[Tex/LaTex] Removing keywords and subject descriptors from a ACM template


I'm trying to customize a ACM template, the Large Format Single Column one [1] and really the main thing I want to get rid of is the subject descriptors and keywords part. I realize how important they are in a journal but if I want to use the template for anything else, it would be really nice to be able to remove them.
In the .tex source, this is present under the abstract like the following:


\category{H.5.2}{Information Interfaces and Presentation}{User
Interfaces}[Evaluation/\break methodology]
\category{H.1.2}{Models and Principles}{User/Machine Systems}[Human Information Processing]
\category{I.5.1}{Pattern\break Recognition}{Models}[Neural Nets]

\terms{Human Factors}
\keywords{Contour perception, flow visualization, perceptual theory, visual cortex, visualization}

\acmformat{Daniel Pineo, Colin Ware, and Sean Fogarty. 2010. Neural Modeling of Flow Rendering Effectiveness.}

If I remove any of this, all the abstract portion is gone, I realize that the .cls file needs to be edited but I am sure what part needs to be edited. I would really appreciate some guidance in where to look for to edit this and remove the cateogories and keywords parts. Thanks for the help!!

Best Answer

Some changes to the way \maketitle is defined by the class are necessary. Do not modify the original .cls file, though; you can either use the redefinition in your .tex document, as I did in my example code below (notice the section between \makeatletter, \makeatother commands), or make a copy of acmlarge.cls, call it myacmlarge.cls and replace the \maketitle definition in the copy with the definition of my example (without using \makeatletter and \makeatother, in this case); save the copy in your local TDS tree or in your current working directory and then use


in your document.

The example code:



\def\maketitle{\newpage \thispagestyle{titlepage}\par
  \begingroup \lineskip = \z@\null \vskip -13.5pt\relax 
  \parindent\z@ {\hyphenpenalty\@M
    {\titlefont \@title \par
      {\vskip 13.5pt\relax \normalsize \authorfont %vskip 13.5pt between title and author
    \begingroup \addtolength{\baselineskip}{2pt}
    \@author\par \vskip -2pt 
    \endgroup }
      {%\ifx \@categories\@empty 
    \baselineskip 17pt\relax
    \hbox{\vrule height .2pt width \@acmWidth}%to eliminate the lines for jacm
      \vskip 8.5pt \footnotesize \box\@abstract \vskip 4pt\relax %vskip8.5 space above abstract
         {\def\and{\unskip\/{\rm ; }}
%          Categories and Subject Descriptors: \@categories \fi
}\par\vskip 4pt\relax
         %\box\@terms \vskip 4pt\relax
         %\box\@keywords \par
             \footnotesize \hsize \@acmWidth \parindent 0pt \noindent
             \vskip 4\p@
             \noindent  {\bf ACM Reference Format:}\\[2pt]
             \@acmformat\vskip 0.5\p@
         {\baselineskip 14pt\relax
         \vskip 23pt\relax
\def\abstract{\global\setbox\@abstract=\vbox\bgroup \everypar{}
  \footnotesize \hsize \@acmWidth \parindent 10pt \noindent 

% Title portion
\title{Neural Modeling of Flow Rendering Effectiveness}
\author{DANIEL PINEO and COLIN WARE \affil{University of New Hampshire}
\affil{University of Illinois at Urbana-Champaign}

It has been previously proposed that understanding the mechanisms of
contour perception can provide a theory for why some flow-rendering
methods allow for better judgments of advection pathways than others.
In the present article, we develop this theory through a numerical
model of the primary visual cortex of the brain (Visual Area 1) where
contour enhancement is understood to occur according to most
neurological theories. We apply a two-stage model of contour
perception to various visual representations of flow fields evaluated
using the advection task of Laidlaw et al. [2001]. In the first
stage, contour {enhancement} is modeled based on Li's cortical model
[Li 1998]. In the second stage, a model of streamline {tracing} is
proposed, designed to support the advection task. We examine the
predictive power of the model by comparing its performance to that of
human subjects on the advection task with four different
visualizations. The results show the same overall pattern for humans
and the model. In both cases, the best performance was obtained with
an aligned streamline-based method, which tied with a LIC-based
method. Using a regular or jittered grid of arrows produced worse
results. The model yields insights into the relative strengths of
different flow visualization methods for the task of visualizing
advection pathways.




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