This paper presents the Temporal Abstract Behavior Representation Model (TABRM). Current techniques for representing behaviors suffer from a lack of abstract representation capability and do not possess the robustness to be used in diverse environments. Without abstraction, the representation of behaviors becomes computationally complex due to the wealth of detail required to enumerate all attributes associated with the environment and the potential courses of action. As a result existing behavior representations tend to be restricted to a limited number of environments or behaviors. TABRM addresses these limitations by using abstraction to define a small number of abstract behaviors and environments. Through the use of abstracted behaviors and environments, TABRM is able to operate using a small decision tree to determine the most appropriate behavior for a given environment. TABRM translates detailed environments into an abstract representation, determines an appropriate abstract behavior, and translates the results back to the detailed environment to produce suitable actions. Decision making within the abstract realm allows an appropriate behavior to be selected regardless of the actual detailed environment. This provides robustness in the model, which is demonstrated within this paper through the application of the model to a range of behavioral domains.

This dissertation has multiple aims in studying hierarchical linear models in biomedical data analysis. In Chapter 1, the novel idea of studying the durations of adolescent growth spurts as a predictor of adulthood obesity is defined, established, and illustrated. The concept of contextual effects modeling is introduced in this first section as we study secular trend of adulthood obesity and how this trend is mitigated by the durations of individual adolescent growth spurts and the secular average length of adolescent growth spurts. It is found that individuals with longer periods of fast height growth in adolescence are more prone to having favorable BMI profiles in adulthood. In Chapter 2 we study the estimation of contextual effects in a hierarchical generalized linear model (HGLM). We simulate data and study the effects using the higher level group sample mean as the estimate for the true mean versus using an Empirical Bayes (EB) approach (Shin and Raudenbush 2010). We study this comparison for logistic, probit, log-linear, ordinal and nominal regression models. We find that in general the EB estimate lends a parameter estimate much closer to the true value, except for cases with very small variability in the upper level, where it is a more complicated situation and there is likely no need for contextual effects analysis. In Chapter 3 the HGLM studies are made clearer with large-scale simulations. These large scale simulations are shown for logistic regression and probit regression models for binary outcome data. With repetition we are able to establish coverage percentages of the confidence intervals of the true contextual effect. Coverage percentages show the percentage of simulations that have confidence intervals containing the true parameter values. Results confirm observations from the preliminary simulations in the previous section of this paper, and an accompanying example of adulthood hypertension shows how these results can be used in an application.

E-commerce applications generate and consume tremendous amount of online information that is typically available as textual documents. Observations of textual document management practices by organizations or individuals suggest the popularity of using categories (or category hierarchies) to organize, archive and access documents. On the other hand, an organization (or individual) also constantly acquires new documents from various Internet sources. Consequently, integration of relevant categorized documents into existent categories of the organization (or individual) becomes an important issue in the e-commerce era. Existing categorization-based approach for document-category integration (specifically, the Enhanced Naïve Bayes classifier) incurs several limitations, including homogeneous assumption on categorization schemes used by master and source catalogs and requirement for a large-sized master categories as training data. In this study, we developed a Clustering-based Category Integration (CCI) technique to deal with integrating two document catalogs each of which is organized non-hierarchically (i.e., in a flat set). Using the Enhanced Naïve Bayes classifier as benchmarks, the empirical evaluation results showed that the proposed CCI technique appeared to improve the effectiveness of document-category integration accuracy in different integration scenarios and seemed to be less sensitive to the size of master categories than the categorization-based approach. Furthermore, to integrate the document categories that are organized hierarchically, we proposed a Clustering-based category-Hierarchy Integration (referred to as CHI) technique extended the CCI technique and for category-hierarchy integration. The empirical evaluation results showed that the CHI technique appeared to improve the effectiveness of hierarchical document-category integration than that attained by CCI under homogeneous and comparable scenarios.

Nanoemulsions are templates that have the potential to fill the gap between micellar systems and latex particles in the preparation of porous materials. A nanoemulsion can also be used as a carrier for uploading the desired materials inside the pore formed after the removal of the template. In this research, oil-in-water (O/W) nanoemulsions were prepared by means of a low-energy method based on a phase inversion composition (PIC) technique, using two nonionic surfactants (Tween 80 and Span 80), which can be mixed in order to adjust the hydrophilic-lipophilic balance (HLB). The influence of a number of parameters on the tunability and stability of such nanoemulsions was also studied. The effect of the simultaneous intercrossing of multifactors on droplet size was explored using a process- mixture design, and the size of the nanoemulsion oil droplets was measured by means of dynamic light scattering (DLS). The nanoemulsions were combined with sol-gel method in order to prepare porous silica with a macroporosity in the 50 nm to 400 nm range. The results demonstrate that a precise synergy between the silica source and the nanoemulsions is essential for effective interactions and homogeneous structures. Depending on the nature of such interactions, a variety of materials were observed, from hollow particles to continuous gels. Changing the size of the oil droplet and the volume of the nanoemulsions produced silica with differing pore sizes and varying total pore volumes. The obtained hierarchical porous silica (HPS) were characterized using mercury porosimetry, small angle X-ray scattering (SAXS), nitrogen isotherms, Fourier transform infrared (FTIR) analysis, transmission electron microscopy (TEM), and scanning electron microscopy (SEM). The parallel use of the oil vesicles as containers for the further synthesis of metal oxide is a novel method of internally functionalizing the silica. When hydrophobic metal precursors are dissolved into the oil phase before the preparation of the nanoemulsion, they are confined within the globular cavities of the silica. The thermal treatment applied to the material to burn the organics then leads to the final formation of metal oxide nanoparticles, which are larger than the porosity of the silica matrix but entrapped within the large cavities, producing a "rattle-like" structure. This method was demonstrated through the synthesis of Fe2O3, Fe3O4, and Co3O4 nanoparticles, and the results showed that a rather large amount of metal oxide (up to a 60 wt.% of metal oxide in nanocomposites) be generated while still maintaining the nanometric size observed at lower concentrations. This method allows control of the type of metal oxide, the concentration of the metal oxide, and the pore size, which enables the creation of different types of nanocomposites. Metal oxide hierarchical porous silica (MHPS) nanocomposites were characterized based on nitrogen isotherms, TEM and SEM observations, FTIR analysis, X-ray diffraction (XRD), and Mossbauer spectroscopy. Magnetic measurements were also taken. This new method, using the new templating objects, is a perfect illustration of the concept of "integrative synthesis,??? whereby the combination of building units and reactional mechanisms leads to complex structures as a result of true synergy among the elements during the reaction. In this case, the size of the nanoemulsion and the total water volume both contribute to the generation of distinctive architectures. In addition, the reaction of the metal oxide precursors within the cavities limits the extension of the final crystal size, but the surrounding solid matrix plays a role as well by keeping the particles apart. The final factor is that the reactive materials cannot leak from the silica because of the rattle-like structure, but the reagents can reach those particles through the porosity of the silica framework.

There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable.

Neste trabalho estudamos a existência de ordem de longo alcance em modelos ferromagnéticos na presença de um campo externo cuja configuração apresenta um padrão tipicamente aleatório. Provamos por meio do argumento de Peierls modificado por Griffiths para o estudo de um antiferromagneto, que o modelo de Ising ferromagnético bidimensional exibe, para um campo alternado de intensidade fraca, ordem de longo alcance `a temperatura finita. Propomos dar um passo além considerando campos auto-similares esparsos, cuja soma é nula em todas as escalas. Estudamos também o modelo hierárquico em duas dimensões, para o qual provamos a existência de ordem de longo alcance a temperatura finita, na ausência de campo externo e para um campo com regiões irregulares esparsas. Provamos que os resultados do modelo de contornos hierárquicos são equivalentes aos resultados do modelo hierárquico em duas dimensões. Por fim, provamos através do método do limite infravermelho existência de ordem de longo alcance no modelo N-vetorial com campo alternado, de intensidade fraca, para d >= 3, sob a hipótese de que a variância do estado associado `a interação com o campo apresenta cardinalidade inferior a do volume do sistema. Mostramos, sob hipóteses similares, que o modelo N-vetorial hierárquico com campo externo, esparso e de intensidade pequena, apresenta ordem de longo alcance a baixas temperaturas. / In this work we study the existence of long range order for ferromagnetic models in the presence of an external field whose configuration has a pattern typically random. We prove, via the Peierls\' argument modified by Griffiths in his study of an antiferromagnet, that the two dimensional ferromagnetic Ising model for a staggered field exhibits long-range order at finite temperature and small field intensity. We propose to give a further step considering sparse self similar fields, whose sum is zero in all scales. We study as well the hierarchical model in two dimensions, where we prove existence of long-range order at finite temperature in the absence of external field and for a field configuration with sparse irregular regions. We prove that the results for the two-dimensional hierarchical contours model are equivalent to the results of the hierarchical model in two dimensions. Lastly, we prove via infrared bound method, existence of long range order in the N-vector model with a staggered and weak external field for d >= 3, under the hypothesis that the variance of the state connected with the field interaction has cardinality lower than volume. We show, under similar hypotheses, that the N-vector hierarchical model with a sparse field of low intensity has long range ordem at low temperatures.

Neste trabalho estudamos a existência de ordem de longo alcance em modelos ferromagnéticos na presença de um campo externo cuja configuração apresenta um padrão tipicamente aleatório. Provamos por meio do argumento de Peierls modificado por Griffiths para o estudo de um antiferromagneto, que o modelo de Ising ferromagnético bidimensional exibe, para um campo alternado de intensidade fraca, ordem de longo alcance `a temperatura finita. Propomos dar um passo além considerando campos auto-similares esparsos, cuja soma é nula em todas as escalas. Estudamos também o modelo hierárquico em duas dimensões, para o qual provamos a existência de ordem de longo alcance a temperatura finita, na ausência de campo externo e para um campo com regiões irregulares esparsas. Provamos que os resultados do modelo de contornos hierárquicos são equivalentes aos resultados do modelo hierárquico em duas dimensões. Por fim, provamos através do método do limite infravermelho existência de ordem de longo alcance no modelo N-vetorial com campo alternado, de intensidade fraca, para d >= 3, sob a hipótese de que a variância do estado associado `a interação com o campo apresenta cardinalidade inferior a do volume do sistema. Mostramos, sob hipóteses similares, que o modelo N-vetorial hierárquico com campo externo, esparso e de intensidade pequena, apresenta ordem de longo alcance a baixas temperaturas. / In this work we study the existence of long range order for ferromagnetic models in the presence of an external field whose configuration has a pattern typically random. We prove, via the Peierls\' argument modified by Griffiths in his study of an antiferromagnet, that the two dimensional ferromagnetic Ising model for a staggered field exhibits long-range order at finite temperature and small field intensity. We propose to give a further step considering sparse self similar fields, whose sum is zero in all scales. We study as well the hierarchical model in two dimensions, where we prove existence of long-range order at finite temperature in the absence of external field and for a field configuration with sparse irregular regions. We prove that the results for the two-dimensional hierarchical contours model are equivalent to the results of the hierarchical model in two dimensions. Lastly, we prove via infrared bound method, existence of long range order in the N-vector model with a staggered and weak external field for d >= 3, under the hypothesis that the variance of the state connected with the field interaction has cardinality lower than volume. We show, under similar hypotheses, that the N-vector hierarchical model with a sparse field of low intensity has long range ordem at low temperatures.

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This thesis focuses on the problem of scalable optimization of dialogue behaviour in speech-based conversational systems using reinforcement learning. Most previous investigations in dialogue strategy learning have proposed flat reinforcement learning methods, which are more suitable for small-scale spoken dialogue systems. This research formulates the problem in terms of Semi-Markov Decision Processes (SMDPs), and proposes two hierarchical reinforcement learning methods to optimize sub-dialogues rather than full dialogues. The first method uses a hierarchy of SMDPs, where every SMDP ignores irrelevant state variables and actions in order to optimize a sub-dialogue. The second method extends the first one by constraining every SMDP in the hierarchy with prior expert knowledge. The latter method proposes a learning algorithm called 'HAM+HSMQ-Learning', which combines two existing algorithms in the literature of hierarchical reinforcement learning. Whilst the first method generates fully-learnt behaviour, the second one generates semi-learnt behaviour. In addition, this research proposes a heuristic dialogue simulation environment for automatic dialogue strategy learning. Experiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: First, both methods scale well at the cost of near-optimal solutions, resulting in slightly longer dialogues than the optimal solutions. Second, dialogue strategies learnt with coherent user behaviour and conservative recognition error rates can outperform a reasonable hand-coded strategy. Third, semi-learnt dialogue behaviours are a better alternative (because of their higher overall performance) than hand-coded or fully-learnt dialogue behaviours. Last, hierarchical reinforcement learning dialogue agents are feasible and promising for the (semi) automatic design of adaptive behaviours in larger-scale spoken dialogue systems. This research makes the following contributions to spoken dialogue systems which learn their dialogue behaviour. First, the Semi-Markov Decision Process (SMDP) model was proposed to learn spoken dialogue strategies in a scalable way. Second, the concept of 'partially specified dialogue strategies' was proposed for integrating simultaneously hand-coded and learnt spoken dialogue behaviours into a single learning framework. Third, an evaluation with real users of hierarchical reinforcement learning dialogue agents was essential to validate their effectiveness in a realistic environment.

Uma forma de extrair e organizar o conhecimento, que tem recebido muita atenção nos últimos anos, é por meio de uma representação estrutural dividida por tópicos hierarquicamente relacionados. Uma vez construída a estrutura hierárquica, é necessário encontrar descritores para cada um dos grupos obtidos pois a interpretação destes grupos é uma tarefa complexa para o usuário, já que normalmente os algoritmos não apresentam descrições conceituais simples. Os métodos encontrados na literatura consideram cada documento como uma bag-of-words e não exploram explicitamente o relacionamento existente entre os termos dos documento do grupo. No entanto, essas relações podem trazer informações importantes para a decisão dos termos que devem ser escolhidos como descritores dos nós, e poderiam ser representadas por regras de associação. Assim, o objetivo deste trabalho é avaliar a utilização de regras de associação para apoiar a identificação de descritores para agrupamentos hierárquicos. Para isto, foi proposto o método SeCLAR (Selecting Candidate Labels using Association Rules), que explora o uso de regras de associação para a seleção de descritores para agrupamentos hierárquicos de documentos. Este método gera regras de associação baseadas em transações construídas à partir de cada documento da coleção, e utiliza a informação de relacionamento existente entre os grupos do agrupamento hierárquico para selecionar candidatos a descritores. Os resultados da avaliação experimental indicam que é possível obter uma melhora significativa com relação a precisão e a cobertura dos métodos tradicionais / One way to organize knowledge, that has received much attention in recent years, is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for each of the obtained clusters, since most algorithms do not produce simple descriptions and the interpretation of these clusters is a difficult task for users. The related works consider each document as a bag-of-words and do not explore explicitly the relationship between the terms of the documents. However, these relationships can provide important information to the decision of the terms that must be chosen as descriptors of the nodes, and could be represented by rass. This works aims to evaluate the use of association rules to support the identification of labels for hierarchical document clusters. Thus, this paper presents the SeCLAR (Selecting Candidate Labels using Association Rules) method, which explores the use of association rules for the selection of good candidates for labels of hierarchical clusters of documents. This method generates association rules based on transactions built from each document in the collection, and uses the information relationship between the nodes of hierarchical clustering to select candidates for labels. The experimental results show that it is possible to obtain a significant improvement with respect to precision and recall of traditional methods