This thesis presents results concerning canonical extensions of bounded lattices and natural dualities for quasivarieties of default bilattices. Part I is dedicated to canonical extensions, while Part II focuses on natural duality for default bilattices. A canonical extension of a lattice-based algebra consists of a completion of the underlying lattice and extensions of the additional operations to the completion. Canonical extensions find rich application in providing an algebraic method for obtaining relational semantics for non-classical logics. Part I gives a new construction of the canonical extension of a bounded lattice. The construction is done via successive applications of functors and thus provides an elegant exposition of the fact that the canonical extension is functorial. Many existing constructions are described via representation and duality theorems. We demonstrate precisely how our new formulation relates to existing constructions as well as proving new results about complete lattices constructed from graphs. Part I ends with an analysis of the untopologised structures used in two methods of construction of canonical extensions of bounded lattices: the untopologised graphs used in our new construction, and the so-called `intermediate structure'. We provide sufficient conditions for the intermediate structure to be a lattice and, for the case of finite lattices, identify when the dual graph is not a minimal representation of the lattice. Part II applies techniques from natural duality theory to obtain dualities for quasivarieties of bilattices used in default logic. Bilattices are doubly-ordered algebraic structures which find application in reasoning about inconsistent and incomplete information. This account is the first attempt to provide dualities or representations when there is little interaction required between the two orders. Our investigations begin by using computer programs to calculate dualities for specific examples, before using purely theoretical techniques to obtain dualities for more general cases. The results obtained are extremely revealing, demonstrating how one of the lattice orders from the original algebra is encoded in the dual structure. We conclude Part II by describing a new class of default bilattices. These provide an alternative way of interpreting contradictory information. We obtain dualities for two newly-described quasivarieties and provide insights into how these dual structures relate to previously described classes of dual structures for bilattices.

A monitoração de variáveis e o diagnóstico de falhas é um aspecto importante a se considerar seja em plantas nucleares ou indústrias de processos, pois um diagnóstico precoce de falha permite a correção do problema proporcionando a não interrupção da produção e a segurança do operador e, assim, não causando perdas econômicas. O objetivo deste trabalho é, dentro do universo de todas as variáveis monitoradas de um processo, construir um conjunto de variáveis, não necessariamente mínimo, que será a entrada de uma rede neural e, com isso, conseguir monitorar, o maior número possível de variáveis. Esta metodologia foi aplicada ao reator de pesquisas IEA-R1 do IPEN. Para isso, as variáveis Potência do reator, Vazão do primário, Posição de barras de controle/segurança e Diferença de pressão no núcleo do reator D P, foram agrupadas, pois por hipótese quase todas as variáveis monitoradas em um reator nuclear tem relação com alguma dessas ou pode ser resultado da interação de duas ou mais. Por exemplo, a Potência está relacionada ao aumento e diminuição de algumas temperaturas bem como à quantidade de radiação devido à fissão do urânio; as Barras são reguladoras de potência e, por conseqüência podem influenciar na quantidade de radiação e/ou temperaturas; a Vazão do Circuito Primário, responsável pelo transporte de energia e pela conseqüente retirada de calor do núcleo. Assim, tomando o grupo de variáveis mencionadas, calculamos a correlação existente entre este conjunto B e todas as outras variáveis monitoradas (coeficiente de correlação múltipla), isto é, através do cálculo da correlação múltipla, que é uma ferramenta proposta pela teoria das Correlações Canônicas, foi possível calcular o quanto o conjunto B pode predizer cada uma das variáveis monitoradas. Uma vez que não seja possível uma boa qualidade de predição com o conjunto B, é acrescentada uma ou mais variáveis que possuam alta correlação com a variável melhorando a qualidade de predição. Finalmente, uma rede pode ser treinada com o novo conjunto e os resultados quanto a monitoração foram bastante satisfatórios quanto às 64 variáveis monitoradas pelo sistema de aquisição de dados do reator IEA-R1 através de sensores e atuadores , pois com um conjunto de 9 variáveis foi possível monitorar 51 variáveis. / The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because an early diagnosis allows the correction of the fault and, like this, do not cause the production interruption, improving operators security and its not provoking economics losses. The objective of this work is, in the whole of all variables monitor of a nuclear power plant, to build a set, not necessary minimum, which will be the set of input variables of an artificial neural network and, like way, to monitor the biggest number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. For this, the variables Power, Rate of flow of primary circuit, Rod of control/security and Difference in pressure in the core of the reactor ( D P) was grouped, because, for hypothesis, almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The Power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the Rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures and the Rate of flow of primary circuit has function of the transport of energy by removing of heat of the nucleus Like this, labeling B= {Power, Rate of flow of Primary Circuit, Rod of Control/Security and D P} was computed the correlation between B and all another variables monitoring (coefficient of multiple correlation), that is, by the computer of the multiple correlation, that is tool of Theory of Canonical Correlations, was possible to computer how much the set B can predict each variable. Due the impossibility of a satisfactory approximation by B in the prediction of some variables, it was included one or more variables that have high correlation with this variable to improve the quality of prediction. In this work an artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables using neural networks.

Im Jahr 2006, in einem Papier in Compositio Titel "Bounds auf kanonische Green-Funktionen" J. Jorgenson und J. Kramer, haben optimale Schranken für den hyperbolischen und kanonischen Green-Funktionen auf einem kompakten hyperbolischen Riemannschen Fläche definiert abgeleitet. Diese Schätzungen wurden im Hinblick auf abgeleitete Invarianten aus hyperbolischen Geometrie der Riemannschen Fläche. Als Anwendung abgeleitet sie Schranken für die kanonische Green-Funktionen durch Abdeckungen und für Familien von Modulkurven. In dieser Arbeit erweitern wir ihre Methoden nichtkompakten hyperbolischen Riemann Oberflächen und leiten ähnliche Schranken für den hyperbolischen und kanonischen Green-Funktionen auf einem nichtkompakten hyperbolischen Riemannschen Fläche definiert. / In 2006, in a paper in Compositio titled "Bounds on canonical Green''s functions", J. Jorgenson and J. Kramer have derived optimal bounds for the hyperbolic and canonical Green''s functions defined on a compact hyperbolic Riemann surface. These estimates were derived in terms of invariants coming from hyperbolic geometry of the Riemann surface. As an application, they deduced bounds for the canonical Green''s functions through covers and for families of modular curves. In this thesis, we extend their methods to noncompact hyperbolic Riemann surfaces and derive similar bounds for the hyperbolic and canonical Green''s functions defined on a noncompact hyperbolic Riemann surface.

A monitoração de variáveis e o diagnóstico de falhas é um aspecto importante a se considerar seja em plantas nucleares ou indústrias de processos, pois um diagnóstico precoce de falha permite a correção do problema proporcionando a não interrupção da produção e a segurança do operador e, assim, não causando perdas econômicas. O objetivo deste trabalho é, dentro do universo de todas as variáveis monitoradas de um processo, construir um conjunto de variáveis, não necessariamente mínimo, que será a entrada de uma rede neural e, com isso, conseguir monitorar, o maior número possível de variáveis. Esta metodologia foi aplicada ao reator de pesquisas IEA-R1 do IPEN. Para isso, as variáveis Potência do reator, Vazão do primário, Posição de barras de controle/segurança e Diferença de pressão no núcleo do reator D P, foram agrupadas, pois por hipótese quase todas as variáveis monitoradas em um reator nuclear tem relação com alguma dessas ou pode ser resultado da interação de duas ou mais. Por exemplo, a Potência está relacionada ao aumento e diminuição de algumas temperaturas bem como à quantidade de radiação devido à fissão do urânio; as Barras são reguladoras de potência e, por conseqüência podem influenciar na quantidade de radiação e/ou temperaturas; a Vazão do Circuito Primário, responsável pelo transporte de energia e pela conseqüente retirada de calor do núcleo. Assim, tomando o grupo de variáveis mencionadas, calculamos a correlação existente entre este conjunto B e todas as outras variáveis monitoradas (coeficiente de correlação múltipla), isto é, através do cálculo da correlação múltipla, que é uma ferramenta proposta pela teoria das Correlações Canônicas, foi possível calcular o quanto o conjunto B pode predizer cada uma das variáveis monitoradas. Uma vez que não seja possível uma boa qualidade de predição com o conjunto B, é acrescentada uma ou mais variáveis que possuam alta correlação com a variável melhorando a qualidade de predição. Finalmente, uma rede pode ser treinada com o novo conjunto e os resultados quanto a monitoração foram bastante satisfatórios quanto às 64 variáveis monitoradas pelo sistema de aquisição de dados do reator IEA-R1 através de sensores e atuadores , pois com um conjunto de 9 variáveis foi possível monitorar 51 variáveis. / The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because an early diagnosis allows the correction of the fault and, like this, do not cause the production interruption, improving operators security and its not provoking economics losses. The objective of this work is, in the whole of all variables monitor of a nuclear power plant, to build a set, not necessary minimum, which will be the set of input variables of an artificial neural network and, like way, to monitor the biggest number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. For this, the variables Power, Rate of flow of primary circuit, Rod of control/security and Difference in pressure in the core of the reactor ( D P) was grouped, because, for hypothesis, almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The Power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the Rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures and the Rate of flow of primary circuit has function of the transport of energy by removing of heat of the nucleus Like this, labeling B= {Power, Rate of flow of Primary Circuit, Rod of Control/Security and D P} was computed the correlation between B and all another variables monitoring (coefficient of multiple correlation), that is, by the computer of the multiple correlation, that is tool of Theory of Canonical Correlations, was possible to computer how much the set B can predict each variable. Due the impossibility of a satisfactory approximation by B in the prediction of some variables, it was included one or more variables that have high correlation with this variable to improve the quality of prediction. In this work an artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables using neural networks.

by Lo Tai-yan, Milton. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 112-114). / Chapter 1 --- Preliminaries --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.1.1 --- The Noncentral Multivariate F distribution --- p.2 / Chapter 1.1.2 --- The Central Problems and the Approach --- p.4 / Chapter 1.2 --- Concepts and Terminology --- p.7 / Chapter 1.3 --- Choice of Estimates --- p.10 / Chapter 1.4 --- Related Work --- p.11 / Chapter 2 --- Estimation of the noncentrality parameter of a Noncentral Mul- tivariate F distribution --- p.19 / Chapter 2.1 --- Unbiased and Linear Estimators --- p.19 / Chapter 2.1.1 --- The unbiased estimate --- p.20 / Chapter 2.1.2 --- The Class of Linear Estimates --- p.24 / Chapter 2.2 --- Optimal Linear Estimate --- p.32 / Chapter 2.3 --- Nonlinear Estimate --- p.34 / Chapter 2.4 --- Monte Carlo Simulation Study --- p.41 / Chapter 2.5 --- Evaluation and Further Investigation --- p.42 / Chapter 3 --- Estimation of Canonical Correlation Coefficients --- p.73 / Chapter 3.1 --- Preliminary --- p.73 / Chapter 3.2 --- The Estimation Problem --- p.76 / Chapter 3.3 --- Orthogonally Invariant Estimates --- p.77 / Chapter 3.3.1 --- The Unbiased Estimate --- p.78 / Chapter 3.3.2 --- The Class of Linear Estimates --- p.78 / Chapter 3.3.3 --- The Class of Nonlinear Estimates --- p.80 / Chapter 3.4 --- Monte Carlo Simulation Study --- p.87 / Chapter 3.5 --- Evaluation and Further Investigation --- p.89 / Chapter A --- p.104 / Chapter A.1 --- Lemma 3.2 --- p.104 / Chapter A.2 --- Theorem 3.3 Leung(1992) --- p.105 / Chapter A.3 --- The Noncentral F Identity --- p.106 / Chapter B --- Bibliography --- p.111

Machine analysis of human facial and body language is a challenging topic in computer vision, impacting on important applications such as human-computer interaction and visual surveillance. In this thesis, we present research building towards computational frameworks capable of automatically understanding facial expression and behavioural body language. The thesis work commences with a thorough examination in issues surrounding facial representation based on Local Binary Patterns (LBP). Extensive experiments with different machine learning techniques demonstrate that LBP features are efficient and effective for person-independent facial expression recognition, even in low-resolution settings. We then present and evaluate a conditional mutual information based algorithm to efficiently learn the most discriminative LBP features, and show the best recognition performance is obtained by using SVM classifiers with the selected LBP features. However, the recognition is performed on static images without exploiting temporal behaviors of facial expression. Subsequently we present a method to capture and represent temporal dynamics of facial expression by discovering the underlying low-dimensional manifold. Locality Preserving Projections (LPP) is exploited to learn the expression manifold in the LBP based appearance feature space. By deriving a universal discriminant expression subspace using a supervised LPP, we can effectively align manifolds of different subjects on a generalised expression manifold. Different linear subspace methods are comprehensively evaluated in expression subspace learning. We formulate and evaluate a Bayesian framework for dynamic facial expression recognition employing the derived manifold representation. However, the manifold representation only addresses temporal correlations of the whole face image, does not consider spatial-temporal correlations among different facial regions. We then employ Canonical Correlation Analysis (CCA) to capture correlations among face parts. To overcome the inherent limitations of classical CCA for image data, we introduce and formalise a novel Matrix-based CCA (MCCA), which can better measure correlations in 2D image data. We show this technique can provide superior performance in regression and recognition tasks, whilst requiring significantly fewer canonical factors. All the above work focuses on facial expressions. However, the face is usually perceived not as an isolated object but as an integrated part of the whole body, and the visual channel combining facial and bodily expressions is most informative. Finally we investigate two understudied problems in body language analysis, gait-based gender discrimination and affective body gesture recognition. To effectively combine face and body cues, CCA is adopted to establish the relationship between the two modalities, and derive a semantic joint feature space for the feature-level fusion. Experiments on large data sets demonstrate that our multimodal systems achieve the superior performance in gender discrimination and affective state analysis.

Advances in next-generation sequencing and ribosomal profiling methods highlight that the proteome is likely orders of magnitude larger than previously thought. This expansion potentially occurs through translational recoding, a process that results in the expression of multiple variations of a protein from a single messenger RNA. Our laboratory demonstrated that cyclooxygenase-3/1b (COX-3/1b), a frameshifted, intron-1-retaining, alternative splice variant from the COX-1 gene, is multiply recoded, which results in the translation of at least seven different COX-3 proteins. Two of the recoded COX-3 proteins that we identified are active prostaglandin synthases and are inhibited by non-steroidal anti-inflammatory drugs (NSAIDs). Here we show that the other non-prostaglandin-generating recoded COX-3 proteins perform new roles in innate immunity, a process in which COX are known to generally function. Our analyses determined that these recoded COX-3 proteins bind at or near the amino-terminal region of ATG9a, a critical regulator of both canonical (i.e. digestive autophagy associated with mTORc inhibition and nutrient deprivation) and non-canonical (i.e. xenophagy involved in the innate immune response to invading organisms) autophagy. We further show that this process requires mTORc signaling activity, which opposes the digestive pathway. As a final confirmation of the biological relevance of these recoded COX-3 proteins and their central role in xenophagy, we demonstrate that expression of these COX-3 proteins in an encephalomyocarditis virus infection model system differentially affects infectious virion production. These COX-3 proteins also associate with recoded cytosolic nucleobindin around large, innate immune-related, large LC3-II positive structures (LLPSs). Through mutagenizing catalytic residues of recoded COX-3 proteins and drug assays, we determine LLPS formation is dependent on oxylipin generation.

There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the models come from? How do we know we have the right models? What does the right model mean anyway? Our work has three major components. The first component deals with how we determine whether a piece of information is relevant to solving a problem. We have three ways of determining relevance: derivational, situational and an order-of-magnitude reasoning process. The second component deals with the defining and building of models for solving problems. We identify these models, determine what we need to know about them, and importantly, determine when they are appropriate. Currently, the system has a collection of four basic models and two hybrid models. This collection of models has been successfully tested on a set of fifteen simple kinematics problems. The third major component of our work deals with how the models are selected.

Synthesis of structures is a very difficult task even with only a small number of components that form a system; yet it is the catalyst of innovation. Molecular structures and nanostructures typically have a large number of similar components but different connections, which manifests a more challenging task for their synthesis. <p> This thesis presents a novel method and its related algorithms and computer programs for the synthesis of structures. This novel method is based on several concepts: (1) the structure is represented by a graph and further by the adjacency matrix; and (2) instead of only exploiting the eigenvalue of the adjacency matrix, both the eigenvalue and the eigenvector are exploited; specifically the components of the eigenvector have been found very useful in algorithm development. This novel method is called the Eigensystem method.<p> The complexity of the Eigensystem method is equal to that of the famous program called Nauty in the combinatorial world. However, the Eigensystem method can work for the weighted and both directed and undirected graph, while the Nauty program can only work for the non-weighted and both directed and undirected graph. The cause for this is the different philosophies underlying these two methods. The Nauty program is based on the recursive component decomposition strategy, which could involve some unmanageable complexities when dealing with the weighted graph, albeit no such an attempt has been reported in the literature. It is noted that in practical applications of structure synthesis, weighted graphs are more useful than non-weighted graphs for representing physical systems. <p> Pivoted at the Eigensystem method, this thesis presents the algorithms and computer programs for the three fundamental problems in structure synthesis, namely the isomorphism/automorphism, the unique labeling, and the enumeration of the structures or graphs.

Mary of Magdala: The Evolution of an Image by Rachel Owen Under the Direction of Louis A. Ruprecht, Jr. ABSTRACT In this study, Mary of Magdala will be presented as a cumulative character consisting of multiple layers rather than as a concrete historical figure, for this allows one to see the opaque connections between her divergent textual and traditional (medieval) images. The “historical” Mary does, however, find a place here--she is presented only as a persistent early Christian belief in the veracity of her figure, and as the foundation for both the textual and traditional Mary. In light of this, the textual, the “historical,” and the medieval will be examined as these comprise the materials out of which Mary’s cumulative layers were made--the understanding of one aids in the understanding of another. Ultimately, this study will examine the many layers of Mary’s character in hopes that the contradictions existing between the “historical,” the textual, and the traditional will diminish, thus giving equal consideration to all. INDEX WORDS: Mary of Magdala, Canonical texts, Gnostic texts, Medieval saint, Apostles, Saint Mary Magdalene, Early Christianity