In this thesis the author presents a history of non-convergent series which, in the past, played an important role in mathematics. Euler's formula, Stirling's series and Poincare's theory are examined to show the development of asymptotic series, a subdivision of divergent series.

Cette thèse a pour origine un projet sur l'optimisation de la chimiothérapie, rassemblant trois directeurs: Jean Clairambault, médecin et mathématicien, Michèle Sabbah, biologiste du cancer, et Emmanuel Trélat, mathématicien spécialisé en contrôle optimal. Ainsi, l'essentiel du travail a été motivé par des questions provenant de la biologie ou la thérapie du cancer. Y répondre a nécessité l'utilisation et le développement d'outils empruntés à diverses disciplines mathématiques, parmi lesquelles l'analyse asymptotique d'équations aux dérivées partielles, leur contrôle optimal théorique et numérique. Ces développements ont posé de nouveaux problèmes mathématiques intéressants en eux-mêmes, avec des applications en dynamique adaptative, dynamique des populations, contrôle optimal ou encore analyse numérique. Plus précisément, nous proposons des résultats d'analyse asymptotique pour certaines équations ou systèmes de sélection/mutation et réaction/diffusion non-locaux. Le contrôle Dirichlet des équations monostable et bistable 1D est étudié dans le détail. On considère l'étude numérique et théorique d'un problème de contrôle optimal pour un système représentant des cellules saines et cancéreuses soumises à de la chimiothérapie. Enfin, l'existence d'instabilités de Turing pour un système de Keller-Segel est prouvée. Pour ces équations, nous développons des schémas numériques aux volumes finis qui préservent la positivité, la dissipation de l'énergie, la conservation de la masse et les états stationnaires. / This PhD originates from a joint project on chemotherapy optimisation, bringing together three advisors: Jean Clairambault, medical doctor and mathematician, Michèle Sabbah, cancer biologist, and Emmanuel Trélat, mathematician specialised in optimal control. Most of the work undertaken has thus been motivated by questions from cancer biology or therapy. Answering them has required using and further developing tools from several different mathematical areas, among them the asymptotic analysis for partial differential equations, and theoretical and numerical optimal control. These developments have in turn posed new mathematical problems, interesting in their own right, with applications in the mathematical fields of adaptive dynamics, population dynamics, optimal control or numerical analysis. More precisely, we propose results of asymptotic analysis for some selection/mutation and reaction/diffusion non-local equations or systems. The Dirichlet control towards homogeneous states of 1D monostable and bistable equations is investigated in detail. A numerical and theoretical analysis for an optimal control is performed on a system representing cancer and healthy cells exposed to chemotherapy. Finally, Turing instabilities are shown to be exhibited by some Keller-Segel equations, for which we design finite-volume numerical schemes preserving positivity, energy dissipation, mass conservation and steady states.

Cette thèse est dédiée au développement d'outils mathématiques innovants améliorant la modélisation en électrophysiologie cardiaque.Une présentation du modèle bidomaine - un système réaction-diffusion - à domaine fixé est proposée en s'appuyant sur la littérature et une justification mathématique du processus d'homogénéisation (convergence «2-scale») est donnée. Enfin, une étude de l'impact des déformations mécaniques dans les lois de conservation avec la théorie des mélanges est faite.Comme les techniques d'imagerie ne fournissent globalement que des surfaces pour les oreillettes cardiaques dont l'épaisseur est très faible, une réduction dimensionnelle du modèle bidomaine dans une couche mince à une formulation posée sur la surface associée est étudiée. À l'aide de techniques développées pour les modèles de coques, une analyse asymptotique des termes de diffusion est faite sous des hypothèses de gradient d'anisotropie fort à travers l'épaisseur. Puis, une modélisation couplée du cœur - asymptotique pour les oreillettes et volumique pour les ventricules - permet la simulation d'électrocardiogramme complet. De plus, les méthodes asymptotiques sont utilisées pour obtenir des résultats de convergence forte pour les modèles de coque-3D.Enfin, afin de «personnaliser» les modèles, une méthode d'estimation est proposée. Les données médicales intégrées dans notre modèle - au moyen d'un filtre d'état de type Luenberger spécialement conçu - sont les cartes d'activation électrique. Ces problématiques apparaissent dans d'autres domaines où les modèles (réaction-diffusion) et les données (position du front) sont similaires, comme la propagation de feux ou la croissance tumorale. / This thesis aims at developing innovative mathematical tools to improve cardiac electrophysiological modeling. A detailed presentation of the bidomain model - a system of reaction-diffusion equations - with a fixed domain is given based on the literature and we mathematically justify the homogenization process using the 2-scale convergence. Then, a study of the impact of the mechanical deformations in the conservation laws is performed using the mixture theory.As the atria walls are very thin and generally appear as thick surfaces in medical imaging, a dimensional reduction of the bidomain model in a thin domain to a surface-based formulation is studied. The challenge is crucial in terms of computational efficiency. Following similar strategies used in shell mechanical modeling, an asymptotic analysis of the diffusion terms is done with assumptions of strong anisotropy through the thickness, as in the atria. Simulations in 2D and 3D illustrate these results. Then, a complete modeling of the heart - with the asymptotic model for the atria and the volume model for the ventricles - allow the simulation of full electrocardiogram cycles. Furthermore, the asymptotic methods are used to obtain strong convergence results for the 3D-shell models.Finally, a specific data assimilation method is proposed in order to «personalize» the electrophysiological models. The medical data assimilated in the model - using a Luenberger-like state filter specially designed - are the maps of electrical activation. The proposed methods can be used in other application fields where models (reaction-diffusion) and data (front position) are very similar, as for fire propagation or tumor growth.

Dans cette thèse, nous nous intéressons au problème de tester en grande dimension l'uniformité sur la sphère-unité $S^{p_n-1}$ (la dimension des observations, $p_n$, dépend de leur nombre, $n$, et être en grande dimension signifie que $p_n$ tend vers l'infini en même temps que $n$). Nous nous restreignons dans un premier temps à des contre-hypothèses ``monotones'' de densité croissante le long d'une direction ${\pmb \theta}_n\in S^{p_n-1}$ et dépendant d'un paramètre de concentration $\kappa_n>0$. Nous commençons par identifier le taux $\kappa_n$ auquel ces contre-hypothèses sont contiguës à l'uniformité ;nous montrons ensuite grâce à des résultats de normalité locale asymptotique, que le test d'uniformité le plus classique, le test de Rayleigh, n'est pas optimal quand ${\pmb \theta}_n$ est connu mais qu'il le devient à $p$ fixé et dans le cas FvML en grande dimension quand ${\pmb \theta}_n$ est inconnu.Dans un second temps, nous considérons des contre-hypothèses ``axiales'', attribuant la même probabilité à des points diamétralement opposés. Elles dépendent aussi d'un paramètre de position ${\pmb \theta}_n\in S^{p_n-1}$ et d'un paramètre de concentration $\kappa_n\in\R$. Le taux de contiguïté s'avère ici plus élevé et suggère un problème plus difficile que dans le cas monotone. En effet, le test de Bingham, le test classique dans le cas axial, n'est pas optimal à ${\pmb \theta}_n$ inconnu et $p$ fixé, et ne détecte pas les contre-hypothèses contiguës en grande dimension. C'est pourquoi nous nous tournons vers des tests basés sur les plus grande et plus petite valeurs propres de la matrice de variance-covariance et nous déterminons leurs distributions asymptotiques sous les contre-hypothèses contiguës à $p$ fixé.Enfin, à l'aide d'un théorème central limite pour martingales, nous montrons que sous certaines conditions et après standardisation, les statistiques de Rayleigh et de Bingham sont asymptotiquement normales sous l'hypothèse d'invariance par rotation des observations. Ce résultat permet non seulement d'identifier le taux auquel le test de Bingham détecte des contre-hypothèses axiales mais aussi celui auquel il détecte des contre-hypothèses monotones. / In this thesis we are interested in testing uniformity in high dimensions on the unit sphere $S^{p_n-1}$ (the dimension of the observations, $p_n$, depends on their number, and high-dimensional data are such that $p_n$ diverges to infinity with $n$).We consider first ``monotone'' alternatives whose density increases along an axis ${\pmb \theta}_n\in S^{p_n-1}$ and depends on a concentration parameter $\kappa_n>0$. We start by identifying the rate at which these alternatives are contiguous to uniformity; then we show thanks to local asymptotic normality results that the most classical test of uniformity, the Rayleigh test, is not optimal when ${\pmb \theta}_n$ is specified but becomes optimal when $p$ is fixed and in the high-dimensional FvML case when ${\pmb \theta}_n$ is unspecified.We consider next ``axial'' alternatives, assigning the same probability to antipodal points. They also depend on a location parameter ${\pmb \theta}_n\in S^{p_n-1}$ and a concentration parameter $\kappa_n\in\R$. The contiguity rate proves to be higher in that case and implies that the problem is more difficult than in the monotone case. Indeed, the Bingham test, the classical test when dealing with axial data, is not optimal when $p$ is fixed and ${\pmb \theta}_n$ is not specified, and is blind to the contiguous alternatives in high dimensions. This is why we turn to tests based on the extreme eigenvalues of the covariance matrix and establish their fixed-$p$ asymptotic distributions under contiguous alternatives.Finally, thanks to a martingale central limit theorem, we show that, under some assumptions and after standardisation, the Rayleigh and Bingham test statistics are asymptotically normal under general rotationally symmetric distributions. It enables us to identify the rate at which the Bingham test detects axial alternatives and also monotone alternatives. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Massive MIMO systems are shown to be a promising technology for next generations of wireless communication networks. The realization of the attractive merits promised by massive MIMO systems requires advanced linear precoding and receiving techniques in order to mitigate the interference in downlink and uplink transmissions. This work considers the precoder and receiver design in massive MIMO systems. We first consider the design of the linear precoder and receiver that maximize the minimum signal-to-interference-plus-noise ratio (SINR) subject to a given power constraint. The analysis is carried out under the asymptotic regime in which the number of the BS antennas and that of the users grow large with a bounded ratio. This allows us to leverage tools from random matrix theory in order to approximate the parameters of the optimal linear precoder and receiver by their deterministic approximations. Such a result is of valuable practical interest, as it provides a handier way to implement the optimal precoder and receiver. To reduce further the complexity, we propose to apply the truncated polynomial expansion (TPE) concept on a per-user basis to approximate the inverse of large matrices that appear on the expressions of 4 the optimal linear transceivers. Using tools from random matrix theory, we determine deterministic approximations of the SINR and the transmit power in the asymptotic regime. Then, the optimal per-user weight coefficients that solve the max-min SINR problem are derived. The simulation results show that the proposed precoder and receiver provide very close to optimal performance while reducing significantly the computational complexity. As a second part of this work, the TPE technique in a per-user basis is applied to the optimal linear precoding that minimizes the transmit power while satisfying a set of target SINR constraints. Due to the emerging research field of green cellular networks, such a problem is receiving increasing interest nowadays. Closed form expressions of the optimal parameters of the proposed low complexity precoding for power minimization are derived. Numerical results show that the proposed power minimization precoding approximates well the performance of the optimal linear precoding while being more practical for implementation.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22587号 / 情博第724号 / 新制||情||124(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 下平 英寿, 教授 田中 利幸, 教授 山下 信雄, 教授 林 和則(大阪市立大学) / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Available statistical methodologies focus more on accommodating continuous variables, however recently dealing with count data has received high interest in the statistical literature. In this dissertation, we propose some statistical approaches to investigate linear and nonlinear dependencies between two discrete random variables, or between a discrete and continuous random variables. Copula functions are powerful tools for modeling dependencies between random variables. We derive copula-based population version of Spearman’s rho when at least one of the marginal distribution is discrete. In each case, the functional relationship between Kendall’s tau and Spearman’s rho is obtained. The asymptotic distributions of the proposed estimators of these association measures are derived and their corresponding confidence intervals are constructed, and tests of independence are derived. Then, we propose a Bayesian copula factor autoregressive model for time series mixed data. This model assumes conditional independence and shares latent factors in both mixed-type response and multivariate predictor variables of the time series through a quadratic timeseries regression model. This model is able to reduce the dimensionality by accommodating latent factors in both response and predictor variables of the high-dimensional time series data. A semiparametric time series extended rank likelihood technique is applied to the marginal distributions to handle mixed-type predictors of the high-dimensional time series, which decreases the number of estimated parameters and provides an efficient computational algorithm. In order to update and compute the posterior distributions of the latent factors and other parameters of the models, we propose a naive Bayesian algorithm with Metropolis-Hasting and Forward Filtering Backward Sampling methods. We evaluate the performance of the proposed models and methods through simulation studies. Finally, each proposed model is applied to a real dataset.

In this thesis a method for studying the asymptotic behavior of solutions to dissipative partial differential equations is developed, motivated by the study of the compressible Navier-Stokes equations in the past works of Hoff and Zumbrun,1995, Hoff and Zumbrun, 1997. In its most basic form, this method allows one to compute n^th order approximations in terms of Hermite functions of solutions of the heat equation having n^th order moments. The main advantage is that these approximations can be efficiently computed, and are often given explicitly in terms of elementary functions. It is shown how this method can be extended to increasingly complicated systems, leading the way toward the asymptotic analysis of the compressible Navier-Stokes equations. A number of challenges must be overcome to apply this method to the compressible Navier-Stokes system. For technical reasons, the analysis is carried out on the divergence and curl of the velocity field, and hence a means of recovering the velocity field from these quantities is established first. The linear part of the evolution is then studied, and an extended version of the artificial viscosity decomposition previously developed (Kawashima, Hoff and Zumbrun1995) is introduced. This decomposition is in terms of the heat and combined heat-wave operators, and hence general estimates on their evolution in weighted L^p spaces are obtained. A modified compressible Navier-Stokes system is then introduced which captures the dominant behavior of the linear evolution and possesses similar nonlinear terms. Solutions to this modified system are proven to exist in weighted spaces, showing that solutions initially having a certain number of moments possess this same number of moments for all time. An analysis of the asymptotic behavior of the modified compressible Navier-Stokes system is then carried out, and it is shown that the method developed herein extends and unifies the approach of Hoff and Zumbrun with that of Gallay and Wayne, 2002a, Gallay and Wayne, 2002b, where it was originally developed to study the behavior of the incompressible Navier-Stokes equations. The thesis is concluded with a discussion of how the results obtained for the modified compressible Navier-Stokes system pave the way for an analysis of the true compressible Navier-Stokes system, the generalization of this asymptotic analysis to arbitrary order, and with a comparison of this asymptotic analysis to that found in the recent work of Kagei and Okita, 2017.

We consider orthogonal polynomials P_n satisfying orthogonality relations where the measure of orthogonality is, in general, a complex-valued Borel measure supported on subsets of the complex plane. In our consideration we will focus on measures of the form d\mu(z) = \rho(z) dz where the function \rho may depend on other auxiliary parameters. Much of the asymptotic analysis is done via the Riemann-Hilbert problem and the Deift-Zhou nonlinear steepest descent method, and relies heavily on notions from logarithmic potential theory.

The spectral distribution function of random matrices is an information-carrying object widely studied within Random matrix theory. In this thesis we combine the results of the theory together with the idea of free independence introduced by Voiculescu (1985). Important theoretical part of the thesis consists of the introduction to Free probability theory, which justifies use of asymptotic freeness with respect to particular matrices as well as the use of Stieltjes and R-transform. Both transforms are presented together with their properties. The aim of thesis is to point out characterizations of those classes of the matrices, which have closed form expressions for the asymptotic spectral distribution function. We consider all matrices which can be decomposed to the sum of asymptotically free independent summands. In particular, explicit calculations are performed in order to illustrate the use of asymptotic free independence to obtain the asymptotic spectral distribution for a matrix Q and generalize Marcenko and Pastur (1967) theorem. The matrix Q is defined as <img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?Q%20=%20%5Cfrac%7B1%7Dn%20X_1X%5E%5Cprime_1%20+%20%5Ccdot%5Ccdot%5Ccdot%20+%20%5Cfrac%7B1%7Dn%20X_kX%5E%5Cprime_k," />  where Xi is p × n matrix following a matrix normal distribution, Xi ~ Np,n(0, \sigma^2I, I). Finally, theorems pointing out classes of matrices Q which lead to closed formula for the asymptotic spectral distribution will be presented. Particularly, results for matrices with inverse Stieltjes transform, with respect to the composition, given by a ratio of polynomials of 1st and 2nd degree, are given.