Nanomanufacturing is critical to the future growth of U.S. manufacturing. Yet the process yield of current nanodevices is typically 10% or less. Particularly in nanomaterials growth, there may exist large variability across the sites on a substrate, which could lead to variability in properties. Essential to the reduction of variability is to mathematically describe the spatial variation of nanostructure. This research therefore aims at a method of modeling and estimating nanostructure morphology variation for process yield improvement. This method consists of (1) morphology variation modeling based on Gaussian Markov random field (GMRF) theory, and (2) maximum likelihood estimation (MLE) of morphology variation model based on measurement data. The research challenge lies in the proper definition and estimation of the interactions among neighboring nanostructures. To model morphology variation, nanostructures on all sites are collectively described as a GMRF. The morphology variation model serves for the space-time growth model of nanostructures. The probability structure of the GMRF is specified by a so-called simultaneous autoregressive scheme, which defines the neighborhood systems for any site on a substrate. The neighborhood system characterizes the interactions among adjacent nanostructures by determining neighbors and their influence on a given site in terms of conditional auto-regression. The conditional auto-regression representation uniquely determines the precision matrix of the GMRF. Simulation of nanostructure morphology variation is conducted for various neighborhood structures. Considering the boundary effects, both finite lattice and infinite lattice models are discussed. The simultaneous autoregressive scheme of the GMRF is estimated via the maximum likelihood estimation (MLE) method. The MLE estimation of morphology variation requires the approximation of the determinant of the precision matrix in the GMRF. The absolute term in the double Fourier expansion of a determinant function is used to approximate the coefficients in the precision matrix. Since the conditional MLE estimates of the parameters are affected by coding the date, different coding schemes are considered in the estimation based on numerical simulation and the data collected from SEM images. The results show that the nanostructure morphology variation modeling and estimation method could provide tools for yield improvement in nanomanufacturing.

In today’s machine learning research, probabilistic graphical models are used extensively to model complicated systems with uncertainty, to help understanding of the problems, and to help inference and predict unknown events. For inference tasks, exact inference methods such as junction tree algorithms exist, but they suffer from exponential growth of cluster size and thus is not able to handle large and highly connected graphs. Approximate inference methods do not try to find exact probabilities, but rather give results that improve as algorithm runs. Gibbs sampling, as one of the approximate inference methods, has gained lots of traction and is used extensively in inference tasks, due to its ease of understanding and implementation. However, as problem size grows, even the faster algorithm needs a speed boost to meet application requirement. The number of variables in an application graphical model can range from tens of thousands to billions, depending on problem domain. The original sequential Gibbs sampling may not return satisfactory result in limited time. Thus, in this thesis, we investigate in ways to speed up Gibbs sampling. We will study ways to do better initialization, blocking variables to be sampled together, as well as using simulated annealing. These are the methods that modifies the algorithm itself. We will also investigate in ways to parallelize the algorithm. An algorithm is parallelizable if some steps do not depend on other steps, and we will find out such dependency in Gibbs sampling. We will discuss how the choice of different hardware and software architecture will affect the parallelization result. We will use optical flow problem as an example to demonstrate the various speed up methods we investigated. An optical flow method tries to find out the movements of small image patches between two images in a temporal sequence. We demonstrate how we can model it using probabilistic graphical model, and solve it using Gibbs sampling. The result of using sequential Gibbs sampling is demonstrated, with comparisons from using various speed up methods and other optical flow methods.

One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

Random heterogeneous, scale-dependent structures can be observed from many image sources, especially from remote sensing and scientific imaging. Examples include slices of porous media data showing pores of various sizes, and a remote sensing image including small and large sea-ice blocks. Meanwhile, rather than the images of phenomena themselves, there are many image processing and analysis problems requiring to deal with \emph{discrete-state} fields according to a labeled underlying property, such as mineral porosity extracted from microscope images, or an ice type map estimated from a sea-ice image. In many cases, if discrete-state problems are associated with heterogeneous, scale-dependent spatial structures, we will have to deal with complex discrete state fields. Although scale-dependent image modeling methods are common for continuous-state problems, models for discrete-state cases have not been well studied in the literature. Therefore, a fundamental difficulty will arise which is how to represent such complex discrete-state fields. Considering the success of hidden field methods in representing heterogenous behaviours and the capability of hierarchical field methods in modeling scale-dependent spatial features, we propose a Hidden Hierarchical Markov Field (HHMF) approach, which combines the idea of hierarchical fields with hidden fields, for dealing with the discrete field modeling challenge. However, to define a general HHMF modeling structure to cover all possible situations is difficult. In this research, we use two image application problems to describe the proposed modeling methods: one for scientific image (porous media image) reconstruction and the other for remote-sensing image synthesis. For modeling discrete-state fields with a spatially separable complex behaviour, such as porous media images with nonoverlapped heterogeneous pores, we propose a Parallel HHMF model, which can decomposes a complex behaviour into a set of separated, simple behaviours over scale, and then represents each of these with a hierarchical field. Alternatively, discrete fields with a highly heterogeneous behaviour, such as a sea-ice image with multiple types of ice at various scales, which are not spatially separable but arranged more as a partition tree, leads to the proposed Tree-Structured HHMF model. According to the proposed approach, a complex, multi-label field can be repeatedly partitioned into a set of binary/ternary fields, each of which can be further handled by a hierarchical field.

No description available.

O presente trabalho estuda o modelo de Blume-Capel na rede aleatória e também analisa a inclusão de um termo de campo cristalino aleatório e de um termo de campo local aleatório. Ao resolver o modelo na rede aleatória, uma técnica de conectividade finita foi utilizada, na qual cada spin é conectado a um número finito de outros spins. Os spins foram conectados de acordo com uma distribuição de Poisson, os termos de campo aleatório seguiram uma distribuição bimodal e as interações entre os spins foram consideradas uniformes. Desse modo, só há desordem nas conexões entre os spins. O foco desse trabalho foi determinar como a natureza da transição de fase é alterada com a conectividade e se há um comportamento reentrante das linhas de transição de fase. A técnica de réplicas é usada para obter equações de ponto de sela para a distribuição de campos locais. Um Ansatz de simetria de réplicas foi utilizado para a função de ordem e esse foi escrito em termos de uma distribuição bidimensional de campos efetivos, onde uma das componentes é associada com um termo linear dos spins e a outra com o termo de campo cristalino. Com isso, equações para as funções de ordem e a energia livre podem ser obtidas. Uma técnica de dinâmica populacional é usada para resolver numericamente a equação auto-consistente para a distribuição de campos locais e outros parâmetros, como a magnetização, a atividade da rede e a energia livre. Os resultados indicam que a natureza da transição ferromagnética-paramagnética, a posição do ponto tricrítico e a existência de reentrância dependem fortemente do valor da conectividade e, nos casos com um termo de campo aleatório, dependem da intensidade dos campos aleatórios. No caso em que o campo cristalino é aleatório, o ponto tricrítico é suprimido para valores acima de um certo valor de aleatoriedade. / The present work studies the Blume-Capel model in a random network and also analyses the inclusion of a random crystal-field term and a random field term. To solve the model in a random network a finite connectivity technique is used, in which each spin is connected to a finite number of other spins. The spins were connected according a Poisson distribution, the random field terms followed a bimodal distribution and the bonds between the spins were considered uniform. Thus, there is only a connection disorder. The focus of this work was on determining how the nature of the phase transition changes with the connectivity and the random fields and if there is a reentrant behavior of the phase boundaries. The replica technique is used to obtain saddle-point equations for the effective local-field distribution. The replica symmetric Ansatz for the order function is written in terms of a two-dimensional effective-field distribution, where one of the components is associated with a linear form in the spins and the other with the crystal-field term. This allows one to derive equations for the order function and for the free-energy. A population dynamics procedure is used to solve numerically a self-consistency equation for the distribution of the local field and with it some physical parameters, like magnetization and free-energy. The results obtained indicate that the nature of the F-P transition, the location of the tricritical point and the presence of a reentrant phase depend strongly on the connectivity. In the cases with a random field term, those are also dependent on the intensity of the fields. For the case with a random crystal-field term, the tricritical point is supressed above a certain value of randomness.

Neste trabalho utilizam-se duas adaptações do modelo originalmente proposto por van Hemmen com o intuito de investigar os efeitos de um campo aleatório hi sob as transições de fases: um modelo com spin 1 estudado na versão clássica e um modelo na formulação fermiônica. A escolha do modelo de van Hemmen está relacionada ao fato de que não e necessário utilizar o método das réplicas para tratar a desordem. No primeiro caso, o modelo clássico conta com um campo cristalino (D) que favorece energeticamente os estados não interagentes. As interações aleatórias Ji j são respons aveis por introduzir desordem e frustração ao problema. Tanto as variáveis aleatórias quanto o campo aleatório seguem uma distribuição de probabilidades bimodal. Analisando o comportamento dos parâmetros de ordem e da energia livre, diagramas de fases da temperatura pelo acoplamento ferromagnético J0 e pelo campo cristalino D para diferentes valores de hi foram construídos. Os resultados indicam que a presença do campo aleatório tende a reduzir o ponto tricrítico das transições de fases e, para determinado valor de hi, uma nova solução da fase vidro de spin (VS) pode ser favorecida. Além disso, para valores relativamente altos de hi, o problema apresenta pontos multicríticos nas transições de fase. Também busca-se investigar nesse modelo se o mesmo e capaz de apresentar algum tipo de transição inversa (TI) As TI são uma classe de transições de fases altamente contraintuitivas, em que uma fase usualmente ordenada tem entropia maior que uma fase desordenada. Elas se manifestam nos diagramas de fases através de uma reentrância da fase desordenada-ordenada-desordenada conforme a temperatura diminui. Embora o modelo apresente diversos pontos tricríticos na transição PM/VS, nenhum tipo de transição reentrante foi observada, não havendo, portanto, nenhuma evidência de transição inversa no sistema. Já o modelo analisado na formulação fermiônica conta com um potencial químico (m), que controla a diluição magnética relacionada ao favorecimento dos sítios duplamente ocupados ou vazios, e com um campo magnético transverso G, que introduz flutuações quânticas ao problema. Nesse caso, as interações de spin Ji j e o campo aleatório seguem uma distribuição gaussiana. A introdução do campo hi, a nível de campo médio, permite investigar as TI sob os efeitos de uma desordem que não e uma fonte de frustração Os resultados mostram uma transição reentrante da fase VS para a fase paramagnética (PM) na ausência de G e hi. A reentrância aparece para um certo intervalo de m, em que se encontra uma fase PM a baixas temperaturas com menor entropia do que a fase VS, caracterizando a transição do tipo congelamento inverso (CI). No entanto o CI e gradualmente suprimido quando os efeitos hi são intensificados. Além disso, o CI e completamente destruído pelas flutuações quânticas provenientes do G. Dessa forma, a desordem combinada com a diluição pode apresentar um cenário favorável a ocorrência de CI, enquanto o campo aleatório e as flutuações quânticas agem contra este tipo de transição. / In this work, two adaptations to the original model proposed by van Hemmen are used with the aim of investigating the e ects of a random eld hi under the phase transitions: a model studied in the classical version and a model in the fermionic formulation. The van Hemmen model was chosen because the disorder can be treated without the use of the replica method. In the rst case, the classic model has a crystal eld (D) which energetically favors the non-interacting states. The random interactions Ji j are responsible for introduce disorder and frustration to the problem. Both random eld and random variables follow a bimodal probability distribution. Analyzing the behavior of the order parameters and the free energy, phase diagrams of temperatura T versus the ferromagnetic coupling J0 and T versus the crystal eld D for di erent values of hi were build. The results indicate that the presence of the random eld tends to reduce the tricritical point of the phase transitions. For a given value of hi, a new solution of phase spin glass (SG) can be favored. In addition, for su ciently high enough values of hi the problem presents multicritical points in phase transitions. It is also intended to investigate if this model is able to present some kind of inverse transition (IT) IT is a class of highly nonintuitive phase transitions in that the usual ordered phase has more entropy than the disordered one. The IT manifests in the phase diagrams as a reentrance of the disordered-ordereddisordered phase according to the temperature decreases. Although the model presents several tricritical points in the transition PM=SG, no type of reentrant transition was observed. Therefore, there is no evidence of inverse transition in this model. The model analyzed in the fermionic formulation has a chemical potential (m), which has the role of controlling the magnetic dilution related to favoring double-occupation or empty sites. This model also counts with a transverse magnetic eld G, which introduces quantum uctuations to the problem. In this case, the spin interactions Ji j and random eld follow a Gaussian distribution The introduction of the hi allows the investigation of IT under the e ects of a disorder that is not a source of frustration. The results show a reentrant transition from the SG phase to the PM phase in the absence of G and hi. The reentrance appears for a certain range of m, in which there is a PM phase at low temperatures with lower entropy than the SG phase, characterizing the inverse freezing (IF) transition. However, IF is gradually suppressed when the e ects hi are intensi ed. Moreover, the IF is completely destroyed by quantum uctuations from G. Thus, the disorder combined with the dilution may present the favorable scenario to the occurrence of IF, while the random eld and the uctuations quantum mechanics act against this kind of transition.

Changes in retinal structure are related to different eye diseases. Various retinal imaging techniques, such as fundus imaging and optical coherence tomography (OCT) imaging modalities, have been developed for non-intrusive ophthalmology diagnoses according to the vasculature changes. However, it is time consuming or even impossible for ophthalmologists to manually label all the retinal structures from fundus images and OCT images. Therefore, computer aided diagnosis system for retinal imaging plays an important role in the assessment of ophthalmologic diseases and cardiovascular disorders. The aim of this PhD thesis is to develop segmentation methods to extract clinically useful information from these retinal images, which are acquired from different imaging modalities. In other words, we built the segmentation methods to extract important structures from both 2D fundus images and 3D OCT images. In the first part of my PhD project, two novel level set based methods were proposed for detecting the blood vessels and optic discs from fundus images. The first one integrates Chan-Vese's energy minimizing active contour method with the edge constraint term and Gaussian Mixture Model based term for blood vessels segmentation, while the second method combines the edge constraint term, the distance regularisation term and the shape-prior term for locating the optic disc. Both methods include the pre-processing stage, used for removing noise and enhancing the contrast between the object and the background. Three automated layer segmentation methods were built for segmenting intra-retinal layers from 3D OCT macular and optic nerve head images in the second part of my PhD project. The first two methods combine different methods according to the data characteristics. First, eight boundaries of the intra-retinal layers were detected from the 3D OCT macular images and the thickness maps of the seven layers were produced. Second, four boundaries of the intra-retinal layers were located from 3D optic nerve head images and the thickness maps of the Retinal Nerve Fiber Layer (RNFL) were plotted. Finally, the choroidal layer segmentation method based on the Level Set framework was designed, which embedded with the distance regularisation term, edge constraint term and Markov Random Field modelled region term. The thickness map of the choroidal layer was calculated and shown.

Neste trabalho utilizam-se duas adaptações do modelo originalmente proposto por van Hemmen com o intuito de investigar os efeitos de um campo aleatório hi sob as transições de fases: um modelo com spin 1 estudado na versão clássica e um modelo na formulação fermiônica. A escolha do modelo de van Hemmen está relacionada ao fato de que não e necessário utilizar o método das réplicas para tratar a desordem. No primeiro caso, o modelo clássico conta com um campo cristalino (D) que favorece energeticamente os estados não interagentes. As interações aleatórias Ji j são respons aveis por introduzir desordem e frustração ao problema. Tanto as variáveis aleatórias quanto o campo aleatório seguem uma distribuição de probabilidades bimodal. Analisando o comportamento dos parâmetros de ordem e da energia livre, diagramas de fases da temperatura pelo acoplamento ferromagnético J0 e pelo campo cristalino D para diferentes valores de hi foram construídos. Os resultados indicam que a presença do campo aleatório tende a reduzir o ponto tricrítico das transições de fases e, para determinado valor de hi, uma nova solução da fase vidro de spin (VS) pode ser favorecida. Além disso, para valores relativamente altos de hi, o problema apresenta pontos multicríticos nas transições de fase. Também busca-se investigar nesse modelo se o mesmo e capaz de apresentar algum tipo de transição inversa (TI) As TI são uma classe de transições de fases altamente contraintuitivas, em que uma fase usualmente ordenada tem entropia maior que uma fase desordenada. Elas se manifestam nos diagramas de fases através de uma reentrância da fase desordenada-ordenada-desordenada conforme a temperatura diminui. Embora o modelo apresente diversos pontos tricríticos na transição PM/VS, nenhum tipo de transição reentrante foi observada, não havendo, portanto, nenhuma evidência de transição inversa no sistema. Já o modelo analisado na formulação fermiônica conta com um potencial químico (m), que controla a diluição magnética relacionada ao favorecimento dos sítios duplamente ocupados ou vazios, e com um campo magnético transverso G, que introduz flutuações quânticas ao problema. Nesse caso, as interações de spin Ji j e o campo aleatório seguem uma distribuição gaussiana. A introdução do campo hi, a nível de campo médio, permite investigar as TI sob os efeitos de uma desordem que não e uma fonte de frustração Os resultados mostram uma transição reentrante da fase VS para a fase paramagnética (PM) na ausência de G e hi. A reentrância aparece para um certo intervalo de m, em que se encontra uma fase PM a baixas temperaturas com menor entropia do que a fase VS, caracterizando a transição do tipo congelamento inverso (CI). No entanto o CI e gradualmente suprimido quando os efeitos hi são intensificados. Além disso, o CI e completamente destruído pelas flutuações quânticas provenientes do G. Dessa forma, a desordem combinada com a diluição pode apresentar um cenário favorável a ocorrência de CI, enquanto o campo aleatório e as flutuações quânticas agem contra este tipo de transição. / In this work, two adaptations to the original model proposed by van Hemmen are used with the aim of investigating the e ects of a random eld hi under the phase transitions: a model studied in the classical version and a model in the fermionic formulation. The van Hemmen model was chosen because the disorder can be treated without the use of the replica method. In the rst case, the classic model has a crystal eld (D) which energetically favors the non-interacting states. The random interactions Ji j are responsible for introduce disorder and frustration to the problem. Both random eld and random variables follow a bimodal probability distribution. Analyzing the behavior of the order parameters and the free energy, phase diagrams of temperatura T versus the ferromagnetic coupling J0 and T versus the crystal eld D for di erent values of hi were build. The results indicate that the presence of the random eld tends to reduce the tricritical point of the phase transitions. For a given value of hi, a new solution of phase spin glass (SG) can be favored. In addition, for su ciently high enough values of hi the problem presents multicritical points in phase transitions. It is also intended to investigate if this model is able to present some kind of inverse transition (IT) IT is a class of highly nonintuitive phase transitions in that the usual ordered phase has more entropy than the disordered one. The IT manifests in the phase diagrams as a reentrance of the disordered-ordereddisordered phase according to the temperature decreases. Although the model presents several tricritical points in the transition PM=SG, no type of reentrant transition was observed. Therefore, there is no evidence of inverse transition in this model. The model analyzed in the fermionic formulation has a chemical potential (m), which has the role of controlling the magnetic dilution related to favoring double-occupation or empty sites. This model also counts with a transverse magnetic eld G, which introduces quantum uctuations to the problem. In this case, the spin interactions Ji j and random eld follow a Gaussian distribution The introduction of the hi allows the investigation of IT under the e ects of a disorder that is not a source of frustration. The results show a reentrant transition from the SG phase to the PM phase in the absence of G and hi. The reentrance appears for a certain range of m, in which there is a PM phase at low temperatures with lower entropy than the SG phase, characterizing the inverse freezing (IF) transition. However, IF is gradually suppressed when the e ects hi are intensi ed. Moreover, the IF is completely destroyed by quantum uctuations from G. Thus, the disorder combined with the dilution may present the favorable scenario to the occurrence of IF, while the random eld and the uctuations quantum mechanics act against this kind of transition.