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presentation.tex
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@ -1,5 +1,5 @@
\documentclass{beamer}
\usetheme{PaloAlto}
\usetheme{Boadilla}
\usecolortheme{seahorse}
% importations
@ -32,15 +32,15 @@
\definecolor{ao(english)}{rgb}{0.0, 0.5, 0.0}
% Beamer
\setbeamertemplate{headline}{}
% \setbeamertemplate{headline}{}
\setbeamertemplate{caption}[numbered]
\setbeamertemplate{footline}{%
\hbox{%
\begin{beamercolorbox}[wd=\paperwidth, right]{title in head/foot}%
\tiny{\insertframenumber{}/\inserttotalframenumber}
\end{beamercolorbox}
}
}
% \setbeamertemplate{footline}{%
% \hbox{%
% \begin{beamercolorbox}[wd=\paperwidth, right]{title in head/foot}%
% \tiny{\insertframenumber{}/\inserttotalframenumber}
% \end{beamercolorbox}
% }
% }
\setbeamerfont{caption}{size=\scriptsize} % Petit titre de figures
@ -77,7 +77,8 @@
% Tikz
%% Tikz Related
\usetikzlibrary{calc,shapes,backgrounds,arrows,automata,shadows,positioning}
\usetikzlibrary{arrows,shapes,positioning,shadows,trees,calc,backgrounds,automata,positioning}
\usetikzlibrary{arrows,shapes,positioning,shadows,trees,calc,backgrounds,
automata,positioning, petri}
\usetikzlibrary{decorations.pathreplacing,calligraphy}
@ -118,7 +119,7 @@
}
\makeatother
\subtitle{Audition candidature de thèse à l'EDMH}
\subtitle{Audition de candidature de thèse à l'EDMH}
\title[Comparaison de structures de réseaux]{Comparaison de structures de
réseaux.
Applications à des réseaux écologiques}

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principal.tex
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@ -48,6 +48,52 @@
rarement par observation directe.
\end{itemize}
\begin{figure}[ht]
\centering
\begin{tikzpicture}[scale=.6]
\tikzstyle{every edge}=[-,>=stealth',shorten >=1pt,auto,thin,draw]
\tikzstyle{every state}=[draw, text=white,scale=0.65, font=\scriptsize, transform shape]
% Upper level
\tikzstyle{every state}=[draw=none,text=white,scale=0.55, font=\scriptsize, transform shape]
% premier cluster
\tikzstyle{every node}=[fill=green!50!blue!20!white]
\node[state] (N1) at (1.1,3) {\includegraphics[width=.1\textwidth]{img/pollen.png}};
\node[state, right = of N1] (N2) {\includegraphics[width=.1\textwidth]{img/pollen.png}}; % at (.75,3)
\node[state, right = of N2] (N3) {\includegraphics[width=.1\textwidth]{img/pollen.png}}; % at (1.5,3)
\node[state, right = of N3] (N4) {\includegraphics[width=.1\textwidth]{img/pollen.png}}; % at (2.25,3)
% \node[state] (N5) at (3,3) {\includegraphics[width=.1\textwidth]{img/pollen.png}};
% \node[state] (N6) at (3.75,3) {\includegraphics[width=.1\textwidth]{img/pollen.png}};
\tikzstyle{every node}=[shape=rectangle,fill=red!50!blue!20!white]
% \node[state, fill = white] (P) at (-1.5, 0) {\includegraphics[width=.08\textwidth]{img/bee.png}};
\node[state, tokens=0] (P1) at (-1, 0) {\includegraphics[width=.1\textwidth]{img/bee.png}};
\node[state, tokens=0, right = of P1] (P2) {\includegraphics[width=.1\textwidth]{img/bee.png}}; % at (-.25, 0)
\node[state, tokens=0, right = of P2] (P3) {\includegraphics[width=.1\textwidth]{img/bee.png}}; %at (.5, 0)
\node[state, tokens=0, right = of P3] (P4) {\includegraphics[width=.1\textwidth]{img/bee.png}}; % at (1.25, 0)
\node[state, tokens=0, right = of P4] (P5) {\includegraphics[width=.1\textwidth]{img/bee.png}};% at (2,0)
\node[state, tokens=0, right = of P5] (P6) {\includegraphics[width=.1\textwidth]{img/bee.png}}; % at (2.75,0)
% \node[state, tokens=0] (P7) at (3.5,0) {\includegraphics[width=.1\textwidth]{img/bee.png}};
% \node[state, tokens=0] (P8) at (4.25,0) {\includegraphics[width=.1\textwidth]{img/bee.png}};
\tikzstyle{every edge}=[>=stealth,shorten >=1pt,auto,thin,draw]
\path (P1) edge (N1);
\path (P2) edge (N1);
\path (P3) edge (N1);
\path (P4) edge (N2);
\path (P4) edge (N1);
\path (P6) edge (N2);
\path (P1) edge (N3);
%\path (P7) edge (N4);
%\path (P8) edge (N5);
%\path (P4) edge (N6);
\path (P5) edge (N3);
\path (P5) edge (N4);
\end{tikzpicture}
\caption{Exemple d'un réseau plantes-pollinisateurs}
\label{fig:plantes-pollin}
\end{figure}
\end{frame}
\begin{frame}{Contexte mathématique}
@ -432,7 +478,7 @@
apprentissage profond pour comparaison des topologies de réseaux}
\label{sec:axe-2}
\begin{frame}[allowframebreaks]
\begin{frame}[allowframebreaks]{\emph{Graph Neural Networks}}
\begin{figure}
\includegraphics[scale=0.4]{img/Message_passing.pdf}
\caption{Illustration du \emph{message passing} au sein d'un graphe.\footnote{Figure adaptée de \cite{sanchez-lengelingGentleIntroductionGraph2021} par Emré Anakok.}}
@ -467,7 +513,8 @@
\subsection[Axe 3]{Axe 3 : Inférence jointe de réseaux}
\label{sec:axe-3}
\begin{frame}
Limites des techniques actuelles \cite{matchadoNetworkAnalysisMethods2021}.
Rôle important pour les réseaux reconstruits notamment en microbiologie.
\end{frame}
\section{Organisation de la thèse}
\label{sec:organisation-these}
@ -478,11 +525,11 @@
\centering
\begin{chronology}[1]{2024}{2028}{\textwidth}[110ex]
\eventspan {\decimaldate{1}{10}{2024}}{\decimaldate{1}{10}{2025}}%
{\textbf{\color{blue} Axe 1}}[blue][.3][0.1][b]
\eventspan {\decimaldate{1}{10}{2025}}{\decimaldate{1}{10}{2026}}%
{\textbf{\color{red} Axe 2}}[red][.3][0.1][b]
{\small\textbf{\color{blue} Collections \& modèles à variables latentes}}[blue][.3][0.1]
\eventspan {\decimaldate{1}{5}{2025}}{\decimaldate{1}{10}{2026}}%
{\textbf{\color{red} \emph{Embedding} de n\oe uds par \emph{Deep Learning}}}[red][.3][0.1]
\eventspan {\decimaldate{1}{4}{2026}}{\decimaldate{1}{4}{2027}}%
{\textbf{\color{ao(english)} Axe 3}}[ao(english)][.3][0.1][b]
{\textbf{\color{ao(english)} Inférence de réseaux}}[ao(english)][.3][0.1]
\eventspan {\decimaldate{1}{4}{2027}}{\decimaldate{1}{10}{2027}}%
{\textbf{\color{gray} Rédaction du manuscrit}}[gray][.3][0.1][b]
% \eventpoint{\decimaldate{11}{4}{1976}}{Launch of Apple I}[red][1][1]

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references.bib
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@ -399,6 +399,23 @@
keywords = {data,plant-pollinator}
}
@article{matchadoNetworkAnalysisMethods2021,
title = {Network Analysis Methods for Studying Microbial Communities: {{A}} Mini Review},
shorttitle = {Network Analysis Methods for Studying Microbial Communities},
author = {Matchado, Monica Steffi and Lauber, Michael and Reitmeier, Sandra and Kacprowski, Tim and Baumbach, Jan and Haller, Dirk and List, Markus},
date = {2021-01-01},
journaltitle = {Computational and Structural Biotechnology Journal},
volume = {19},
pages = {2687--2698},
issn = {2001-0370},
doi = {10.1016/j.csbj.2021.05.001},
url = {https://www.sciencedirect.com/science/article/pii/S2001037021001823},
urldate = {2024-05-16},
abstract = {Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.},
keywords = {Microbial co-occurrence networks,Microbial interactions,Network analysis,Trans-kingdom interactions},
file = {/home/polarolouis/Zotero/storage/NAEQFHE8/j.csbj.2021.05.001.pdf.pdf;/home/polarolouis/Zotero/storage/SXJYNPP7/Matchado et al. - 2021 - Network analysis methods for studying microbial co.pdf;/home/polarolouis/Zotero/storage/B6NZVP7Y/S2001037021001823.html}
}
@book{ottawafield-naturalistsclubCanadianFieldnaturalist1976,
title = {The {{Canadian}} Field-Naturalist},
author = {Ottawa Field-Naturalists' Club and Club, Ottawa Field-Naturalists'},