Ca2+ and calmodulin play important roles in many physiologically crucial pathways. The conformational landscape of calmodulin is intriguing. Conformational changes allow for binding target-proteins, while binding Ca2+ yields population shifts within the landscape. Thus, target-proteins become Ca2+-sensitive upon calmodulin binding. Calmodulin regulates more than 300 target-proteins, and mutations are linked to lethal disorders. The mechanisms underlying Ca2+ and target-protein binding are complex and pose interesting questions. Such questions are typically addressed with experiments which fail to provide simultaneous molecular and dynamics insights. In this thesis, questions on binding mechanisms are probed with molecular dynamics simulations together with tailored unsupervised learning and data analysis. In Paper 1, a free energy landscape estimator based on Gaussian mixture models with cross-validation was developed and used to evaluate the efficiency of regular molecular dynamics compared to temperature-enhanced molecular dynamics. This comparison revealed interesting properties of the free energy landscapes, highlighting different behaviors of the Ca2+-bound and unbound calmodulin conformational ensembles. In Paper 2, spectral clustering was used to shed light on Ca2+ and target protein binding. With these tools, it was possible to characterize differences in target-protein binding depending on Ca2+-state as well as N-terminal or C-terminal lobe binding. This work invites data-driven analysis into the field of biomolecule molecular dynamics, provides further insight into calmodulin’s Ca2+ and targetprotein binding, and serves as a stepping-stone towards a complete understanding of calmodulin’s Ca2+-dependent conformational ensembles. / <p>QC 20180912</p>

Finite mixture modelling is a powerful and well-developed paradigm, having proven useful in unsupervised learning and, to a lesser extent supervised learning (mixture discriminant analysis), especially in the case(s) of data with local variation and/or latent variables. It is the aim of this thesis to improve upon mixture discriminant analysis by introducing two types of random forest analogues which are called Mix- Forests. The first MixForest is based on Gaussian mixture models from the famous family of Gaussian parsimonious clustering models and will be useful in classify- ing lower dimensional data. The second MixForest extends the technique to higher dimensional data via the use of mixtures of factor analyzers from the well-known family of parsimonious Gaussian mixture models. MixForests will be utilized in the analysis of real data to demonstrate potential increases in classification accuracy as well as inferential procedures such as generalization error estimation and variable importance measures. / Thesis / Doctor of Philosophy (PhD)

As user experience can be a competitive edge, it lies in the interest of businesses to be aware of how users perceive the services they provide. For telecommunications operators, how network performance influences user experience is critical. To attain this knowledge, one can survey users. However, sometimes users are not available or willing to answer. For this reason, there exists an interest in estimating the quality of user experience without having to ask users directly. Previous research has studied how the relationship between network performance and the quality of experience can be modelled over time through a fixed window classification approach. This work aims to extend this research by investigating the applicability of a regression approach without the fixed window limitation by the application of an Long Short Term Memmory based Machine Learning model. Aggregation of both network elements and user feedback through the application of three different clustering techniques was used to overcome challenges in user feedback sparsity. The performance while using each clustering technique was evaluated. It was found that all three methods can outperform a baseline based on the weekly average of the user feedback. The effect of applying different levels of detrending was also examined. It was shown that detrending the time series based on a smaller superset may increase overall performance but hinder relative model improvement, indicating that some helpful information may be lost in this process. The results should inspire future works to consider a regression approach for modelling Quality of Experience as a function of network performance as an avenue worthy of further study. This work should also motivate further research into the generalizability of models trained on network elements that reside in areas of different urban and rural conditions. / Användarupplevelsen kan utgöra en konkurrensfördel och således ligger det i marknadsaktörernas intressen att vara medvetna om hur användarna upplever det tjänster de erbjuder. Före telekommunikationsoperatörer är det kritiskt at vare varse om hur nätverkets prestanda influerar användarnas upplevelse. För att förskaffa sig den informationen kan operatörer välja att fråga användarna direkt. Detta kan dock vara svårt då användare kanske inte finns tillgängliga för eller inte är villiga att besvara operatörens frågor. Med detta som utgångspunkt finns det därför ett intresse för att estimera kundernas upplevelse utan att direkt fråga dem. Tidigare studier har undersökt möjligheten att genom klassificeringsmetoder som tillämpats på avgränsade tidsfönster modellera förhållandet mellan nätverksprestanda och kundupplevelse. Detta arbete syftar till att utvidga forskningsområdet genom att studera tillämparbarheten av att använda regressionsmetoder utan begränsningen av ett avgränsat tidsfönster. Detta ska göras genom att tillämpa en Long Short Term Memmory baserad maskininlärningsmodell. Genom att aggregera både nätverkselement och användarfeedback i en process som nyttjat tre olika klustringstekniker har utmaningar med glesfördelad feedback från användare hanterats. Resultaten av att använda vardera klustringsteknik har utvärderats. Från utvärderingen fans att alla tre metoder presterar bättre än ett jämförelsemått bestående av ett veckovis genomsnitt av användarnas återkoppling. Effekten av att applicera olika nivåer av aggregering för att ta bort trender i data. Resultaten visar att modellerna presenterat bättre då den övermängd som används för att ta bort trenden i en given delmängd då skillnaden mellan dessa är mindre. Dock försämrades den relative förbättringen hos modellerna då skillnaden mellan delmängd och övermängd minskade. Detta tror indikera att nyttig information i sammanhanget går förlorad i processen av att ta bort trenden i datamängden. De uppnådda resultaten bör inspirera framtida studier till att ha regressionsmodeller i åtanke när användarupplevelsen skall modelleras som en funktion av närverkets prestanda. Detta arbete borde även motivera vidare forskning kring huruvida modeller som tränats på nätverkselement belägna i urbana eller lantliga områden generaliserar till nätverks element i andra områden.

This thesis investigates out-of-distribution recognition for time-series data of pulsedradar signals. The classifier is a naive Bayesian classifier based on Gaussian mixturemodels and Dirichlet process mixture models. In the mixture models, we model thedistribution of three pulse features in the time series, namely radio-frequency in thepulse, duration of the pulse, and pulse repetition interval which is the time betweenpulses. We found that simple thresholds on the likelihood can effectively determine ifsamples are out-of-distribution or belong to one of the classes trained on. In addition,we present a simple method that can be used for deinterleaving/pulse classification andshow that it can robustly classify 100 interleaved signals and simultaneously determineif pulses are out-of-distribution. / Det här examensarbetet undersöker hur en maskininlärnings-modell kan anpassas för attkänna igen när pulserade radar-signaler inte tillhör samma fördelning som modellen är tränadmed men också känna igen om signalen tillhör en tidigare känd klass. Klassifieringsmodellensom används här är en naiv Bayesiansk klassifierare som använder sig av Gaussian mixturemodels och Dirichlet Process mixture models. Modellen skapar en fördelning av tidsseriedatan för pulserade radar-signaler och specifikt för frekvensen av varje puls, pulsens längd och tiden till nästa puls. Genom att sätta gränser i sannolikheten av varje puls eller sannolikhetenav en sekvens kan vi känna igen om datan är okänd eller tillhör en tidigare känd klass.Vi presenterar även en enkel metod för att klassifiera specifika pulser i sammanhang närflera signaler överlappar och att metoden kan användas för att robust avgöra om pulser ärokända.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The importance of Language Identification for African languages is seeing a dramatic increase due to the development of telecommunication infrastructure and, as a result, an increase in volumes of data and speech traffic in public networks. By automatically processing the raw speech data the vital assistance given to people in distress can be speeded up, by referring their calls to a person knowledgeable in that language. To this effect a speech corpus was developed and various algorithms were implemented and tested on raw telephone speech data. These algorithms entailed data preparation, signal processing, and statistical analysis aimed at discriminating between languages. The statistical model of Gaussian Mixture Models (GMMs) were chosen for this research due to their ability to represent an entire language with a single stochastic model that does not require phonetic transcription. Language Identification for African languages using GMMs is feasible, although there are some few challenges like proper classification and accurate study into the relationship of langauges that need to be overcome. Other methods that make use of phonetically transcribed data need to be explored and tested with the new corpus for the research to be more rigorous. / AFRIKAANSE OPSOMMING: Die belang van die Taal identifiseer vir Afrika-tale is sien ’n dramatiese toename te danke aan die ontwikkeling van telekommunikasie-infrastruktuur en as gevolg ’n toename in volumes van data en spraak verkeer in die openbaar netwerke.Deur outomaties verwerking van die ruwe toespraak gegee die noodsaaklike hulp verleen aan mense in nood kan word vinniger-up ”, deur te verwys hul oproepe na ’n persoon ingelichte in daardie taal. Tot hierdie effek van ’n toespraak corpus het ontwikkel en die verskillende algoritmes is gemplementeer en getoets op die ruwe telefoon toespraak gegee.Hierdie algoritmes behels die data voorbereiding, seinverwerking, en statistiese analise wat gerig is op onderskei tussen tale.Die statistiese model van Gauss Mengsel Modelle (GGM) was gekies is vir hierdie navorsing as gevolg van hul vermo te verteenwoordig ’n hele taal met’ n enkele stogastiese model wat nodig nie fonetiese tanscription nie. Taal identifiseer vir die Afrikatale gebruik GGM haalbaar is, alhoewel daar enkele paar uitdagings soos behoorlike klassifikasie en akkurate ondersoek na die verhouding van TALE wat moet oorkom moet word.Ander metodes wat gebruik maak van foneties getranskribeerde data nodig om ondersoek te word en getoets word met die nuwe corpus vir die ondersoek te word strenger.

Apesar da ampla diversidade de línguas indígenas no Brasil, poucas pesquisas estudam estas línguas e suas relações. Inúmeros esforços têm sido dedicados a procurar similaridades entre as palavras das línguas indígenas e classificá-las em famílias de línguas. Seguindo a classificação mais aceita das línguas indígenas do Brasil, esta pesquisa propõe comparar palavras de 10 línguas indígenas brasileiras. Para isso, considera-se que estas palavras são sinais de fala e estima-se a função de distribuição de probabilidade (PDF) de cada palavra, usando um modelo de mistura de gaussianas (GMM). A PDF foi considerada um modelo para representar as palavras. Os modelos foram comparados utilizando medidas de distância para construir estruturas hierárquicas que evidenciaram possíveis relações entre as palavras. Seguindo esta linha, a hipótese levantada nesta pesquisa é que as PDFs baseadas em GMM conseguem caracterizar as palavras das línguas indígenas, permitindo o emprego de medidas de distância entre elas para estabelecer relações entre as palavras, de forma que tais relações confirmem algumas das classificações. Os parâmetros do GMM foram calculados utilizando o algoritmo Maximização da Expectância (em inglês, Expectation Maximization (EM)). A divergência Kullback Leibler (KL) foi empregada para medir semelhança entre as PDFs. Esta divergência serve de base para estabelecer as estruturas hierárquicas que ilustram as relações entre os modelos. A estimativa da PDF, baseada em GMM foi testada com o auxílio de sinais simulados, sendo possível confirmar que os parâmetros obtidos são próximos dos originais. Foram implementadas várias medidas de distância para avaliar se a semelhança entre os modelos estavam determinadas pelos modelos e não pelas medidas adotadas neste estudo. Os resultados de todas as medidas foram similares, somente foi observada alguma diferença nos agrupamentos realizados pela distância C2, por isso foi proposta como complemento da divergência KL. Estes resultados sugerem que as relações entre os modelos dependem das suas características, não das métricas de distância selecionadas no estudo e que as PDFs baseadas em GMM, conseguem fazer uma caracterização adequada das palavras. Em geral, foram observados agrupamentos entre palavras que pertenciam a línguas de um mesmo tronco linguístico, assim como se observou uma tendência a incluir línguas isoladas nos agrupamentos dos troncos linguísticos. Palavras que pertenciam a determinada língua apresentaram um comportamento padrão, sendo identificadas por esse tipo de comportamento. Embora os resultados para as palavras das línguas indígenas sejam inconclusivos, considera-se que o estudo foi útil para aumentar o conhecimento destas 10 línguas estudadas, propondo novas linhas de pesquisas dedicadas à análise destas palavras. / Although there exists a large diversity of indigenous languages in Brazil, there are few researches on these languages and their relationships. Numerous efforts have been dedicated to search for similarities among words of indigenous languages to classify them into families. Following the most accepted classification of Brazilian indigenous languages, this research proposes to compare words of 10 Brazilian indigenous languages. The words of the indigenous languages are considered speech signals and the Probability Distribution Function (PDF) of each word was estimated using the Gaussian Mixture Models (GMM). This estimation was considered a model to represent each word. The models were compared using distance measures to construct hierarchical structures that illustrate possible relationships among words. The hypothesis in this research is that the estimation of the PDF, based on GMM can characterize the words of indigenous languages, allowing the use of distance measures between the PDFs to establish relationships among the words and confirm some of the classifications. The Expectation Maximization algorithm (EM) was implemented to estimate the parameters that describe the GMM. The Kullback Leibler (KL) divergence was used to measure similarities between two PDFs. This divergence is the basis to establish the hierarchical structures that show the relationships among the models. The PDF estimation, based on GMM was tested using simulated signals, allowing confirming the useful approximation of the original parameters. Several distance measures were implemented to prove that the similarities among the models depended on the model of each word, and not on the distance measure adopted in this study. The results of all measures were similar, however, as the clustering results of the C2 distances showed some differences from the other clusters, C2 distance was proposed to complement the KL divergence. The results suggest that the relationships between models depend on their characteristics, and not on the distance measures selected in this study, and the PDFs based on GMM can properly characterize the words. In general, relations among languages that belong to the same linguistic branch were illustrated, showing a tendency to include isolated languages in groups of languages that belong to the same linguistic branches. As the GMM of some language families presents a standard behavior, it allows identifying each family. Although the results of the words of indigenous languages are inconclusive, this study is considered very useful to increase the knowledge of these types of languages and to propose new research lines directed to analyze this type of signals.

Apesar da ampla diversidade de línguas indígenas no Brasil, poucas pesquisas estudam estas línguas e suas relações. Inúmeros esforços têm sido dedicados a procurar similaridades entre as palavras das línguas indígenas e classificá-las em famílias de línguas. Seguindo a classificação mais aceita das línguas indígenas do Brasil, esta pesquisa propõe comparar palavras de 10 línguas indígenas brasileiras. Para isso, considera-se que estas palavras são sinais de fala e estima-se a função de distribuição de probabilidade (PDF) de cada palavra, usando um modelo de mistura de gaussianas (GMM). A PDF foi considerada um modelo para representar as palavras. Os modelos foram comparados utilizando medidas de distância para construir estruturas hierárquicas que evidenciaram possíveis relações entre as palavras. Seguindo esta linha, a hipótese levantada nesta pesquisa é que as PDFs baseadas em GMM conseguem caracterizar as palavras das línguas indígenas, permitindo o emprego de medidas de distância entre elas para estabelecer relações entre as palavras, de forma que tais relações confirmem algumas das classificações. Os parâmetros do GMM foram calculados utilizando o algoritmo Maximização da Expectância (em inglês, Expectation Maximization (EM)). A divergência Kullback Leibler (KL) foi empregada para medir semelhança entre as PDFs. Esta divergência serve de base para estabelecer as estruturas hierárquicas que ilustram as relações entre os modelos. A estimativa da PDF, baseada em GMM foi testada com o auxílio de sinais simulados, sendo possível confirmar que os parâmetros obtidos são próximos dos originais. Foram implementadas várias medidas de distância para avaliar se a semelhança entre os modelos estavam determinadas pelos modelos e não pelas medidas adotadas neste estudo. Os resultados de todas as medidas foram similares, somente foi observada alguma diferença nos agrupamentos realizados pela distância C2, por isso foi proposta como complemento da divergência KL. Estes resultados sugerem que as relações entre os modelos dependem das suas características, não das métricas de distância selecionadas no estudo e que as PDFs baseadas em GMM, conseguem fazer uma caracterização adequada das palavras. Em geral, foram observados agrupamentos entre palavras que pertenciam a línguas de um mesmo tronco linguístico, assim como se observou uma tendência a incluir línguas isoladas nos agrupamentos dos troncos linguísticos. Palavras que pertenciam a determinada língua apresentaram um comportamento padrão, sendo identificadas por esse tipo de comportamento. Embora os resultados para as palavras das línguas indígenas sejam inconclusivos, considera-se que o estudo foi útil para aumentar o conhecimento destas 10 línguas estudadas, propondo novas linhas de pesquisas dedicadas à análise destas palavras. / Although there exists a large diversity of indigenous languages in Brazil, there are few researches on these languages and their relationships. Numerous efforts have been dedicated to search for similarities among words of indigenous languages to classify them into families. Following the most accepted classification of Brazilian indigenous languages, this research proposes to compare words of 10 Brazilian indigenous languages. The words of the indigenous languages are considered speech signals and the Probability Distribution Function (PDF) of each word was estimated using the Gaussian Mixture Models (GMM). This estimation was considered a model to represent each word. The models were compared using distance measures to construct hierarchical structures that illustrate possible relationships among words. The hypothesis in this research is that the estimation of the PDF, based on GMM can characterize the words of indigenous languages, allowing the use of distance measures between the PDFs to establish relationships among the words and confirm some of the classifications. The Expectation Maximization algorithm (EM) was implemented to estimate the parameters that describe the GMM. The Kullback Leibler (KL) divergence was used to measure similarities between two PDFs. This divergence is the basis to establish the hierarchical structures that show the relationships among the models. The PDF estimation, based on GMM was tested using simulated signals, allowing confirming the useful approximation of the original parameters. Several distance measures were implemented to prove that the similarities among the models depended on the model of each word, and not on the distance measure adopted in this study. The results of all measures were similar, however, as the clustering results of the C2 distances showed some differences from the other clusters, C2 distance was proposed to complement the KL divergence. The results suggest that the relationships between models depend on their characteristics, and not on the distance measures selected in this study, and the PDFs based on GMM can properly characterize the words. In general, relations among languages that belong to the same linguistic branch were illustrated, showing a tendency to include isolated languages in groups of languages that belong to the same linguistic branches. As the GMM of some language families presents a standard behavior, it allows identifying each family. Although the results of the words of indigenous languages are inconclusive, this study is considered very useful to increase the knowledge of these types of languages and to propose new research lines directed to analyze this type of signals.

In this master's thesis, hidden Markov models (HMM) are evaluated as a tool for forecasting movements in a currency cross. With an ever increasing electronic market, making way for more automated trading, or so called algorithmic trading, there is constantly a need for new trading strategies trying to find alpha, the excess return, in the market. HMMs are based on the well-known theories of Markov chains, but where the states are assumed hidden, governing some observable output. HMMs have mainly been used for speech recognition and communication systems, but have lately also been utilized on financial time series with encouraging results. Both discrete and continuous versions of the model will be tested, as well as single- and multivariate input data. In addition to the basic framework, two extensions are implemented in the belief that they will further improve the prediction capabilities of the HMM. The first is a Gaussian mixture model (GMM), where one for each state assign a set of single Gaussians that are weighted together to replicate the density function of the stochastic process. This opens up for modeling non-normal distributions, which is often assumed for foreign exchange data. The second is an exponentially weighted expectation maximization (EWEM) algorithm, which takes time attenuation in consideration when re-estimating the parameters of the model. This allows for keeping old trends in mind while more recent patterns at the same time are given more attention. Empirical results shows that the HMM using continuous emission probabilities can, for some model settings, generate acceptable returns with Sharpe ratios well over one, whilst the discrete in general performs poorly. The GMM therefore seems to be an highly needed complement to the HMM for functionality. The EWEM however does not improve results as one might have expected. Our general impression is that the predictor using HMMs that we have developed and tested is too unstable to be taken in as a trading tool on foreign exchange data, with too many factors influencing the results. More research and development is called for.

<p>In this master's thesis, hidden Markov models (HMM) are evaluated as a tool for forecasting movements in a currency cross. With an ever increasing electronic market, making way for more automated trading, or so called algorithmic trading, there is constantly a need for new trading strategies trying to find alpha, the excess return, in the market.</p><p>HMMs are based on the well-known theories of Markov chains, but where the states are assumed hidden, governing some observable output. HMMs have mainly been used for speech recognition and communication systems, but have lately also been utilized on financial time series with encouraging results. Both discrete and continuous versions of the model will be tested, as well as single- and multivariate input data.</p><p>In addition to the basic framework, two extensions are implemented in the belief that they will further improve the prediction capabilities of the HMM. The first is a Gaussian mixture model (GMM), where one for each state assign a set of single Gaussians that are weighted together to replicate the density function of the stochastic process. This opens up for modeling non-normal distributions, which is often assumed for foreign exchange data. The second is an exponentially weighted expectation maximization (EWEM) algorithm, which takes time attenuation in consideration when re-estimating the parameters of the model. This allows for keeping old trends in mind while more recent patterns at the same time are given more attention.</p><p>Empirical results shows that the HMM using continuous emission probabilities can, for some model settings, generate acceptable returns with Sharpe ratios well over one, whilst the discrete in general performs poorly. The GMM therefore seems to be an highly needed complement to the HMM for functionality. The EWEM however does not improve results as one might have expected. Our general impression is that the predictor using HMMs that we have developed and tested is too unstable to be taken in as a trading tool on foreign exchange data, with too many factors influencing the results. More research and development is called for.</p>

Modern speech communication technologies expose users to perceptual quality degradations that were not experienced earlier with conventional telephone systems. Since perceived speech quality is a major contributor to the end user's perception of quality of service, speech quality estimation has become an important research field. In this dissertation, perceptual quality estimators are proposed for several emerging speech communication applications, in particular for i) wireless communications with noise suppression capabilities, ii) wireless-VoIP communications, iii) far-field hands-free speech communications, and iv) text-to-speech systems. First, a general-purpose speech quality estimator is proposed based on statistical models of normative speech behaviour and on innovative techniques to detect multiple signal distortions. The estimators do not depend on a clean reference signal hence are termed ``blind." Quality meters are then distributed along the network chain to allow for both quality degradations and quality enhancements to be handled. In order to improve estimation performance for wireless communications, statistical models of noise-suppressed speech are also incorporated. Next, a hybrid signal-and-link-parametric quality estimation paradigm is proposed for emerging wireless-VoIP communications. The algorithm uses VoIP connection parameters to estimate a base quality representative of the packet switching network. Signal-based distortions are then detected and quantified in order to adjust the base quality accordingly. The proposed hybrid methodology is shown to overcome the limitations of existing pure signal-based and pure link parametric algorithms. Temporal dynamics information is then investigated for quality diagnosis for hands-free speech communications. A spectro-temporal signal representation, where speech and reverberation tail components are shown to be separable, is used for blind characterization of room acoustics. In particular, estimators of reverberation time, direct-to-reverberation energy ratio, and reverberant speech quality are developed. Lastly, perceptual quality estimation for text-to-speech systems is addressed. Text- and speaker-independent hidden Markov models, trained on naturally produced speech, are used to capture normative spectral-temporal information. Deviations from the models, computed by means of a log-likelihood measure, are shown to be reliable indicators of multiple quality attributes including naturalness, fluency, and intelligibility. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-12-22 14:54:49.28