Update Readme.md

Readme.md

@@ -1,8 +1,8 @@
 # LoadTensorDecompositions
 
-**LoadTensorDecompositions** is a module required by [NTFk](https://github.com/TensorDecompositions/NTFk.jl). For more information, visit [tensors.lanl.gov](http://tensors.lanl.gov)
+**LoadTensorDecompositions** is a module of **SmartTensors**.
 
-### Tensor Decomposition
+## Tensor Decomposition
 
 **NTFk** performs a novel unsupervised Machine Learning (ML) method based on Tensor Decomposition coupled with sparsity and nonnegativity constraints.
 
@@ -46,7 +46,7 @@ A popular approach is to add sparsity and nonnegative constraints.
 Sparsity constraints on the elements of G reduce the number of features and their mixing (by having as many zero entries as possible).
 Nonnegativity enforces parts-based representation of the original data which also allows the Tensor Decomposition results for <img src="https://latex.codecogs.com/svg.latex?\Large&space;G" /> and <img src="https://latex.codecogs.com/svg.latex?\Large&space;A_1,A_2,\ldots,A_n" /> to be easily interrelated [Cichocki et al, 2009](https://books.google.com/books?hl=en&lr=&id=KaxssMiWgswC&oi=fnd&pg=PR5&ots=Lta2adM6LV&sig=jNPDxjKlON1U3l46tZAYH92mvAE#v=onepage&q&f=false).
 
-### Publications:
+## Publications:
 
 - Vesselinov, V.V., Mudunuru, M., Karra, S., O'Malley, D., Alexandrov, B.S., Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing, 10.1016/j.jcp.2019.05.039, Journal of Computational Physics, 2019. [PDF](https://gitlab.com/monty/monty.gitlab.io/raw/master/papers/Vesselinov%20et%20al%202018%20Unsupervised%20Machine%20Learning%20Based%20on%20Non-Negative%20Tensor%20Factorization%20for%20Analyzing%20Reactive-Mixing.pdf)
 - Vesselinov, V.V., Alexandrov, B.S., O'Malley, D., Nonnegative Tensor Factorization for Contaminant Source Identification, Journal of Contaminant Hydrology, 10.1016/j.jconhyd.2018.11.010, 2018. [PDF](https://gitlab.com/monty/monty.gitlab.io/raw/master/papers/Vesselinov%20et%20al%202018%20Nonnegative%20Tensor%20Factorization%20for%20Contaminant%20Source%20Identification.pdf)
@@ -59,7 +59,7 @@ Nonnegativity enforces parts-based representation of the original data which als
 
 Research papers are also available at [Google Scholar](http://scholar.google.com/citations?user=sIFHVvwAAAAJ&hl=en), [ResearchGate](https://www.researchgate.net/profile/Velimir_Vesselinov) and [Academia.edu](https://lanl.academia.edu/monty)
 
-### Presentations:
+## Presentations:
 
 - Vesselinov, V.V., Physics-Informed Machine Learning Methods for Data Analytics and Model Diagnostics, M3 NASA DRIVE Workshop, Los Alamos, 2019. [PDF](http://monty.gitlab.io/presentations/Vesselinov%202019%20Physics-Informed%20Machine%20Learning%20Methods%20for%20Data%20Analytics%20and%20Model%20Diagnostics.pdf)
 - Vesselinov, V.V., Unsupervised Machine Learning Methods for Feature Extraction, New Mexico Big Data &amp; Analytics Summit, Albuquerque, 2019. [PDF](http://monty.gitlab.io/presentations/vesselinov%202019%20Unsupervised%20Machine%20Learning%20Methods%20for%20Feature%20Extraction%20LA-UR-19-21450.pdf)
@@ -72,7 +72,7 @@ Research papers are also available at [Google Scholar](http://scholar.google.com
 
 Presentations are also available at [slideshare.net](https://www.slideshare.net/VelimirmontyVesselin), [ResearchGate](https://www.researchgate.net/profile/Velimir_Vesselinov) and [Academia.edu](https://lanl.academia.edu/monty)
 
-### Videos:
+## Videos:
 
 - Progress of nonnegative matrix factorization process:
 
@@ -82,51 +82,14 @@ Presentations are also available at [slideshare.net](https://www.slideshare.net/
 
 Videos are also available at [YouTube](https://www.youtube.com/playlist?list=PLpVcrIWNlP22LfyIu5MSZ7WHp7q0MNjsj)
 
-### Patent:
+## Patent:
 
 Alexandrov, B.S., Vesselinov, V.V., Alexandrov, L.B., Stanev, V., Iliev, F.L., Source identification by non-negative matrix factorization combined with semi-supervised clustering, [US20180060758A1](https://patents.google.com/patent/US20180060758A1/en)
 
 For more information, visit [monty.gitlab.io](http://monty.gitlab.io)
 
-### Examples:
+## Examples:
 
 * [Machine Learning](https://madsjulia.github.io/Mads.jl/Examples/machine_learning/index.html)
 * [Blind Source Separation (i.e. Feature Extraction)](https://madsjulia.github.io/Mads.jl/Examples/blind_source_separation/index.html)
 * [Source Identification](https://madsjulia.github.io/Mads.jl/Examples/contaminant_source_identification/index.html)
-
-Installation behind a firewall
-------------------------------
-
-Julia uses git for package management. Add in the `.gitconfig` file in your home directory:
-
-```
-[url "https://"]
-        insteadOf = git://
-```
-
-or execute:
-
-```
-git config --global url."https://".insteadOf git://
-```
-
-Julia uses git and curl to install packages. Set proxies:
-
-```
-export ftp_proxy=http://proxyout.<your_site>:8080
-export rsync_proxy=http://proxyout.<your_site>:8080
-export http_proxy=http://proxyout.<your_site>:8080
-export https_proxy=http://proxyout.<your_site>:8080
-export no_proxy=.<your_site>
-```
-
-For example, if you are doing this at LANL, you will need to execute the
-following lines in your bash command-line environment:
-
-```
-export ftp_proxy=http://proxyout.lanl.gov:8080
-export rsync_proxy=http://proxyout.lanl.gov:8080
-export http_proxy=http://proxyout.lanl.gov:8080
-export https_proxy=http://proxyout.lanl.gov:8080
-export no_proxy=.lanl.gov
-```