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\lhead{}
\chead{Central Massachusetts Amateur Radio Association}
\rhead{}
\lfoot{\texttt{https://github.com/mide/cmara-meeting-minutes/}}
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\begin{document}

\begin{center}
{\huge April 2017 Board of Directors Meeting}\\
\emph{of the}\\
{\Large Central Massachusetts Amateur Radio Association}\\
\emph{Submitted by Mark Ide \texttt{W1IDE}, Secretary}
\end{center}

\section{Meeting Called to Order}
The CMARA April 2017 board meeting was called to order on April 20, 2017 by CMARA president Bob Peloquin (\texttt{KB1VUA}).

\section{Attendance}

\subsection{Officers Present}
\begin{tabular}{|l|l|l|c|}
  \hline
  \textbf{Position} & \textbf{Name}  & \textbf{Callsign} & \textbf{Present} \\ \hline
  President         & Bob Peloquin   & \texttt{KB1VUA}   & Yes \\
  Vice President    & Brian Loverro  & \texttt{K1BML}    & Yes \\
  Secretary         & Mark Ide       & \texttt{W1IDE}    & No \\
  Treasurer         & Jim Singer     & \texttt{N1EKO}    & No \\
  Webmaster         & Lyn Glagowski  & \texttt{WB1CCL}   & No \\
  \hline
\end{tabular}

\subsection{Board of Directors Present}
\begin{tabular}{|l|l|c|}
  \hline
  \textbf{Name}     & \textbf{Callsign} & \textbf{Present} \\ \hline
  Adrian Zeffert    & \texttt{AB2IX}    & Yes \\
  Harold Carlson    & \texttt{N1ZC}     & Yes \\
  Greg Algieri      & \texttt{WA1JXR}   & Yes \\
  Terry Glagowski   & \texttt{W1TR}     & Yes \\
  Randy Dore        & \texttt{W4FEB}    & Yes \\
  Johnathan Sherman & \texttt{WW2JS}    & Yes \\
  \hline
\end{tabular}

\subsection{Members Present}
\texttt{KC1GIB}

\subsection{Guests \& Visitors}
Christopher Wentworth

\section{Primary Discussion}

\subsection{Club Projector}
Bob (\texttt{KB1VUA}) brought up the need for the club to purchase a new projector for the presentations. The old one is just not up to the task anymore. The board voted to spend no more than \$600.00 on a new projector. We will try to sell the old projector to members first, then the general public. Voted in by voice vote, all in agreement.

\subsection{Field Day}
\begin{enumerate}
  \item We are going to try to get new members to help out during field day.
  \item We are going to look at easier options for field day foods. It is felt that we can cut down the time cooking and preparing.
  \item Weather permitting, we'd like to have telescope observing at the field on Saturday night. When we set up for this we don't want white lights (flashlights or headlights) shining in our eyes because it takes about half an hour to recover from being blinded by white light. If we are observing we will request that all super-bright flashlights be turned off and no cars will be allowed in or out with headlights lit so we may open up the observatory and set up another scope outside as well. The use of red flashlights will be required from dark utill we close up the observatory for the night.
  \item We will work with Gerry (\texttt{W1GD}) to get a group formed specifically to assemble the antennas.
\end{enumerate}

\subsection{Unpaid Members}
Bob (\texttt{KB1VUA}) and Brian (\texttt{K1BML}) went through the list of unpaid members.

\subsection{Radio Donation Sales}
We can now roll the ``Radio Donation Sales'' funds into the ``General Fund''. We need to let Jim (\texttt{N1EKO}) know.

\subsection{ARRL Listing}
Greg (\texttt{WA1JXR}) updated the CMARA listing on ARRL.

\section{Next Month's Presentation}
Next month's presentation will be an open discussion on Field Day planning.

\section{Meeting Adjourned}
The meeting was adjourned April 20, 2017 at 9:49 PM by CMARA president Bob Peloquin (\texttt{KB1VUA}).

\end{document}

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%----------------------
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% Author : Nischay Jain
% Github : nischay13may
% License :
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%-------------------------------------------
%%%%%%  CV STARTS HERE  %%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{document}

%----------HEADING-----------------
\begin{tabular*}{\textwidth}{l@{\extracolsep{\fill}}r}
  \textbf{\href{http://nischay13may.github.io/}{\Large Nischay Jain}} & Email : \href{mailto:nischay13may@gmail.com}{nischay13may@gmail.com}\\
  \href{http://nischay13may.github.io/}{http://nischay13may.github.io/} & Mobile : +91-9075093938 \\
\end{tabular*}

%----------PROFESSIONAL SUMMARY----------
\section{Summary}
  \resumePointsListStart
    \resumePoint{Full Stack Developer with \textbf{6 years} of hands-on experience in application development using web technologies like \textbf{Angular, Java, Spring Boot and Hibernate.}}
    \resumePoint{Strong experience in designing, developing and supporting REST services using Spring Boot and Java}
    \resumePoint{Strong experience in front-end web development using HTML5, CSS3, Javascript, Angular 8+}
    \resumePoint{Strong understanding of Cloud Technologies like Docker and IBM Cloud.}
    \resumePoint{Good knowledge of Data Structures and Algorithms and good problem solving skills.}
    \resumePoint{Robust skills of trouble shooting, debugging and fixing various logical and technical issues.}
  \resumePointsListEnd

%-----------EXPERIENCE-----------------
\section{Experience}
  \resumeSubHeadingListStart

    \resumeSubheading
      {IBM}{Bengaluru, KA, India}
      {Full Stack Developer}{Aug 2019 - Present}
      \resumeItemListStart
        \resumeItem{Master Data Hub}
          {: Master Data Hub Configurator (MDH) is a centralized interface of IBM Products and Offerings and its associated components sourced from various trusted source.}
          \resumePointsListStart
              \resumePoint{Responsible for designing and developing Promotion module and Access Management module.}
              \resumePoint{Improved the application load time by 20\% by implementing lazy loading.}
              \resumePoint{Built extensive test coverage for all new features and some legacy code which reduced the number of bugs and complaints by 10\%.}
          \resumePointsListEnd
      \resumeItemListEnd

%--------Multiple Positions Heading------------
%    \resumeSubSubheading
%     {Software Engineer I}{Oct 2014 - Sep 2016}
%     \resumeItemListStart
%        \resumeItem{Apache Beam}
%          {Apache Beam is a unified model for defining both batch and streaming
%          data-parallel processing pipelines}
%     \resumeItemListEnd
%    \resumeSubHeadingListEnd
%-------------------------------------------

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      {Tata Consultancy Services Ltd.}{Nagpur, MH, India}
      {Full Stack Developer}{Nov 2015 - Jul 2019}
      \resumeItemListStart
        \resumeItem{General Door Access Requisition}
          {: A Web application for providing access management of Front Door Access across all locations available to all TCS associates.}
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              \resumePoint{Responsible for developing responsive Front End for the application using Bootstrap.}
              \resumePoint{Developed Profile management module for the Cron Job used for managing User Profiles at the EBI Server.}
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        \resumeItem{Print/Encode Id Card}
          {: A Web application to manage printing and personalizing identity cards of associates.}
          \resumePointsListStart
              \resumePoint{Developed Layout Configuration and User Management module for the Admin Team which is used for configuring card layout and managing users.}
              \resumePoint{Responsible for API design and development of RESTful Services for the in-house application.}
          \resumePointsListEnd
      \resumeItemListEnd

  \resumeSubHeadingListEnd

%-----------PROJECTS-----------------
%\section{Projects}
%  \resumeSubHeadingListStart
%  \resumeSubHeadingListEnd

%--------TECHNICAL SKILLS------------
\section{Technical Skills}
  \resumeSubHeadingListStart
    \resumeSubItem{Languages}{: Java, Javascript, SQL}
    \resumeSubItem{Technologies}{: Angular, Spring Boot, Hibernate, JUnit, IBM Cloud}
    \resumeSubItem{Tools}{: Github, SVN, Jenkins, Maven}
  \resumeSubHeadingListEnd
%-------------------------------------------

%-------CERTIFICATIONS--------------
\section{Certifications}
    \resumeSubHeadingListStart
    \resumeSubItem{Oracle Certified Associate Java SE 8 Programmer}{}
    \resumeSubHeadingListEnd

%-----------EDUCATION-----------------
\section{Education}
  \resumeSubHeadingListStart
    \resumeSubheading
      {Bengal College of Engg. and Technology}{Durgapur, WB, India}
      {Bachelor of Technology in Information Technology;  CGPA: 7.68}{May. 2015}
    \resumeSubheading
      {Kendriya Vidyalaya No. 2}{Kalaikunda, WB, India}
      {Higher Secondary,  76.4\%}{Apr, 2011}
  \resumeSubHeadingListEnd

%----------LINKS----------------------
\section{Links}
  \resumeSubHeadingListStart
    \item{
      \textbf{LinkedIn}{: \href{https://www.linkedin.com/in/nischay13may/}{nischay13may}}
      \hfill
      \textbf{Github}{: \href{https://github.com//nischay13may/}{nischay13may}}
      \hfill
      \textbf{LeetCode}{: \href{https://leetcode.com/nischay13may/}{nischay13may}}
    }
  \resumeSubHeadingListEnd

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ext: tex


text:
\documentclass{article}

\begin{document}
	\section{Introduction}
	\subsection{Kernel}
	\subsection{Concepts}
	\section{Types of Operating Systems}
	\section{Process Management} hardware file memory
	\section{Memory Management}
	\section{File Management}
	\section{Handling of I/O}
	\section{Interrupt Handling}

\end{document}

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max_forks_repo_name: Girogio/My-LaTeX


text:
The principle difficulties of previous numerical studies can be summarized as follows:
\begin{enumerate}

\item Much higher eccentricities of the final planets than the real solar system planets.
\item Differences in the formation sites of the bodies in the semi-major axis domain with respect to the planet.
\item Very large number of final bodies.
\end{enumerate}

First and last points are related.Low eccentricities of planetesimals is required in order for the final eccentricities to be small.This unfortunately isolates the feeding zones of the embryos preventing further evolution thereby.This could be avoided by assigning some eccentricity to the initial planetesimal distribution (Cox \& Lewis 1980), which is rather large ($\sim 0.15$).

The model can be improved in two ways: first, by introducing more realistic collision model and, secondly, by a more careful treatment of perturbations.\\
In the Physical processes to simulate, collisions with outcomes other than accretion can occur, and their dynamical and evolutionary consequences are very different. Hence, most important physical processes are included.
\\
A model to be realistic must be able to treat the outcome of collisions both as a function of dynamic characteristics  of the impact (Relative velocity and the mass of the body involved) and the physical properties of the bodies. This helps in ordaining possible collisional outcome. Especially in cases where there is a choice between accreting and fragmenting.
As there is no direct information about what happens when two planetesimals collide, the lab experiments on collisions under wide range of impact velocities and materials (Fujiwara \& Tsuamoto 1980; Hartmann 1978) form the basis to infer behaviour and outcomes. The experimental results however cannot be fully extrapolated to celestial bodies and hence is a rough approximation of these processes no matter how detailed a collision model is. The equipartition of impact energy (Impact energy is distributed evenly over both of the colliding bodies) is assumed throughout. The model employed (Greenberg et al. (1978) and Spaute et al. (1985)) considers the collision of two planetesimals of total mass, $M = m_{1}+m_{2}$ ($m_{1} \geq m_{2}$) and velocities $v_{1}$ and $v_{2}$ in the inertial frame respectively. The outcome from the collision are possibly divided into three categories: rebound, rebound with crater formation and fragmentation.

The total impact energy due to the motion of the bodies relative to the center of mass of the colliding pair is defined by: \
\begin{equation}
E = \frac{1}{2} \frac {m_{1}\cdot m_{2}} {m_{1}+m_{2}}\cdot V_{r}^{2}
\end{equation}
where ${\bf V_{r}} = \bf v_{2}-v_{1}$ is the relative velocity.

The collision between planetesimals is not expected to totally elastic, and hence the part of the impact energy is dissipated as heat during the encounter. The fraction of $E$ lost in this manner is $\eta$ and the energy after the collision is
\begin{equation}
E' = (1-\eta)E.
\end{equation}

Using eqn 1, we define the rebound velocity
\begin{equation}
{\bf V_{reb}} = -\sqrt{1-\eta}{\bf V_{r}}
\end{equation}
Which is the velocity at which both bodies separate.The magnitude of n depends on the physical characteristics of the bodies. The value $\sqrt{1-\eta}$, denoted by $c_{i}$ sometimes and called the restitution coefficient, which is a ratio between the rebound velocity and the impact velocity.

\subsubsection{Rebound}
For a collision at significantly low relative velocities or impact energy, the result will be a rebound of both the bodies without cratering or chipping of either surface.Maximum strain on the surface of the body $(\frac{c \rho V_{r}}{2})$ must be lower than the crushing strength in order to prevent local fracture i.e. the relative velocity at he impact must satisfy the relation
\begin{equation}
V_{r}<V_{c} = \frac{2 S}{c \rho}
\end{equation}
Where, $S$ is the impact strength, $c$ is the speed of sound and $\rho$, the density of the body. Hence, if $V_{r}<V_{c}$ we have a rebound, in which case both bodies separate with no change of mass, and with new velocities given by

\begin{equation}
{\bf v'_{1}}= {\bf V_{G}}-\frac {m_{2}}{M}{\bf V_{reb}}
\end{equation}

\begin{equation}
{\bf v'_{2}} = V_{G}-\frac{m_{1}}{M}{\bf V_{reb}}
\end{equation}

with ${\bf V _{G}}$ the velocity of the center of mass of the two-body system,

\begin{equation}
{\bf V_{G}} = \frac{m_{1}{\bf{v_{1}}}+m_{2}{\bf{v_{2}}}}{m_{1}+m_{2}}
\end{equation}

In the simulation, the impact strength and density of the bodies are  input. The velocity at which both bodies separate after the impact is affected by the coefficients of restitution, which is also taken as another model parameter ( If this value is smaller than the mutual escape velocity of the two-body system, the two  are accreted into one body, using the center of mass approximation).

\subsubsection{Rebound with crater formation:}
If $V_{r}>V_{c}$ and impact energy is not very high as to shatter the bodies and the structure not strong enough to propagate the strain with sufficient speed, the surface will suffer some local damage (crater formation), after with which both bodies will separate at some fraction of their impact velocity. The cratering process removes a certain amount of mass from each body and the ratio of rebound over impact velocity (Restitution co-efficient) is smaller than that in the normal rebound (modified restitution coefficient denoted by $c_{ii}$), due to additional loss of energy in cratering and mass ejection at the impact site.
The amount of mass excavated in such a collision and the velocity distribution of the debris are the two aspects of the cratering process that need to be modelled. The mass excavated in the crater of a body of mass $M$ that is struck by a projectile of mass $m_{pr}$ with velocity $V_{r}$ can be approximated(From Lab experiments of high energy impacts on semi infinite targets)
\begin{equation}
M_{ej} =  Km_{pr}{V_{r}}^{2}
\end{equation}
where $K$ is a constant that depends only on the material of the body. If  two bodies of mass $m_{1}$ and $m_{2}$ are considered and  if they collide with relative velocity $V_{1}$, the mass excavated b $m_{2}$ from $m_{1}$ can be written as
\begin{equation}
\Delta m_{1} = Km_{2}{V_{1}}^2
\end{equation}

And similarly for $m_{2}$. It is not necessary for new mass of $m_{1}$ to be $m_{1}-\Delta m_{1}$ as some of the fragments of the excavated mass ($\Delta m_{1}$) may have velocities (relative to $m_{1}$) smaller than the escape velocity and would therefore fall back to the surface.
fraction of $\Delta m_{1}$ and $\Delta m_{2}$ can be estimated  by a law for the cumulative mass of ejecta versus velocity according to which for solar system materials, the cumulative fraction (f) of ejecta with velocity greater than $v$ may be approximated by:

\begin{equation}
f(v) = c_{ej}v^{-9/4}
\end{equation}

The coefficient $c_{ej}$ is a parameter which only depends on the physical properties of the bodies involved( assumed constant for this particular model). Making use of this expression, the fraction of ejecta escaping from the parent body is given by the value of f which corresponds to the parent's escape velocity. he amount of mass that actually escapes from  $m$ therefore is
\begin{equation}
{m_{ej}}^{(1)} = c_{ej}\Delta m_{1} {[{V_{e}}^{(1)}]}^{-9/4}
\end{equation}
or,introducing explicitly the expression for $\Delta m_{1}$:
\begin{equation}
{m_{ej}}^{(1)} = c_{ej}Km_{2}{V_{r}}^{2}{[{V_{e}}^{(1)}]}^{-9/4}
\end{equation}
where ${V_{e}}^{(1)}$ is the escape velocity from $m_{1}$.The same procedure is also valid for $m_{2}$. in both cases, the remaining mass returns to the body.
The fraction of excavated mass that actually escapes he parent's potential well is very small hence it is not necessary o create other bodies out of this mass. The escaped mass of $m_{1}$ is added to $m_{2}$ instead and vice versa. This conserves mass of the system and as the values are small, this approximation will not significantly influence the evolution of the bodies. The new masses of the bodies are then
\begin{equation}
m'_{1} = m_{1}-{m_{ej}}^{(1)}={m_{ej}}^{(2)}
\end{equation}

\begin{equation}
m'_{2} = m_{2}-{m_{ej}}^{(2)}+{m_{ej}}^{(1)}
\end{equation}

The corresponding new velocities are

\begin{equation}
{\bf v'_{1}} = {\bf V_{G}} - (\frac{m2}{M}){\bf V'_{reb}}
\end{equation}

\begin{equation}
{\bf v'_{2}} = {\bf V_{G}} + (\frac{m_{1}}{M}){\bf V'_{reb}}
\end{equation}

with ${\bf V'_{reb}} = -c_{ii}{\bf V_{r}}$

\subsubsection{Fragmentation}

 If the impact energy $E$ in the collision is very high, the internal structure of the body cannot resist the impact and shatters. From collision experiments, fragmentation is likely to occur if the impact energy per unit volume $(E/W)$ is greater than a certain critical value $S$ (impact energy). Experiments also show  that this parameter independent of body size and impact velocity, and depends only on the physical properties of the body itself. Hence, if $E>S_{i}W_{i}$, where $S_{i}$ and $W_{i}$ are the impact strength and volume of $m_{i}$ (i = 1,2), the body will shatter.

One or both bodies fragmenting, consequently.
the fragments of a shattered pair (just like crater ejecta) follow a cumulative mass distribution of the type $N(m) = Cm^{-b}$(Greenberg et al. (1970)) The values of the parameters $C$ and $b$ are related to the mass of the biggest fragment ($m_{max}$) by

\begin{equation}
b = {(1+\frac {m_{max}}{M})}^{-1},
c = {(m_{max)}}^{b}
\end{equation}

$m_{max}$ is determined based on the work of Fujiwara,Kamimoto \& Tsukamoo (1977) found for basalt

\begin{equation}
m_{max} = {DM(E/M)}^{-1.24}
\end{equation}

where $D$ is a constant.For other materials ,Greenberg et al (1978) suggested that, the value of $D$ should be modified to ensure $m_{max} = M/2$ for marginal fragmentation.Marginal fragmentation occurs when $E = SW$, and therefore, for this value

\begin{equation}
\frac{M}{2} = DM{(\frac{SW}{M})}^{-1.24}
\end{equation}

An explicit expression for $D$ can then be introduced into eqn (2.17) yields for an material

\begin{equation}
m_{max} = \frac{M}{2}{(\frac{E}{SW})}^{-1.24}
\end{equation}

The velocity distribution of the fragments relative to parent body is again based on the cumulative function $f(v)$. Therefore part of the previous discussion also holds in this case as only part of the fragmented mass will have velocities greater than the escape velocity. The amount of mass the leaves the two-body system is

\begin{equation}
m_{ej} = c_{ej}M{(V_{e})}^{-9/4}
\end{equation}
where $V_{e}$ is the escape velocity from mass $M = m_{1}+m_{2}$. The remaining mass is gravitationally bound, and accretes into a single core. For the mass distribution of the fragments escaping, a small number of bodies, each with the appropriate mass to approximate the theoretical mass distribution.Each fragmentation is represented by four escaping bodies, as well as a remaining core. he chosen number is a compromise in order to prevent the particle number from  becoming too large. The new fragment masses are chosen according to

For $\alpha \geq 1$
\begin{equation}
M_{1} = \frac{m_{ej}m_{max}}{M},
M_{2} = \frac{1}{alpha} m_{max},
M_{3} = M_{4} = \frac {3-alpha}{2alpha} m_{max}
\end{equation}

for $\alpha < 1$
\begin{equation}
M_{1}=M_{2}=M_{3}=M_{4} = \frac{m_{ej}}{4}
\end{equation}

where $a = \frac{4m_{max}}{M}$.Here, $\alpha$ is an indicator of impact energy. For marginal fragmentation $\alpha = 2$, and
this value decreases with increasing impact energy. Through experimentation, the fragments were distributed at $4R$ ( Where $R$ is the radius of $M$) from the center of mass, with phase difference of $\frac{\pi}{2}$ between them and were assigned parabolic velocities with respect to each new subsystem.


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\setlength{\droptitle}{-2em}

  \title{Linguistic Geocomputation with R}
    \pretitle{\vspace{\droptitle}\centering\huge}
  \posttitle{\par}
    \author{George Moroz}
    \preauthor{\centering\large\emph}
  \postauthor{\par}
      \predate{\centering\large\emph}
  \postdate{\par}
    \date{2018-06-26}

\usepackage{booktabs}
\usepackage{amsthm}
\makeatletter
\def\thm@space@setup{%
  \thm@preskip=8pt plus 2pt minus 4pt
  \thm@postskip=\thm@preskip
}
\makeatother

\usepackage{amsthm}
\newtheorem{theorem}{Theorem}[chapter]
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\newtheorem{corollary}{Corollary}[chapter]
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\newtheorem*{remark}{Remark}
\newtheorem*{solution}{Solution}
\begin{document}
\maketitle

{
\setcounter{tocdepth}{1}
\tableofcontents
}
This book is about

\chapter{Introduction}\label{intro}

\section{Why linguistic
geocomputations?}\label{why-linguistic-geocomputations}

\section{Why do we need geostatistics in
linguistics?}\label{why-do-we-need-geostatistics-in-linguistics}

\section{Why R?}\label{why-r}

\chapter{Introduction to R language}\label{introduction-to-r-language}

Since this book includes a lot of R code examples, this chapter will
describe some basics for those, who is not familiar with R. For purposes
of understanding R code in this book you don't need any deep knowledge
of R. In case you want to learn more, there are a lot of good books on
it. I will list only few of them:

\begin{itemize}
\item
\item
\end{itemize}

\section{Instalation}\label{instalation}

\subsection{R instalation}\label{r-instalation}

To download R, go to \href{https://cran.r-project.org/}{CRAN}. Don't try
to pick a mirror that's close to you, instead it is better to use the
cloud mirror, \url{https://cloud.r-project.org}.

\subsection{RStudio}\label{rstudio}

\href{https://www.rstudio.com/products/rstudio/download/}{RStudio} is an
integrated development environment, or IDE, for R programming. There are
two possibilities for using it:

\begin{itemize}
\tightlist
\item
  type R code in the R console pane, and press enter to run it;
\item
  type R code in the Code editor pane, and press Control/Command + Enter
  to run selected part. It is easier to correct and it is possible to
  save the result as a script.
\end{itemize}

\begin{figure}

{\centering \includegraphics[width=5in]{images/02-rstudio}

}

\caption{RStudio layout}\label{fig:rstudio}
\end{figure}

When you first launch RStudio it is more likely, that you won't see the
Code Editor pane. It is possible to decrease R Console pane on icons in
the pane's right upper corner.

Everything from this book will be availible without RStudio instalation.
There are a lot of possibilities to work with R not using RStudio such
as R console, command line, Jupyter Notebook, some plugins for working
in Sublime, Vim, Emacs, Atom, Notepad++ and other programming text
editors.

\subsection{RStuio cloud}\label{rstuio-cloud}

It is also possible not to install anything on your own PC, using
\href{https://rstudio.cloud/}{RStudio Cloud}, a web-based interface for
Rstudio and R. In RStudio Cloud it is also possible to share your R
projects and collaborate with a select group in a private space. RStudio
Cloud is currently free to use, but soon there will be free and paid
options.

\section{Basic elements, variables, vectors,
dataframe}\label{basic-elements-variables-vectors-dataframe}

\subsection{Basic elements}\label{basic-elements}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{7}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 7
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\OperatorTok{-}\FloatTok{5.7}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] -5.7
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\StringTok{"bonjour"}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] "bonjour"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\StringTok{"bon mot"}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] "bon mot"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\OtherTok{TRUE}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] TRUE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\OtherTok{FALSE}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] FALSE
\end{verbatim}

\subsection{Arithmetic operations}\label{arithmetic-operations}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{7}\OperatorTok{+}\DecValTok{7}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 14
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{21}\OperatorTok{-}\DecValTok{8}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 13
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{4}\OperatorTok{*}\DecValTok{3}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 12
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{12}\OperatorTok{/}\DecValTok{4}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 3
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{4}\OperatorTok{^}\DecValTok{3}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 64
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{4}\OperatorTok{**}\DecValTok{3}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 64
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{sum}\NormalTok{(}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{,}\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 10
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{prod}\NormalTok{(}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{,}\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 24
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{log}\NormalTok{(}\DecValTok{1}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 0
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{log}\NormalTok{(}\DecValTok{100}\NormalTok{, }\DataTypeTok{base =} \DecValTok{10}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 2
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{pi}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 3.141593
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{exp}\NormalTok{(}\DecValTok{5}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 148.4132
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{sin}\NormalTok{(}\DecValTok{13}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 0.420167
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{cos}\NormalTok{(}\DecValTok{13}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 0.9074468
\end{verbatim}

\subsection{Variables}\label{variables}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_var <-}\StringTok{ }\DecValTok{7}
\NormalTok{my_var}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 7
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_var}\OperatorTok{+}\DecValTok{7}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 14
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_var}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 7
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_var <-}\StringTok{ }\NormalTok{my_var }\OperatorTok{+}\StringTok{ }\DecValTok{7}
\end{Highlighting}
\end{Shaded}

\subsection{Vectors}\label{vectors}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{5}\OperatorTok{:}\DecValTok{9}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 5 6 7 8 9
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\DecValTok{11}\OperatorTok{:}\DecValTok{4}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 11 10  9  8  7  6  5  4
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{numbers <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{7}\NormalTok{, }\FloatTok{9.9}\NormalTok{, }\DecValTok{24}\NormalTok{)}
\NormalTok{multiple_strings <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"the"}\NormalTok{, }\StringTok{"quick"}\NormalTok{, }\StringTok{"brown"}\NormalTok{, }\StringTok{"fox"}\NormalTok{, }\StringTok{"jumps"}\NormalTok{, }\StringTok{"over"}\NormalTok{, }\StringTok{"the"}\NormalTok{, }\StringTok{"lazy"}\NormalTok{, }\StringTok{"dog"}\NormalTok{)}
\NormalTok{one_string <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"the quick brown fox jumps over the lazy dog"}\NormalTok{)}
\NormalTok{true_false <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\OtherTok{TRUE}\NormalTok{, }\OtherTok{FALSE}\NormalTok{, }\OtherTok{FALSE}\NormalTok{, }\OtherTok{TRUE}\NormalTok{)}
\KeywordTok{length}\NormalTok{(numbers)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 3
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{length}\NormalTok{(multiple_strings)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 9
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{length}\NormalTok{(one_string)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 1
\end{verbatim}

\subsection{Dataframes}\label{dataframes}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_df <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{latin =} \KeywordTok{c}\NormalTok{(}\StringTok{"a"}\NormalTok{, }\StringTok{"b"}\NormalTok{, }\StringTok{"c"}\NormalTok{),}
                    \DataTypeTok{cyrillic =} \KeywordTok{c}\NormalTok{(}\StringTok{"а"}\NormalTok{, }\StringTok{"б"}\NormalTok{, }\StringTok{"в"}\NormalTok{),}
                    \DataTypeTok{greek =} \KeywordTok{c}\NormalTok{(}\StringTok{"α"}\NormalTok{, }\StringTok{"β"}\NormalTok{, }\StringTok{"γ"}\NormalTok{),}
                    \DataTypeTok{numbers =} \KeywordTok{c}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{3}\NormalTok{),}
                    \DataTypeTok{is.vowel =} \KeywordTok{c}\NormalTok{(}\OtherTok{TRUE}\NormalTok{, }\OtherTok{FALSE}\NormalTok{, }\OtherTok{FALSE}\NormalTok{),}
                    \DataTypeTok{stringsAsFactors =} \OtherTok{FALSE}\NormalTok{)}
\NormalTok{my_df}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
  latin cyrillic greek numbers is.vowel
1     a        а     α       1     TRUE
2     b        б     β       2    FALSE
3     c        в     γ       3    FALSE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{nrow}\NormalTok{(my_df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 3
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{ncol}\NormalTok{(my_df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 5
\end{verbatim}

\subsection{Indexing}\label{indexing}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{numbers[}\DecValTok{3}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 24
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{multiple_strings[}\DecValTok{9}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] "dog"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_df[}\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] "β"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_df[}\DecValTok{2}\NormalTok{,]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
  latin cyrillic greek numbers is.vowel
2     b        б     β       2    FALSE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_df[,}\DecValTok{3}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] "α" "β" "γ"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_df}\OperatorTok{$}\NormalTok{is.vowel}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1]  TRUE FALSE FALSE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{my_df}\OperatorTok{$}\NormalTok{is.vowel[}\DecValTok{2}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] FALSE
\end{verbatim}

\section{Reading files}\label{reading-files}

We can read to R a dataset about Numeral Classifiers from
\href{https://github.com/autotyp/autotyp-data}{AUTOTYP database}.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{new_df <-}\StringTok{ }\KeywordTok{read.csv}\NormalTok{(}\StringTok{"https://raw.githubusercontent.com/autotyp/autotyp-data/master/data/Numeral_classifiers.csv"}\NormalTok{)}
\KeywordTok{head}\NormalTok{(new_df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
  LID NumClass.n NumClass.Presence
1 148          0             FALSE
2  65          0             FALSE
3  75          0             FALSE
4  85          0             FALSE
5 111         NA                NA
6 163          0             FALSE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{tail}\NormalTok{(new_df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
     LID NumClass.n NumClass.Presence
250 1397          0             FALSE
251 2994          5              TRUE
252 2779          0             FALSE
253  192          0             FALSE
254  551          0             FALSE
255 2564          2              TRUE
\end{verbatim}

It could be also a file on your computer, just provide a whole path to
the file. Windows users need to change backslashes
\texttt{\textbackslash{}} to slashes \texttt{/}.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{new_df_}\DecValTok{2}\NormalTok{ <-}\StringTok{ }\KeywordTok{read.csv}\NormalTok{(}\StringTok{"/home/agricolamz/my_file.csv"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\section{Writing files from R}\label{writing-files-from-r}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{write.csv}\NormalTok{(new_df_}\DecValTok{2}\NormalTok{, }\StringTok{"/home/agricolamz/my_new_file.csv"}\NormalTok{,}
          \DataTypeTok{row.names =} \OtherTok{FALSE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\section{Missing data}\label{missing-data}

In R, missing values are represented by the symbol \texttt{NA} (not
available).

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{is.na}\NormalTok{(new_df}\OperatorTok{$}\NormalTok{NumClass.Presence)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
  [1] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[100] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[111] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[122] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[144] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[155] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[166] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[177]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
[188] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[199] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[210] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[232] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[243] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[254] FALSE FALSE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{sum}\NormalTok{(}\KeywordTok{is.na}\NormalTok{(new_df}\OperatorTok{$}\NormalTok{NumClass.Presence))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 5
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{sum}\NormalTok{(}\KeywordTok{is.na}\NormalTok{(new_df))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
[1] 22
\end{verbatim}

\section{How to get help in R}\label{how-to-get-help-in-r}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{?nchar}
\end{Highlighting}
\end{Shaded}

\section{Packages}\label{packages}

There are a lot of R packages for solving a lot of different problems.
There are two way for install them (you need an internet connection):

\begin{itemize}
\tightlist
\item
  packages on CRAN are checked in multiple ways and should be stable
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{install.packages}\NormalTok{(}\StringTok{"lingtypology"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{itemize}
\tightlist
\item
  packages on GitHub are NOT checked and could contain anything, but it
  is the place where all package developers keep the last vertion of
  they work.
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{install.packages}\NormalTok{(}\StringTok{"devtools"}\NormalTok{)}
\NormalTok{devtools}\OperatorTok{::}\KeywordTok{install_github}\NormalTok{(}\StringTok{"ropensci/lingtypology"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{itemize}
\tightlist
\item
  or package file
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{install.packages}\NormalTok{(}\StringTok{"lingtypology"}\NormalTok{,}
                 \DataTypeTok{destdir =} \StringTok{"/path/to/your/package"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

After the package is installed you need to load the package using the
following command:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(}\StringTok{"lingtypology"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

There is a nice picture from
\href{https://bookdown.org/ndphillips/YaRrr/}{Phillips N. D. (2017)
YaRrr! The Pirate's Guide to R}:

\begin{figure}

{\centering \includegraphics[width=6.89in]{images/02-package}

}

\caption{Lamp metaphore}\label{fig:lamp}
\end{figure}

\chapter{Map creation}\label{map-creation}

\chapter{Linguistical databases}\label{db}

\section{Linguistical databases APIs}\label{api}

\section{Linguistical databases creation}\label{db-creation}

Look \ref{api} and \ref{map-creation}

\chapter{Spatial statistics}\label{statistics}

\section{Not all similarities are the
same}\label{not-all-similarities-are-the-same}

In previous chapters we have learnt how to visualize different
linguistic features on the map. But, as title says, not all similarities
observed in data are the same. Some languages share some features,
because the trait was inherited from a common ancestor. This type of
similarities are called \textbf{homologous}. For example the majority of
the Slavic languages (except Bulgarian and Macedonian) have a case
system, because they inherited this from Proto-Slavic. Despite coming
from different ancestors some languages can independently evolve some
analogous traits. This type of similarities are called
\textbf{analogous}. For example despite Proto-Slavic, Latin and
Proto-Germanic had case systems, few offspring languages such as
Bulgarian, English and French independently lost their case systems.

Analogous similarities are commonly divided into areal and typological.
Areal similarities are tend to arise due to the language contact in some
multilingual area. Classic example of such an area is Balkans, that have
been firstly introduced as a linguistic area in \citep{trubetzkoy28}.
Several other areas were introduced later \citep[see][]{muysken08}.
Other similarities that could be found in languages of the world are
typological similarities --- traits that are independently evolved in
some languages. For example ejective sounds could be found mostly in
Caucasus, Central Africa and North America and it seems like this
spatial distribution couldn't be explained neither by a common ancestor
nor by a language contact.

To sum up the whole similarity typology:

\begin{itemize}
\tightlist
\item
  homologous
\item
  analogous

  \begin{itemize}
  \tightlist
  \item
    areal
  \item
    typological
  \end{itemize}
\end{itemize}

It is really important to keep this typology in mind providing the
spatial analysis. During such an analysis we try to find a patterns in
data, describe correlation between some variables etc. But all kind of
analyses discussed in this chapter can not distinguish all kinds of
similarities listed above. So during the analysis researcher need to
remmember that similarities observed in data could have different
nature.

\chapter{Conclusion}\label{conclusion}

\bibliography{book.bib}

\end{document}


text:
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\begin{document}
\begin{poster}{eyecatcher=false}
% Eyecatcher
{

}
% Title
{
    \fontfamily{phv}\selectfont\Large Superpixel Segmentation: An Evaluation
}
% Authors
{
    \fontfamily{phv}\selectfont\normalsize \vspace*{5pt} David Stutz\\david.stutz@rwth-aachen.de
}
% University logo
{
    \includegraphics[scale=0.14]{rwth_vci_weiss_rgb.png}
}

    \headerbox{Introduction}{name=introduction,column=0,span=1}{
       %  The term superpixel is used to describe a group of pixels similar in color or other low-level properties (Ren \etal, 2003). The concept is motivated by two aspects: pixels do not represent natural entities but are a result of discretization; and the usually high number of pixels prevents many algorithms from being computationally feasible.
       Superpixel algorithms have become a standard tool in computer vision. However, varying experimental setups and metrics used for evaluation make direct comparison difficult. We address this shortcoming with a thorough comparison of thirteen state-of-the-art superpixel algorithms including algorithms utilizing depth information.
    }

    \headerbox{Superpixel Algorithms}{name=algorithms,column=0,span=1,below=introduction,boxpaddingx=0em}{

        %{\footnotesize Table 1: Evaluated superpixel algorithms; M refers to MatLab.}
        %\vskip 0.25em

        \def\arraystretch{1.25}
        \begin{tabularx}{\textwidth}{l X c c c c}
            % \rowcolor{RWTHblue!60}& \multicolumn{1}{c}{\cellcolor{RWTHblue!60}\rotatebox{90}{Reference}} & \rotatebox{90}{Year} & \rotatebox{90}{Implementation\phantom{n}} & \rotatebox{90}{\#Superpixels} & \rotatebox{90}{Compactness} & \rotatebox{90}{Depth}\\\hline\hline
            % ------------------------------------------------------------------
            \multicolumn{5}{l}{\cellcolor{RWTHblue!50}\textbf{Superpixel algorithms}}\\\hline\hline
            \textbf{NC} & Ren \etal & 2003 & C/MatLab & \ref{plot:evaluation-comparison-nc-ue-nyu} \\ % \cite{RenMalik:2003}
            \rowcolor{RWTHblue!30}\textbf{FH} & Felzenswalb \etal & 2004 & C++ & \ref{plot:evaluation-comparison-fh-ue-nyu} \\ % \cite{FelzenswalbHuttenlocher:2004}
            \textbf{QS} & Vedaldi \etal & 2008 & C/MatLab & \ref{plot:evaluation-comparison-qs-ue-nyu}\\ %  \cite{VedaldiSoatto:2008}
            \rowcolor{RWTHblue!30}\textbf{TP} & Levinshtein \etal & 2009 & C/MatLab & \ref{plot:evaluation-comparison-tp-ue-nyu} \\ % \cite{LevinshteinStereKutulakosFleetDickinsonSiddiqi:2009}
            \textbf{SLIC} & Achanta \etal & 2010 & C++ & \ref{plot:evaluation-comparison-orislic-ue-nyu} \\ % \cite{AchantaShajiSmithLucchiFuaSuesstrunk:2010}
            \rowcolor{RWTHblue!30}\textbf{CIS} & Veksler \etal & 2010 & C++ & \ref{plot:evaluation-comparison-cis-ue-nyu} \\ % \cite{VekslerBoykovMehrani:2010}
            \textbf{ERS} & Liu \etal & 2011 & C++ & \ref{plot:evaluation-comparison-ers-ue-nyu} \\ % \cite{LiuTuzelRamalingamChellappa:2011}
            \rowcolor{RWTHblue!30}\textbf{PB} & Zhang \etal & 2011 & C++ & \ref{plot:evaluation-comparison-pb-ue-nyu} \\ % \cite{ZhangHartleyMashfordBurn:2011}
            \textbf{CRS} & Mester \etal & 2011 & C++ & \ref{plot:evaluation-comparison-crs-ue-nyu} \\ % \cite{MesterConradGuevara:2011}
            \rowcolor{RWTHblue!30}\textbf{SEEDS} & Van den Bergh \etal & 2012 & C++ & \ref{plot:evaluation-comparison-oriseedsmp-ue-nyu} \\ % \cite{VanDenBerghBoixRoigCapitaniVanGool:2012}
            \textbf{reSEEDS} & \textbf{SEEDS} reimplementation & -- & C++ & \ref{plot:evaluation-comparison-reseedssm-ue-nyu} \\
            \rowcolor{RWTHblue!30}\textbf{TPS} & Tang \etal & 2012 & C/MatLab & \ref{plot:evaluation-comparison-tps-ue-nyu} \\\addlinespace % \cite{DaiTangHuazhaFuXiaochunCao:2012}
            % ------------------------------------------------------------------
            \multicolumn{5}{l}{\cellcolor{RWTHblue!50}\textbf{Superpixel algorithms using depth information}}\\\hline\hline
             \textbf{reSEEDS3D} & \textbf{reSEEDS} using depth & -- & C++ & \ref{plot:evaluation-comparison-seeds3d-ue-nyu} \\
            \rowcolor{RWTHblue!30}\textbf{DASP} & Weikersdorfer \etal & 2012 & C++ & \ref{plot:evaluation-comparison-dasp-ue-nyu} \\ % \cite{WeikersdorferGossowBeetz:2012}
            \textbf{VCCS} & Papon \etal & 2013 & C++ & \ref{plot:evaluation-comparison-vccs-ue-nyu} \\ % \cite{PaponAbramovSchoelerWoergoetter:2013}
        \end{tabularx}
    }

    \headerbox{Datasets \& Benchmark}{name=datasets,column=0,span=1,below=algorithms}{
        \textbf{BSDS500} (Arbel\'{a}ez \etal, 2011)\textbf{.} 100 training and 200 test images of size 481 $\times$ 321.
        % 5 ground truth segmentations per image.
        \vskip 4pt

        \textbf{NYUV2} (Silberman \etal, 2012)\textbf{.} 200 training and 400 test images of size 608 $\times$ 448 including pre-processed depth.
        % semantic ground truth segmentations with instance labels.
        \vskip 4pt

        \textbf{Benchmark.} $N$ set of pixels; $G = \{G_i \subseteq N\}$ ground truth segmentation; $S = \{S_j \subseteq N\}$ superpixel segmentation; $TP(G,S)$, $FN(G,S)$ true positive and false negative boundary pixels.
        \vskip 2px

        {Boundary Recall}:
        \vskip -1em
        \begin{align}
            Rec(G, S) = \frac{|TP(G, S)|}{|FN(G, S)| + |TP(G, S)|}.\notag
        \end{align}

        {Undersegmentation Error}:
        \vskip -1em
        \begin{align}
            UE(G, S) = \frac{1}{|N|} \sum_{G_i \in G} \sum_{S_j \cap G_i \neq \emptyset} \min\{|S_j \cap G_i|, |S_j - G_i|\}.\notag
        \end{align}
    }

    \headerbox{Qualitative Results}{name=qualitative,column=0,span=1,below=datasets}{
        \begin{tabularx}{\linewidth}{@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}}
            \resizebox{0.99\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2}
            }
            &
            %\resizebox{\linewidth}{!}{
            %    \includegraphics{../submission/pictures/bsd-test-2-contours-excerpt}
            %}
            %&
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-nc-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-fh-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-qs-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-tp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-orislic-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-cis-excerpt}
            }
            \\
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-ers-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-pb-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-oriseedsmp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-reseedsmp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-tps-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/bsd-test-2-crs-excerpt}
            }
        \end{tabularx}

        {\footnotesize Figure 1: Superpixel segmentations obtained on an image from the BSDS500.}
        \vskip 0.5em

        \begin{tabularx}{\linewidth}{@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}}
            \resizebox{0.99\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1}
            }
            &
            %\resizebox{\linewidth}{!}{
            %    \includegraphics[scale=0.2016]{../submission/pictures/nyu-test-1-contours-excerpt}
            %}
            %&
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-nc-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-fh-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-qs-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-tp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-orislic-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-cis-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-ers-excerpt}
            }
            \\
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-pb-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-oriseedsmp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-reseedsmp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-tps-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-crs-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-seeds3d-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-dasp-beta-0-05-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{../submission/pictures/nyu-test-1-vccs-excerpt}
            }
        \end{tabularx}
        {\footnotesize Figure 2: Superpixel segmentations obtained on an image from the NYUV2.}
    }

    \headerbox{Qualitative Results (cont'd)}{name=qualitative,column=1,span=1}{
        \begin{tabularx}{\linewidth}{@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}}
            \resizebox{0.99\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-excerpt}
            }
            &
            %\resizebox{\linewidth}{!}{
            %    \includegraphics{../submission/pictures/bsd-test-2-contours-excerpt}
            %}
            %&
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-cis-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-fh-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-qs-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-tp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-orislic-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-cis-excerpt}
            }
            \\
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-ers-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-pb-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-oriseedsmp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-reseedsmp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-tps-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/bsd-test-3-crs-excerpt}
            }
        \end{tabularx}

        {\footnotesize Figure 3: Superpixel segmentations obtained on an image from the BSDS500.}
        \vskip 0.5em

        \begin{tabularx}{\linewidth}{@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}Y@{}}
            \resizebox{0.99\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-excerpt}
            }
            &
            %\resizebox{\linewidth}{!}{
            %    \includegraphics[scale=0.2016]{../submission/pictures/nyu-test-1-contours-excerpt}
            %}
            %&
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-nc-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-fh-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-qs-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-tp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-orislic-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-cis-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-ers-excerpt}
            }
            \\
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-pb-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-oriseedsmp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-reseedsmp-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-tps-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-crs-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-seeds3d-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-dasp-beta-0-05-excerpt}
            }
            &
            \resizebox{\linewidth}{!}{
                \includegraphics{pictures/nyu-test-2-vccs-excerpt}
            }
        \end{tabularx}
        {\footnotesize Figure 4: Superpixel segmentations obtained on an image from the NYUV2.}
    }

    \headerbox{Quantitative Results}{name=quantitative,column=1,span=1,below=qualitative}{

        \begin{tabularx}{\linewidth}{@{}Y@{}Y@{} !{\color{RWTHblue}\vrule} Y@{}Y@{}Y@{}}
            \resizebox{0.98\linewidth}{!}{
		        \begin{tikzpicture}
			        \begin{axis}[
					        height=7.3cm,
					        width=3cm,
					        y label style={at={(axis description cs:0.45,1.02)},anchor=south,rotate=-90},
					        xlabel=Superpixels,
					        xlabel shift={0.1cm},
					        ylabel=$Rec$ (BSDS500),
					        ymin=0.9,
					        ymax=1,
					        xmin=100,
					        xmax=1200,
					        xticklabel shift={.1cm},
					        xtick={400,1000},
					        yticklabel shift={.1cm},
					        cycle list name=comparison bsd]

				        % NC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/nc-bsd.csv};
				        \label{plot:evaluation-comparison-nc-rec-bsd}

				        % FH
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/fh-bsd-test.csv};
				        \label{plot:evaluation-comparison-fh-rec-bsd}

				        % QS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/qs-bsd-test.csv};
				        \label{plot:evaluation-comparison-qs-rec-bsd}

				        % TP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/tp-bsd-test.csv};
				        \label{plot:evaluation-comparison-tp-rec-bsd}

				        % oriSLIC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/orislic-bsd-test.csv};
				        \label{plot:evaluation-comparison-orislic-rec-bsd}

				        % CIS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/cis-bsd-test.csv};
				        \label{plot:evaluation-comparison-cis-rec-bsd}

				        % ERS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/ers-bsd-test.csv};
				        \label{plot:evaluation-comparison-ers-rec-bsd}

				        % PB
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/pb-bsd-test.csv};
				        \label{plot:evaluation-comparison-pb-rec-bsd}

				        % SEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/oriseedsmp-bsd-test.csv};
				        \label{plot:evaluation-comparison-oriseedsmp-rec-bsd}

                        % reSEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedsmp-bsd-test.csv};
				        \label{plot:evaluation-comparison-reseedssm-rec-bsd}

				        % reSEEDSmp*
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedssm-bsd-test.csv};
				        %\label{plot:evaluation-comparison-reseedssm-rec-bsd}

				        % TPS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/tps-bsd-test.csv};
				        \label{plot:evaluation-comparison-tps-rec-bsd}

				        % CRS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/crs-bsd-test.csv};
				        \label{plot:evaluation-comparison-crs-rec-bsd}
			        \end{axis}
		        \end{tikzpicture}
		    }
	        &
	        \resizebox{0.98\linewidth}{!}{
		        \begin{tikzpicture}
			        \begin{axis}[
					        height=7.3cm,
					        width=3cm,
					        y label style={at={(axis description cs:0.45,1.02)},anchor=south,rotate=-90},
					        xlabel=Superpixels,
					        xlabel shift={0.1cm},
					        ylabel=$UE$ (BSDS500),
					        ymin=0.025,
					        ymax=0.1,
					        xmin=100,
					        xmax=1200,
					        xticklabel shift={.1cm},
					        xtick={400,1000},
					        yticklabel shift={.1cm},
					        cycle list name=comparison bsd]

				        % NC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/nc-bsd.csv};
				        \label{plot:evaluation-comparison-nc-ue-bsd}

				        % FH
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/fh-bsd-test.csv};
				        \label{plot:evaluation-comparison-fh-ue-bsd}

				        % QS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/qs-bsd-test.csv};
				        \label{plot:evaluation-comparison-qs-ue-bsd}

				        % TP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/tp-bsd-test.csv};
				        \label{plot:evaluation-comparison-tp-ue-bsd}

				        % oriSLIC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/orislic-bsd-test.csv};
				        \label{plot:evaluation-comparison-orislic-ue-bsd}

				        % CIS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/cis-bsd-test.csv};
				        \label{plot:evaluation-comparison-cis-ue-bsd}

				        % ERS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/ers-bsd-test.csv};
				        \label{plot:evaluation-comparison-ers-ue-bsd}

				        % PB
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/pb-bsd-test.csv};
				        \label{plot:evaluation-comparison-pb-ue-bsd}

				        % SEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/oriseedsmp-bsd-test.csv};
				        \label{plot:evaluation-comparison-oriseedsmp-ue-bsd}

				        % reSEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedsmp-bsd-test.csv};
				        \label{plot:evaluation-comparison-reseedssm-ue-bsd}

                        % reSEEDSmp*
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedssm-bsd-test.csv};
				        %\label{plot:evaluation-comparison-reseedssm-ue-bsd}

				        % TPS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/tps-bsd-test.csv};
				        \label{plot:evaluation-comparison-tps-ue-bsd}

				        % CRS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/crs-bsd-test.csv};
				        \label{plot:evaluation-comparison-crs-ue-bsd}
			        \end{axis}
		        \end{tikzpicture}
		    }
	        &
	        \resizebox{\linewidth}{!}{
		        \begin{tikzpicture}
			        \begin{axis}[
					        height=7.3cm,
					        width=3cm,
					        y label style={at={(axis description cs:0.4,1.02)},anchor=south,rotate=-90},
					        xlabel=Superpixels,
					        xlabel shift={0.1cm},
					        ylabel=$Rec$ (NYUV2),
					        ymin=0.9,
					        ymax=1,
					        xmin=300,
					        xmax=1700,
					        xticklabel shift={.1cm},
					        xtick={500,1500},
					        yticklabel shift={.1cm},
					        cycle list name=comparison nyu]

				        % NC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/nc-nyu.csv};
				        \label{plot:evaluation-comparison-nc-rec-nyu}

				        % FH
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/fh-nyu-test.csv};
				        \label{plot:evaluation-comparison-fh-rec-nyu}

				        % QS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/qs-nyu-test.csv};
				        \label{plot:evaluation-comparison-qs-rec-nyu}

				        % TP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/tp-nyu-test.csv};
				        \label{plot:evaluation-comparison-tp-rec-nyu}

				        % oriSLIC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/orislic-nyu-test.csv};
				        \label{plot:evaluation-comparison-orislic-rec-nyu}

				        % SLIC3D
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/slic3d-nyu-test.csv};
				        %\label{plot:evaluation-comparison-slic3d-rec-nyu}

				        % CIS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/cis-nyu-test.csv};
				        \label{plot:evaluation-comparison-cis-rec-nyu}

				        % ERS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/ers-nyu-test.csv};
				        \label{plot:evaluation-comparison-ers-rec-nyu}

				        % PB
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/pb-nyu-test.csv};
				        \label{plot:evaluation-comparison-pb-rec-nyu}

				        % SEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/oriseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-oriseedsmp-rec-nyu}

                        % reSEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-reseedssm-rec-nyu}

				        % reSEEDSmp*
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedssm-nyu-test.csv};
				        %\label{plot:evaluation-comparison-reseedssm-rec-nyu}

				        % reSEEDS3D
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/seeds3d-nyu-test.csv};
				        \label{plot:evaluation-comparison-seeds3d-rec-nyu}

				        % DASP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/dasp-nyu-test.csv};
				        \label{plot:evaluation-comparison-dasp-rec-nyu}

				        % TPS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/tps-nyu-test.csv};
				        \label{plot:evaluation-comparison-tps-rec-nyu}

				        % CRS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/crs-nyu-test.csv};
				        \label{plot:evaluation-comparison-crs-rec-nyu}

				        % DCRS

				        % VCCS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/vccs-depth-test.csv};
				        \label{plot:evaluation-comparison-vccs-rec-nyu}
			        \end{axis}
		        \end{tikzpicture}
		    }
	        &
	        \resizebox{\linewidth}{!}{
		        \begin{tikzpicture}
			        \begin{axis}[
					        height=7.3cm,
					        width=3cm,
					        y label style={at={(axis description cs:0.4,1.02)},anchor=south,rotate=-90},
					        xlabel=Superpixels,
					        xlabel shift={0.1cm},
					        ylabel=$UE$ (NYUV2),
					        ymin=0.07,
					        ymax=0.17,
					        xmin=300,
					        xmax=1700,
					        xticklabel shift={.1cm},
					        xtick={500,1500},
					        yticklabel shift={.1cm},
					        cycle list name=comparison nyu]

				        % NC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/nc-nyu.csv};
				        \label{plot:evaluation-comparison-nc-ue-nyu}

				        % FH
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/fh-nyu-test.csv};
				        \label{plot:evaluation-comparison-fh-ue-nyu}

				        % QS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/qs-nyu-test.csv};
				        \label{plot:evaluation-comparison-qs-ue-nyu}

				        % TP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/tp-nyu-test.csv};
				        \label{plot:evaluation-comparison-tp-ue-nyu}

				        % oriSLIC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/orislic-nyu-test.csv};
				        \label{plot:evaluation-comparison-orislic-ue-nyu}

				        % SLIC3D
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/slic3d-nyu-test.csv};
				        %\label{plot:evaluation-comparison-slic3d-ue-nyu}

				        % CIS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/cis-nyu-test.csv};
				        \label{plot:evaluation-comparison-cis-ue-nyu}

				        % ERS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/ers-nyu-test.csv};
				        \label{plot:evaluation-comparison-ers-ue-nyu}

				        % PB
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/pb-nyu-test.csv};
				        \label{plot:evaluation-comparison-pb-ue-nyu}

				        % SEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/oriseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-oriseedsmp-ue-nyu}

				        % reSEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-reseedssm-ue-nyu}

				        % reSEEDSmp*
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedssm-nyu-test.csv};
				        %\label{plot:evaluation-comparison-reseedssm-ue-nyu}

				        % reSEEDS3D
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/seeds3d-nyu-test.csv};
				        \label{plot:evaluation-comparison-seeds3d-ue-nyu}

				        % DASP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/dasp-nyu-test.csv};
				        \label{plot:evaluation-comparison-dasp-ue-nyu}

				        % TPS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/tps-nyu-test.csv};
				        \label{plot:evaluation-comparison-tps-ue-nyu}

				        % CRS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/crs-nyu-test.csv};
				        \label{plot:evaluation-comparison-crs-ue-nyu}

				        % DCRS

				        % VCCS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/vccs-depth-test.csv};
				        \label{plot:evaluation-comparison-vccs-ue-nyu}
			        \end{axis}
		        \end{tikzpicture}
		    }
	    \end{tabularx}

	    {\footnotesize Figure 5: Average Boundary Recall and Undersegmentation Error on the test sets of\\[-2pt] the BSDS500 (left) and the NYUV2 (right); parameters have been optimized on the\\[-2pt] corresponding training sets using discrete grid search.}
    }

    \headerbox{Runtime}{name=runtime,column=1,span=1,below=quantitative}{
        \begin{tabularx}{\linewidth}{@{}Y@{}Y@{}Y@{}Y@{}}
	        \resizebox{\linewidth}{!}{
		        \begin{tikzpicture}
			        \begin{axis}[
					        height=3.91cm,
					        width=3.75cm,
					        y label style={at={(axis description cs:0.25,1.04)},anchor=south,rotate=-90},
					        xlabel=Superpixels,
					        xlabel shift={0.1cm},
					        ylabel=$Rec$ (NYUV2),
					        ymin=0.9,
					        ymax=1,
					        xmin=300,
					        xmax=1700,
					        xtick={500,1500},
					        xticklabel shift={.1cm},
					        yticklabel shift={.1cm},
					        cycle list name=runtime nyu]

				        % FH
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/fh-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-fh-rec-nyu}

				        % oriSLIC 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/orislic1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-orislic1it-rec-nyu}

				        % oriSLIC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/orislic-nyu-test.csv};
				        \label{plot:evaluation-comparison-orislic-rec-nyu}

				        % SEEDSmp 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/oriseedsmp1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-oriseedsmp1it-rec-nyu}

				        % SEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/oriseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-oriseedsmp-rec-nyu}

				        % reSEEDSmp
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedsmp1it-nyu-test.csv};
				        %\label{plot:evaluation-comparison-iteration-reseedsmp-rec-nyu}

				        % reSEEDSmp 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedsmp1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-reseedssm1it-rec-nyu}

				        % reSEEDSmp* 1it
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedssm1it-nyu-test.csv};
				        %\label{plot:evaluation-comparison-iteration-reseedssm1it-rec-nyu}

				        % reSEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-reseedssm-rec-nyu}

				        % reSEEDSmp*
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/reseedssm-nyu-test.csv};
				        %\label{plot:evaluation-comparison-iteration-reseedssm-rec-nyu}

				        % reSEEDS3D
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/seeds3d1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-seeds3d-rec-nyu}

				        % DASP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/dasp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-dasp-rec-nyu}

				        % VCCS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=Rec] {../submission/data/vccs-depth-test.csv};
				        \label{plot:evaluation-comparison-iteration-vccs-rec-nyu}

			        \end{axis}
		        \end{tikzpicture}
		    }
		    &
		    \resizebox{\linewidth}{!}{
		        \begin{tikzpicture}
			        \begin{axis}[
					        height=3.91cm,
					        width=3.75cm,
					        y label style={at={(axis description cs:0.25,1.04)},anchor=south,rotate=-90},
					        xlabel=Superpixels,
					        xlabel shift={0.1cm},
					        ylabel=$UE$ (NYUV2),
					        ymin=0.07,
					        ymax=0.17,
					        xmin=300,
					        xmax=1700,
					        xtick={500,1500},
					        xticklabel shift={.1cm},
					        yticklabel shift={.1cm},
					        cycle list name=runtime nyu]

				        % FH
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/fh-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-fh-ue-nyu}

				        % oriSLIC 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/orislic1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-orislic1it-ue-nyu}

				        % oriSLIC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/orislic-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-orislic-ue-nyu}

				        % SLIC3D
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/slic3d-nyu-test.csv};
				        %\label{plot:evaluation-comparison-slic3d-ue-nyu}

				        % SEEDSmp 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/oriseedsmp1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-oriseedsmp1it-ue-nyu}

				        % SEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/oriseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-oriseedsmp-ue-nyu}

				        % reSEEDSmp
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedsmp1it-nyu-test.csv};
				        %\label{plot:evaluation-comparison-iteration-reseedsmp-ue-nyu}

				        % reSEEDSmp 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedsmp1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-reseedssm1it-ue-nyu}

				        % reSEEDSmp* 1it
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedssm1it-nyu-test.csv};
				        %\label{plot:evaluation-comparison-iteration-reseedssm1it-ue-nyu}

				        % reSEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-reseedssm-ue-nyu}

				        % reSEEDSmp*
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/reseedssm-nyu-test.csv};
				        %\label{plot:evaluation-comparison-iteration-reseedssm-ue-nyu}

				        % reSEEDS3D
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/seeds3d1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-seeds3d-ue-nyu}

				        % DASP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/dasp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-dasp-ue-nyu}

				        % VCCS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=UE] {../submission/data/vccs-depth-test.csv};
				        \label{plot:evaluation-comparison-iteration-vccs-ue-nyu}

			        \end{axis}
		        \end{tikzpicture}
	        }
	        &
	        \resizebox{\linewidth}{!}{
		        \begin{tikzpicture}
			        \begin{axis}[
					        height=3.8cm,
					        width=3.75cm,
					        y label style={at={(axis description cs:0.275,1.04)},anchor=south,rotate=-90},
					        xlabel=Superpixels,
					        xlabel shift={0.1cm},
					        ylabel=$t$ in $s$ (NYUV2),
					        ymin=0,
					        ymax=0.45,
					        xmin=300,
					        xmax=1700,
					        xtick={500,1500},
					        xticklabel shift={.1cm},
					        yticklabel shift={.1cm},
					        cycle list name=runtime nyu]

				        % FH
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/fh-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-fh-t-nyu}

				        % oriSLIC 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/orislic1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-orislic1it-t-nyu}

				        % oriSLIC
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/orislic-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-orislic-t-nyu}

				        % SEEDSmp 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/oriseedsmp1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-oriseedsmp1it-t-nyu}

				        % SEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/oriseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-oriseedsmp-t-nyu}

				        % reSEEDSmp 1it
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/reseedsmp1it-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-reseedssm1it-t-nyu}

				        % reSEEDSmp* 1it
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/reseedssm1it-nyu-test.csv};
				        %\label{plot:evaluation-comparison-iteration-reseedssm1it-t-nyu}

				        % reSEEDSmp
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/reseedsmp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-reseedssm-t-nyu}

				        % reSEEDSmp*
				        %\addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/reseedssm-nyu-test.csv};
				        %\label{plot:evaluation-comparison-iteration-reseedssm-t-nyu}

				        % reSEEDS3D
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/seeds3d-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-seeds3d-t-nyu}

				        % DASP
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/dasp-nyu-test.csv};
				        \label{plot:evaluation-comparison-iteration-dasp-t-nyu}

				        % VCCS
				        \addplot+[thick] table [row sep=newline,trim cells=true,x=K,y=t] {../submission/data/vccs-depth-test.csv};
				        \label{plot:evaluation-comparison-iteration-vccs-t-nyu}
			        \end{axis}
		        \end{tikzpicture}
		    }
	    \end{tabularx}

	    {\footnotesize Figure 6: Average Boundary Recall, Undersegmentation Error and runtime on the test\\[-2pt] set of the NYUV2; $^\star$ marks algorithms optimized for low runtime:\\[-2pt] \ref{plot:evaluation-comparison-iteration-orislic1it-t-nyu} \textbf{SLIC} $^\star$; \ref{plot:evaluation-comparison-iteration-oriseedsmp1it-t-nyu} \textbf{SEEDS} $^\star$; \ref{plot:evaluation-comparison-iteration-reseedssm1it-t-nyu} \textbf{reSEEDS} $^\star$.}
    }

    \headerbox{Conclusion}{name=conclusion,column=1,span=1,below=runtime}{
        In conclusion, several algorithms demonstrate excellent performance and low runtime. Furthermore, using depth information does not improve performance in all cases (e.g. \textbf{DASP}, \textbf{reSEEDS3D}). However, directly segmenting point clouds turns out to be advantageous (e.g. \textbf{VCCS}). While additional aspects may be necessary to asses superpixel algorithms with regard to specific applications, the presented results offer a general overview and allow to select suitable superpixel algorithms for a wide range of applications.

        %\vskip 4px
        %Code and further results available at {\ttfamily \bfseries davidstutz.de}.

        \begin{minipage}{0.825\textwidth}
            Further results and source code: {\ttfamily \bfseries davidstutz.de}
            \vskip 4px

            Acknowledgments:\\Alexander Hermans, Prof. Dr. Bastian Leibe
            \vfill
        \end{minipage}
        \begin{minipage}{0.15\textwidth}
            \includegraphics[scale=0.15]{qrcode}
        \end{minipage}
    }

\end{poster}
\end{document}