This study starts from the perspective of a future increase availability of water quality data at the water treatment facility at Lovön and aims to use the existing data to identify a pattern in the role of the different sub-basin that constitute the Mälaren basin. The data are analyzed with the graphical tool of the scatterplot and a Non-linear Canonical Correlation Analysis, a variation of the classical multivariate method, that by using a neural network model is able to handle not linear relationships. From the data analysis, it is possible to identify that different areas have different contribution in shaping the water quality at the facility of Lovön, but also that this pattern of contribution is strongly affected by the season inside the analyzed year.

Genome-wide associations studies (GWASs) have identified hundreds of loci that are strongly associated with coronary artery disease and its risk factors. However, the causal variants and genes remain unknown for the vast majority of the identified loci. Zebrafish model systems coupled with clustered regularly interspaced short palindromic repeats-C–associated 9 (CRISPR Cas-9) mutagenesis have enabled the possibility to systematically characterize candidate genes in GWAS-identified loci. In this thesis, canonical correlation analysis (CCA) was used to identify putative causal genes in multiplexed genetic screens for atherogenic traits in zebrafish larvae in an efficient manner. The two datasets used in this thesis contained genes and phenotypes obtained through sequencing and high-throughput imaging of fish larvae. Dataset 1 contained (7 genes, 11 phenotypes, n = 384) and dataset 2 (4 genes, 11 phenotypes, n = 384). CCA’s multiple genes vs. multiple phenotype analysis in dataset 1 identified the genes met, pepd, timd4 and vegfa to have an association with the total cholesterol, triglycerides, glucose, corrected lipid disposition, as well as co- localization of (macrophage and lipid deposition,) (neutrophils and lipid deposition) and (macrophage and neutrophils). In dataset 2, CCA found previously reported correlation of genes apobb1 and apoea with total cholesterol, low-density lipoprotein and triglycerides as well as co localization of neutrophils and lipids. In comparison with hierarchical linear model, CCA represents a powerful and promising tool to identify causal genes for cardiovascular diseases in data from zebrafish model systems.

The use of conversational agents has increased across the world. From providing automated support for companies to being virtual psychologists they have moved from an academic curiosity to an application with real world relevance. While many researchers have focused on the content of the dialogue and synthetic speech to give the agents a voice, more recently animating these characters has become a topic of interest. An additional use for character animation technology is in the film and video game industry where having characters animated without needing to pay for expensive labour would save tremendous costs. When animating characters there are many aspects to consider, for example the way they walk. However, to truly assist with communication automated animation needs to duplicate the body language used when speaking. In particular conversational agents are often only an animation of the upper parts of the body, so head motion is one of the keys to a believable agent. While certain linguistic features are obvious, such as nodding to indicate agreement, research has shown that head motion also aids understanding of speech. Additionally head motion often contains emotional cues, prosodic information, and other paralinguistic information. In this thesis we will present our research into synthesising head motion using only recorded speech as input. During this research we collected a large dataset of head motion synchronised with speech, examined evaluation methodology, and developed a synthesis system. Our dataset is one of the larger ones available. From it we present some statistics about head motion in general. Including differences between read speech and story telling speech, and differences between speakers. From this we are able to draw some conclusions as to what type of source data will be the most interesting in head motion research, and if speaker-dependent models are needed for synthesis. In our examination of head motion evaluation methodology we introduce Forced Canonical Correlation Analysis (FCCA). FCCA shows the difference between head motion shaped noise and motion capture better than standard methods for objective evaluation used in the literature. We have shown that for subjective testing it is best practice to use a variation of MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) based testing, adapted for head motion. Through experimentation we have developed guidelines for the implementation of the test, and the constraints on the length. Finally we present a new system for head motion synthesis. We make use of simple templates of motion, automatically extracted from source data, that are warped to suit the speech features. Our system uses clustering to pick the small motion units, and a combined HMM and GMM based approach for determining the values of warping parameters at synthesis time. This results in highly natural looking motion that outperforms other state of the art systems. Our system requires minimal human intervention and produces believable motion. The key innovates were the new methods for segmenting head motion and creating a process similar to language modelling for synthesising head motion.

The counseling profession seeks to support and enrich the quality of life of the general public by providing effective clinical services. Many counselors struggle with practicing self-care regularly, increasing the risk of burnout. When counselors provide services while experiencing burnout, they risk harming clients being served. The conservation of resources theory suggests that there is an increased risk of maladaptive coping and burnout when there is a decrease in resources used to protect someone from experiencing stress. A quantitative survey research study using a nonprobability convenience sampling was used to explore the relationship between counselor burnout, life balance, and self-compassion among fully licensed and provisionally licensed counselors throughout the United States with at least 2 years of experience (N = 331). Two canonical correlation analyses were conducted to determine (a) if there was any significant relationship between the subscales of the Juhnke-Balkin Life Balance Inventory, measuring life balance, and the Counselor Burnout Inventory (CBI), measuring burnout, and (b) if there was a significant relationship between the subscales of the CBI, measuring burnout, and the Self-Compassion Scale, measuring self-compassion. Both canonical correlation analyses indicated a statistically significant relationship. Particularly, professional counselors are experiencing poor work-life balance, decreased attention in their personal life, decreased quality of their relationships, negative work environment, and lower levels of self-compassion. The potential social change impact from this research study is that a better understanding of how to mitigate and/or prevent experiences of burnout in counselors may improve counselor’s quality of life, mitigate turnover, counselor burnout, reduce client harm, and increase the quality of clinical services.

In this dissertation we introduce two variable selection procedures for multivariate responses. Our procedures are based on sufficient dimension reduction concepts and are model-free. In the first procedure we consider the dual marginal coordinate hypotheses, where the role of the predictor and the response is not important. Motivated by canonical correlation analysis (CCA), we propose a CCA-based test for the dual marginal coordinate hypotheses, and devise a joint backward selection algorithm for dual model-free variable selection. The second procedure is based on ordinary least squares (OLS). We derive and study the asymptotic properties of the OLS-based test under the normality assumption of the predictors as well as an asymmetry assumption. When these assumptions are violated, the asymptotic test with elliptical trimming and clustering is still valid with desirable numerical performances. A backward selection algorithm for the predictor is also provided for the OLS-based test. The performances of the proposed tests and the variable selection procedures are evaluated through synthetic examples and a real data analysis. / Statistics

Online communities of practice (CoPs) provide social spaces for people to connect, learn, and engage with one another around shared interests and passions. CoPs are innovatively employed within industry and education for their inherent knowledge management characteristics and as a means of improving professional practice. Measuring the success of a CoP is a challenge researchers are examining through various strategies. Recent literature supports measuring community effectiveness through the perceptions of its members; however, evaluating a community by means of member perception introduces complicating factors from outside the community. In order to gain insight into the importance of external factors, this quantitative study examined the influence of factors in the professional lives of educators on their perceptions of their CoP experience. Through an empirical examination of CoPs employed to connect educators and advance their professional learning, canonical correlation analysis was used to examine correlations between factors believed to be influential on the experiences of community members.

Este trabalho destina-se a realizar um estudo sobre o mercado imobiliário de lançamentos residenciais da Região Metropolitana de São Paulo, tendo como base a utilização de modelos de comercialização hedônica. Para tanto, utiliza-se da Teoria dos Atributos proposta por Lancaster e dos modelos hedônicos e de equilíbrio de sub-mercados propostos por Rosen e Palmquist, a partir dos quais é possível analisar a importância relativa de “pacotes" de atributos, em função dos diferentes perfis sócio-demográficos determinados previamente por meio de análise fatorial elaborada com um grupo de 11 variáveis sócio-demográficas de cada Município da Região Metropolitana e de cada distrito do Município de São Paulo. Por meio de um levantamento realizado com especialistas, com compradores de imóveis residenciais e por meio de anúncios específicos, definiram-se as variáveis hedônicas explicativas e dependentes a serem incluídas nos modelos de regressão múltipla de Box-Cox e de correlação canônica, sob a ótica da demanda e da oferta, para cada perfil sócio-demográfico definido. O método proposto permite a determinação e a avaliação dos “pacotes" representativos de atributos para a composição das condições comerciais dos imóveis residenciais em lançamento na Região Metropolitana de São Paulo, propiciando a verificação da existência de eventuais descolamentos entre o comportamento da demanda e da oferta e possibilitando a comparação da importância relativa de cada variável entre os perfis sócio-demográficos. Portanto, a aplicação do método proposto neste trabalho pode propiciar a implementação de iniciativas privadas e políticas públicas voltadas ao estabelecimento de novas estratégias de lançamento imobiliário, designadas para cada tipo específico de empreendimento e de acordo com as preferências dos consumidores e das características de cada localidade. / This work intends to research about the residential launchings of the real estate market in the Metropolitan Region of Sao Paulo, using hedonic models. It’s based on the approach to Theory of Attributes, proposed by Lancaster, and on the hedonic models and the sub-markets equilibrium approach proposed by Rosen and Palmquist, that make possible the analysis of the relative importance of the attributes “bundles" for each different social and demographic group previously defined by the factorial analysis statistical technique, that used 11 social and demographic variables related to each Municipality of the Metropolitan Region of Sao Paulo and each district of the City of Sao Paulo. Using a survey realized with specialists, residential launchings buyers and through specific advertisements, many explicative and dependent hedonic variables were defined and are to be included in the models of Box-Cox multiple regression and canonical correlation, under the perspective of demand and supply, for each social and demographic defined group. The proposed method allows the determination and the evaluation of the representative “bundles" of attributes to the composition of the residential launchings commercial conditions of the Metropolitan Region of Sao Paulo real estate market, making possible to verify the existence of eventual gaps between the demand and supply behaviors and allowing the comparison of the relative importance of each variable among the social and demographic groups. Thus, the method application can facilitate private and public implementations, allowing the establishment of new strategies designated to each specific kind of real estate, according to the consumers’ preferences and local characteristics.

Orientador: Douglas Domingues Bueno / Resumo: A busca por uma melhor compreensão das regiões do cérebro e suas funções nas ações humanas tem sido uma tarefa árdua, porém muito útil, principalmente para aplicações da engenharia de interface cérebro-máquina (ICM), bem como para o auxílio a diagnósticos médicos a partir de sinais obtidos dos pacientes em avaliação. No contexto do presente trabalho, destacam-se os trabalhos de interface cérebro-máquina (ICM) pela abrangência no envolvimento de técnicas, métodos e ferramentas comumente estudadas nos cursos de engenharia. Em particular, análises envolvendo técnicas de processamento de sinais de eletroencefalograma (EEG) têm se mostrado de significativa importância para o desenvolvimento dessa área. Uma abordagem amplamente utilizada nesse contexto é a ICM usando Potenciais Visuais Evocados de Estados Estacionários (SSVEP, do inglês Steady-State Visual Evoked Potentials), que, de forma geral, são sinais caracterizados pela resposta evocada do cérebro a estímulos visuais modulados em uma frequência específica. Assim, este trabalho tem o objetivo de propor uma generalização do coeficiente de correlação, conceito-base da análise de correlação canônica (CCA), técnica que tem se mostrado robusta e eficiente no reconhecimento de padrões, especialmente no caso dos SSVEP, e detalhar seu comportamento em função dos parâmetros relevantes para se estabelecer melhores práticas de uso em aplicações de ICM, incluindo fatores fisiológicos, técnicos e operacionais. / Abstract: The search for a better understanding of the brain's anatomy and its functions on human actions has been a harsh yet very useful task, especially for brain-computer interface engineering applications, as well as for medical diagnosis using signals from patients. In the context of this work, brain-computer interface (BCI) applications are highlighted due to their compreehensiveness related to techniques, methods and tools commonly studied in engineering. In particular, analyses involving eletroencephalogram (EEG) signals processing have proven to be of great significance for developing this field of study. A widely used approach is Steady State Visual Evoked Potentials (SSVEP) based BCI, which, in general, are signals characterized by the brain’s evoked response to visual stimuli modulated at a certain frequency. This work aims thus to propose a generalization of the correlation coefficient, which entails Canonical Correlation Analysis (CCA), a technique that has presented robustness and efficiency for pattern recognition, especially in SSVEP-based BCIs, and describe its behavior under relevant varying parameters to stablish better use practices in BCI applications, comprising physiological, technical and operational factors. / Mestre

隨著典型相關分析應用的日益廣泛，線性典型相關分析並不足以描述兩個主題事務間確實的關連及與其個別相關事務的互動關係，為了更適切地解釋資料數據背後所表達的現象，近來不斷地有關於非線性典型相關分析的理論發表，本文利用1992年Sheng所發表的非線性典型相關分析的理論，將之應用在對臺灣地區民國八十二年的前二十五名最有聲望的企業的資料分析上，並比較應用線性典型相關分析與非線性典型相關分析做資料分析的結果。

In medicine, data are produced from different sources and continuously stored in data depositories. Examples of these growing databases are quality registries. In Sweden, there are many cancer registries where data on cancer patients are gathered and recorded and are used mainly for reporting survival analyses to high level health authorities. In this thesis, a breast cancer quality registry operating in South-East of Sweden is used as the data source for newer analytical techniques, i.e. data mining as a part of knowledge discovery in databases (KDD) methodology. Analyses are done to sift through these data in order to find interesting information and hidden knowledge. KDD consists of multiple steps, starting with gathering data from different sources and preparing them in data pre-processing stages prior to data mining. Data were cleaned from outliers and noise and missing values were handled. Then a proper subset of the data was chosen by canonical correlation analysis (CCA) in a dimensionality reduction step. This technique was chosen because there were multiple outcomes, and variables had complex relationship to one another. After data were prepared, they were analyzed with a data mining method. Decision tree induction as a simple and efficient method was used to mine the data. To show the benefits of proper data pre-processing, results from data mining with pre-processing of the data were compared with results from data mining without data pre-processing. The comparison showed that data pre-processing results in a more compact model with a better performance in predicting the recurrence of cancer. An important part of knowledge discovery in medicine is to increase the involvement of medical experts in the process. This starts with enquiry about current problems in their field, which leads to finding areas where computer support can be helpful. The experts can suggest potentially important variables and should then approve and validate new patterns or knowledge as predictive or descriptive models. If it can be shown that the performance of a model is comparable to domain experts, it is more probable that the model will be used to support physicians in their daily decision-making. In this thesis, we validated the model by comparing predictions done by data mining and those made by domain experts without finding any significant difference between them. Breast cancer patients who are treated with mastectomy are recommended to receive radiotherapy. This treatment is called postmastectomy radiotherapy (PMRT) and there is a guideline for prescribing it. A history of this treatment is stored in breast cancer registries. We analyzed these datasets using rules from a clinical guideline and identified cases that had not been treated according to the PMRT guideline. Data mining revealed some patterns of non-compliance with the PMRT guideline. Further analysis with data mining revealed some reasons for guideline non-compliance. These patterns were then compared with reasons acquired from manual inspection of patient records. The comparisons showed that patterns resulting from data mining were limited to the stored variables in the registry. A prerequisite for better results is availability of comprehensive datasets. Medicine can take advantage of KDD methodology in different ways. The main advantage is being able to reuse information and explore hidden knowledge that can be obtained using advanced analysis techniques. The results depend on good collaboration between medical informaticians and domain experts and the availability of high quality data.