--- a/project/templates/about/tutorial.html	Wed Nov 30 13:25:05 2011 +0530
+++ b/project/templates/about/tutorial.html	Wed Nov 30 15:39:35 2011 +0530
@@ -207,19 +207,19 @@
 	maps.
 	</li>
 	<li>
-	T This tutorial will give a practical introduction to tools and techniques
+	This tutorial will give a practical introduction to tools and techniques
  	available for processing spatial information and, through hands-on
-	 exercises, give the participants a sense of how to manipulate spatial data
-	 using Python. Depending on time, topics covered include reading and writing
-	 of important data formats for both raster and vector data, looking at the
+	exercises, give the participants a sense of how to manipulate spatial data
+	using Python. Depending on time, topics covered include reading and writing
+	of important data formats for both raster and vector data, looking at the
  	layers with qgis, awareness of issues with datums and projections,
  	calculating area and centroids of polygons, performance enhancement using
  	vector operations, numerical stability issues, calculation of distance
-	 between points on the surface of Earth, interpolation from raster grids to
-	 points etc. The tutorial has been developed for Ubuntu Linux 11.04/11.10 and
-	 will provide source code, tests and data for this platform. However, the
-	 content and messages should be general and apply to any self-respecting
-	 platform.
+	between points on the surface of Earth, interpolation from raster grids to
+	points etc. The tutorial has been developed for Ubuntu Linux 11.04/11.10 and
+	will provide source code, tests and data for this platform. However, the
+	content and messages should be general and apply to any self-respecting
+	platform.
 	</li>
 	<li>
 	I assume that participants know how to write and run Python scripts and are
@@ -228,9 +228,9 @@
  	Geographic Information Systems (GIS). The tutorial depends on the
  	packages qgis and gdal-bin as well as the python dependencies python-numpy
  	and python-gdal which are preloaded on the distributed live-DVD. The
- 	tutorial material itself will be available in the Subversion repository
- 	http://oles-tutorials.googlecode.com/svn/trunk/scipy2011 and also on a USB
- 	stick that I will bring along.		
+ 	tutorial material itself will be available in the 
+	<a href="http://oles-tutorials.googlecode.com/svn/trunk/scipy2011">Subversion repository</a> 
+	and also on a usb stick that I will bring along.		
 	</li>
 	<li>
 	If you have some spatial data you want to manipulate in Python feel free to

--- a/project/templates/talk/conf_schedule.html	Wed Nov 30 13:25:05 2011 +0530
+++ b/project/templates/talk/conf_schedule.html	Wed Nov 30 15:39:35 2011 +0530
@@ -24,11 +24,12 @@
 <tr><td class="right">13:55-14:15</td><td class="left">Bala Subrahmanyam Varanasi</td><td class="left"><a href="#sec2.6">Sentiment Analysis</a></td></tr>
 <tr><td class="right">14:15-14:45</td><td class="left">Vishal Kanaujia</td><td class="left"><a href="#sec2.7">Exploiting the power of multicore for scientific computing in Python</a></td></tr>
 <tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr>
-<tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
-<tr><td class="right">14:25-15:55</td><td class="left">Jayneil Dalal</td><td class="left"><a href="#sec2.8">Building Embedded Systems for Image Processing using Python</a></td></tr>
-<tr><td class="right">15:55-16:25</td><td class="left">Kunal Puri</td><td class="left"><a href="#sec2.9">Smoothed Particle Hydrodynamics with Python</a></td></tr>
-<tr><td class="right">16:25-16:45</td><td class="left">Nivedita Datta</td><td class="left"><a href="#sec2.10">Encryptedly yours : Python & Cryptography</a></td></tr>
-<tr><td class="right">16:45-17:30</td><td class="left">Gael</td><td class="left"><a href="#sec2.23"><b>Machine learning as a tool for Neuroscience</b></td></tr>
+<tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea Break</b></td></tr>
+<tr><td class="right">15:25-16:10</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited Talk</b></td></tr>
+<tr><td class="right">16:10-16:40</td><td class="left">Jayneil Dalal</td><td class="left"><a href="#sec2.8">Building Embedded Systems for Image Processing using Python</a></td></tr>
+<tr><td class="right">16:40-17:10</td><td class="left">Kunal Puri</td><td class="left"><a href="#sec2.9">Smoothed Particle Hydrodynamics with Python</a></td></tr>
+<tr><td class="right">17:10-17:30</td><td class="left">Nivedita Datta</td><td class="left"><a href="#sec2.10">Encryptedly yours : Python & Cryptography</a></td></tr>
+
 </tbody>
 </table>
 
@@ -44,22 +45,20 @@
 <tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr>
 </thead>
 <tbody>
-<tr><td class="right">09:00-09:45</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited</b></td></tr>
-<tr><td class="right">09:45-10:05</td><td class="left">Mahendra Naik</td><td class="left"><a href="#sec2.13">Large amounts of data downloading and processing in python with facebook data as reference</a></td></tr>
-<tr><td class="right">10:05-10:15</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr>
+<tr><td class="right">09:00-09:45</td><td class="left">Gael</td><td class="left"><a href="#sec2.23">Invited Speaker: <b>Machine learning as a tool for Neuroscience</b></td></tr>
+<tr><td class="right">09:45-10:15</td><td class="left">Kannan Moudgalya</td><td class="left"><b>Invited</b></td></tr>
 <tr><td class="right">10:15-10:45</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
 <tr><td class="right">10:45-11:05</td><td class="left">Hrishikesh Deshpande</td><td class="left"><a href="#sec2.14">Higher Order Statistics in Python</a></td></tr>
 <tr><td class="right">11:05-11:25</td><td class="left">Shubham Chakraborty</td><td class="left"><a href="#sec2.15">Combination of Python and Phoenix-M as a low cost substitute for PLC</a></td></tr>
 <tr><td class="right">11:25-12:10</td><td class="left">Emmanuelle</td><td class="left"><b>Invited</b></td></tr>
 <tr><td class="right">12:10-13:10</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr>
-<tr><td class="right">13:10-13:55</td><td class="left">Asokan</td><td class="left"><b>Invited</b></td></tr>
-<tr><td class="right">13:55-14:15</td><td class="left">Jaidev Deshpande</td><td class="left"><a href="#sec2.18">A Python Toolbox for the Hilbert-Huang Transform</a></td></tr>
-<tr><td class="right">14:15-14:45</td><td class="left">Chetan Giridhar</td><td class="left"><a href="#sec2.19">Diving in to Byte-code optimization in Python</a></td></tr>
-<tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning  Talks</b></td></tr>
-<tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
-<tr><td class="right">15:25-16:05</td><td class="left">Ole Nielsen</td><td class="left"><b>Invited</b></td></tr>
-<tr><td class="right">16:05-16:35</td><td class="left">Kunal puri</td><td class="left"><a href="#sec2.21">GPU Accelerated Computational Fluid Dynamics with Python</a></td></tr>
-<tr><td class="right">16:35-16:45</td><td class="left">Sachin Shinde</td><td class="left"><a href="#sec2.22">Reverse Engineering and python</a></td></tr>
+<tr><td class="right">13:10-13:30</td><td class="left">Mahendra Naik</td><td class="left"><a href="#sec2.13">Large amounts of data downloading and processing in python with facebook data as reference</a></td></tr>
+<tr><td class="right">13:30-14:10</td><td class="left">Ole Nielsen</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">14:10-14:30</td><td class="left">Jaidev Deshpande</td><td class="left"><a href="#sec2.18">A Python Toolbox for the Hilbert-Huang Transform</a></td></tr>
+<tr><td class="right">14:30-15:00</td><td class="left">Chetan Giridhar</td><td class="left"><a href="#sec2.19">Diving in to Byte-code optimization in Python</a></td></tr>
+<tr><td class="right">15:00-15:30</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
+<tr><td class="right">15:305-16:00</td><td class="left">Kunal puri</td><td class="left"><a href="#sec2.21">GPU Accelerated Computational Fluid Dynamics with Python</a></td></tr>
+<tr><td class="right">16:00-16:10</td><td class="left">Sachin Shinde</td><td class="left"><a href="#sec2.22">Reverse Engineering and python</a></td></tr>
 <tr><td class="right">16:10-16:40</td><td class="left">Jarrod Millman</td><td class="left"><b>Invited</b></td></tr>
 </tbody>
 </table>
@@ -381,7 +380,7 @@
 computational challenges that are well-known in large data analytics. The
 <b>scipy</b> stack, including <b>Cython</b> and <b>scikit-learn</b>, used with care, can
 provide a high-performance environment, matching dedicated solutions. I
-will highlight how the *scikit-learn* performs efficient data analysis in
+will highlight how the <b>scikit-learn</b> performs efficient data analysis in
 Python. 
 </p>
 <p>