The present Ph.D. thesis, primarily conceived to support and reinforce the relation between academic and industrial worlds, was developed in collaboration with Shell Global Solutions (Amsterdam, The Netherlands) in the endeavour of applying and possibly extending well-established latent variable-based approaches (i.e. Principal Component Analysis - PCA - Partial Least Squares regression - PLS - or Partial Least Squares Discriminant Analysis - PLSDA) for complex problem solving not only in the fields of manufacturing troubleshooting and optimisation, but also in the wider environment of multivariate data analysis. To this end, novel efficient algorithmic solutions are proposed throughout all chapters to address very disparate tasks, from calibration transfer in spectroscopy to real-time modelling of streaming flows of data. The manuscript is divided into the following six parts, focused on various topics of interest: Part I - Preface, where an overview of this research work, its main aims and justification is given together with a brief introduction on PCA, PLS and PLSDA; Part II - On kernel-based extensions of PCA, PLS and PLSDA, where the potential of kernel techniques, possibly coupled to specific variants of the recently rediscovered pseudo-sample projection, formulated by the English statistician John C. Gower, is explored and their performance compared to that of more classical methodologies in four different applications scenarios: segmentation of Red-Green-Blue (RGB) images, discrimination of on-/off-specification batch runs, monitoring of batch processes and analysis of mixture designs of experiments; Part III - On the selection of the number of factors in PCA by permutation testing, where an extensive guideline on how to accomplish the selection of PCA components by permutation testing is provided through the comprehensive illustration of an original algorithmic procedure implemented for such a purpose; Part IV - On modelling common and distinctive sources of variability in multi-set data analysis, where several practical aspects of two-block common and distinctive component analysis (carried out by methods like Simultaneous Component Analysis - SCA - DIStinctive and COmmon Simultaneous Component Analysis - DISCO-SCA - Adapted Generalised Singular Value Decomposition - Adapted GSVD - ECO-POWER, Canonical Correlation Analysis - CCA - and 2-block Orthogonal Projections to Latent Structures - O2PLS) are discussed, a new computational strategy for determining the number of common factors underlying two data matrices sharing the same row- or column-dimension is described, and two innovative approaches for calibration transfer between near-infrared spectrometers are presented; Part V - On the on-the-fly processing and modelling of continuous high-dimensional data streams, where a novel software system for rational handling of multi-channel measurements recorded in real time, the On-The-Fly Processing (OTFP) tool, is designed; Part VI - Epilogue, where final conclusions are drawn, future perspectives are delineated, and annexes are included. / La presente tesis doctoral, concebida principalmente para apoyar y reforzar la relación entre la academia y la industria, se desarrolló en colaboración con Shell Global Solutions (Amsterdam, Países Bajos) en el esfuerzo de aplicar y posiblemente extender los enfoques ya consolidados basados en variables latentes (es decir, Análisis de Componentes Principales - PCA - Regresión en Mínimos Cuadrados Parciales - PLS - o PLS discriminante - PLSDA) para la resolución de problemas complejos no sólo en los campos de mejora y optimización de procesos, sino también en el entorno más amplio del análisis de datos multivariados. Con este fin, en todos los capítulos proponemos nuevas soluciones algorítmicas eficientes para abordar tareas dispares, desde la transferencia de calibración en espectroscopia hasta el modelado en tiempo real de flujos de datos. El manuscrito se divide en las seis partes siguientes, centradas en diversos temas de interés: Parte I - Prefacio, donde presentamos un resumen de este trabajo de investigación, damos sus principales objetivos y justificaciones junto con una breve introducción sobre PCA, PLS y PLSDA; Parte II - Sobre las extensiones basadas en kernels de PCA, PLS y PLSDA, donde presentamos el potencial de las técnicas de kernel, eventualmente acopladas a variantes específicas de la recién redescubierta proyección de pseudo-muestras, formulada por el estadista inglés John C. Gower, y comparamos su rendimiento respecto a metodologías más clásicas en cuatro aplicaciones a escenarios diferentes: segmentación de imágenes Rojo-Verde-Azul (RGB), discriminación y monitorización de procesos por lotes y análisis de diseños de experimentos de mezclas; Parte III - Sobre la selección del número de factores en el PCA por pruebas de permutación, donde aportamos una guía extensa sobre cómo conseguir la selección de componentes de PCA mediante pruebas de permutación y una ilustración completa de un procedimiento algorítmico original implementado para tal fin; Parte IV - Sobre la modelización de fuentes de variabilidad común y distintiva en el análisis de datos multi-conjunto, donde discutimos varios aspectos prácticos del análisis de componentes comunes y distintivos de dos bloques de datos (realizado por métodos como el Análisis Simultáneo de Componentes - SCA - Análisis Simultáneo de Componentes Distintivos y Comunes - DISCO-SCA - Descomposición Adaptada Generalizada de Valores Singulares - Adapted GSVD - ECO-POWER, Análisis de Correlaciones Canónicas - CCA - y Proyecciones Ortogonales de 2 conjuntos a Estructuras Latentes - O2PLS). Presentamos a su vez una nueva estrategia computacional para determinar el número de factores comunes subyacentes a dos matrices de datos que comparten la misma dimensión de fila o columna y dos planteamientos novedosos para la transferencia de calibración entre espectrómetros de infrarrojo cercano; Parte V - Sobre el procesamiento y la modelización en tiempo real de flujos de datos de alta dimensión, donde diseñamos la herramienta de Procesamiento en Tiempo Real (OTFP), un nuevo sistema de manejo racional de mediciones multi-canal registradas en tiempo real; Parte VI - Epílogo, donde presentamos las conclusiones finales, delimitamos las perspectivas futuras, e incluimos los anexos. / La present tesi doctoral, concebuda principalment per a recolzar i reforçar la relació entre l'acadèmia i la indústria, es va desenvolupar en col·laboració amb Shell Global Solutions (Amsterdam, Països Baixos) amb l'esforç d'aplicar i possiblement estendre els enfocaments ja consolidats basats en variables latents (és a dir, Anàlisi de Components Principals - PCA - Regressió en Mínims Quadrats Parcials - PLS - o PLS discriminant - PLSDA) per a la resolució de problemes complexos no solament en els camps de la millora i optimització de processos, sinó també en l'entorn més ampli de l'anàlisi de dades multivariades. A aquest efecte, en tots els capítols proposem noves solucions algorítmiques eficients per a abordar tasques dispars, des de la transferència de calibratge en espectroscopia fins al modelatge en temps real de fluxos de dades. El manuscrit es divideix en les sis parts següents, centrades en diversos temes d'interès: Part I - Prefaci, on presentem un resum d'aquest treball de recerca, es donen els seus principals objectius i justificacions juntament amb una breu introducció sobre PCA, PLS i PLSDA; Part II - Sobre les extensions basades en kernels de PCA, PLS i PLSDA, on presentem el potencial de les tècniques de kernel, eventualment acoblades a variants específiques de la recentment redescoberta projecció de pseudo-mostres, formulada per l'estadista anglés John C. Gower, i comparem el seu rendiment respecte a metodologies més clàssiques en quatre aplicacions a escenaris diferents: segmentació d'imatges Roig-Verd-Blau (RGB), discriminació i monitorització de processos per lots i anàlisi de dissenys d'experiments de mescles; Part III - Sobre la selecció del nombre de factors en el PCA per proves de permutació, on aportem una guia extensa sobre com aconseguir la selecció de components de PCA a través de proves de permutació i una il·lustració completa d'un procediment algorítmic original implementat per a la finalitat esmentada; Part IV - Sobre la modelització de fonts de variabilitat comuna i distintiva en l'anàlisi de dades multi-conjunt, on discutim diversos aspectes pràctics de l'anàlisis de components comuns i distintius de dos blocs de dades (realitzat per mètodes com l'Anàlisi Simultània de Components - SCA - Anàlisi Simultània de Components Distintius i Comuns - DISCO-SCA - Descomposició Adaptada Generalitzada en Valors Singulars - Adapted GSVD - ECO-POWER, Anàlisi de Correlacions Canòniques - CCA - i Projeccions Ortogonals de 2 blocs a Estructures Latents - O2PLS). Presentem al mateix temps una nova estratègia computacional per a determinar el nombre de factors comuns subjacents a dues matrius de dades que comparteixen la mateixa dimensió de fila o columna, i dos plantejaments nous per a la transferència de calibratge entre espectròmetres d'infraroig proper; Part V - Sobre el processament i la modelització en temps real de fluxos de dades d'alta dimensió, on dissenyem l'eina de Processament en Temps Real (OTFP), un nou sistema de tractament racional de mesures multi-canal registrades en temps real; Part VI - Epíleg, on presentem les conclusions finals, delimitem les perspectives futures, i incloem annexos. / Vitale, R. (2017). Novel chemometric proposals for advanced multivariate data analysis, processing and interpretation [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90442

Organisms relevant for nature conservation dont follow administrative borders. Because of this there is a need for a landscape perspective within conservation and planning, and a need for the species of interest to have legal protection. Network analysis adapted for ecological purposes has grown to become a powerful tool for studying and communicating the relationships between species dispersion and access to habitat. In this study the following question is posed: How is the Osmoderma eremita and the Pernis apivorus dispersal possibilities in the small scale cultivated landscape of Borås affected by exploitation in respect to a) dispersal ability, b) habitat quality, c) position of habitat patches in a network? The analysis were based on municipal and regional nature conservation data, which in due to confidentiality is not accounted for in the report by maps, coordinates, etc. Several networks were established for both species to indicate how habitat patches are distributed today and how the species dispersal changes depending on which patches are excluded – this was done to imitate how exploitation can affect the species future survival and dispersion. The results showed that the O.e. is mainly inhibited by its poor dispersal abilities, followed by patch position, while the P.a. is the most affected by degrading habitat quality. The most important conclusions of the study were that the O.e. natural dispersal may be restricted but can be improved by linking small network components together and by maintaining the largest components. As for the P.a. it was concluded that a different type of analysis, focusing on its behaviour and need for different patches for different purposes, would generate more interesting results. / Eftersom skyddsvärda organismer inte följer administrativa gränser behövs ett landskapsperspektiv i naturvårds- och planarbete, och de arter som studeras behöver ha juridiska belägg för att skyddas. Nätverksanalyser anpassade för ekologi har vuxit fram som ett kraftfullt verktyg för att studera och kommunicera sambanden mellan arters spridning över större områden. I denna rapport ställs därför frågan: hur läderbaggens (Osmoderma eremita) respektive bivråkens (Pernis apivorus) spridningsmöjligheter i odlingslandskapet i Borås kommun påverkas vid exploatering, med avseende på a)spridningsförmåga, b) habitatkvalitet c) habitatpatchers position i ett nätverk? Analyserna baserades på kommunal och regional naturvårdsdata, som p.g.a. sekretess inte redovisas med kartmaterial, koordinater eller liknande. Flera nätverk etablerades för varje art för att indikera hur nätverken av patcher ser ut idag och hur arternas spridning förändras beroende på vilka patcher som utesluts – detta för att imitera hur exploatering kan påverka arternas fortsatta överlevnad och spridning. Resultaten visade att läderbaggens största begränsning är dess dåliga spridningsförmåga, tätt följd av patchernas position, medan bivråken påverkas mer av habitatkvalitet. De viktigaste slutsatserna som kunde dras var att läderbaggens naturliga spridning må vara begränsad men kan förbättras genom att länka samman små nätverkskomponenter och fortsätta sköta de som är störst idag. För bivråkens del skulle en annan typ av analys med mer fokus på artens beteende och behov av olika patcher för olika aktiviteter ge ett bättre underlag.

Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed to be Gaussian distributed. This thesis introduces two new models, named PPCA-AR and ProbCCA-AR, that augment PPCA and ProbCCA respectively with autoregressive processes over the latent variables to additionally capture temporal relationships between the observations. To make PPCA-AR and ProbCCA-AR robust to outliers and able to model leptokurtic data, the Gaussian assumptions are replaced with infinite scale mixtures of Gaussians, using the Student-t distribution. Bayesian inference calculates posterior probability distributions for each of the parameter variables, from which we obtain a measure of confidence in the inference. It avoids the pitfalls associated with the maximum likelihood method: integrating over all possible values of the parameter variables guards against overfitting. For these new models the integrals required for exact Bayesian inference are intractable; instead a method of approximation, the variational Bayesian approach, is used. This enables the use of automatic relevance determination to estimate the model orders. PPCA-AR and ProbCCA-AR can be viewed as linear dynamical systems, so the forward-backward algorithm, also known as the Baum-Welch algorithm, is used as an efficient method for inferring the posterior distributions of the latent variables. The exact algorithm is tractable because Gaussian assumptions are made regarding the distribution of the latent variables. This thesis introduces a variational Bayesian forward-backward algorithm based on Student-t assumptions. The new models are demonstrated on synthetic datasets and on real remote sensing and EEG data.

Many applications lead to signals with nonnegative function values. Understanding the structure of the spaces of nonnegative signals is therefore of interest in many different areas. Hence, constructing effective representation spaces with suitable metrics and natural transformations is an important research topic. In this thesis, we present our investigations of the structure of spaces of nonnegative signals and illustrate the results with applications in the fields of multispectral color science and content-based image retrieval. The infinite-dimensional Hilbert space of nonnegative signals is conical and convex. These two properties are preserved under linear projections onto lower dimensional spaces. The conical nature of these coordinate vector spaces suggests the use of hyperbolic geometry. The special case of three-dimensional hyperbolic geometry leads to the application of the SU(1,1) or SO 2,1) groups. We introduce a new framework to investigate nonnegative signals. We use PCA-based coordinates and apply group theoretical tools to investigate sequences of signal coordinate vectors. We describe these sequences with oneparameter subgroups of SU(1,1) and show how to compute the one-parameter subgroup of SU(1,1) from a given set of nonnegative signals. In our experiments we investigate the following signal sequences: (i) blackbody radiation spectra; (ii) sequences of daylight/twilight spectra measured in Norrk¨oping, Sweden and in Granada, Spain; (iii) spectra generated by the SMARTS2 simulation program; and (iv) sequences of image histograms. The results show that important properties of these sequences can be modeled in this framework. We illustrate the usefulness with examples where we derive illumination invariants and introduce an efficient visualization implementation. Content-Based Image Retrieval (CBIR) is another topic of the thesis. In such retrieval systems, images are first characterized by descriptor vectors. Retrieval is then based on these content-based descriptors. Selection of contentbased descriptors and defining suitable metrics are the core of any CBIR system. We introduce new descriptors derived by using group theoretical tools. We exploit the symmetry structure of the space of image patches and use the group theoretical methods to derive low-level image filters in a very general framework. The derived filters are simple and can be used for multispectral images and images defined on different sampling grids. These group theoretical filters are then used to derive content-based descriptors, which will be used in a real implementation of a CBIR.

We analyse the Swedish central bank, the Riksbank’s, interest setting policy in a Taylor rule framework. In particular, we examine whether or not the Riksbank has reacted to fluctuations in asset prices during the period 1995:Q1 to 2016:Q2. This is done by estimating a forward-looking Taylor rule with interest rate smoothing, augmented with stock prices, house prices and the real exchange rate, using IV GMM. In general, we find that the Riksbank’s interest setting policy is well described by a forward-looking Taylor rule with interest rate smoothing and that the use of factors as instruments, derived from a PCA, serves to alleviate the weak-identification problem that tend to plague GMM. Moreover, apart from finding evidence that the Riksbank exhibit a substantial degree of policy rate inertia and has acted so as to stabilize inflation and the real economy, we also find evidence that the Riksbank has been reacting to fluctuations in stock prices, house prices, and the real exchange rate.

Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permitting probabilistic inference on hierarchical, generative models of data. This thesis primarily develops Bayesian analysis techniques for magnetic resonance imaging (MRI), which is a noninvasive neuroimaging tool for probing function, perfusion, and structure in the human brain. The first part of this work fits nonlinear biophysical models to multimodal functional MRI data within a variational Bayes framework. Simultaneously-acquired multimodal data contains mixtures of different signals and therefore may have common noise sources, and a method for automatically modelling this correlation is developed. A Gaussian process prior is also used to allow spatial regularization while simultaneously applying informative priors on model parameters, restricting biophysically-interpretable parameters to reasonable values. The second part introduces a novel data fusion framework for multivariate data analysis which finds a joint decomposition of data across several modalities using a shared loading matrix. Each modality has its own generative model, including separate spatial maps, noise models and sparsity priors. This flexible approach can perform supervised learning by using target variables as a modality. By inferring the data decomposition and multivariate decoding simultaneously, the decoding targets indirectly influence the component shapes and help to preserve useful components. The same framework is used for unsupervised learning by placing independent component analysis (ICA) priors on the spatial maps. Linked ICA is a novel approach developed to jointly decompose multimodal data, and is applied to combined structural and diffusion images across groups of subjects. This allows some of the benefits of tensor ICA and spatially-concatenated ICA to be combined, and allows model comparison between different configurations. This joint decomposition framework is particularly flexible because of its separate generative models for each modality and could potentially improve modelling of functional MRI, magnetoencephalography, and other functional neuroimaging modalities.

The purpose of this work is to present the development and validation of a novel method for reconstructing time-dependent, or 4D, cone-beam CT (4DCBCT) images. 4DCBCT can have a variety of applications in the radiotherapy of moving targets, such as lung tumors, including treatment planning, dose verification, and real time treatment adaptation. However, in its current incarnation it suffers from poor reconstruction quality and limited temporal resolution that may restrict its efficacy. Our algorithm remedies these issues by deforming a previously acquired high quality reference fan-beam CT (FBCT) to match the projection data in the 4DCBCT data-set, essentially creating a 3D animation of the moving patient anatomy. This approach combines the high image quality of the FBCT with the fine temporal resolution of the raw 4DCBCT projection data-set. Deformation of the reference CT is accomplished via a patient specific motion model. The motion model is constrained spatially using eigenvectors generated by a principal component analysis (PCA) of patient motion data, and is regularized in time using parametric functions of a patient breathing surrogate recorded simultaneously with 4DCBCT acquisition. The parametric motion model is constrained using forward iterative projection matching (FIPM), a scheme which iteratively alters model parameters until digitally reconstructed radiographs (DRRs) cast through the deforming CT optimally match the projections in the raw 4DCBCT data-set. We term our method FIPM-PCA 4DCBCT. In developing our algorithm we proceed through three stages of development. In the first, we establish the mathematical groundwork for the algorithm and perform proof of concept testing on simulated data. In the second, we tune the algorithm for real world use; specifically we improve our DRR algorithm to achieve maximal realism by incorporating physical principles of image formation combined with empirical measurements of system properties. In the third stage we test our algorithm on actual patient data and evaluate its performance against gold standard and ground truth data-sets. In this phase we use our method to track the motion of an implanted fiducial marker and observe agreement with our gold standard data that is typically within a millimeter.

L'objectif de cette étude était de réaliser des revêtements de TiO2 présentant une large variété de morphologies et d'établir des corrélations entre la structure de ces couches et leurs propriétés fonctionnelles, notamment la photocatalyse. Deux voies de synthèse employant le même précurseur, le tétra-isopropropoxide (TTIP) de titane, ont été utilisées, le procédé sol-gel et le dépôt chimique en phase vapeur (MOCVD). L'emploi de ces deux techniques permet de produire TiO2 sous une large gamme de morphologies mais avec des variétés polymorphiques similaires. Les revêtements synthétisés ont été caractérises afin de déterminer leur composition polymorphique, la taille des cristallites, la surface spécifique, la rugosité et l'épaisseur. Puis leur activité photocalytique pour la dégradation du bleu de méthylène a été déterminée. Par voie sol-gel, des dispersions de nano-cristallites de TiO2 dans l'eau, stables sur une longue durée (plus d'un an) en termes de composition polymorphique, taille d'agglomérats et de cristallites ont été synthétisées. Les revêtements ont été réalisés par tape-casting et dip-coating. Pour la synthèse en MOCVD, un plan d'expérience (PeX) a été utilisé, à notre connaissance pour la première fois. Il a permis de déterminer, d'une manière efficace et économique (avec un nombre minimum de tests expérimentaux), les paramètres les plus importants du procédé contrôlant les diverses propriétés quantifiables du revêtement. Il a aussi permis de mettre en évidence les interactions entre les paramètres de synthèse et leur effet sur la structure du revêtement. Les conclusions tirées du PeX sont en accord avec les résultats obtenus lors des études précédentes. L'analyse en composantes principales (ACP) a été réalisée pour avoir une vue globale de la façon dont les diverses propriétés des revêtements sont reliées entre elles / The overall objectives of this study was to find an environmental-friendly and simple procedure to synthesize titanium-dioxide, as well as, to determine the relation between the structural and functional properties of titanium dioxide coatings. Both of these objective have been attained in this study. By the sol-gel technique, titanium dioxide sols were synthesized by the hydrolysis of titanium(IV)isopropoxide. Nanocrystalline dispersions of TiO2 in water were prepared that were suitable for coatings and having long-term stability (more than 1 year) in terms of polymorphic composition, crystallite and agglomerate size. A design of experiments (DoE) was utilised, to our knowledge, for the first time in MOCVD for the synthesis of TiO2 coatings. It was employed to determine, in a timely and economical manner, the most significant process parameters for any quantifiable property of the coating and to highlight the interaction between these operating parameters, as well as, the correlation between the structure of the coating and the process. The conclusions drawn from the DoE were compared to results obtained by previous studies and were found to concur. Therefore, the DoE was successful in screening the most important process parameters, with a minimum number of experimental trials. For most of the properties that were under investigation, the DoE showed that, the deposition temperature and reactor pressure were, often-times, the most significant. Therefore, to change the microstructure and composition of MOCVD coatings, changing these process parameters will ensure the highest impact. It has to be stressed that the conclusions drawn from the DoE are restricted to the experimental range that was under investigation. Principal Component Analysis (PCA) was conducted to have an overall view of how the different properties of the coatings related with one another. The interpretations made from this analysis were that the photocatalytic (PC) activity of the coatings produced did not relate strongly to the polymorphic composition, which is contrary to literature review and is explained to be a result of the different morphologies that lead to different porosities and specific surface area. The PC activity did not depend on the mass over a critical mass. With this analysis it appeared to be clear that the porosity and specific surface area played a larger role than polymorphic composition. This hypothesis has to be verified because we did not succeed in determining the specific surface area of our coatings during this study. However, some preliminary tests have been conducted showing that cyclic voltametry could be used to evaluate the surface area of our films

The term "personality" nowadays occurs more often not only in psychological studies of humans but also in animal studies. Studying of personality help us to define the behavioural characteristics which can vary within the age, sexes, species or enviroments. Behavioral experiments are used to detect these behavioral patterns and they can divide the animals into the different groups. The subject of our research became three populations of house mouse (Mus musculus sensu lato) which we tested in a series of experiments involving free exploration, forced exploration, hole- board test, test of vertical activity and Elevated plus-maze. These experiments should reveal wheter the mice differ in their behaviour through the context of sex, comensalism or subspecies. We found (with in excepcion of one test) that intrapopulation variability differences are very small but interpopulation differences purely increase in the cas of comensalism and effects of subspecies. Keywords: Mus musculus, comensalism, open fieldtest, Elevated plus-maze, Principal Component Analysis (PCA)

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