Arbuscular mycorrhizal (AM) fungi are key soil symbiotic microorganisms, intensively studied for their roles in improving plant fitness and their ubiquity in terrestrial ecosystems. Research on AM fungi is difficult because their obligate biotrophic nature makes it impossible to culture them in the absence of a host. Over the last three decades, Ri T-DNA transformed roots have been the gold standard to study AM fungi under in vitro conditions. However, only two host plant species (Daucus carota and Cichorium intybus) have been routinely used to in vitro propagate less than 5% of the known AM fungal species. There is much evidence that host identity can significantly affect AM symbioses, therefore, we investigated any potential host-specific effects of two novel Ri T-DNA transformed root species, Medicago truncatula and Nicotiana benthamiana, by associating them with seven AM fungal species selected based on their contrasting behaviors when grown with Ri T-DNA transformed D. carota roots. To evaluate the performance of new Ri T-DNA transformed roots to host and propagate AM fungal species, a factorial set-up was used to generate nine unique pairs of hosts (M. truncatula, N. benthamiana, D. carota) and AM fungi (Rhizophagus irregularis, R. clarus, Glomus sp.). Using statistical modeling, all pairs of hosts and AM fungi were compared by their symbiosis development (SD) and sporulation patterns in the hyphal compartments (HCs) of two-compartment Petri dishes. Our results show that 1) most of the variation between host and AM fungus pairs relating to SD or HC sporulation was explained by an interaction between host and AM fungal identity, i.e., host identity alone was not sufficient to explain AM fungal behaviour, 2) AM symbioses involving different combinations of symbiont identities trigger heterogenous fungal behaviours. This work provides a robust framework to develop and evaluate new Ri T-DNA roots for the in vitro propagation of AM fungi, an important asset for germplasm collections and biodiversity preservation.

Today many societies rely on fast mobile networks, and the future seem to place even larger demand on the networks performance. This thesis analyzes which parameters affects the downlink speed of mobile networks. Various statistical analyses are performed on a large dataset provided by Bredbandskollen. We find that parameters such as the internet service provider, the type of phone, the time of day and the density of population affect the downlink speed. We also find that the downlink speeds are significantly higher in urban areas compared to more rural regions.

The two-phase flow approach has been the conventional method designed to study the sediment transport rate. Due to the complexity of sediment transport, the precisely numerical models computed from that approach require initial assumptions and, as a result, may not yield accurate output for all conditions. This research work proposes that Machine Learning algorithms can be an alternative way to predict the processes of sediment transport in two-dimensional directions under oscillating sheet flow conditions, by utilizing the available dataset of the SedFoam multidimensional two-phase model. The assessment utilized linear regression and gradient boosting algorithm to analyze the lowest average mean squared error in each case and search for the best partition method based on the domain height of the simulation setup.

The school board of a school district in South Carolina has proposed to increase class size in all schools due to mandatory budgetary reductions. However, at the secondary school level, the literature on the effect of larger class size on student achievement is conflicting. The theoretical framework by Lazear suggested that the minimization of negative externalities (i.e., problematic behavioral and academic characteristics of students) achieved through the mechanism of smaller class size impacts student learning. Reducing the number of students in a classroom alters the entire classroom environment, creating a more positive learning environment in which students are able to forge better relationships with classmates and teachers. The research question for this study examined whether class size in secondary school predicted student achievement as measured by teacher-issued end-of-course numerical student grades (TIECNSG). The study used a correlational design with a sample of 17,582 TIECNSG from 5 secondary schools in the district. The effect of smaller class sizes on TIECNSG was determined through the use of a linear regression model. For 9 course offerings, an increase in class size resulted in a decrease in TIECNSG, whereas for 8 course offerings, an increase in class size resulted in an increase in TIECNSG. The results of this study, therefore, were inconclusive, suggesting that other unaccounted confounding variables may have affected student achievement. This study can be used to promote positive social change by creating a dialogue between parents and school administrators who often have opposing points of view in terms of the effects of class size. In addition, it is recommended that a district's school board should authorize additional studies prior to taking any course of action that would affect class size at the secondary school level.

Cette thèse traite les problèmes statistiques suivants : la sélection de variables dans le modèle de régression linéaire en grande dimension, le clustering dans le modèle de mélange Gaussien, quelques effets de l'adaptabilité sous l'hypothèse de parcimonie ainsi que la simulation des processus Gaussiens.Sous l'hypothèse de parcimonie, la sélection de variables correspond au recouvrement du "petit" ensemble de variables significatives. Nous étudions les propriétés non-asymptotiques de ce problème dans la régression linéaire en grande dimension. De plus, nous caractérisons les conditions optimales nécessaires et suffisantes pour la sélection de variables dans ce modèle. Nous étudions également certains effets de l'adaptation sous la même hypothèse. Dans le modèle à vecteur parcimonieux, nous analysons les changements dans les taux d'estimation de certains des paramètres du modèle lorsque le niveau de bruit ou sa loi nominale sont inconnus.Le clustering est une tâche d'apprentissage statistique non supervisée visant à regrouper des observations proches les unes des autres dans un certain sens. Nous étudions le problème de la détection de communautés dans le modèle de mélange Gaussien à deux composantes, et caractérisons précisément la séparation optimale entre les groupes afin de les recouvrir de façon exacte. Nous fournissons également une procédure en temps polynomial permettant un recouvrement optimal des communautés.Les processus Gaussiens sont extrêmement utiles dans la pratique, par exemple lorsqu'il s'agit de modéliser les fluctuations de prix. Néanmoins, leur simulation n'est pas facile en général. Nous proposons et étudions un nouveau développement en série à taux optimal pour simuler une grande classe de processus Gaussiens. / This PhD thesis deals with the following statistical problems: Variable selection in high-Dimensional Linear Regression, Clustering in the Gaussian Mixture Model, Some effects of adaptivity under sparsity and Simulation of Gaussian processes.Under the sparsity assumption, variable selection corresponds to recovering the "small" set of significant variables. We study non-asymptotic properties of this problem in the high-dimensional linear regression. Moreover, we recover optimal necessary and sufficient conditions for variable selection in this model. We also study some effects of adaptation under sparsity. Namely, in the sparse vector model, we investigate, the changes in the estimation rates of some of the model parameters when the noise level or its nominal law are unknown.Clustering is a non-supervised machine learning task aiming to group observations that are close to each other in some sense. We study the problem of community detection in the Gaussian Mixture Model with two components, and characterize precisely the sharp separation between clusters in order to recover exactly the clusters. We also provide a fast polynomial time procedure achieving optimal recovery.Gaussian processes are extremely useful in practice, when it comes to model price fluctuations for instance. Nevertheless, their simulation is not easy in general. We propose and study a new rate-optimal series expansion to simulate a large class of Gaussian processes.

Nonparametric convex regression has been extensively studied over the last two decades. It has been shown any Lipschitz convex function can be approximated with arbitrarily accuracy with a max of linear functions. Using this framework, in this thesis we generalize convex regression to learning an arbitrary Bregman divergence and learning a difference of convex functions. We provide approximation guarantees and sample complexity bounds for both these extensions. Furthermore, we provide algorithms to solve the resulting optimization problems based on 2-block alternative direction method of multipliers (ADMM). For this algorithm, we provide convergence guarantees with iteration complexity of O(n√d/𝜖) for a dataset X 𝝐 ℝ^n,d and arbitrary positive 𝜖. Finally we provide experiments for both the Bregman divergence learning and difference of convex functions learning based on UCI datasets that demonstrate the state of the art on regression and classification datasets.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20369号 / 工博第4306号 / 新制||工||1667(附属図書館) / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 田中 功, 教授 酒井 明, 教授 中村 裕之 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20688号 / 工博第4385号 / 新制||工||1681(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小池 克明, 教授 三ｹ田 均, 准教授 須崎 純一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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This project applies statistical inference methods to energy data from the island of Luzon in the Philippines. The goal of the project is to explore different ways of creating predictive models and to understand the assumptions that are made about reality when a certain model is selected. The main models discussed in the project are Simple Linear Regression and Markov Chain Models. The predictions were used to assess Luzon's progress towards the sustainable development goals. All models considered in this project suggest that they are not on target to meet the sustainability goal.