SciPy Conference: comparison project/templates/talk/conf

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-:3fcab949fc59
+:97c978a24a6d
 <thead>
 <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">09:00-09:15</td><td class="left"></td><td class="left">Inauguration</td></tr>
-<tr><td class="right">09:15-10:15</td><td class="left">Eric Jones</td><td class="left"><b>Keynote</b></td></tr>
+<tr><td class="right">09:15-10:15</td><td class="left">[Invited Speaker] Eric Jones</td><td class="left"><b>Keynote: What Matters in Scientific Software Projects? 10 Years of Success and Failure Distilled</b></td></tr>
 <tr><td class="right">10:15-10:45</td><td class="left"></td><td class="left">Tea Break</td></tr>
 <tr><td class="right">10:45-11:05</td><td class="left">Ankur Gupta</td><td class="left"><a href="#sec2.2">Multiprocessing module and Gearman</a></td></tr>
 <tr><td class="right">11:05-11:35</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">11:35-12:20</td><td class="left">Mateusz Paprocki</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">11:35-12:20</td><td class="left">[Invited Speaker] Mateusz Paprocki</td><td class="left"><b><a href = "#sec2.26">Understanding importance of automated software testing</b></a></td></tr>
 <tr><td class="right">12:20-13:20</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr>
-<tr><td class="right">13:20-14:05</td><td class="left">Ajith Kumar</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">13:20-14:05</td><td class="left">[Invited Speaker] Ajith Kumar</td><td class="left"><b>Invited Talk</b></td></tr>
 <tr><td class="right">14:05-14:25</td><td class="left">Bala Subrahmanyam Varanasi</td><td class="left"><a href="#sec2.6">Sentiment Analysis</a></td></tr>
 <tr><td class="right">14:25-14:55</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">14:55-15:05</td><td class="left">IITB Students</td><td class="left"><a href="#sec2.24">Project Presentation</a></td></tr>
 <tr><td class="right">15:05-15:35</td><td class="left"></td><td class="left"><b>Tea Break</b></td></tr>
-<tr><td class="right">15:35-16:20</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited Talk</b></td></tr>
+<tr><td class="right">15:35-16:20</td><td class="left">[Invited Speaker] Prabhu Ramachandran</td><td class="left"><b>Invited Talk</b></td></tr>
 <tr><td class="right">16:20-16:40</td><td class="left">William Natharaj P.S</td><td class="left"><a href="#sec2.3">Automated Measurement of Magnetic properties of Ferro-Magnetic materials using Python</a></td></tr>
 <tr><td class="right">16:40-17:00</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">17:10-17:30</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr>
 </tbody>
 </colgroup>
 <thead>
 <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">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">09:00-09:45</td><td class="left">[Invited Speaker] Gaël Varoquaux</td><td class="left"><a href="#sec2.23"><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">09:45-10:15</td><td class="left">[Invited Speaker] Kannan Moudgalya</td><td class="left"><b>National Mission on Education Through ICT</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">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">11:25-12:10</td><td class="left">Emmanuelle</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">11:25-12:10</td><td class="left">[Invited Speaker] Emmanuelle Gouillart</td><td class="left"><a href="#sec2.27"><b>3-D image processing and visualization with the scientific-Python stack</b></a></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">13:10-13:50</td><td class="left">Ole Nielsen</td><td class="left"><a href="#sec2.25">Invited Speaker: <b>7 Steps to Python Software That Works</b></a></td></tr>
+<tr><td class="right">13:10-13:50</td><td class="left">[Invited Speaker] Ole Nielsen/Panel Discussion with Invited Speakers</td><td class="left"><a href="#sec2.25"><b>7 Steps to Python Software That Works<a/> / Community Building in Open Source Projects</b></td></tr>
 <tr><td class="right">13:50-14:20</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">14:20-14:50</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">14:50-15:20</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">15:20-15:50</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
 <tr><td class="right">15:50-16:10</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>
 <h4>Abstract</h4>
 <p>In many signal and image processing applications, correlation and power spectrum have been used as primary tools; the information contained in the power spectrum is provided by auto-correlation and is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there exist some practical situations where one needs to look beyond auto-correlation operation to extract information pertaining to deviation from Gaussianity and the presence of phase relations. Higher Order Statistics (HOS) are the extensions of second order measures to higher orders and have proven to be useful in problems where non-gaussianity, non-minimal phase or non-linearity has some role to play. In recent years, the field of HOS has continued its expansion, and applications have been found in fields as diverse as economics, speech, medical, seismic data processing, plasma physics and optics. In this paper, we present a module named PyHOS, which provides elementary higher order statistics functions in Python and further discuss an application of HOS for biomedical signals. This module makes use of SciPy, Numpy and Matplotlib libraries in Python. To evaluate the module, we experimented with several complex signals and compared the results with equivalent procedures in MATLAB. The results showed that PyHOS is excellent module to analyze or study signals using their higher order statistics features.</p>
 <h4>Slides</h4>
 <p>To be uploaded</p>
-<h3 id="sec2.15">Shubham Chakraborty : Combination of Python and Phoenix-M as a low cost substitute for PLC</h3>
-<h4>Abstract</h4>
-<p>In this paper I will show how the combination of Python programming language and Phoenix-M interface (created by IUAC, New Delhi) can be used as a low cost substitute for PLC (Programmable Logic Controllers). In Home Automation this combination can be used for a variety of purposes. </p>
-<h4>Slides</h4>
-<p>To be uploaded</p>
 <h3 id="sec2.18">Jaidev Deshpande : A Python Toolbox for the Hilbert-Huang Transform</h3>
 <h4>Abstract</h4>
 <p>This paper introduces the PyHHT project. The aim of the project is to develop a Python toolbox for the Hilbert-Huang Transform (HHT) for nonlinear and nonstationary data analysis. The HHT is an algorithmic tool particularly useful for the time-frequency analysis of nonlinear and nonstationary data. It uses an iterative algorithm called Empirical Mode Decomposition (EMD) to break a signal down into so-called Intrinsic Mode Functions (IMFs). These IMFs are characterized by being piecewise narrowband and amplitude/frequency modulated, thus making them suitable for Hilbert spectral analysis.</p>
 testing, source control, style guides, exception handling etc were
 observed more generally. To keep it real, I'll show real examples where appropriate.
 </p>
 <h4>Slides</h4>
 <p>To be uploaded</p>
+<h3 id="sec2.26">Mateusz Paprocki : Understanding importance of automated software testing</h3>
+<h4>Abstract</h4>
+<p>
+Development of scientific programs isn't much different than development of computer programs of any other kind. One of the key characteristic of computer programs is correctness. No matter whether we create programs for our own purpose or for other parties, we do not want to spent hours or days waiting for results of computations that will be flawed from the very beginning. As long as programs consist of few lines of code, we may be able to verify correctness of all cases in those programs manually after every change or even try to prove their correctness. However, real life programs consist of thousands, hundred thousands or even millions of lines of code, and even more states. In such a setup we need tools and methods that would allow to automate the process of software testing.
+</p>
+<p>
+Python, a programming language with a weak dynamic type system, makes the use of automated software testing even more important because in this case test suites and the testing framework of choice have to accommodate for the weaknesses of the language. Also, agile software development techniques may intrinsically require automated testing as their core component to guarantee effectiveness of those methods.
+</p>
+<p>
+In this talk I will show how to do automated testing of programs written in Python. Test automation tools will be described and common issues and pitfalls outlined. I will also discuss the notion of code coverage with tests and testing via examples (doctests).
+</p>
+<h4>Slides</h4>
+<p>To be uploaded</p>
+<h3 id="sec2.27">Emmanuelle Gouillart (joint laboratory CNRS/Saint-Gobain UMR 125,
+39 quai Lucien Lefranc 93303 Aubervilliers, France): 3-D image processing and visualization with the scientific-Python stack</h3>
+<h4>Abstract</h4>
+<p>
+Synchrotron X-ray tomography images the inner 3-D micro-structure of
+objects. Recent progress bringing acquisition rates down to a few seconds
+have opened the door to in-situ monitoring of material transformations
+during, e.g., mechanical or heat treatments. However, this powerful
+imaging technique presents many challenges, such as the huge size of
+typical datasets, or the poor signal over noise ratio. In this talk, we
+will present how the standard modules of the scientific Python stack,
+combined with a few additional developments, are used to process and
+visualize such 3-D tomography images for research purposes. The data
+presented in this talk consist of 3-D images of window-glass raw
+materials, that react together at high temperature to form liquids, and
+images of glasses undergoing phase separation.3
+</p>
+<p>
+Using the Traits module, it was possible to write at minimal cost a
+custom graphical application with an embedded Mayavi scene to perform
+"4-D visualization", that is, to display cuts through a 3-D volume that
+can be updated with the next or previous image of the dataset. Easy
+interaction with the data (placing markers) could also be added at
+minimal cost. Efficient state-of-the-art algorithms for denoising images
+and segmenting (extracting) objects were implemented using scipy, and
+PyAMG for multigrid resolution of linear systems.
+</p>
+<p>
+Finally, we will show how this work led us "naturally" to take part in
+development efforts of open-source Scientific-python packages. Improving
+the documentation of scipy.ndimage on the documentation wiki was a first
+easy contribution. Then, one segmentation algorithm as well as one
+denoising algorithm were contributed to the scikits-image package. We
+will finish the talk by a brief overview of scikits-image and its
+development process.
+</p>
+<p>
+<h4>Slides</h4>
+<p>To be uploaded</p>
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branch	2011
changeset 493	97c978a24a6d
parent 492	3fcab949fc59
parent 484	de1331cbbbf3
child 510	a7e85cdc6ed0