Concurrency is concerned with systems of multiple computing agents that interact with each other. Bisimilarity is one of the main representatives of these. Concurrent Constrain Programming (ccp) is a formalism that combines the traditional and algebraic view of process calculi with a declarative one based upon first-order logic. The standard definition of bisimilarity is not completely satisfactory for ccp since it yields an equivalence that is too fine grained. By building upon recent foundational investigations, we introduce a labeled transition semantics and a novel notion of bisimilarity that is fully abstract w.r.t. the observational equivalence in ccp. When the state space of a system is finite, the ordinary notion of bisimilarity can be computed via the partition refinement algorithm, but unfortunately, this algorithm does not work for ccp bisimilarity. Hence, we provide an algorithm that allows us to verify strong bisimilarity for ccp, modifying the algorithm by using a pre-refinement and a partition function based on the irredundant bisimilarity. Weak bisimilarity is a central behavioral equivalence in process calculi and it is obtained from the strong case by taking into account only the actions that are observable in the system. Typically the standard partition refinement can also be used for deciding weak bisimilarity simply by using Milner's reduction from weak to strong; a technique referred to as saturation. We demonstrate that the above-mentioned saturation technique does not work for ccp. We give a reduction that allows us to use the ccp partition refinement algorithm for deciding this equivalence.

Reovirus is internalized after interaction of the outer proteins μ1, σ1 and σ3 with the host cell. Proteolysis of σ3 and cleavage of μ1 (into δ and φ) eventually leads to the formation of a more infectious subviral particle named “ISVP”. The infectious entry of viruses, but not of ISVPs, can be blocked using various entry inhibitors and therefore, suggests that there is a threshold of σ3 digestion required to allow particle to bypass entry blockers. By combining protease and detergent to the digestion of virions, data from this work showed distinct particles generated along the transition pathway. In addition, studies involving flow cytometry and specific antibodies (anti-μ1) showed that between virus and ISVP there is a gradual yet heterogeneous particle proteolysis that is directly related to the virus infectivity. The findings and approaches taken for this thesis work can possibly be extended for studying other non-enveloped viruses. Moreover, it may help to shed some light on the development of safe and effective oncolytic agents.

Reovirus is internalized after interaction of the outer proteins μ1, σ1 and σ3 with the host cell. Proteolysis of σ3 and cleavage of μ1 (into δ and φ) eventually leads to the formation of a more infectious subviral particle named “ISVP”. The infectious entry of viruses, but not of ISVPs, can be blocked using various entry inhibitors and therefore, suggests that there is a threshold of σ3 digestion required to allow particle to bypass entry blockers. By combining protease and detergent to the digestion of virions, data from this work showed distinct particles generated along the transition pathway. In addition, studies involving flow cytometry and specific antibodies (anti-μ1) showed that between virus and ISVP there is a gradual yet heterogeneous particle proteolysis that is directly related to the virus infectivity. The findings and approaches taken for this thesis work can possibly be extended for studying other non-enveloped viruses. Moreover, it may help to shed some light on the development of safe and effective oncolytic agents.

This dissertation develops computational models to address outstanding problems in the domain of expectation-related cognitive processes and their neuroimaging markers in functional MRI or EEG. The new models reveal a way to unite diverse phenomena within a common framework focused on dynamic neural encoding shifts, which can arise from robust interactive effects of M-currents and chloride currents in pyramidal neurons. By specifying efficient, biologically realistic circuits that achieve predictive coding (e.g., Friston, 2005), these models bridge among neuronal biophysics, systems neuroscience, and theories of cognition. Chapter one surveys data types and neural processes to be examined, and outlines the Dynamically Labeled Predictive Coding (DLPC) framework developed during the research. Chapter two models hippocampal prediction and mismatch, using the DLPC framework. Chapter three presents extensions to the model that allow its application for modeling neocortical EEG genesis. Simulations of this extended model illustrate how dynamic encoding shifts can produce Mismatch Negativity (MMN) phenomena, including pharmacological effects on MMN reported for humans or animals. Chapters four and five describe new modeling studies of possible neural bases for alpha-induced information suppression, a phenomenon associated with active ignoring of stimuli. Two models explore the hypothesis that in simple rate-based circuits, information suppression might be a robust effect of neural saturation states arising near peaks of resonant alpha oscillations. A new proposal is also introduced for how the basal ganglia may control onset and offset of alpha-induced information suppression. Although these rate models could reproduce many experimental findings, they fell short of reproducing a key electrophysiological finding: phase-dependent reduction in spiking activity correlated with power in the alpha frequency band. Therefore, chapter five also specifies how a DLPC model, adapted from the neocortical model developed in chapter three, can provide an expectation-based model of alpha-induced information suppression that exhibits phase-dependent spike reduction during alpha-band oscillations. The model thus can explain experimental findings that were not reproduced by the rate models. The final chapter summarizes main theses, results, and basic research implications, then suggests future directions, including expanded models of neocortical mismatch, applications to artificial neural networks, and the introduction of reward circuitry.

Esta dissertação aborda o problema da utilização de classificadores paramétricos em dados de alta dimensionalidade. As vantagens trazidas pelos dados em alta dimensionalidade são bem conhecidas. Classes que são muito semelhantes podem, não obstante, ser separadas com um alto grau de acurácia desde que a classificação dos dados seja realizada em um espaço de alta dimensionalidade e que as matrizes de covariância das classes difiram significativamente. Sistemas sensores capazes de adquirir dados de imagem em alta dimensionalidade (dados de imagens hiperespectrais) foram, em parte, desenvolvidos para tirar proveito dessa condição. Nas condições do mundo real, no entanto, temos de enfrentar o problema de estimar um grande número de parâmetros, geralmente, com um número limitado de amostras. Amostras de treinamento são geralmente caras e demoradas para adquirir. Diferentes abordagens para resolver ou, pelo menos, atenuar este problema tem sido um tópico de investigação por parte da comunidade internacional em sensoriamento remoto. Entre outras, uma possível abordagem que tem sido proposta na literatura consiste em aumentar o número de amostras pela adição de amostras semi-rotuladas ao processo de estimação dos parâmetros do classificador. A metodologia investigada nesta dissertação segue esta abordagem geral. O foco principal deste estudo consiste em investigar uma abordagem para estimar os pesos a serem associados às amostras semi-rotuladas. A abordagem proposta inclui duas etapas. Na primeira, as estimativas iniciais para os pesos são realizadas de forma interativa, por meio da utilização de informações espectrais somente. Em uma segunda etapa, os pesos estimados são refinados por meio de informações de contexto espacial. A metodologia proposta é avaliada através de experimentos que fazem uso de dados de imagens hiperespectrais AVIRIS. Os resultados são apresentados e discutidos. Sugestões para futuras pesquisas neste tópico também são apresentados. / This dissertation deals with the problem of using parametric classifiers in high dimensional data settings. The advantages brought by high dimensional data are well known. Classes that are very similar can nonetheless be separated with a high degree of accuracy provided that the classification is performed in high dimensional data settings and that the classes’ covariance matrices differ significantly. Sensor system capable of acquiring high dimensional image data (hyperspectral image data) were in part developed to take advantage of this condition. In real world conditions, however, we have to face the problem of estimating a resulting large number of parameters with a generally limited number of samples. Training samples are usually expensive and time consuming to acquire. Different approaches to solve or at least mitigate this problem have been a topic of investigation by the international community in remote sensing. Among others, one possible approach that has been proposed in the literature consists in increasing the number of samples by adding semilabeled samples to the process of estimating the classifier’s parameters. The methodology investigated in this dissertation follows this general approach. The main focus in this study consists in investigating an approach to estimate the weights to be associated with the semilabeled samples. The proposed approach includes two steps. In the first one, initial estimates for the weights are performed in an iterative way, by making use of spectral information only. In a second step, the estimated weights are further adjusted by means of spatial context information. The proposed methodology is evaluated by experiments making use of AVIRIS hyperspectral image data. The results are presented and discussed. Suggestions for further research in this topic are also presented.

Esta dissertação aborda o problema da utilização de classificadores paramétricos em dados de alta dimensionalidade. As vantagens trazidas pelos dados em alta dimensionalidade são bem conhecidas. Classes que são muito semelhantes podem, não obstante, ser separadas com um alto grau de acurácia desde que a classificação dos dados seja realizada em um espaço de alta dimensionalidade e que as matrizes de covariância das classes difiram significativamente. Sistemas sensores capazes de adquirir dados de imagem em alta dimensionalidade (dados de imagens hiperespectrais) foram, em parte, desenvolvidos para tirar proveito dessa condição. Nas condições do mundo real, no entanto, temos de enfrentar o problema de estimar um grande número de parâmetros, geralmente, com um número limitado de amostras. Amostras de treinamento são geralmente caras e demoradas para adquirir. Diferentes abordagens para resolver ou, pelo menos, atenuar este problema tem sido um tópico de investigação por parte da comunidade internacional em sensoriamento remoto. Entre outras, uma possível abordagem que tem sido proposta na literatura consiste em aumentar o número de amostras pela adição de amostras semi-rotuladas ao processo de estimação dos parâmetros do classificador. A metodologia investigada nesta dissertação segue esta abordagem geral. O foco principal deste estudo consiste em investigar uma abordagem para estimar os pesos a serem associados às amostras semi-rotuladas. A abordagem proposta inclui duas etapas. Na primeira, as estimativas iniciais para os pesos são realizadas de forma interativa, por meio da utilização de informações espectrais somente. Em uma segunda etapa, os pesos estimados são refinados por meio de informações de contexto espacial. A metodologia proposta é avaliada através de experimentos que fazem uso de dados de imagens hiperespectrais AVIRIS. Os resultados são apresentados e discutidos. Sugestões para futuras pesquisas neste tópico também são apresentados. / This dissertation deals with the problem of using parametric classifiers in high dimensional data settings. The advantages brought by high dimensional data are well known. Classes that are very similar can nonetheless be separated with a high degree of accuracy provided that the classification is performed in high dimensional data settings and that the classes’ covariance matrices differ significantly. Sensor system capable of acquiring high dimensional image data (hyperspectral image data) were in part developed to take advantage of this condition. In real world conditions, however, we have to face the problem of estimating a resulting large number of parameters with a generally limited number of samples. Training samples are usually expensive and time consuming to acquire. Different approaches to solve or at least mitigate this problem have been a topic of investigation by the international community in remote sensing. Among others, one possible approach that has been proposed in the literature consists in increasing the number of samples by adding semilabeled samples to the process of estimating the classifier’s parameters. The methodology investigated in this dissertation follows this general approach. The main focus in this study consists in investigating an approach to estimate the weights to be associated with the semilabeled samples. The proposed approach includes two steps. In the first one, initial estimates for the weights are performed in an iterative way, by making use of spectral information only. In a second step, the estimated weights are further adjusted by means of spatial context information. The proposed methodology is evaluated by experiments making use of AVIRIS hyperspectral image data. The results are presented and discussed. Suggestions for further research in this topic are also presented.

Esta dissertação aborda o problema da utilização de classificadores paramétricos em dados de alta dimensionalidade. As vantagens trazidas pelos dados em alta dimensionalidade são bem conhecidas. Classes que são muito semelhantes podem, não obstante, ser separadas com um alto grau de acurácia desde que a classificação dos dados seja realizada em um espaço de alta dimensionalidade e que as matrizes de covariância das classes difiram significativamente. Sistemas sensores capazes de adquirir dados de imagem em alta dimensionalidade (dados de imagens hiperespectrais) foram, em parte, desenvolvidos para tirar proveito dessa condição. Nas condições do mundo real, no entanto, temos de enfrentar o problema de estimar um grande número de parâmetros, geralmente, com um número limitado de amostras. Amostras de treinamento são geralmente caras e demoradas para adquirir. Diferentes abordagens para resolver ou, pelo menos, atenuar este problema tem sido um tópico de investigação por parte da comunidade internacional em sensoriamento remoto. Entre outras, uma possível abordagem que tem sido proposta na literatura consiste em aumentar o número de amostras pela adição de amostras semi-rotuladas ao processo de estimação dos parâmetros do classificador. A metodologia investigada nesta dissertação segue esta abordagem geral. O foco principal deste estudo consiste em investigar uma abordagem para estimar os pesos a serem associados às amostras semi-rotuladas. A abordagem proposta inclui duas etapas. Na primeira, as estimativas iniciais para os pesos são realizadas de forma interativa, por meio da utilização de informações espectrais somente. Em uma segunda etapa, os pesos estimados são refinados por meio de informações de contexto espacial. A metodologia proposta é avaliada através de experimentos que fazem uso de dados de imagens hiperespectrais AVIRIS. Os resultados são apresentados e discutidos. Sugestões para futuras pesquisas neste tópico também são apresentados. / This dissertation deals with the problem of using parametric classifiers in high dimensional data settings. The advantages brought by high dimensional data are well known. Classes that are very similar can nonetheless be separated with a high degree of accuracy provided that the classification is performed in high dimensional data settings and that the classes’ covariance matrices differ significantly. Sensor system capable of acquiring high dimensional image data (hyperspectral image data) were in part developed to take advantage of this condition. In real world conditions, however, we have to face the problem of estimating a resulting large number of parameters with a generally limited number of samples. Training samples are usually expensive and time consuming to acquire. Different approaches to solve or at least mitigate this problem have been a topic of investigation by the international community in remote sensing. Among others, one possible approach that has been proposed in the literature consists in increasing the number of samples by adding semilabeled samples to the process of estimating the classifier’s parameters. The methodology investigated in this dissertation follows this general approach. The main focus in this study consists in investigating an approach to estimate the weights to be associated with the semilabeled samples. The proposed approach includes two steps. In the first one, initial estimates for the weights are performed in an iterative way, by making use of spectral information only. In a second step, the estimated weights are further adjusted by means of spatial context information. The proposed methodology is evaluated by experiments making use of AVIRIS hyperspectral image data. The results are presented and discussed. Suggestions for further research in this topic are also presented.

The thesis discusses in detail some concepts of speech and prosody that can contribute to build a speech corpus for the speaker segmentation purpose. Moreover, the Elan multimedia annotator used for labeling is described. The theoretical part highlights some frequently used speech features such as MFCC, PLP and LPC and deals with currently most popular speech segmentation methods. Some classification algorithms are also mentioned. The practical part describes implementation of Bayesian information criterium algorithm in system for automatic speaker segmentation. For classification of speaker change point in speech, were used different speech features. The results of tests were evaluated by the graphic method of receiver operating characteristic (ROC) and his quantitative indices. As the best speech features for this system were provided MFCC and HFCC.

Providing high quality of topics inference in today's large and dynamic corpora, such as Twitter, is a challenging task. This is especially challenging taking into account that the content in this environment contains short texts and many abbreviations. This project proposes an improvement of a popular online topics modelling algorithm for Latent Dirichlet Allocation (LDA), by incorporating supervision to make it suitable for Twitter context. This improvement is motivated by the need for a single algorithm that achieves both objectives: analyzing huge amounts of documents, including new documents arriving in a stream, and, at the same time, achieving high quality of topics’ detection in special case environments, such as Twitter. The proposed algorithm is a combination of an online algorithm for LDA and a supervised variant of LDA - labeled LDA. The performance and quality of the proposed algorithm is compared with these two algorithms. The results demonstrate that the proposed algorithm has shown better performance and quality when compared to the supervised variant of LDA, and it achieved better results in terms of quality in comparison to the online algorithm. These improvements make our algorithm an attractive option when applied to dynamic environments, like Twitter. An environment for analyzing and labelling data is designed to prepare the dataset before executing the experiments. Possible application areas for the proposed algorithm are tweets recommendation and trends detection. / Tillhandahålla högkvalitativa ämnen slutsats i dagens stora och dynamiska korpusar, såsom Twitter, är en utmanande uppgift. Detta är särskilt utmanande med tanke på att innehållet i den här miljön innehåller korta texter och många förkortningar. Projektet föreslår en förbättring med en populär online ämnen modellering algoritm för Latent Dirichlet Tilldelning (LDA), genom att införliva tillsyn för att göra den lämplig för Twitter sammanhang. Denna förbättring motiveras av behovet av en enda algoritm som uppnår båda målen: analysera stora mängder av dokument, inklusive nya dokument som anländer i en bäck, och samtidigt uppnå hög kvalitet på ämnen "upptäckt i speciella fall miljöer, till exempel som Twitter. Den föreslagna algoritmen är en kombination av en online-algoritm för LDA och en övervakad variant av LDA - Labeled LDA. Prestanda och kvalitet av den föreslagna algoritmen jämförs med dessa två algoritmer. Resultaten visar att den föreslagna algoritmen har visat bättre prestanda och kvalitet i jämförelse med den övervakade varianten av LDA, och det uppnådde bättre resultat i fråga om kvalitet i jämförelse med den online-algoritmen. Dessa förbättringar gör vår algoritm till ett attraktivt alternativ när de tillämpas på dynamiska miljöer, som Twitter. En miljö för att analysera och märkning uppgifter är utformad för att förbereda dataset innan du utför experimenten. Möjliga användningsområden för den föreslagna algoritmen är tweets rekommendation och trender upptäckt.

This thesis projects presents a system for gathering and using data in the context of a smarthome research enviroment. The system was developed at the Human Health and ActivityLaborty, H2Al, at Luleå University of Technology and consists of two distinct parts. First, a data export application that runs in the H2Al enviroment. This application syn-chronizes data from various sensor systems and forwards the data for further analysis. Thisanalysis was performed in the iMotions platform in order to visualize, record and export data.As a delimitation, the only sensor used was the WideFind positional system installed at theH2Al. Secondly, an activity recognition application that uses data generated from the iMotionsplatform and data export application. This includes several scripts which transforms rawdata into labeled datasets and translates them into activity recognition models with the helpof machine learning algorithms. As a delimitation, activity recognition was limited to falldetection. These fall detection models were then hosted on a basic server to test accuracyand to act as an example use case for the rest of the project. The project resulted in an effective data gathering system and was generally successful asa tool to create datasets. The iMotions platform was especially successful in both visualizingand recording data together with the data export application. The example fall detectionmodels trained showed theoretical promise, but failed to deliver good results in practice,partly due to the limitations of the positional sensor system used. Some of the conclusions drawn at the end of the project were that the data collectionprocess needed more structure, planning and input from professionals, that a better positionalsensor system may be required for better fall detection results but also that this kind of systemshows promise in the context of smart homes, especially within areas like elderly healthcare.