discuss main parts of designing distributed application to solve factorization problem. Finally the comparison of distributed and dedicated application is made.

Cette thèse s'articule autour des problèmes d'optimisation à grande échelle, et plus particulièrement autour des méthodes de factorisation matricielle sur des problèmes de grandes tailles. L'objectif des méthodes de factorisation de grandes matrices est d'extraire des variables latentes qui permettent d'expliquer les données dans un espace de dimension réduite. Nous nous sommes intéressés au domaine d'application de la recommandation et plus particulièrement au problème de prédiction de préférences d'utilisateurs.Dans une contribution, nous nous sommes intéressés à l'application de méthodes de factorisation dans un environnement de recommandation contextuelle et notamment dans un contexte social.Dans une seconde contribution, nous nous sommes intéressés au problème de sélection de modèle pour la factorisation où l'on cherche à déterminer de façon automatique le rang de la factorisation par estimation de risque. / This thesis focuses on large scale optimization problems and especially on matrix factorization methods for large scale problems. The purpose of such methods is to extract some latent variables which will explain the data in smaller dimension space. We use our methods to address the problem of preference prediction in the framework of the recommender systems. Our first contribution focuses on matrix factorization methods applied in context-aware recommender systems problems, and particularly in socially-aware recommandation.We also address the problem of model selection for matrix factorization which ails to automatically determine the rank of the factorization.