Merge branch 'master' of https://git.frank-ebner.de/FHWS/DSem1
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presentation/chapters/toni.tex
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presentation/chapters/toni.tex
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\section{Status Stelle und Thema}
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\begin{frame}[fragile]
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\frametitle{Status}
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\begin{itemize}
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\item Promotion an der Universit"at Siegen in der Pattern Recognition Group von Prof. Dr. Marcin Grzegorzek
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\item Offizieller Start: 01.2017
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\item Wehrpflichtersatzstelle seit 03.2016 bis 03.2019 an FIW auf \SI{50}{\percent}
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\item Geplanter Abschluss der Promotion: \sout{03.2019} 12.2019
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\item Publikationen: 10 (+ 1 wird aktuell geschrieben)
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\item Fortschritt thematische Bearbeitung: \SI{75}{\percent}
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\item \textbf{Problem:} Fehlende Finanzierung von 03. bis 12.2019
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Indoor Localisation System}
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\begin{figure}
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\centering
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\includegraphics[width=\linewidth]{gfx/info_graphic}
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\end{figure}%
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Probleme herk\"ommlicher Systeme: Multimodalit\"aten}
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\begin{figure}
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\centering
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\def\svgwidth{0.9\columnwidth}
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\input{gfx/multimodalpath.eps_tex}
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\end{figure}%
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{L\"osung: Smoothing}
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Die klassische rekursive Zustandssch\"atzung kann nun durch Nutzung eines bekannten Intervalls $1:T$ wie folgt erweitert werden:
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\begin{equation*}
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p(\vec{q}_{t} \mid \vec{o}_{1:T}) = \underbrace{p(\vec{q}_{t} \mid \vec{o}_{1:t})}_{\text{Filtered}} \int \frac{\overbrace{p(\vec{q}_{t+1} \mid \vec{o}_{1:T})}^{\text{Smoothed}} \overbrace{p(\vec{q}_{t+1} \mid \vec{q}_{t})}^{\text{Transition}}}{\underbrace{\int p(\vec{q}_{t+1} \mid \vec{q}_{t}) p(\vec{q}_{t} \mid \vec{o}_{1:t}) d\vec{q}_{t}}_{\text{State Prediction}}} d\vec{q}_{t+1}\enspace .
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\end{equation*}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{L\"osung: Smoothing}
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\begin{algorithm}[H]
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\caption{Backward Simulation Smoothing}
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\label{alg:backwardSimulation}
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\begin{algorithmic}[1] % The number tells where the line numbering should start
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\Statex{\textbf{Input:} Prior $\mu(\vec{X}^i_1)$}
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\Statex{~}
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\For{$t = 1$ \textbf{to} $T$} \Comment{Filtering}
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\State{Perform particle filtering to obtain the weighted trajectories $ \{ W^i_t, \vec{X}^i_t\}^N_{i=1}$}
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\EndFor
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\For{ $k = 1$ \textbf{to} $N_{\text{sample}}$}
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\State{Choose $\tilde{\vec{q}}^k_T = \vec{X}^i_T$ with probability $W^i_T$} \Comment{Initialize realization}
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\For{$t = T-1$ \textbf{to} $1$} \Comment{Smoothing}
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\For{$j = 1$ \textbf{to} $N$}
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\State{Compute the weights \fcolorbox{FHWSOrange}{white}{$W^j_{t \mid t+1} = W^j_t ~ p(\tilde{\vec{q}}_{t+1} \mid \vec{X}^j_{t})$}}
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\EndFor
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\State{\fcolorbox{FHWSOrange}{white}{Choose $\tilde{\vec{q}}^k_t = \vec{X}^j_t$ with probability $W^j_{t\mid t+1}$}}
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\EndFor
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\State{$\tilde{\vec{q}}^k_{1:T} = (\tilde{\vec{q}}^k_1, \tilde{\vec{q}}^k_2, ..., \tilde{\vec{q}}^k_T)$ is one approximate realization from $p(\vec{q}_{1:T} \mid \vec{o}_{1:T})$}
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\EndFor
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\end{algorithmic}
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\end{algorithm}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Probleme herk\"ommlicher Systeme: Impoverishment}
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\begin{figure}
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\centering
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\def\svgwidth{0.9\columnwidth}
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\input{gfx/multimodalPath2.eps_tex}
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\end{figure}%
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\end{frame}
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\begin{frame}
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\frametitle{L\"osung: Markov Transition Process mit IMMPF}
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\only<1>{%
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\vspace{0.48cm}
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\centering
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\input{gfx/GeneralIdeaExample.eps_tex}
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}
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\only<2>{%
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\centering
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\input{gfx/GeneralIdeaExample2.eps_tex}
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}
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\only<3>{%
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\centering
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\input{gfx/GeneralIdeaExample3.eps_tex}
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}
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\only<4>{%
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\centering
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\input{gfx/GeneralIdeaExample5.eps_tex}
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}
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\end{frame}
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\section{Lessons learned}
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\begin{frame}[fragile]
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\frametitle{Lessons learned}
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\begin{itemize}
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\item Klare thematische Ziele setzen!
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\item Gute Deadlines - Schlechte Deadlines
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\item Gute Projekte - Schlechte Projekte
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\item Themen auch mal liegen lassen und Abstand gewinnen
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\item Priorit\"aten setzen und verteidigen
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\item Klare Grenzen zwischen Anstellung und Promotion definieren
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\item Regelm\"a\ss ige Kommunikation / Team-Meetings
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\item Viel publizieren oder Tagebuch f\"uhren
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\item Intelligentes Tooling (Mendeley, LaTeX, Simplenote etc. pp.)
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\end{itemize}
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\end{frame}
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