Banach's contraction principle is probably one of the most important theorems in fixed point theory. It has been used to develop much of the rest of fixed point theory. Another key result in the field is a theorem due to Browder, Göhde, and Kirk involving Hilbert spaces and nonexpansive mappings. Several applications of Banach's contraction principle are made. Some of these applications involve obtaining new metrics on a space, forcing a continuous map to have a fixed point, and using conditions on the boundary of a closed ball in a Banach space to obtain a fixed point. Finally, a development of the theorem due to Browder et al. is given with Hilbert spaces replaced by uniformly convex Banach spaces.

Mathematical models have extensively been used in problems related to engineering, computer sciences, economics, social, natural and medical sciences etc. It has become very common to use mathematical tools to solve, study the behavior and different aspects of a system and its different subsystems. Because of various uncertainties arising in real world situations, methods of classical mathematics may not be successfully applied to solve them. Thus, new mathematical theories such as probability theory and fuzzy set theory have been introduced by mathematicians and computer scientists to handle the problems associated with the uncertainties of a model. But there are certain deficiencies pertaining to the parametrization in fuzzy set theory. Soft set theory aims to provide enough tools in the form of parameters to deal with the uncertainty in a data and to represent it in a useful way. The distinguishing attribute of soft set theory is that unlike probability theory and fuzzy set theory, it does not uphold a precise quantity. This attribute has facilitated applications in decision making, demand analysis, forecasting, information sciences, mathematics and other disciplines. In this thesis we will discuss several algebraic and topological properties of soft sets and fuzzy soft sets. Since soft sets can be considered as setvalued maps, the study of fixed point theory for multivalued maps on soft topological spaces and on other related structures will be also explored. The contributions of the study carried out in this thesis can be summarized as follows: i) Revisit of basic operations in soft set theory and proving some new results based on these modifications which would certainly set a new dimension to explore this theory further and would help to extend its limits further in different directions. Our findings can be applied to develop and modify the existing literature on soft topological spaces ii) Defining some new classes of mappings and then proving the existence and uniqueness of such mappings which can be viewed as a positive contribution towards an advancement of metric fixed point theory iii) Initiative of soft fixed point theory in framework of soft metric spaces and proving the results lying at the intersection of soft set theory and fixed point theory which would help in establishing a bridge between these two flourishing areas of research. iv) This study is also a starting point for the future research in the area of fuzzy soft fixed point theory. / Abbas, M. (2014). Soft Set Theory: Generalizations, Fixed Point Theorems, and Applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48470 / TESIS

The structural Ramsey theory is a field on the boundary of combinatorics and model theory with deep connections to topological dynamics. Most of the known Ramsey classes in finite binary symmetric relational language can be shown to be Ramsey by utilizing a variant of the shortest path completion (e.g. Sauer's S-metric spaces, Conant's generalised metric spaces, Braunfeld's Λ-ultrametric spaces or Cherlin's metrically homogeneous graphs). In this thesis we explore the limits of the shortest path completion. We offer a unifying framework - semigroup-valued metric spaces - for all the aforementioned Ramsey classes and study their Ramsey expansions and EPPA (the extension property for partial automorphisms). Our results can be seen as evidence for the importance of studying the completion problem for amalgamation classes and have some further applications (such as the stationary independence relation). As a corollary of our general theorems, we reprove results of Hubička and Nešetřil on Sauer's S-metric spaces, results of Hubička, Nešetřil and the author on Conant's generalised metric spaces, Braunfeld's results on Λ-ultrametric spaces and the results of Aranda et al. on Cherlin's primitive 3-constrained metrically homogeneous graphs. We also solve several open problems such as EPPA for Λ-ultrametric...

<p>Merenje kvaliteta slike korišćenjem indeksa za kvalitet slike, ne mora da odražava i praktični kvalitet slike, odnosno nije baziran na HVS (Human visual system) modelu. Formiranje razmatranih funkcija, koje se koriste u algoritmu filtriranja za određivanje rastojanja među pikselima, može se vršiti&nbsp; na različite načine, što se može videti u radovima iz oblasti filtriranja slike, daje širok spektar mogućnosti da se ispita uticaj fazi rastojanja npr. fazi T-metrike ili fazi Ѕ-metrike mogu imati na sam proces filtriranja slike. Cilj je poboljšanje kvaliteta slike u odnosu na medijanski filter. U okviru teorijskih razmatranja prostora sa fazi rastojanjem dobijeni su i rezultati iz teorije nepokretne tačke koji pružaju mogućnost dalje primene ovih prostora u tehnici.</p> / <p>Measuring the image quality using a given image quality index does not necessarily reflect the practical quality of the image, that is, it is not based on the HVS (Human Visual System) model. The formation of given functions, which are used in the filtering algorithm for determining the distance between the pixels, can be done in different ways, which can be seen in works in the field of image filtering, provides a wide range of possibilities to examine the effect of fuzzy distance, for example, of the fuzzy T-metric or the fuzzy S-metric can have on the image filtering process itself. The goal is to improve image quality in relation to a vector median filter. Within the theoretical considerations of space with fuzzy distance, results from the fixed point theory have been obtained which provide the possibility of further application of these spaces in the technique.</p>

No description available.

A complexidade dos dados armazenados em grandes bases de dados aumenta cada vez mais, criando a necessidade de novas operações de consulta. Uma classe de operações que tem apresentado interesse crescente são as chamadas Consultas por Similaridade, sendo as mais conhecidas as consultas por Abrangência (\'R IND. q\') e por k-Vizinhos mais Proximos (kNN), sendo que esta ultima obtem quais são os k elementos armazenados mais similares a um dado elemento de referência. Outra consulta que é interessante tanto para consultas diretas quanto como parte de operações de análises mais complexas e a operação de consulta aos k-Vizinhos mais Próximos Reversos (RkNN). Seu objetivo e obter todos os elementos armazenados que têm um dado elemento de referência como um dos seus k elementos mais similares. Devido a complexidade de execução da operação de RkNN, a grande maioria das soluções existentes restringem-se a dados representados em espaços multidimensionais euclidianos (nos quais estão denidas tambem operações cardinais e topológicas, além de se considerar a similaridade como sendo a distância Euclidiana entre dois elementos), ou então obtém apenas respostas aproximadas, sujeitas a existência de falsos negativos. Várias aplicações de análise de dados científicos, médicos, de engenharia, financeiros, etc. requerem soluções eficientes para o problema da operação de RkNN sobre dados representados em espaços métricos, onde os elementos não podem ser considerados estar em um espaço nem Euclidiano nem multidimensional. Num espaço métrico, além dos próprios elementos armazenados existe apenas uma função de comparação métrica entre pares de objetos. Neste trabalho, são propostas novas podas de espaço de busca e o algoritmo RkNN-MG que utiliza essas novas podas para solucionar o problema de consultas RkNN exatas em espaços métricos sem limitações. Toda a proposta supõe que o conjunto de dados esta em um espaço métrico imerso isometricamente em espaço euclidiano e utiliza propriedades da geometria métrica válida neste espaço para realizar podas eficientes por lei dos cossenos combinada com as podas tradicionais por desigualdade triangular. Os experimentos demonstram comparativamente que as novas podas são mais eficientes que as tradicionais podas por desigualdade triangular, tendo desempenhos equivalente quando comparadas em conjuntos de alta dimensionalidade ou com dimensão fractal alta. Assim, os resultados confirmam as novas podas propostas como soluções alternativas eficientes para o problema de consultas RkNN / Data stored in large databases present an ever increasing complexity, pressing for the development of new classes of query operators. One such class, which is enticing an increasing interest, is the so-called Similarity Queries, where the most common are the similarity range queries (\'R IND. q\') and the k-nearest neighbor queries (kNN). A k-nearest neighbor query aims at retrieving the k stored elements nearer (or more similar) to a given reference element. Another important similarity query is the reverse k-nearest neighbor (RkNN), useful both for queries posed directly by the analyst and for queries that are part of more complex analysis processes. The objective of a reverse k-nearest neighbor queries is obtaining the stored elements that has the query reference element as one of their k-nearest neighbors. As the RkNN operation is a rather expensive operation, from the computational standpoint, most existing solutions only solve the query when applied over Euclidean multidimensional spaces (as these spaces also define cardinal and topological operations besides the Euclidean distance between pairs of elements) or retrieve only approximate answers, where false negatives can occur. Several applications, like the analysis of scientific, medical, engineering or financial data, require efficient and exact answers for the RkNN queries over data which is frequently represented in metric spaces, that is where no other property besides the similarity measure exists. Therefore, for applications handling metrical data, the assumption of Euclidean metric or even multidimensional data cannot be used. In this work, we propose new pruning rules based on the law of cosines, and the RkNN-MG algorithm, which uses them to solve RkNN queries in a way that is exact, faster than the existing approaches, that is not limited for any value of k, and that can be applied both over static and over dynamic datasets. The new pruning rules assume that the data set is in a metric space that can be embedded into an Euclidean space and use metric geometry properties valid in this space to perform effective pruning based on the law of cosines combined with the traditional pruning based on the triangle inequality property. The experiments show that the new pruning rules are alkways more efficient than the traditional pruning rules based solely on the triangle inequality. The experiments show that for high high dimensionality datasets, or for metric datasets with high fractal dimensionality, the performance improvement is smaller than for for lower dimensioinality datasets, but it\'s never worse. Thus, the results confirm that the our pruning rules are efficient alternative to solve RkNN queries in general

Os volumes de dados armazenados em grandes bases de dados aumentam em ritmo sempre crescente, pressionando o desempenho e a flexibilidade dos Sistemas de Gerenciamento de Bases de Dados (SGBDs). Os problemas de se tratar dados em grandes quantidades, escopo, complexidade e distribuição vêm sendo tratados também sob o tema de big data. O aumento da complexidade cria a necessidade de novas formas de busca - representar apenas números e pequenas cadeias de caracteres já não é mais suficiente. Buscas por similaridade vêm se mostrando a maneira por excelência de comparar dados complexos, mas até recentemente elas não estavam disponíveis nos SGBDs. Agora, com o início de sua disponibilidade, está se tornando claro que apenas os operadores de busca por similaridade fundamentais não são suficientes para lidar com grandes volumes de dados. Um dos motivos disso é que similaridade\' é, usualmente, definida considerando seu significado quando apenas poucos estão envolvidos. Atualmente, o principal foco da literatura em big data é aumentar a eficiência na recuperação dos dados usando paralelismo, existindo poucos estudos sobre a eficácia das respostas obtidas. Esta tese visa propor e desenvolver variações dos operadores de busca por similaridade para torná-los mais adequados para processar big data, apresentando visões mais abrangentes da base de dados, aumentando a eficácia das respostas, porém sem causar impactos consideráveis na eficiência dos algoritmos de busca e viabilizando sua execução escalável sobre grandes volumes de dados. Para alcançar esse objetivo, este trabalho apresenta quatro frentes de contribuições: A primeira consistiu em um modelo de diversificação de resultados que pode ser aplicado usando qualquer critério de comparação e operador de busca por similaridade. A segunda focou em definir técnicas de amostragem e de agrupamento de dados com o modelo de diversificação proposto, acelerando o processo de análise dos conjuntos de resultados. A terceira contribuição desenvolveu métodos de avaliação da qualidade dos conjuntos de resultados diversificados. Por fim, a última frente de contribuição apresentou uma abordagem para integrar os conceitos de mineração visual de dados e buscas por similaridade com diversidade em sistemas de recuperação por conteúdo, aumentando o entendimento de como a propriedade de diversidade pode ser aplicada. / The data being collected and generated nowadays increase not only in volume, but also in complexity, requiring new query operators. Health care centers collecting image exams and remote sensing from satellites and from earth-based stations are examples of application domains where more powerful and flexible operators are required. Storing, retrieving and analyzing data that are huge in volume, structure, complexity and distribution are now being referred to as big data. Representing and querying big data using only the traditional scalar data types are not enough anymore. Similarity queries are the most pursued resources to retrieve complex data, but until recently, they were not available in the Database Management Systems. Now that they are starting to become available, its first uses to develop real systems make it clear that the basic similarity query operators are not enough to meet the requirements of the target applications. The main reason is that similarity is a concept formulated considering only small amounts of data elements. Nowadays, researchers are targeting handling big data mainly using parallel architectures, and only a few studies exist targeting the efficacy of the query answers. This Ph.D. work aims at developing variations for the basic similarity operators to propose better suited similarity operators to handle big data, presenting a holistic vision about the database, increasing the effectiveness of the provided answers, but without causing impact on the efficiency on the searching algorithms. To achieve this goal, four mainly contributions are presented: The first one was a result diversification model that can be applied in any comparison criteria and similarity search operator. The second one focused on defining sampling and grouping techniques with the proposed diversification model aiming at speeding up the analysis task of the result sets. The third contribution concentrated on evaluation methods for measuring the quality of diversified result sets. Finally, the last one defines an approach to integrate the concepts of visual data mining and similarity with diversity searches in content-based retrieval systems, allowing a better understanding of how the diversity property is applied in the query process.

O problema da descontinuidade semântica tem sido um dos principais focos de pesquisa no desenvolvimento de sistemas de recuperação de imagens baseada em conteúdo (CBIR). Neste contexto, as pesquisas mais promissoras focam principalmente na inferência de pesos de características contínuos e na seleção de características. Entretanto, os processos tradicionais de inferência de pesos contínuos são computacionalmente caros e a seleção de características equivale a uma ponderação binária. Visando tratar adequadamente o problema de lacuna semântica, este trabalho propõe dois métodos de transformação de espaço de características métricos baseados na inferência de funções de transformação por meio de algoritmo genético. O método WF infere funções de ponderação para ajustar a função de dissimilaridade e o método TF infere funções para transformação das características. Comparados às abordagens de inferência de pesos contínuos da literatura, ambos os métodos propostos proporcionam uma redução drástica do espaço de busca ao limitar a busca à escolha de um conjunto ordenado de funções de transformação. Análises visuais do espaço transformado e de gráficos de precisão vs. revocação confirmam que TF e WF superam a abordagem tradicional de ponderação de características. Adicionalmente, foi verificado que TF supera significativamente WF em termos de precisão dos resultados de consultas por similaridade por permitir transformação não lineares no espaço de característica, conforme constatado por análise visual. / The semantic gap problem has been a major focus of research in the development of content-based image retrieval (CBIR) systems. In this context, the most promising research focus primarily on the inference of continuous feature weights and feature selection. However, the traditional processes of continuous feature weighting are computationally expensive and feature selection is equivalent to a binary weighting. Aiming at alleviating the semantic gap problem, this master dissertation proposes two methods for the transformation of metric feature spaces based on the inference of transformation functions using Genetic Algorithms. The WF method infers weighting functions and the TF method infers transformation functions for the features. Compared to the existing methods, both proposed methods provide a drastic searching space reduction by limiting the search to the choice of an ordered set of transformation functions. Visual analysis of the transformed space and precision. vs. recall graphics confirm that both TF and WF outperform the traditional feature eighting methods. Additionally, we found that TF method significantly outperforms WF regarding the query similarity accuracy by performing non linear feature space transformation, as found in the visual analysis.

<p>This PhD thesis, consisting of an introduction, four papers, and some supplementary results, studies the problem of finding an <i>optimal realization</i> of a given finite metric space: a weighted graph which preserves the metric's distances and has minimal total edge weight. This problem is known to be NP-hard, and solutions are not necessarily unique.</p><p>It has been conjectured that <i>extremally weighted</i> optimal realizations may be found as subgraphs of the <i>hereditarily optimal realization</i> Γ_d, a graph which in general has a higher total edge weight than the optimal realization but has the advantages of being unique, and possible to construct explicitly via the <i>tight span</i> of the metric.</p><p>In Paper I, we prove that the graph Γ_d is equivalent to the 1-skeleton of the tight span precisely when the metric considered is <i>totally split-decomposable</i>. For the subset of totally split-decomposable metrics known as <i>consistent</i> metrics this implies that Γ_d is isomorphic to the easily constructed <i>Buneman graph</i>.</p><p>In Paper II, we show that for any metric on at most five points, any optimal realization can be found as a subgraph of Γ_d.</p><p>In Paper III we provide a series of counterexamples; metrics for which there exist extremally weighted optimal realizations which are not subgraphs of Γ_d. However, for these examples there also exists at least one optimal realization which is a subgraph.</p><p>Finally, Paper IV examines a weakened conjecture suggested by the above counterexamples: can we always find some optimal realization as a subgraph in Γ_d? Defining <i>extremal</i> optimal realizations as those having the maximum possible number of shortest paths, we prove that any embedding of the vertices of an extremal optimal realization into Γ_d is injective. Moreover, we prove that this weakened conjecture holds for the subset of consistent metrics which have a 2-dimensional tight span</p>

This PhD thesis, consisting of an introduction, four papers, and some supplementary results, studies the problem of finding an optimal realization of a given finite metric space: a weighted graph which preserves the metric's distances and has minimal total edge weight. This problem is known to be NP-hard, and solutions are not necessarily unique. It has been conjectured that extremally weighted optimal realizations may be found as subgraphs of the hereditarily optimal realization Γd, a graph which in general has a higher total edge weight than the optimal realization but has the advantages of being unique, and possible to construct explicitly via the tight span of the metric. In Paper I, we prove that the graph Γd is equivalent to the 1-skeleton of the tight span precisely when the metric considered is totally split-decomposable. For the subset of totally split-decomposable metrics known as consistent metrics this implies that Γd is isomorphic to the easily constructed Buneman graph. In Paper II, we show that for any metric on at most five points, any optimal realization can be found as a subgraph of Γd. In Paper III we provide a series of counterexamples; metrics for which there exist extremally weighted optimal realizations which are not subgraphs of Γd. However, for these examples there also exists at least one optimal realization which is a subgraph. Finally, Paper IV examines a weakened conjecture suggested by the above counterexamples: can we always find some optimal realization as a subgraph in Γd? Defining extremal optimal realizations as those having the maximum possible number of shortest paths, we prove that any embedding of the vertices of an extremal optimal realization into Γd is injective. Moreover, we prove that this weakened conjecture holds for the subset of consistent metrics which have a 2-dimensional tight span