org-mode-poster/src/org-mode-poster_poster.org

Preamble   ignore

General comments   ignore

Specific comments for this manuscript   ignore

org specific settings   ignore

Latex header   ignore

Authors and affiliations   ignore

Buffer-wide source code blocks   ignore

# # #

End preamble   ignore

The poster

Code   ignore

Left column   BMCOL

Background   B_block

• Org-mode is not only useful for producing blog posts and even scientific manuscripts; it is also perfectly suitable to make decent looking scientific posters
• We combine a relatively simple custom \LaTeX style file and common org-mode syntax
• The nice thing about org-mode is that we can populate the poster with code, graphs and numbers from inline code in languages such as R, python, Matlab and even shell scripting
• Inline code would look like this, which will produce a graph (Fig. /emacs/org-mode-poster/src/commit/0a4af1326003ca3c8715fa5a9b03a2dabebf3c80/src/figcode1):

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x <- rnorm(100, 0, 1)
hist(x, col="gray")

This is the output.

Inline code and tables   B_block

• In addition to inline code, we can also produce tables
• Tables are very powerful in org-mode, they even include spreadsheet capabilities
• 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 /emacs/org-mode-poster/src/commit/0a4af1326003ca3c8715fa5a9b03a2dabebf3c80/src/tabcode2) :

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m <- round(mean(x), 2)
s <- round(sd(x), 2)
data.frame(Mean=m, SD=s)

\vspace{2cm}

Mean	SD
-0.07	0.97

Right column   BMCOL

Graphics   B_block

• Of course we can also include graphics
• Here, we use shell scripting to grab an image with curl from the internet (Fig. /emacs/org-mode-poster/src/commit/0a4af1326003ca3c8715fa5a9b03a2dabebf3c80/src/figcode3):

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\footnotesize

curl -0 https://www.gnu.org/software/emacs/images/emacs.png

\normalsize

\vspace{2cm}

This is the downloaded image.

Math   B_block

• We can easily include math:

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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

Left

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Right

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Conclusions   B_block

• This little example is meant to show how incredibly versatile org-mode is
• One can now produce scientific posters with a simple text editor