Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Title: Pupils' problems when solving problems requiring substitution Abstract: The work focuses on 8th and 9th graders' difficulties and mistakes when solving problems using substitution. The theoretical part defines the used concepts and discusses selected results from international comparative studies and studies focusing on substitution. The main goal of the work is to identify pupils' difficulties and mistakes they make when solving problems using substitution and to discover their origin or cause. To achieve this goal the empirical part begins with an analysis of three sets of textbooks used by pupils involved in this research study. This is followed by a description of conducting and subsequent analysis of fifteen clinical interviews on problems with substitution with 14 to 15 year old pupils (9th graders from elementary schools and 3rd graders from lower secondary grammar schools). In the final part the most common mistakes are summarised and relations to their possible origin in the set of textbooks used looked for. The conclusion is that pupils do not have problems with substitution as such when substituting natural numbers into first power variables. However, this does not hold for substitution of variable in the second power. Pupils have problems when substituting for a variable with a negative...

This paper uses data from The Survey on Income and Living Conditions (SILC) for year 2013 to estimate the effect of family size on parent's wellbeing. To address the possible endogeneity in family size we use "multiple births" as exogenous origin of variation in family size. First finding shows insignificant effect of the additional child on parent's wellbeing. However, when we examine if the effect of number of children is significantly different for men and for women, we receive significant results. The number of children positively influences mother's wellbeing, but for fathers, there do not exist clear result. Finally, we examine if big family is poor family and our finding reveals, that number of children positively increases income of household. JEL Classification D31, I31, J13 Keywords wellbeing, family size, instrumental variable, income Author's e-mail andrea.havrilova@gmail.com Supervisor's e-mail gebicka@fsv.cuni.cz

Dans cette thèse, nous nous intéressons à la restauration d'image tridimensionnelle de microscopie de fluorescence. Deux difficultés majeures dans ce système d'imagerie sont traitées. La première est le flou variable en profondeur qui est dû aux aberrations induites par la variation des indices de réfraction dans le système optique et le spécimen imagé. La deuxième est le bruit qui est principalement dû au processus de comptage de photons. L'objectif de cette thèse est de réduire ces distorsions afin de fournir aux biologistes une image de meilleure qualité possible. Dans la première partie de cette thèse, nous étudions les modèles d'approximation du flou variable en profondeur et nous choisissons un modèle adéquat au problème d'inversion. Dans ce modèle, la réponse impulsionnelle (RI) variable en profondeur est approchée par une combinaison convexe d'un ensemble de RIs invariables spatialement. Nous développons pour ce modèle deux méthodes rapides de restauration non-aveugle par minimisation d'un critère régularisé, chacune d'elles est adaptée au type de bruit présent dans les images de microscopie confocale ou à champ large. Dans la deuxième partie, nous abordons le problème de restauration aveugle et proposons deux méthodes dans lesquelles le flou variable en profondeur et l'image sont conjointement estimés. Dans la première méthode, la RI est estimée en chaque voxel du volume considéré afin de laisser une grande liberté sur la forme de la RI, tandis que dans la deuxième méthode, la forme de la RI est contrainte par une fonction gaussienne afin de réduire le nombre de variables inconnues et l'espace des solutions possibles. Dans ces deux méthodes d'estimation aveugle, l'effet des aberrations optiques n'est pas efficacement estimé en raison du manque d'information. Nous améliorons ces méthodes d'estimation en alternant des contraintes dans les domaines fréquentiel et spatial. Des résultats sont montrés en simulation et sur des données réelles. / In this thesis, we focus on the restoration of three-dimensional image of fluorescence microscopy. Two major difficulties in this imaging system are considered. The first one is the depth-variant blur due to aberrations induced by the refractive index variation in the optical system and the imaged specimen. The second difficulty is the noise due to the photon counting process. The goal of this thesis is to reduce these distortions in order to provide biologists with a better image quality. In the first part of this thesis, we study the approximation models of the depth-variant blur and choose an appropriate model for the inversion problem. In that model, the depth-variant point spread function (PSF) is approximated by a convex combination of a set of space-invariant PSFs. We then develop for that model two fast non-blind restoration methods by minimizing a regularized criterion, each of these methods is adapted to the type of noise present in images of confocal or wide field microscopy. In the second part, we address the problem of blind restoration and propose two methods where the depth-variant blur and the image are jointly estimated. In the first method, the PSF is estimated at each voxel in the considered volume in order to allow high degree of freedom on the PSF shape while in the second method, the shape of the PSF is constrained by a Gaussian function in order to reduce the number of unknown variables and the space of possible solutions. In both blind estimation methods, the effect of optical aberrations is not effectively estimated due to the lack of information. We thus improve these estimation methods by alternating some constraints in the frequency and spatial domains. Results on simulated and real data are shown.

Scheduling problems and constraint satisfaction problems are generally known to be extremely hard. This thesis proposes a new evolutionary al- gorithm approach to solve a constrained-based scheduling problem. In this approach, variable orderings are evolved. The variable ordering serves as a parameter for the constraint solver. Its purpose is to determine the order in which variables are labelled by the solver. Hence the evolving individuals may be encoded as permutations. Therefore, our approach can be applied to a wider range of constraint satisfaction problems. Methods for generating the initial population of individuals based on the analysis of the precedence constraints graph are proposed. New genetic operators are presented and successfully applied. Our approach succeeded in finding a range of diverse schedules with the optimal makespan. Furthermore, multi-objective opti- mization was successfully attempted with the NSGA-II. 1

Master of Agribusiness / Department of Agricultural Economics / Kevin Dhuyvetter / This thesis was commissioned by Cooperative Producers, Inc. (CPI) of Hastings, Nebraska in order to better understand the preferences and uses of precision agriculture by customers within the company’s trade territory. With the rapid increase of precision agriculture (hardware, software, services, etc.) it is necessary to get a better understanding of what drives growers to adopt and implement precision agriculture practices. A paper survey was sent out in CPI’s monthly statements to patrons that also included instructions to be able to fill out an online survey if that was preferred. From that offering there were a total of 114 responses providing data from which several technology adoption models were estimated. Based on prior experience with precision agriculture and the development of services offered to growers, it is hypothesized that there are three primary variables influencing a grower’s decision to adopt precision agriculture. If the operation is managed by a younger grower (<40 years old), farms with a larger number of acres, and if a high percent of the operation’s acres are irrigated they will be more likely to adopt precision agriculture practices. The survey results generally revealed that younger farmers, larger farm size, and a higher percentage of irrigated acres did not increase the likelihood of utilizing precision agriculture. The questions asked in the survey were designed to provide information for the development of a tool that salespeople offering precision agriculture services could use to determine if a potential customer with be inclined to adopt and utilize precision agriculture. While some of the results were contrary to expectations they do offer insight into what type of customer adopts precision agriculture and a direction for CPI to move in order to maximize market penetration.

The objective of this study was to develop a 3-D model for The Pontchartrain Estuary that was capable of long-term mass conservative simulation of salinities. This was accomplished in a multi-stage approach involving: a physical model of salinity exchange through a pass; a 3-D FVCOM model of the physical experiment; the development and testing of an FVCOM model for an idealized Pontchartrain Basin; and for the entire estuary. The data from the physical model tests were used to validate the performance of the FVCOM model with density-driven flows. These results showed that hydrostatic FVCOM captured the primary internal wave movement. The idealized basin simulations were used to evaluate several issues related to salinity transport, namely the relative importance of baroclinic forcing, tidal forcing and hydrology. The idealized domain also permitted the testing of sigma-gradients, spatial distribution of friction coefficients, wind stress and various boundary treatments. The results showed that the density-driven exchange of saltwater at the open boundary required a baroclinic boundary condition for salinity as well as a lateral filter at the boundary on each sigma layer. A new radiative baroclinic open boundary condition was developed for FVCOM. When tides and hydrology were included, the FVCOM model was shown to reproduce the seasonal salinity that has been observed for long-term periods. It was also found that the simulation of tides and salinity in FVCOM is very sensitive to the spatial distribution of the friction coefficient; relatively low friction was required in the open water regions and high friction was needed in the passes and waterways to reproduce the tides and salinity distribution. A variable friction coefficient option was coded on FVCOM. The findings from the idealized model were utilized to setup two models for the actual estuary. Both models extend from Lake Maurepas, one to the Chandeleurs Islands and the other to Mobile Bay. The baroclinic open boundary and variable friction were implemented in these models. They were calibrated for tides and salinity. The 2008 Bonnet Carré Spillway Opening was applied to the first model. A tidal pumping effect in Lake Pontchartrain was observed and captured by the model.

Background: Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects must be included a priori as mandatory covariates while allowing the selection of a large number of candidate or optional variables. As genomic studies routinely require mandatory covariates, it is of interest to propose principled methods of variable selection that can incorporate mandatory covariates. Methods: In this article, we propose the ridge-lasso hybrid estimator (ridle), a new penalized regression method that simultaneously estimates coefficients of mandatory covariates while allowing selection for others. The ridle provides a principled approach to mitigate effects of multicollinearity among the mandatory covariates and possible dependency between mandatory and optional variables. We provide detailed empirical and theoretical studies to evaluate our method. In addition, we develop an efficient algorithm for the ridle. Software, based on efficient Fortran code with R-language wrappers, is publicly and freely available at https://sites.google.com/site/zhongyindaye/software. Results: The ridle is useful when mandatory predictors are known to be significant due to prior knowledge or must be kept for additional analysis. Both theoretical and comprehensive simulation studies have shown that the ridle to be advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves. A microarray gene expression analysis of the histologic grades of breast cancer has identified 24 genes, in which 2 genes are selected only by the ridle among current methods and found to be associated with tumor grade. Conclusions: In this article, we proposed the ridle as a principled sparse regression method for the selection of optional variables while incorporating mandatory ones. Results suggest that the ridle is advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves.

<p>Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space), and the challenge arise in defining an algorithm with low communication, theoretical guarantees and excellent practical performance in general settings. For sample space partitioning, I propose a MEdian Selection Subset AGgregation Estimator ({\em message}) algorithm for solving these issues. The algorithm applies feature selection in parallel for each subset using regularized regression or Bayesian variable selection method, calculates the `median' feature inclusion index, estimates coefficients for the selected features in parallel for each subset, and then averages these estimates. The algorithm is simple, involves very minimal communication, scales efficiently in sample size, and has theoretical guarantees. I provide extensive experiments to show excellent performance in feature selection, estimation, prediction, and computation time relative to usual competitors.</p><p>While sample space partitioning is useful in handling datasets with large sample size, feature space partitioning is more effective when the data dimension is high. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In the thesis, I propose a new embarrassingly parallel framework named {\em DECO} for distributed variable selection and parameter estimation. In {\em DECO}, variables are first partitioned and allocated to m distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number m. Extensive numerical experiments are provided to illustrate the performance of the new framework.</p><p>For datasets with both large sample sizes and high dimensionality, I propose a new "divided-and-conquer" framework {\em DEME} (DECO-message) by leveraging both the {\em DECO} and the {\em message} algorithm. The new framework first partitions the dataset in the sample space into row cubes using {\em message} and then partition the feature space of the cubes using {\em DECO}. This procedure is equivalent to partitioning the original data matrix into multiple small blocks, each with a feasible size that can be stored and fitted in a computer in parallel. The results are then synthezied via the {\em DECO} and {\em message} algorithm in a reverse order to produce the final output. The whole framework is extremely scalable.</p> / Dissertation

L’entreprise Rio Tinto effectue la gestion du système hydrique de la rivière Nechako, situé en Colombie-Britannique (Canada), à partir de règles de gestion optimisées à l’aide d’un algorithme de programmation dynamique stochastique (PDS) et de scénarios d’apports historiques. Les récents développements en recherche opérationnelle tendent à démontrer que la mise à jour des règles de gestion en mode prévisionnel permet d’améliorer la performance des règles de gestion lorsque des prévisions d’ensemble sont utilisées pour mieux cerner les incertitudes associées aux apports à venir. La modélisation hydrologique permet de suivre l’évolution d’un ensemble de processus hydrologiques qui varient dans le temps et dans l’espace (réserve de neige, humidité du sol, etc.). L’utilisation de modèles hydrologiques, en plus d’offrir la possibilité de construire des prévisions d’ensemble qui tiennent compte de l’ensemble des processus simulés, permet de suivre l’évolution de variables d’état qui peuvent être utilisées à même l’algorithme d’optimisation pour construire les probabilités de transition utiles à l’évaluation de la valeur des décisions futures. À partir d’un banc d’essais numériques dans lequel le comportement du bassin versant de la rivière Nechako est simulé à l’aide du modèle hydrologique CEQUEAU, les résultats du présent projet démontrent que la mise à jour des règles avec l’algorithme de PDS en mode prévisionnel permet une amélioration de la gestion du réservoir Nechako lorsque comparée aux règles optimisées avec l’algorithme en mode historique. Le mode prévisionnel utilisant une variable hydrologique combinant un modèle autorégressif d’ordre 5 (AR5) et la valeur maximale de l’équivalent en eau de la neige (ÉENM) a permis de réduire les déversements non-productifs et les inondations tout en maintenant des productions similaires à celles obtenues à l’aide de règles optimisées en mode historique utilisant l’ÉENM comme variable hydrologique. De plus, les résultats du projet démontrent que l’utilisation de prévisions hydrologiques d’ensemble en mode historique pour construire une variable hydrologique permettant d’émettre une prévision du volume d’apport médian pour les huit mois à venir (PVAM) ne permettait pas d’obtenir des résultats de gestion supérieurs à ceux obtenus avec la variable d’ÉENM.