One would like to evaluate and compare complex digital communication systems based upon their overall bit error rate. Unfortunately, analytical expressions for bit error rate for even simple communication systems are notoriously difficult to evaluate accurately. Therefore, communication engineers often resort to simulation techniques to evaluate these error probabilities. In this thesis importance sampling techniques (variations of standard Monte Carlo methods) are studied in relation to both linear and non-linear detectors. Quick simulation, an importance sampling method based upon the asymptotics of the error estimator, is studied in detail. The simulated error probabilities are compared to values obtained by numerically inverting Laplace Transform expressions for these quantities.

Magistro darbe tiriamas gegužraibinių (Orchidaceae) šeimos augalų populiacijų gyvybingumas priklausomai nuo EB buveinių tvarkymo intensyvumo Šveicarijos miško BAST. Darbo objektas – Šveicarijos miško BAST gegužraibinių (Orchidaceae) augalų populiacijos. Darbo tikslas – įvertinti gegužraibinių (Orchidaceae) šeimos populiacijų gausumą skirtinguose tvarkymo plotuose, priklausomai nuo gamtotvarkos darbų reguliarumo. Darbo metodai – gegužraibinių (Orchidaceae) augalų individų apskaita. Darbo rezultatai. Vykdant gamtotvarkos projektą, Neries regioniniam parkui priskirtoje teritorijoje, Šveicarijos miško pelkinėse buveinėse, kurios nuolatos turi vandens perteklių ir yra svarbios biologinei įvairovei buvo vertinamas gegužraibinių (Orchidaceae) augalų gausumas priklausomai nuo buveinių tvarkymo intensyvumo. Projekto metu, siekiant nustatyti augalų gausumą buveinėse, buvo įrengta 20 vieno kvadratinio metro barelių 4 skirtingose EU buveinėse. Populiacijos buveinėse buvo vertintos pagal tokį tvarkymo intensyvumą: intensyviai prižiūrimos, neintensyviai prižiūrimos, natūralios arba netvarkomos buveinės. Pagal tyrimų rezultatus, galima patvirtinti hipotezę, kad gegužraibinių (Orchidaceae) šeimos populiacijos, Šveicarijos BAST, tiesiogiai priklauso nuo buveinių tvarkymo intensyvumo buveinėse. / This article analyses the continuity of nature restoration project, ongoing since 2008 in the area of Šveicarija forest, in Vilnius district, which occupies 210 ha, influence for European importance habitats and protected species populations. Subject of the research – Orchid (Orchidaceae) plants populations of Šveicarija forest. Aim of the research – to evaluate the orchid (Orchidaceae) plants density in Šveicarija forest, European importance habitats, due to restoration intensity. Methodology – evaluation of orchid (Orchidaceae) plants in 1m2. Results. During the project in the area supervised by Neris Regional Park, wetlands and habitats with excess water are very important for biodiversity and are always maintained. During the study, abundance of orchid (Orchidaceae) plants populations was assessed in 4 EU habitats, where 20 different 1 square meter plots were established. Populations of plants were evaluated according to the intensity of habitat management: regularly managed, partially managed and naturally managing itself. According to the research results, we can confirm findings that the populations of orchids (Orchidaceae) plants species in the Šveicarija forest directly depend on the management intensity of forest habitat.

Mark-recapture methods use repeated captures of individually identifiable animals to provide estimates of properties of populations. Different models allow estimates to be obtained for population size and rates of processes governing population dynamics. State-space models consist of two linked processes evolving simultaneously over time. The state process models the evolution of the true, but unknown, states of the population. The observation process relates observations on the population to these true states. Mark-recapture models specified within a state-space framework allow population dynamics models to be embedded in inference ensuring that estimated changes in the population are consistent with assumptions regarding the biology of the modelled population. This overcomes a limitation of current mark-recapture methods. Two alternative approaches are considered. The "conditional" approach conditions on known numbers of animals possessing capture history patterns including capture in the current time period. An animal's capture history determines its state; consequently, capture parameters appear in the state process rather than the observation process. There is no observation error in the model. Uncertainty occurs only through the numbers of animals not captured in the current time period. An "unconditional" approach is considered in which the capture histories are regarded as observations. Consequently, capture histories do not influence an animal's state and capture probability parameters appear in the observation process. Capture histories are considered a random realization of the stochastic observation process. This is more consistent with traditional mark-recapture methods. Development and implementation of particle filtering techniques for fitting these models under each approach are discussed. Simulation studies show reasonable performance for the unconditional approach and highlight problems with the conditional approach. Strengths and limitations of each approach are outlined, with reference to Soay sheep data analysis, and suggestions are presented for future analyses.

Automatic analysis of facial actions (AFA) can reveal a person’s emotion, intention, and physical state, and make possible a wide range of applications. To enable reliable, valid, and efficient AFA, this thesis investigates automatic analysis of facial actions through transductive, supervised and unsupervised learning. Supervised learning for AFA is challenging, in part, because of individual differences among persons in face shape and appearance and variation in video acquisition and context. To improve generalizability across persons, we propose a transductive framework, Selective Transfer Machine (STM), which personalizes generic classifiers through joint sample reweighting and classifier learning. By personalizing classifiers, STM offers improved generalization to unknown persons. As an extension, we develop a variant of STM for use when partially labeled data are available. Additional challenges for supervised learning include learning an optimal representation for classification, variation in base rates of action units (AUs), correlation between AUs and temporal consistency. While these challenges could be partly accommodated with an SVM or STM, a more powerful alternative is afforded by an end-to-end supervised framework (i.e., deep learning). We propose a convolutional network with long short-term memory (LSTM) and multi-label sampling strategies. We compared SVM, STM and deep learning approaches with respect to AU occurrence and intensity in and between BP4D+ [282] and GFT [93] databases, which consist of around 0.6 million annotated frames. Annotated video is not always possible or desirable. We introduce an unsupervised Branch-and-Bound framework to discover correlated facial actions in un-annotated video. We term this approach Common Event Discovery (CED). We evaluate CED in video and motion capture data. CED achieved moderate convergence with supervised approaches and enabled discovery of novel patterns occult to supervised approaches.

Turnover is one of the most important phenomena for management scholars and practitioners. Yet, researchers and practitioners are often frustrated by their inability to accurately predict why individuals leave their jobs. This should be worrisome given that total replacement costs can exceed 100% of an employee’s salary (Cascio, 2006) and can represent up to 40% of a firm’s pre-tax income (Allen, 2008). Motivated by these concerns, the purpose of this study was to assess the predictive validity of commonly-investigated correlates and, by extension, conceptualizations of employee turnover using a large-scale database of scientific findings. Results indicate that job satisfaction, organizational commitment, and embeddedness (e.g., person-job fit, person-organization fit) may be the most valid proximal predictors of turnover intention. Results for a tripartite analysis of the potential empirical redundancy between job satisfaction and organizational commitment when predicting turnover intention align well with previous research on this topic and generally suggest that the two constructs may be empirically indistinguishable in the turnover context. Taken together, this study has important implications for the turnover and sensitivity analysis literatures. With regard to the sensitivity analysis literature, this study demonstrates the application of a sensitivity analysis for relative importance weights in the meta-analytic context. This new method takes into account variance around the meta-analytic mean effect size estimate when imputing relative importance weights and may be adapted to other correlation matrix-based techniques (i.e., structural equation modeling) that are often used to test theory.

Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnPLS model interpretability when analyzing a large number of data sets simultaneously (Paper IV). The results of this thesis, and their enclosed papers, showed that VIPOPLS, VIPO2PLS, and MB-VIOP methods successfully assess the most relevant variables for model interpretation in PLS, OPLS, O2PLS, and OnPLS models. In addition, predictability, robustness, dimensionality reduction, and other variable selection purposes, can be potentially improved/achieved by using these methods.

Pharmacometric model-based analysis using nonlinear mixed-effects models (NLMEM) has to date mainly been applied to learning activities in drug development. However, such analyses can also serve as the primary analysis in confirmatory studies, which is expected to bring higher power than traditional analysis methods, among other advantages. Because of the high expertise in designing and interpreting confirmatory studies with other types of analyses and because of a number of unresolved uncertainties regarding the magnitude of potential gains and risks, pharmacometric analyses are traditionally not used as primary analysis in confirmatory trials. The aim of this thesis was to address current hurdles hampering the use of pharmacometric model-based analysis in confirmatory settings by developing strategies to increase model compliance to distributional assumptions regarding the residual error, to improve the quantification of parameter uncertainty and to enable model prespecification. A dynamic transform-both-sides approach capable of handling skewed and/or heteroscedastic residuals and a t-distribution approach allowing for symmetric heavy tails were developed and proved relevant tools to increase model compliance to distributional assumptions regarding the residual error. A diagnostic capable of assessing the appropriateness of parameter uncertainty distributions was developed, showing that currently used uncertainty methods such as bootstrap have limitations for NLMEM. A method based on sampling importance resampling (SIR) was thus proposed, which could provide parameter uncertainty in many situations where other methods fail such as with small datasets, highly nonlinear models or meta-analysis. SIR was successfully applied to predict the uncertainty in human plasma concentrations for the antibiotic colistin and its prodrug colistin methanesulfonate based on an interspecies whole-body physiologically based pharmacokinetic model. Lastly, strategies based on model-averaging were proposed to enable full model prespecification and proved to be valid alternatives to standard methodologies for studies assessing the QT prolongation potential of a drug and for phase III trials in rheumatoid arthritis. In conclusion, improved methods for handling residual error, parameter uncertainty and model uncertainty in NLMEM were successfully developed. As confirmatory trials are among the most demanding in terms of patient-participation, cost and time in drug development, allowing (some of) these trials to be analyzed with pharmacometric model-based methods will help improve the safety and efficiency of drug development.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Le calcul de la fiabilité des réseaux est en général un problème NP-difficile. On peut par exemple s’intéresser à la fiabilité des systèmes de télécommunications où l'on veut évaluer la probabilité qu’un groupe sélectionné de nœuds peuvent communiquer. Dans ce cas, un ensemble de nœuds déconnectés peut avoir des conséquences critiques, que ce soit financières ou au niveau de la sécurité. Une estimation précise de la fiabilité est ainsi nécessaire. Dans le cadre de ce travail, on s'intéresse à l’étude et au calcul de la fiabilité des réseaux hautement fiables. Dans ce cas la défiabilité est très petite, ce qui rend l’approche standard de Monte Carlo inutile, car elle nécessite un grand nombre d’itérations. Pour une bonne estimation de la fiabilité des réseaux au moindre coût, nous avons développé de nouvelles techniques de simulation basées sur la réduction de variance par échantillonnage préférentiel. / Network reliability determination, is an NP-hard problem. For instance, in telecommunications, it is desired to evaluate the probability that a selected group of nodes communicate or not. In this case, a set of disconnected nodes can lead to critical financials security consequences. A precise estimation of the reliability is, therefore, needed. In this work, we are interested in the study and the calculation of the reliability of highly reliable networks. In this case the unreliability is very small, which makes the standard Monte Carlo approach useless, because it requires a large number of iterations. For a good estimation of system reliability with minimum cost, we have developed new simulation techniques based on variance reduction using importance sampling.

SummaryThis study intends to give the reader explanations of attitudes regarding the importance of global warming issues and modification of behaviors in order to mitigate problems. Measurements of optimism, locus of control and self-esteem have been used as mediat-ing factors. Data is collected from Indonesia and Sweden through questionnaires.BackgroundGlobal warming problems are today one of the most important missions politicians together with scientists have to resolve, and this includes an economic division of re-sponsibility worldwide. Psychology has a huge role in order to understand and change individuals' attitudes toward global warming issues.ObjectiveOur purpose with the study is to explore cultural differences in attitudes regarding global warming, specifically regarding the importance of global warming issues and regarding modification of behaviors in order to mitigate problems.MethodWith the use of questionnaires a quantitative study was conducted in the two cultures Indonesia and Sweden. Measurements of individualism/collectivism, Locus of control, Optimism and Self-esteem were used as mediating variables between culture and attitudes regarding global warming.ResultIndonesian respondents found global warming issues to be more important, than Swedish respondents. Swedish respondents were more willing to modify their behavior in order to mitigate global warming problems, than Indonesian respondents.