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

Latex macros   ignore

Authors and affiliations   ignore

Buffer-wide source code blocks   ignore

# # #

End preamble   ignore

The poster

Code   ignore

Left column   BMCOL

Background   B_block

• Here we show how org-mode (version

  org-version

  ) and emacs (version

  emacs-version

  ) can be used to make decent looking scientific posters
• With org-mode we can populate the poster with code, graphs and numbers from inline code in languages such as R, python, Matlab and even shell scripting
• For example, this poster was created on on

  lsb_release -sd} {{{results(Ubuntu 17.10)}}

  .
• Inline code could look like this (which will produce a graph; Fig. /emacs/org-mode-poster/src/commit/795f8708f2acf732701223e14337033a8051c6c4/src/figcode1):

Block

set.seed(20180402)
x1 <- rnorm(100, 0, 1)
x2 <- rnorm(100, 0.5, 1)
hist(x1, col="red")
hist(x2, col="blue", add=TRUE)

This is the output.

Inline code and tables   B_block

• Tables are powerful in org-mode and even include spreadsheet capabilities
• Some code to process the first 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/795f8708f2acf732701223e14337033a8051c6c4/src/tabcode2) :

Block

library(broom)
library(dplyr)
t1 <- tidy(round(summary(x1), 2)) 
t2 <- tidy(round(summary(x2), 2))

# This will export as a table
rbind(t1, t2) %>%
mutate(name=c("x1", "x2"))

\vspace{2cm} \small

minimum	q1	median	mean	q3	maximum	name
-2.29	-0.49	0.11	0.14	0.8	2.47	x1
-2.17	-0.45	0.07	0.13	0.85	2.23	x2

Right column   BMCOL

Graphics   B_block

• We can use shell scripting to grab an image with curl from the internet (Fig. /emacs/org-mode-poster/src/commit/795f8708f2acf732701223e14337033a8051c6c4/src/figcode3):

Block

\footnotesize

# Download emacs icon from gnu.org
curl -0 https://www.gnu.org/software/emacs/images/emacs.png

\normalsize

\vspace{2cm}

This is the downloaded image.

Math   B_block

• Let's describe how to compute the distance between the two simulated distributions $x1$ and $x2$ from before:

Block

\small 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: # \small

\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

∩tionsetup{justification=justified,width=.85\linewidth}

This is the left figure of a two-column block, showing the density of $x1$.

Right

∩tionsetup{justification=justified,width=.85\linewidth}

This is the right figure. It shows the density of $x2$.

Conclusions   B_block

• This example is meant to show how versatile org-mode is
• Scientific posters can be produced with a simple text editor