Topic hierarchies are efficient ways of organizing document collections. These structures help users to manage the knowledge contained in textual data. These hierarchies are usually obtained through unsupervised hierarchical clustering algorithms. By not considering the context of the user in the formation of the hierarchical groups, unsupervised topic hierarchies may not attend the user\'s expectations in some cases. One possible solution for this problem is to employ semi-supervised clustering algorithms. These algorithms incorporate the user\'s knowledge through the usage of constraints to the clustering process. However, in the context of semi-supervised hierarchical clustering, the works in the literature do not efficient explore the selection of cases (instances or cluster) to add constraints, neither the interaction of the user with the clustering process. In this sense, in this work we introduce two semi-supervised hierarchical clustering algorithms: HCAC (Hierarchical Confidence-based Active Clustering) and HCAC-LC (Hierarchical Confidence-based Active Clustering with Limited Constraints). These algorithms employ an active learning approach based in the confidence of cluster merges. When a low confidence merge is detected, the user is invited to decide, from a pool of candidate pairs of clusters, the best cluster merge in that point. In this work, we employ HCAC and HCAC-LC in the extraction of topic hierarchies through the SMITH framework, which is also proposed in this thesis. This framework provides a series of well defined activities that allow the user\'s interaction in the generation of topic hierarchies. The active learning approach used in the HCAC-based algorithms, the kind of queries employed in these algorithms, as well as the SMITH framework for the generation of semi-supervised topic hierarchies are innovations to the state of the art proposed in this thesis. Our experimental results indicate that HCAC and HCAC-LC outperform other semi-supervised hierarchical clustering algorithms in diverse scenarios. The results also indicate that semi-supervised topic hierarchies obtained through the SMITH framework are more intuitive and easier to navigate than unsupervised topic hierarchies / Hierarquias de tópicos são formas eficientes de organização de coleções de documentos, auxiliando usuários a gerir o conhecimento materializado nessas publicações textuais. Tais hierarquias são usualmente construídas por meio de algoritmos de agrupamento hierárquico não supervisionado. Entretanto, por não considerarem o contexto do usuário na formação dos grupos, hierarquias de tópicos não supervisionadas nem sempre conseguem atender as suas expectativas. Uma solução para este problema e o emprego de algoritmos de agrupamento semissupervisionado, os quais incorporam o conhecimento de domínio do usuário por meio de restrições. Entretanto, para o contexto de agrupamento hierárquico semissupervisionado, não são eficientemente explorados na literatura métodos de seleção de casos (instâncias ou grupos) para receber restrições, bem como não há formas eficientes de interação do usuário com o processo de agrupamento hierárquico. Dessa maneira, neste trabalho, dois algoritmos de agrupamento hierárquico semissupervisionado são propostos: HCAC (Hierarchical Confidence-based Active Clustering) e HCAC-LC (Hierarchical Confidence-based Active Clustering with Limited Constraints). Estes algoritmos empregam uma abordagem de aprendizado ativo baseado na confiança de uma junção de clusters. Quando uma junção de baixa confiança e detectada, o usuário e convidado a decidir, em um conjunto de pares de grupos candidatos, a melhor junção naquele ponto. Estes algoritmos são aqui utilizados na extração de hierarquias de tópicos por meio do framework SMITH, também proposto nesse trabalho. Este framework fornece uma série de atividades bem definidas que possibilitam a interação do usuário para a obtenção de hierarquias de tópicos. A abordagem de aprendizado ativo utilizado nos algoritmos HCAC e HCAC-LC, o tipo de restrição utilizada nestes algoritmos, bem como o framework SMITH para obtenção de hierarquias de tópicos semissupervisionadas são inovações ao estado da arte propostos neste trabalho. Os resultados obtidos indicam que os algoritmos HCAC e HCAC-LC superam o desempenho de outros algoritmos hierárquicos semissupervisionados em diversos cenários. Os resultados também indicam que hierarquias de tópico semissupervisionadas obtidas por meio do framework SMITH são mais intuitivas e fáceis de navegar do que aquelas não supervisionadas

O objetivo da presente tese foi investigar o efeito do nível de dificuldade do adversário nas respostas hormonais, comportamentais e de desempenho de jovens jogadores de basquetebol. Para tanto, 29 atletas de basquetebol do sexo masculino, das categorias sub-15, sub-16 e sub-17 de um mesmo clube (15,3 ± 1,1 anos; 85,5 ± 15,0 kg; 189,0 ± 7,9 cm) foram avaliados na primeira fase do Campeonato Paulista em três partidas com diferentes níveis de dificuldade do adversário (Difícil, Médio e Fácil) para cada uma das três categorias, somando um total de nove partidas. As concentrações salivares de cortisol (C) e testosterona (T) pré e pós-partida, a ansiedade pré-competitiva, o desempenho técnico através do número de envolvimentos técnico-táticos com bola de cada jogador, a percepção subjetiva de esforço da sessão (PSE da sessão) e o desempenho percebido, foram consideradas em cada partida. A comparação de T e C, e o desempenho técnico foi realizada com uma análise de modelos mistos de um (nível de dificuldade) ou dois fatores (nível de dificuldade e momento), com medidas repetidas. Por sua vez, a comparação das variáveis ansiedade pré-competitiva, PSE da sessão e desempenho percebido foi realizada pelo teste de Friedman, seguido do teste de Wilcoxon, quando necessário. Em ambas as análises foi utilizado o post hoc de Bonferroni. Além disso, a associação entre as repostas normalizadas pelo score Z da variação de T com o desempenho percebido e técnico foi verificada por meio da correlação de Pearson. Em todas as análises foi adotado o nível de significância de 5%. Foi verificado aumento de T do pré para o pós nas partidas contra adversários de nível de dificuldade Fácil (p = 0,0064) e Médio (p = 0,0375). A C pós-partida foi superior aos valores pré (p < 0,0001), independentemente do nível de dificuldade; e C no nível Fácil foi inferior ao nível Médio (p = 0,0351) e Difícil (p = 0,0035). A ansiedade cognitiva foi maior no nível Difícil em relação ao nível Fácil (p < 0.01). A ansiedade somática, por sua vez, foi maior no nível Difícil em relação aos níveis Fácil (p<0,001) e Médio (p = 0,01), e maior no nível Médio em relação ao nível Fácil (p = 0,004). A PSE da sessão foi maior no nível Difícil em relação aos níveis Fácil (p = 0,003) e Médio (p = 0,003). Não foi observado efeito do nível de dificuldade para autoconfiança (p = 0,118), desempenho técnico (p = 0,728) e desempenho percebido (p = 0,113). Além disso, não foi verificada correlação significante entre as variáveis desempenho técnico e percebido com a variação da concentração de T (r < 0,35 e p > 0,07 para todas as comparações). Os resultados da presente tese indicam que quanto maior o nível de dificuldade do adversário maior a ansiedade pré-competitiva e a PSE da sessão. A concentração de C aumentou independente do nível do adversário. Já a T aumentou nos jogos de menor dificuldade, mas não no jogo Difícil. Esse aumento da T não se correlacionou significativamente com o desempenho técnico ou percebido. Não houve influência do nível do adversário no desempenho técnico ou percebido / The aim of this thesis was to investigate the effect of the adversary difficulty level on hormonal and behavioral responses, technical performance and perceived performance in young basketball players. Thereunto, 29 male young basketball players, from under-15, under-16 and under-17 categories from the same club (15.3 ± 1.1 years, 85.5 ± 15.0 kg, 189.0 ± 7.9 cm) were evaluated in the first phase of the Paulista Championship in three matches with different opponent difficulty levels (Hard, Medium and Easy) for each of the three categories, totaling nine matches. Salivary concentrations of cortisol (C) and testosterone (T) pre and post-match, pre-competitive anxiety, technical performance through individual technical-tactical ball involvements, session rating of perceived exertion (session-RPE) and perceived performance were considered in each match. The comparisons of T and C, and technical performance were performed with a mixed model analysis with one (difficulty level) or two factors (difficulty level and moment), with repeated measures. On the other hand, the comparisons of pre-competitive anxiety, session-RPE and perceived performance were performed by the Friedman test, followed by the Wilcoxon test, when it was necessary. In both analyses, Bonferroni post hoc was used. Moreover, the association between the responses normalized by the Z score of T variation with the perceived and technical performance was verified through the Pearson correlation. A significance level of 5% was adopted for all analyzes. It was verified an increase in T from pre- to post-match against Easy (p = 0,0064) and Medium (p = 0,0375) opponent levels. C level increased from pre- to post-match, regardless of the difficulty level; and C in the Easy level was lower than the Medium (p = 0.0351) and Hard (p = 0.0035) difficulty levels. Cognitive anxiety was higher in the Hard level compared to the Easy one (p <0.01). Somatic anxiety, on the other hand, was higher in the Hard level compared to the Easy (p <0.001) and Medium (p = 0.01) levels and was higher in the Medium level compared to the Easy one (p = 0.004). Session-RPE was higher in the Hard level compared to the Easy (p = 0.003) and Medium (p = 0.003) levels. No effect of difficulty level for self-confidence (p = 0.118), technical performance (p = 0.728) and perceived performance (p = 0.113) was observed. In addition, no significant correlation was observed between the technical and perceived performance with T variation (r <0.35 and p> 0.07 for all comparisons). The results of this thesis indicate that the higher the opponent difficulty level, the greater the pre-competitive anxiety and the session-RPE. C increased independently of the opponent level. T increased in less difficult matches, but not in the Hard one. This increase in T did not correlate significantly with the technical or perceived performance. There was no influence of the opponent\'s level on technical or perceived performance

A problemática central deste trabalho é a multifacetada relação estabelecida entre as estruturas eclesiásticas da América portuguesa e o Santo Ofício por intermédio do clero (e de suas carreiras) no decorrer do século XVIII. Elucidamos aspectos ainda pouco conhecidos dos instrumentos e estratégias que tornaram possível a presença inquisitorial no Brasil, território que nunca sediou um tribunal da Inquisição. Tal ausência hipertrofiava as conexões entre a esfera eclesiástica local e a esfera inquisitorial. Investigamos também o impacto destes aparatos institucionais na sociedade colonial, evidenciando principalmente seus papéis na estruturação das hierarquias sociais. / The main question of this work is the multifaceted relationship established between the ecclesiastical structures of Portuguese America and the Holly Office through the clergy (and their careers) during the eighteenth century. We clarify these little known aspects of the strategies that made possible the presence of the Inquisition in Brazil, a land that has never hosted a tribunal of the Inquisition. This lack hypertrophied the connections between the local ecclesiastical sphere and the inquisitorial sphere. We investigate also the impact of these institutional apparatuses in colonial society, particularly highlighting their roles in the process of structuring of social hierarchies.

A necessidade de extrair conhecimento útil e inovador de grandes massas de dados textuais, tem motivado cada vez mais a investigação de métodos para Mineração de Textos. Dentre os métodos existentes, destacam-se as iniciativas para organização de conhecimento por meio de hierarquias de tópicos, nas quais o conhecimento implícito nos textos é representado em tópicos e subtópicos, e cada tópico contém documentos relacionados a um mesmo tema. As hierarquias de tópicos desempenham um papel importante na recupera ção de informação, principalmente em tarefas de busca exploratória, pois permitem a análise do conhecimento de interesse em diversos níveis de granularidade e exploração interativa de grandes coleções de documentos. Para apoiar a construção de hierarquias de tópicos, métodos de agrupamento hierárquico têm sido utilizados, uma vez que organizam coleções textuais em grupos e subgrupos, de forma não supervisionada, por meio das similaridades entre os documentos. No entanto, a maioria dos métodos de agrupamento hierárquico não é adequada em cenários que envolvem coleções textuais dinâmicas, pois são exigidas frequentes atualizações dos agrupamentos. Métodos de agrupamento que respeitam os requisitos existentes em cenários dinâmicos devem processar novos documentos assim que são adicionados na coleção, realizando o agrupamento de forma incremental. Assim, neste trabalho é explorado o uso de métodos de agrupamento incremental para o aprendizado não supervisionado de hierarquias de tópicos em coleções textuais dinâmicas. O agrupamento incremental é aplicado na construção e atualização de uma representação condensada dos textos, que mantém um sumário das principais características dos dados. Os algoritmos de agrupamento hierárquico podem, então, ser aplicados sobre as representa ções condensadas, obtendo-se a organização da coleção textual de forma mais eficiente. Foram avaliadas experimentalmente três estratégias de agrupamento incremental da literatura, e proposta uma estratégia alternativa mais apropriada para hierarquias de tópicos. Os resultados indicaram que as hierarquias de tópicos construídas com uso de agrupamento incremental possuem qualidade próxima às hierarquias de tópicos construídas por métodos não incrementais, com significativa redução do custo computacional / The need to extract new and useful knowledge from large textual collections has motivated researchs on Text Mining methods. Among the existing methods, initiatives for the knowledge organization by topic hierarchies are very popular. In the topic hierarchies, the knowledge is represented by topics and subtopics, and each topic contains documents of similar content. They play an important role in information retrieval, especially in exploratory search tasks, allowing the analysis of knowledge in various levels of granularity and interactive exploration of large document collections. Hierarchical clustering methods have been used to support the construction of topic hierarchies. These methods organize textual collections in clusters and subclusters, in an unsupervised manner, using similarities among documents. However, most existing hierarchical clustering methods is not suitable for scenarios with dynamic text collections, since frequent clustering updates are necessary. Clustering methods that meet these requirements must process new documents that are inserted into textual colections, in general, through incremental clustering. Thus, we studied the incremental clustering methods for unsupervised learning of topic hierarchies for dynamic text collections. The incremental clustering is used to build and update a condensed representation of texts, which maintains a summary of the main features of the data. The hierarchical clustering algorithms are applied in these condensed representations, obtaining the textual organization more efficiently. We experimentally evaluate three incremental clustering algorithms available in the literature. Also, we propose an alternative strategy more appropriate for construction of topic hieararchies. The results indicated that the topic hierarchies construction using incremental clustering have quality similar to non-incremental methods. Furthermore, the computational cost is considerably reduced using incremental clustering methods

This work presents new models and algorithms for creating, modifying, and controlling access to complex text. The digitization of texts opens new opportunities for preservation, access, and analysis, but at the same time raises questions regarding how to represent and collaboratively edit such texts. Two issues of particular interest are modelling the relationships of markup (annotations) in complex texts, and controlling the creation and modification of those texts. This work addresses and connects these issues, with emphasis on data modelling, algorithms, and computational complexity; and contributes new results in these areas of research. Although hierarchical models of text and markup are common, complex texts often exhibit layers of overlapping structure that are best described by multihierarchical markup. We develop a new model of multihierarchical markup, the globally ordered GODDAG, that combines features of both graph- and range-based models of markup, allowing documents to be unambiguously serialized. We describe extensions to the XPath query language to support globally ordered GODDAGs, provide semantics for a set of update operations on this structure, and provide algorithms for converting between two different representations of the globally ordered GODDAG. Managing the collaborative editing of documents can require restricting the types of changes different editors may make, while not altogether restricting their access to the document. Fine-grained access control allows precisely these kinds of restrictions on the operations that a user is or is not permitted to perform on a document. We describe a rule-based model of fine-grained access control for updates of hierarchical documents, and in this context analyze the document generation problem: determining whether a document could have been created without violating a particular access control policy. We show that this problem is undecidable in the general case and provide computational complexity bounds for a number of restricted variants of the problem. Finally, we extend our fine-grained access control model from hierarchical to multihierarchical documents. We provide semantics for fine-grained access control policies that control splice-in, splice-out, and rename operations on globally ordered GODDAGs, and show that the multihierarchical version of the document generation problem remains undecidable.

Racism in the United States is persistent and its negative effects are widespread. The social hierarchy in the United States positions White people as the dominant culture and Black people, among other races, as a minority culture. Current literature provides insight into explicit and implicit individual expressions of racism; however, very little research clarifies the effects racism has on the continuance and structure of the social race hierarchy. This study utilizes social gender hierarchy research to investigate how racism-induced hostility toward the dominant culture relates to an individual's perception of the stability of the race hierarchy. This quantitative survey study compared a prime versus a non-prime condition. In the prime condition, Black participants (n = 129) were presented with racist statements to elicit a "hostility toward White individuals" response. A 6-point Likert-type scale quantified participants' perceptions of the stability of the race hierarchy. A one-way between subjects ANOVA was conducted by comparing the perceived stability means as measured by the Race Hierarchy Scale. Contrary to expectation, the prime did not produce a statistically significant change in the perceived stability of the race hierarchy. The data did reveal a chronic individual perception of the race hierarchy as unchanging. This study contributes to positive social change by illuminating social structure aspects and how individual perception functions to maintain the race hierarchy in America. This knowledge will help direct future research, policy makers, the legal system, and the private sector. Attempting to understand the effects of racism from the perspective used in this study, may encourage other researchers to generate novel approaches and methods to combat discrimination.

We give a brief survey on some new developments on elliptic operators on manifolds with polyhedral singularities. The material essentially corresponds to a talk given by the author during the Conference “Elliptic and Hyperbolic Equations on Singular Spaces”, October 27 - 31, 2008, at the MSRI, University of Berkeley.

The representation and manipulation of visual content in a computer vision system requires a suitable abstraction of raw visual content such as pixels in an image. In this thesis, we study region-based feature representations and in particular, hierarchical segmentations because they do make no assumptions about region granularity. Hierarchical segmentations create a large feature space that increases the cost of subsequent processing in computer vision systems. We introduce a segment filter to reduce the feature space of hierarchical segmentations by identifying unique regions in the images. The filter uses appearance-based properties of the regions and the structure of the segmentation for the selection of a small set of descriptive regions. The filter works in two phases: selection with a criteria based on relative region size and a sorting based on a variational criteria. The filter is applicable to any hierarchical segmentation algorithm, in particular to bottom-up and region growing approaches. We evaluate the filter's performance against an extensive set of ground-truth regions from a dataset containing image sequences with scenes of different complexity. We demonstrate a novel region-to-region image matching approach as a possible application of our segment filter. A reduced segmentation tree is reconstructed based on the set of regions provided by the filtering. The reduction of the feature space by the segment filter simplifies our region-to-region matching approach. The correspondences between regions from two different images is established by a similarity measure. We use a modified mutual information measurement to compute the similarity of regions. The identified region correspondences are refined using the reduced segmentation tree. Our region-to-region matching approach is evaluated with an extensive set of ground-truth correspondences. This evaluation shows the large potential of both, our filtering and our matching approach.

The representation and manipulation of visual content in a computer vision system requires a suitable abstraction of raw visual content such as pixels in an image. In this thesis, we study region-based feature representations and in particular, hierarchical segmentations because they do make no assumptions about region granularity. Hierarchical segmentations create a large feature space that increases the cost of subsequent processing in computer vision systems. We introduce a segment filter to reduce the feature space of hierarchical segmentations by identifying unique regions in the images. The filter uses appearance-based properties of the regions and the structure of the segmentation for the selection of a small set of descriptive regions. The filter works in two phases: selection with a criteria based on relative region size and a sorting based on a variational criteria. The filter is applicable to any hierarchical segmentation algorithm, in particular to bottom-up and region growing approaches. We evaluate the filter's performance against an extensive set of ground-truth regions from a dataset containing image sequences with scenes of different complexity. We demonstrate a novel region-to-region image matching approach as a possible application of our segment filter. A reduced segmentation tree is reconstructed based on the set of regions provided by the filtering. The reduction of the feature space by the segment filter simplifies our region-to-region matching approach. The correspondences between regions from two different images is established by a similarity measure. We use a modified mutual information measurement to compute the similarity of regions. The identified region correspondences are refined using the reduced segmentation tree. Our region-to-region matching approach is evaluated with an extensive set of ground-truth correspondences. This evaluation shows the large potential of both, our filtering and our matching approach.

We study three problems related to searching and sorting in multisets in the cache-oblivious model: Finding the most frequent element (the mode), duplicate elimination and finally multi-sorting. We are interested in minimizing the cache complexity (or number of cache misses) of algorithms for these problems in the context under which the cache size and block size are unknown. We start by showing the lower bounds in the comparison model. Then we present the lower bounds in the cache-aware model, which are also the lower bounds in the cache-oblivious model. We consider the input multiset of size <i>N</i> with multiplicities <i>N</i>₁,. . . , <i>N_k</i>. The lower bound for the cache complexity of determining the mode is &Omega;({<i>N</i> over <i>B</i>} log {<i>M</i> over <i>B</i>} {<i>N</i> over <i>fB</i>}) where &fnof; is the frequency of the mode and <i>M</i>, <i>B</i> are the cache size and block size respectively. Cache complexities of duplicate removal and multi-sorting have lower bounds of &Omega;({<i>N</i> over <i>B</i>} log {<i>M</i> over <i>B</i>} {<i>N</i> over <i>B</i>} - £{<i>k</i> over <i>i</i>}=1{<i>N_i</i> over <i>B</i>}log {<i>M</i> over <i>B</i>} {<i>N_i</i> over <i>B</i>}). We present two deterministic approaches to give algorithms: selection and distribution. The algorithms with these deterministic approaches differ from the lower bounds by at most an additive term of {<i>N</i> over <i>B</i>} loglog <i>M</i>. However, since loglog <i>M</i> is very small in real applications, the gap is tiny. Nevertheless, the ideas of our deterministic algorithms can be used to design cache-aware algorithms for these problems. The algorithms turn out to be simpler than the previously-known cache-aware algorithms for these problems. Another approach to design algorithms for these problems is the probabilistic approach. In contrast to the deterministic algorithms, our randomized cache-oblivious algorithms are all optimal and their cache complexities exactly match the lower bounds. All of our algorithms are within a constant factor of optimal in terms of the number of comparisons they perform.