title

Author: qgeissmann

report/report.tex

@@ -66,7 +66,7 @@
 
 \pagenumbering{roman}
 
-\title{abcd}
+\title{Vigilance state prediction using random forest and time interval feature space}
 \author{Quentin Geissmann\\
 \\	
 \\
@@ -76,6 +76,11 @@
 \\
 \\
 Imperial College London
+\\
+\\
+\\
+Words count in main text: 4996\\
+\\
 }
 \date{\today}
 
@@ -138,14 +143,14 @@
 In order to optimise feature extraction and pave the way for a future software
 implementation, \pr{}, a \py{} package was also developed. \pr{} is here shown
 to outperform an alternative runtime implementation by several orders of magnitude.
-	\\
-	\\
-\begin{itemize}
-\item Words in main text: 4996
-\item Words in headers: 84
-\item Words outside text (captions, etc.): 943
-\end{itemize}
-%~ 
+	%~ \\
+	%~ \\
+%~ \begin{itemize}
+%~ \item Words in main text: 4996
+%~ \item Words in headers: 84
+%~ \item Words outside text (captions, etc.): 943
+%~ \end{itemize}
+
 \end{abstract}
 
 \newpage