https://twitter.com/CsEverything
Website: http://everythingcomputerscience.com/
https://www.youtube.com/channel/UCaV_0qp2NZd319K4_K8Z5SQ
Barry Grant.
https://www.youtube.com/channel/UCuop0rFv1hi84ZkONOZtdJA
Kenberley Norris Russell, facebook, playposit
https://www.facebook.com/groups/134847691291662/permalink/276908243752272/
R twitter with illustration
Kelly Bodin, decode your R code with flair
https://education.rstudio.com/blog/2020/05/flair/
This bug was solved by re-install R.
https://community.rstudio.com/t/rstudio-freezing-and-qt-webengine-eating-cpu/28019
tryCatch( info <- getMeta(lat = metaTb$Lat[i], lon = metaTb$Long_[i], plot=FALSE),
error=function(e) {
print( paste( "*** tryCaught error:", metaTb[i, ]) )
}
)
print(i)
https://terra.bio/covid19
https://support.terra.bio/hc/en-us/articles/360041068771--COVID-19-workspaces-data-and-tools-in-Terra
https://terra.bio/covid19
Some broad sequences in SRA
Webinar series on cellular and protein homeostasis
Organised by: P. De Los Rios, N.B. Nillegoda, A. Barducci and P. Goloubinoff
https://tube.switch.ch/channels/4ed71569
https://arxiv.org/abs/2010.14925
"We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28 * 28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets."
https://github.com/MedMNIST/MedMNIST
Fourier Neural Operator for Parametric Partial Differential Equations
Zongyi Li∗ , Nikola Kovachki∗ , Kamyar Azizzadenesheli† , Burigede Liu∗ , Kaushik Bhattacharya∗ , Andrew Stuart∗ , Anima Anandkumar∗ October 20, 2020
"The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation. In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers’ equation, Darcy flow, and the Navier-Stokes equation (including the turbulent regime). Our Fourier neural operator shows state-of-the-art performance compared to existing neural network methodologies and it is up to three orders of magnitude faster compared to traditional PDE solvers."
https://www.technologyreview.com/2020/10/30/1011435/ai-fourier-neural-network-cracks-navier-stokes-and-partial-differential-equations/?utm_term=Autofeed&utm_campaign=site_visitor.unpaid.engagement&utm_medium=tr_social&utm_source=Facebook&fbclid=IwAR3HPDxmTVGrYLdfUzBFL7KeHiSlfN57dmZx2IStwA4dNpywqccY6Ip_9sk#Echobox=1604049241
# Hong will install anaconda R403 in a conda environment on ts117. This strategy worked.
conda create --name condaR403
environment location: /home/hqin/.conda/envs/condaR403
-bash-4.2$ conda activate condaR403
conda install -c r r-base #??
# which R shows an R403 inside an conda environment.
R
install.packages('tidyverse') #this seems worked.
install.packages('EpiNow2') #this run for a while
non-zero exit again due to V8.
(condaR403) -bash-4.2$ conda install -c conda-forge libv8
$ conda install -c conda-forge r-randomcolor
R
install.packages('EpiNow2') #this worked!!!!
> library(EpiNow2)
>
module load sge
qsub epinow2.pbs #this runs!!!!
(condaR403) -bash-4.2$ cat epinow2.pbs
#!/bin/bash -l
#$ -S /bin/bash
#$ -N epinow_job
#$ -V
#$ -cwd
. /etc/profile.d/modules.sh
module load anaconda/5.2.0
source activate condaR403
R -f batch_Rt_by_county.R --args 900 901 1 4/1/2020 5/1/2020
# THIS DID NOT WORK
-bash-4.2$ module load anaconda/5.2.0
-bash-4.2$ conda create --name tsR403
Collecting package metadata: done
Solving environment: done
## Package Plan ##
environment location: /home/hqin/.conda/envs/tsR403
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate tsR403
#
# To deactivate an active environment, use
#
# $ conda deactivate
-bash-4.2$
-bash-4.2$ conda activate tsR403
(tsR403) -bash-4.2$ conda install -c conda-forge libv8
Collecting package metadata: done
Hong then run R
install.packages('tidyverse') #this seem to worked.
-----------------------------[ ANTICONF ]-------------------------------
Configuration failed to find the libv8 engine library. Try installing:
* deb: libv8-dev or libnode-dev (Debian / Ubuntu)
* rpm: v8-devel (Fedora, EPEL)
* brew: v8 (OSX)
* csw: libv8_dev (Solaris)
To use a custom libv8, set INCLUDE_DIR and LIB_DIR manually via:
R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...'
---------------------------[ ERROR MESSAGE ]----------------------------
<stdin>:1:16: fatal error: v8.h: No such file or directory
compilation terminated.
-------------------------------------------------------
(tsR403) -bash-4.2$ conda install -c conda-forge r-randomcolor
Collecting package metadata: \
#this install many packages
Ref: https://github.com/iaconogi/bigSCale2/issues/19
I then tried:
R: install.packages('EpiNow2') #this seems to be running now.
library(EpiNow2) #it worked!
install.packages('woldmet')
#The entire install seem to take almost 2 hours.
Checked a few hour later, EpiNow2 installation did not work.
