Add Organizations to main: (#288)

* Add Organizations to main:
Josef Stefan Institute (JSI)
University of Nebraska Lincoln (UNL)

Add Participating Organizations to projects:
CERN, JSI -> DD4HEP
CMS -> HahRd - 3DClustering
UNL -> ROOT - RootPackageManager

Change project titles:

Old: Machine Learning Project - Convolutional Deep Neural Networks on GPUs for Particle Physics Applications
New: Convolutional Deep Neural Networks on GPUs for Particle Physics Applications (DONE)

Old: Optimization of the ultra-fast detector simulation package FALCON and multi-objective regression  
New: FALCON - optimize fast detector simulation package and multi-objective regression (DONE)

Author: omazapa

_gsocorgs/2018/unl.md

@@ -0,0 +1,11 @@
+---
+title: "University of Nebraska Lincoln"
+author: "Omar Zapata"
+layout: default
+organization: UNL
+logo: unl.png
+description: |
+    The University of Nebraska–Lincoln, often referred to as Nebraska, UNL or NU, is a public research university in the city of Lincoln, in the state of Nebraska in the Midwestern United States.[6] It is the state's oldest university, and the largest in the University of Nebraska system.
+---
+
+{% include gsoc_proposal.ext %}

_gsocproposals/2018/proposal_DD4heptessellation.md

@@ -4,7 +4,7 @@ layout: gsoc_proposal
 project:
   - DD4hep
 year: 2018
-organization:
+organization: 
   - CERN
 ---
 

_gsocproposals/2018/proposal_FALCON.md

@@ -1,5 +1,5 @@
 ---
-title: Optimization of the ultra-fast detector simulation package FALCON and multi-objective regression
+title: FALCON - optimize fast detector simulation package and multi-objective regression
 layout: gsoc_proposal
 project: FALCON
 year: 2018

_gsocproposals/2018/proposal_HAhRD3D-Clustering.md

@@ -3,7 +3,8 @@ title: 3D-Clustering
 layout: gsoc_proposal
 project: HAhRD
 year: 2018
-organization: LLR
+organization:
+  - LLR
 ---
 
 ## Description

_gsocproposals/2018/proposal_ROOTmodularization.md

@@ -3,7 +3,9 @@ title: ROOT Package Manager
 layout: gsoc_proposal
 project: ROOT
 year: 2018
-organization: CERN
+organization: 
+  - CERN
+  - UNL
 ---
 
 ## Description

_gsocproposals/2018/proposal_TMVACNN.md

@@ -1,5 +1,5 @@
 ---
-title: Machine Learning Project - Convolutional Deep Neural Networks on GPUs for Particle Physics Applications
+title: Convolutional Deep Neural Networks on GPUs for Particle Physics Applications
 layout: gsoc_proposal
 project: TMVA
 year: 2018

_gsocproposals/2018/proposal_TMVAother.md

@@ -19,7 +19,10 @@ organization:
 Toolkit for Multivariate Analysis (TMVA) is a multi-purpose machine learning toolkit integrated into the ROOT scientific software framework, used in many particle physics data analyses and applications. The following are also areas of interest with impactful applications to particle physics.
 
 ## Task ideas and expected results
-
+ * Deep Q-Learning: a deep reinforcement learning technique combining the standard Fully-Connected Networks using a biologically inspired technique called experience replay [https://arxiv.org/abs/1312.5602](https://arxiv.org/abs/1312.5602).
+ * Optimization module: Momentum-based, Adam, RMSProp. The optimizers can be developed separately, because they only need the labels and the objective function of the model.
+ * Deep Learning Module Input: low-level implementation of a 3D (or possible higher order) tensor, which will manage the memory better. Also, a better interface to read a ROOT file and supply it directly to the Deep Learning Network.
+ * Gaussian Processes: this is a classical, but powerful method for variational inference.
  * Unsupervised learning:  
     * deep auto-encoders, restricted boltzmann machines (RBMs)  
  * Deep learning: