From 745a034d437b05cf4daf5f1c3cae5845d429c5f7 Mon Sep 17 00:00:00 2001
From: Tiago Gomes <tgomes@fc.up.pt>
Date: Sat, 13 Apr 2013 00:49:59 +0100
Subject: [PATCH] Reduce the size of the section labels

---
 packages/CLPBN/html/index.html | 21 +++++++++++++--------
 packages/CLPBN/html/pfl.css    |  9 ++++++++-
 packages/CLPBN/pfl.tex         |  4 ++--
 3 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/packages/CLPBN/html/index.html b/packages/CLPBN/html/index.html
index d25008cc0..cdb95ece0 100644
--- a/packages/CLPBN/html/index.html
+++ b/packages/CLPBN/html/index.html
@@ -16,10 +16,15 @@
 <div class="header">
 <div id="leftcolumn"><h1>Prolog Factor Language</h1></div>
 <div id="rightcolumn">
-  <table>
-    <tr><td>vsc</td><td>at gmail.com</td></tr>
-    <tr><td>tiago.avv</td><td>at gmail.com</td></tr>
-  </table>
+  <div>
+    <div id=name>Vítor Costa</div>
+    <div id=email>vsc at gmail.com </div>
+  </div>
+  <div style="padding-top:10px">
+    <div id=name>Tiago Gomes</div>
+    <div id=email>tiago.avv at gmail.com</div>
+  </div>
+
 </div>
 <div style="clear: both"></div>
   <nav id="menu">
@@ -28,8 +33,8 @@
     <li><a href="#installation">Installation</a></li>
     <li><a href="#language">Language</a></li>
     <li><a href="#querying">Querying</a></li>
-    <li><a href="#inference_options">Inference Options</a></li>
-    <li><a href="#parameter_learning">Parameter Learning</a></li>
+    <li><a href="#options">Options</a></li>
+    <li><a href="#learning">Learning</a></li>
     <li><a href="#external_interface">External Interface</a></li>
     <li><a href="#papers">Papers</a></li>
     </ul>
@@ -238,7 +243,7 @@ In this section we demonstrate how to use PFL to solve probabilistic queries. We
 <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
 <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
 <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
-<h2 id="inference_options">Inference Options</h2>
+<h2 id="options">Options</h2>
 PFL supports both ground and lifted inference methods. The inference algorithm can be chosen by calling <span class=texttt>set_solver/1</span>. The following are supported:
 
 <ul>
@@ -352,7 +357,7 @@ This option allows to print a textual representation of the factor graph.
 <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
 <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
 <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
-<h2 id="parameter_learning">Parameter Learning</h2>
+<h2 id="learning">Learning</h2>
 PFL is capable to learn the parameters for bayesian networks, through an implementation of the expectation-maximization algorithm.
 
 <p>Next we show an example of parameter learning for the sprinkler network.</p>
diff --git a/packages/CLPBN/html/pfl.css b/packages/CLPBN/html/pfl.css
index 3c115b512..e51d78b1b 100644
--- a/packages/CLPBN/html/pfl.css
+++ b/packages/CLPBN/html/pfl.css
@@ -43,9 +43,16 @@ body {
   padding-top:15px;
   padding-right:0px;
   margin-right:0px;
+}
+
+#name {
   color:#2798CA;
 }
 
+#email {
+  color:gray;
+}
+
 .mainbody {
   padding-top:10px;
 }
@@ -58,7 +65,7 @@ body {
 
 #menu ul {
   padding:0px;
-  margin:0px;
+  margin: 20px 0px;
   background-color:#EDEDED;
   list-style:none;
   text-align: center;
diff --git a/packages/CLPBN/pfl.tex b/packages/CLPBN/pfl.tex
index 9793b0e9f..d0d5a2634 100644
--- a/packages/CLPBN/pfl.tex
+++ b/packages/CLPBN/pfl.tex
@@ -274,7 +274,7 @@ PFL also supports calculating joint probability distributions. For instance, we
 %------------------------------------------------------------------------------
 %------------------------------------------------------------------------------
 %------------------------------------------------------------------------------
-\section{Inference Options}
+\section{Options}
 PFL supports both ground and lifted inference methods. The inference algorithm can be chosen by calling \texttt{set\_solver/1}. The following are supported:
 \begin{itemize}
  \item \texttt{ve},  variable elimination (written in Prolog)
@@ -397,7 +397,7 @@ This option allows to print a textual representation of the factor graph.
 %------------------------------------------------------------------------------
 %------------------------------------------------------------------------------
 %------------------------------------------------------------------------------
-\section{Parameter Learning}
+\section{Learning}
 PFL is capable to learn the parameters for bayesian networks, through an implementation of the expectation-maximization algorithm.
 
 Next we show an example of parameter learning for the sprinkler network.