tests and fixes

Author: emanueleusai

_gsocproposals/2023/proposal_EXXA.md

@@ -33,12 +33,7 @@ Total project length: 175/350 hours.
 
 ## Test
 Use [this link](https://docs.google.com/document/d/10jZ7aubVkfkcpURQQnvrvbC7o3XgglsJwjS0UA7SRBE/edit?usp=sharing) for instructions on completing the test.
-<!-- ## Test
 
-No evaluation test for this project, however, we encourage you to:
-  * submit your proposal by April 19
-  * submit an evaluation test for a different ML4SCI project to show your ML skills
-  * consider submitting an additional proposal for another ML4SCI project to increase your overall chances (this is a very popular project) -->
 
 ## Links
 * [Previous Paper](https://iopscience.iop.org/article/10.3847/1538-4357/aca477)

_gsocproposals/2023/proposal_EXXA1.md

@@ -31,71 +31,16 @@ Total project length: 175/350 hours.
 * Python, PyTorch, C/Fortran
 * Background in astronomy is a bonus but not a requirement
 
-## Mentors
-
-* [Sergei Gleyzer](http://sergeigleyzer.com/) (University of Alamaba)
-
-Please DO NOT contact mentors directly by email. Instead, please email [email protected] with Project Title and include your CV and test results. The relevant mentors will then get in touch with you.
-
-## Corresponding Project
-
-* [EXXA](https://ml4sci.org/gsoc/2023/proposal_EXXA.html)
-
-## Links
-
-* [Previous Paper](https://iopscience.iop.org/article/10.3847/1538-4357/aca477)
-
-## Participating Organizations
-
-* [University of Alabama](https://physics.ua.edu/)---
-title: Finding Exoplanets with Astronomical Observations
-layout: gsoc_proposal
-project: EXXA
-year: 2023
-organization:
- - Alabama
- - EPFL
- - Georgia
----
-
-## Description
-
-The purpose of this project is to use publicly available data from observations intended to identify exoplanets in order to determine whether or not a given stellar system contains one or more exoplanets.
-
-An exoplanet is any planet not in our solar system. Since the early 1990s, thousands of exoplanets have been discovered. Exoplanets are identified through a variety of methods, including, but not limited to, transit light curve analysis, direct imaging, and radial velocity measurements. One of the most successful attempts was the Kepler Mission, which studied light curves of hundreds of thousands of stars and identified over 2,500 planets. Each detection technique is biased towards detecting different types of planets at different distances from stars, so a variety of methods is necessary to fully explore the exoplanetary parameter space.
-
-As is exemplified by the Kepler Mission, identifying exoplanets – using any method – relies on massive datasets and signals that can be subtle and noisy. In fact, the first exoplanet was observed many years before it was identified because the researchers did not originally notice it in the data. An unfortunate side effect of this delay was that the “first” discovered exoplanet was announced in the interim despite being observed years later. Machine learning may be a useful avenue for systematically, accurately, and quickly identifying planets and avoiding such situations. 
-
-__This is a popular project.__
-
-## Duration
-
-Total project length: 175 hours.
-
-## Task ideas and expected results
-  * Using publicly available observational data, models will be constructed that predict the presence of a planet. The participant may use data from any type of detection of method, and there is no limit to the number of detection methods that may be used. 
-  * A successful project should be able to confirm the presence of previously identified planets with extremely high confidence; a fantastic project will identify new ones.
-   
-## Requirements 
-  * Python, previous experience in machine learning
-  * Basic knowledge of astronomy and observations is useful but not required
-
 ## Test
 Use [this link](https://docs.google.com/document/d/10jZ7aubVkfkcpURQQnvrvbC7o3XgglsJwjS0UA7SRBE/edit?usp=sharing) for instructions on completing the test.
-<!-- ## Test
-
-No evaluation test for this project, however, we encourage you to:
-  * submit your proposal by April 19
-  * submit an evaluation test for a different ML4SCI project to show your ML skills
-  * consider submitting an additional proposal for another ML4SCI project to increase your overall chances (this is a very popular project) -->
 
 ## Links
+
 * [Previous Paper](https://iopscience.iop.org/article/10.3847/1538-4357/aca477)
 
 ## Mentors
-  * [Sergei Gleyzer](mailto:[email protected]) (University of Alabama)
-  * [Emanuele Usai](mailto:[email protected]) (University of Alabama)
-  * [Ali Hariri](mailto:[email protected]) (EPFL)
-  * [Jason Terry](mailto:[email protected]) (University of Georgia)
+
+* [Sergei Gleyzer](mailto:[email protected]) (University of Alabama)
 
 Please **DO NOT** contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and **include your CV**. The mentors will then get in touch with you.
+

_gsocproposals/2023/proposal_PROSPECTOR.md

@@ -25,12 +25,9 @@ Total project length: 175/350 hours.
 ## Expected results
   * Identification of a suitable model with a proof-of-concept using Lunar Prospector data.
 
-## Test
-The test will be available by Feb 28 2023.
-
-<!-- ## Test
 
-Please use [this link](https://drive.google.com/file/d/1QoxSdeL0JOqJJKCea75AwIyaujIV8gxB/view?usp=sharing) to access the test for this project. -->
+## Test
+Please use [this link](https://ml4sci.org/assets/GSOC_2023_Evaluation_Test.pdf) to access the test for this project.
 
 ## Requirements 
 Python and relevant past experience in Machine Learning.

_layouts/main.html

@@ -77,7 +77,7 @@ <h2>ML4SCI in GSoC 2023</h2>
   <p class="lead">If you are interested in our activities please join our <a href="https://simba3.web.cern.ch/simba3/SelfSubscription.aspx?groupName=ml4sci-announce" target="_blank">announcements mailing list</a>. 
 To join, you will need to create a <a href="https://account.cern.ch/account/externals/" target="_blank">CERN lightweight account</a>.</p>
 
-  <p class="lead">You can also find us on <a href="https://gitter.im/ML4SCI" target="_blank">Gitter</a>.</p>
+  <p class="lead">You can also find us on <a href="https://matrix.to/#/#ML4SCI_general:gitter.im" target="_blank">Gitter</a>.</p>