diff --git a/src/org-mode-poster_poster.org b/src/org-mode-poster_poster.org index d3561c0..41e70cb 100644 --- a/src/org-mode-poster_poster.org +++ b/src/org-mode-poster_poster.org @@ -36,6 +36,10 @@ #+LATEX_HEADER: \usepackage{listings} #+LATEX_HEADER: \usepackage{textcomp} #+LATEX_HEADER: \usepackage{bibentry} +#+LATEX_HEADER: \newcommand\sumin{\sum_{i=1}^{n}} +#+LATEX_HEADER: \newcommand{\Xoi}[1]{#1(i)} +#+LATEX_HEADER: \newcommand{\frakPQ}[2]{\frac{\Xoi{#1}}{\Xoi{#2}}} +#+LATEX_HEADER: \newcommand{\DKLPQ}[3]{D_{\mathrm{KL}}(#1 #3 #2)} #+LATEX_HEADER: \date{} # ---------------------------------------------------------------------- ** Authors and affiliations :ignore: @@ -133,7 +137,6 @@ :BEGIN: :BEAMER_env: fullframe :END: - ** Code :ignore: # Babel code can go here to populate the poster with dynamic output from # statistical calculations @@ -178,7 +181,7 @@ hist(x, col="gray") #+CAPTION: This is the output. [[file:3.png]] -*** Methods: Inline code and tables :B_block: +*** Inline code and tables :B_block: :PROPERTIES: :BEAMER_env: block :END: @@ -213,14 +216,12 @@ data.frame(Mean=m, SD=s) | -0.07 | 0.97 | |-------+------| - - ** Right column :BMCOL: :PROPERTIES: :BEAMER_col: 0.45 :BEAMER_opt: [t] :END: -*** Results: graphics :B_block: +*** Graphics :B_block: :PROPERTIES: :BEAMER_env: block :END: @@ -250,7 +251,40 @@ curl -0 https://www.gnu.org/software/emacs/images/emacs.png #+RESULTS: code3 [[file:emacs.png]] -*** Results: columns :B_block: +*** Math :B_block: +:PROPERTIES: +:BEAMER_env: block +:END: + +- We can easily include math: + +**** Block +:PROPERTIES: +:BEAMER_col: 0.78 +:BEAMER_opt: [T] +:END: + +The Kullback-Leibler (KL) divergence measures the difference between two +probability distributions (i.e., the loss of information when one +distribution is used to approximate another). The KL divergence is thus +defined as + +\begin{align} +\label{eq:KL} +\DKLPQ{P}{Q}{\|} = \sumin \Xoi{P} \log \frakPQ{P}{Q} +\end{align} + +with $P$ and $Q$ being two probability distribution functions and $n$ +the number of sample points. Since $\DKLPQ{P}{Q}{\|}$ is not equal to +$\DKLPQ{Q}{P}{\|}$, a symmetric variation of the KL divergence can be +derived as follows: + +\begin{align} +\label{eq:KL2} +\DKLPQ{P}{Q}{,} = \sumin \Big(\Xoi{P} \log \frakPQ{P}{Q} + \Xoi{Q} \log \frakPQ{Q}{P} \Big). +\end{align} + +*** Columns :B_block: :PROPERTIES: :BEAMER_env: block :END: diff --git a/src/org-mode-poster_poster.pdf b/src/org-mode-poster_poster.pdf index 7a482f1..dbe6eea 100644 Binary files a/src/org-mode-poster_poster.pdf and b/src/org-mode-poster_poster.pdf differ diff --git a/src/org-mode-poster_poster.tex b/src/org-mode-poster_poster.tex index a9a5ca9..49e368e 100644 --- a/src/org-mode-poster_poster.tex +++ b/src/org-mode-poster_poster.tex @@ -1,4 +1,4 @@ -% Created 2018-04-02 Mon 15:25 +% Created 2018-04-02 Mon 18:38 % Intended LaTeX compiler: pdflatex \documentclass[final]{beamer} \usetheme{ph} @@ -19,6 +19,10 @@ \usepackage{listings} \usepackage{textcomp} \usepackage{bibentry} +\newcommand\sumin{\sum_{i=1}^{n}} +\newcommand{\Xoi}[1]{#1(i)} +\newcommand{\frakPQ}[2]{\frac{\Xoi{#1}}{\Xoi{#2}}} +\newcommand{\DKLPQ}[3]{D_{\mathrm{KL}}(#1 #3 #2)} \date{} \author{ Philipp Homan$^{1}$, @@ -29,11 +33,11 @@ Philipp Homan$^{1}$, \normalsize{Hempstead, NY} } \usetheme{default} -\date{2018-04-02 15:25} +\date{2018-04-02 18:38} \title{A scientific poster entirely written in org-mode using GNU emacs and the beamer library} \begin{document} -\begin{frame}[fragile,label={sec:orga07b5dd}]{} +\begin{frame}[fragile,label={sec:org727f6f5}]{} \begin{columns} \begin{column}[t]{0.45\columnwidth} \begin{block}{Background} @@ -47,7 +51,7 @@ org-mode syntax code, graphs and numbers from inline code in languages such as R, python, Matlab and even shell scripting \item Inline code would look like this, which will produce a graph -(Fig. \ref{fig:orgfe25245}): +(Fig. \ref{fig:orgaca79ae}): \end{itemize} \begin{columns} @@ -61,21 +65,21 @@ hist(x, col="gray") \begin{figure}[htbp] \centering \includegraphics[width=.9\linewidth]{3.png} -\caption{\label{fig:orgfe25245} +\caption{\label{fig:orgaca79ae} This is the output.} \end{figure} \end{column} \end{columns} \end{block} -\begin{block}{Methods: Inline code and tables} +\begin{block}{Inline code and tables} \begin{itemize} \item In addition to inline code, we can also produce tables \item Tables are very powerful in org-mode, they even include spreadsheet capabilities \item Some code to process the vector from above to make a table out of its summary could look like this, which would result in a little table -(Table \ref{tab:org6afde98}) : +(Table \ref{tab:org6921627}) : \end{itemize} \begin{columns} @@ -93,9 +97,9 @@ data.frame(Mean=m, SD=s) \begin{tabular}{rr} Mean & SD\\ \hline --0.14 & 0.97\\ +0.07 & 0.98\\ \end{tabular} -\caption{\label{tab:org6afde98} +\caption{\label{tab:org6921627} A table.} \end{table} @@ -104,14 +108,12 @@ A table.} \end{block} \end{column} - - \begin{column}[t]{0.45\columnwidth} -\begin{block}{Results: graphics} +\begin{block}{Graphics} \begin{itemize} \item Of course we can also include graphics \item Here, we use shell scripting to grab an image with curl from the -internet (Fig. \ref{fig:org1ba5872}): +internet (Fig. \ref{fig:orgf35b3ea}): \end{itemize} \begin{columns} @@ -127,21 +129,51 @@ curl -0 https://www.gnu.org/software/emacs/images/emacs.png \begin{figure}[htbp] \centering \includegraphics[page=9,width=0.2\textwidth]{emacs.png} -\caption{\label{fig:org1ba5872} +\caption{\label{fig:orgf35b3ea} This is the downloaded image.} \end{figure} \end{column} \end{columns} \end{block} -\begin{block}{Results: columns} +\begin{block}{Math} +\begin{itemize} +\item We can easily include math: +\end{itemize} + +\begin{columns} +\begin{column}[T]{0.78\columnwidth} +The Kullback-Leibler (KL) divergence measures the difference between two +probability distributions (i.e., the loss of information when one +distribution is used to approximate another). The KL divergence is thus +defined as + +\begin{align} +\label{eq:KL} +\DKLPQ{P}{Q}{\|} = \sumin \Xoi{P} \log \frakPQ{P}{Q} +\end{align} + +with \(P\) and \(Q\) being two probability distribution functions and \(n\) +the number of sample points. Since \(\DKLPQ{P}{Q}{\|}\) is not equal to +\(\DKLPQ{Q}{P}{\|}\), a symmetric variation of the KL divergence can be +derived as follows: + +\begin{align} +\label{eq:KL2} +\DKLPQ{P}{Q}{,} = \sumin \Big(\Xoi{P} \log \frakPQ{P}{Q} + \Xoi{Q} \log \frakPQ{Q}{P} \Big). +\end{align} +\end{column} +\end{columns} +\end{block} + +\begin{block}{Columns} \begin{columns} \begin{column}[T]{0.48\columnwidth} \captionsetup{justification=justified,width=.8\linewidth} \begin{figure}[htbp] \centering \includegraphics[page=3,width=0.9\textwidth]{org-mode-poster-4.png} -\caption{\label{fig:org1748fcb} +\caption{\label{fig:org623938b} \textbf{This is the left figure of a two-column block}} \end{figure} \end{column} @@ -151,7 +183,7 @@ This is the downloaded image.} \begin{figure}[htbp] \centering \includegraphics[page=9,width=0.9\textwidth]{org-mode-poster-4.png} -\caption{\label{fig:org5a59fdf} +\caption{\label{fig:orgb76a1ef} \textbf{This is the right figure.}} \end{figure} \end{column} diff --git a/src/org-mode-poster_poster.tex~ b/src/org-mode-poster_poster.tex~ index 03c1759..9f582cb 100644 --- a/src/org-mode-poster_poster.tex~ +++ b/src/org-mode-poster_poster.tex~ @@ -1,4 +1,4 @@ -% Created 2018-04-02 Mon 15:24 +% Created 2018-04-02 Mon 18:37 % Intended LaTeX compiler: pdflatex \documentclass[final]{beamer} \usetheme{ph} @@ -19,6 +19,10 @@ \usepackage{listings} \usepackage{textcomp} \usepackage{bibentry} +\newcommand\sumin{\sum_{i=1}^{n}} +\newcommand{\Xoi}[1]{#1(i)} +\newcommand{\frakPQ}[2]{\frac{\Xoi{#1}}{\Xoi{#2}}} +\newcommand{\DKLPQ}[3]{D_{\mathrm{KL}}(#1 #3 #2)} \date{} \author{ Philipp Homan$^{1}$, @@ -29,11 +33,11 @@ Philipp Homan$^{1}$, \normalsize{Hempstead, NY} } \usetheme{default} -\date{2018-04-02 15:24} +\date{2018-04-02 18:37} \title{A scientific poster entirely written in org-mode using GNU emacs and the beamer library} \begin{document} -\begin{frame}[fragile,label={sec:orgbaeb4b8}]{} +\begin{frame}[fragile,label={sec:org4b06f5e}]{} \begin{columns} \begin{column}[t]{0.45\columnwidth} \begin{block}{Background} @@ -47,7 +51,7 @@ org-mode syntax code, graphs and numbers from inline code in languages such as R, python, Matlab and even shell scripting \item Inline code would look like this, which will produce a graph -(Fig. \ref{fig:org8727911}): +(Fig. \ref{fig:org042638b}): \end{itemize} \begin{columns} @@ -61,21 +65,21 @@ hist(x, col="gray") \begin{figure}[htbp] \centering \includegraphics[width=.9\linewidth]{3.png} -\caption{\label{fig:org8727911} +\caption{\label{fig:org042638b} This is the output.} \end{figure} \end{column} \end{columns} \end{block} -\begin{block}{Methods: Inline code and tables} +\begin{block}{Inline code and tables} \begin{itemize} \item In addition to inline code, we can also produce tables \item Tables are very powerful in org-mode, they even include spreadsheet capabilities \item Some code to process the vector from above to make a table out of its summary could look like this, which would result in a little table -(Table \ref{tab:orga4b22ee}) : +(Table \ref{tab:orge51644d}) : \end{itemize} \begin{columns} @@ -93,9 +97,9 @@ data.frame(Mean=m, SD=s) \begin{tabular}{rr} Mean & SD\\ \hline --0.11 & 0.92\\ +0.23 & 1.07\\ \end{tabular} -\caption{\label{tab:orga4b22ee} +\caption{\label{tab:orge51644d} A table.} \end{table} @@ -104,19 +108,17 @@ A table.} \end{block} \end{column} - - \begin{column}[t]{0.45\columnwidth} -\begin{block}{Results: graphics} +\begin{block}{Graphics} \begin{itemize} \item Of course we can also include graphics \item Here, we use shell scripting to grab an image with curl from the -internet (Fig. \ref{fig:orgb4430e3}): +internet (Fig. \ref{fig:orga669d9a}): \end{itemize} \begin{columns} \begin{column}[T]{0.78\columnwidth} -\tiny +\footnotesize \begin{minted}[linenos=true]{bash} curl -0 https://www.gnu.org/software/emacs/images/emacs.png \end{minted} @@ -127,21 +129,46 @@ curl -0 https://www.gnu.org/software/emacs/images/emacs.png \begin{figure}[htbp] \centering \includegraphics[page=9,width=0.2\textwidth]{emacs.png} -\caption{\label{fig:orgb4430e3} +\caption{\label{fig:orga669d9a} This is the downloaded image.} \end{figure} \end{column} \end{columns} \end{block} -\begin{block}{Results: columns} +\begin{block}{Math} +\begin{itemize} +\item We can easily include math: +\end{itemize} + +\begin{columns} +\begin{column}[T]{0.78\columnwidth} +The Kullback-Leibler (KL) divergence measures the difference between two +probability distributions (i.e., the loss of information when one +distribution is used to approximate another). The KL divergence is thus +defined as + +\begin{align} +\label{eq:KL} +\DKLPQ{P}{Q}{\|} = \sumin \Xoi{P} \log \frakPQ{P}{Q} +\end{align} + +with \(P\) and \(Q\) being two probability distribution functions and \(n\) +the number of sample points. Since \(\DKLPQ{P}{Q}{\|}\) is not equal to +\(\DKLPQ{Q}{P}{\|}\), a symmetric variation of the KL divergence can be +derived as follows: +\end{column} +\end{columns} +\end{block} + +\begin{block}{Columns} \begin{columns} \begin{column}[T]{0.48\columnwidth} \captionsetup{justification=justified,width=.8\linewidth} \begin{figure}[htbp] \centering \includegraphics[page=3,width=0.9\textwidth]{org-mode-poster-4.png} -\caption{\label{fig:orgb45d477} +\caption{\label{fig:org88ad0c6} \textbf{This is the left figure of a two-column block}} \end{figure} \end{column} @@ -151,7 +178,7 @@ This is the downloaded image.} \begin{figure}[htbp] \centering \includegraphics[page=9,width=0.9\textwidth]{org-mode-poster-4.png} -\caption{\label{fig:orgdcbb6b3} +\caption{\label{fig:org9e1f136} \textbf{This is the right figure.}} \end{figure} \end{column}