dewpoint weakly correlates with RH

 NOAA dewpoint  correlate weakly with RH, R2=0.11 for Hamilton, TN

ERA5 dewpoint correlates with NOAA dewpoint well. R2 = 0.97

ERA5 Grib t2m correlates well with NOAA air_temp at Hamilton, TN

 source code:  
https://github.com/QinLab/COVID19_transmission_MS/blob/main/hamiltonTN_compare_T2m_NOAA.Rmd 

Call:
lm(formula = tb_comp$K ~ tb_comp$air_temp)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.7346 -0.9967 -0.0165  0.9527  7.6325 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      271.41583    0.14303  1897.6   <2e-16 ***
tb_comp$air_temp   0.96368    0.00688   140.1   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.492 on 597 degrees of freedom
Multiple R-squared:  0.9705,	Adjusted R-squared:  0.9704 
F-statistic: 1.962e+04 on 1 and 597 DF,  p-value: < 2.2e-16

data science, last lecture, Q&A about final project and final presentation video

 

== pre-class to do: 

calendar email invitation: including guests;  done. 

socrative questions (midterm exam, questions on contents from last lecture ).  done

update Canvas course materials, update learning objectives. assignments as needed.   done

* make sure grades are NOT shown on Canvas. 

Test-run code:  ipynb. NA

learning objectives:  updated on canvas, NA

== In-class to do: 

clean up desktop space, calendars": 

ZOOM, live transcript (start video recording).  Turn the computer speaker on. 

Socrative sign in 

Give time for students to take course surveys:     

* Show my ERA5 land weather data, PBS jobs, run errors and outputs. 

 Arts and Science Dean cited some books on STEM in general education. 

Robot-Proof

Higher Education in the Age of Artificial Intelligence

https://mitpress.mit.edu/books/robot-proof

李宏毅对抗生成网络(GAN)国语教程(2018)

https://www.bilibili.com/video/BV1Up411R7Lk?from=search&seid=11284843292198463798&spm_id_from=333.337.0.0

awesome public data sets

 

https://github.com/awesomedata/awesome-public-datasets

APEER by ZEISS

deep learning image analysis provided by ZEISS

https://www.apeer.com/blog

training set, validation set, and test test for machine learing / deep learning

 

from: https://machinelearningmastery.com/difference-test-validation-datasets/

– Training set: A set of examples used for learning, that is to fit the parameters of the classifier.

– Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network.

– Test set: A set of examples used only to assess the performance of a fully-specified classifier.

secure python, unit 14, 15, regular expression

Socrative: review of while loop

Slides
ask students to share their screen 
Last times, I let students worked on 
whileExample1.py
csvHandler.py
Lab exercise 1.

=======lesson plan ==========

15 minutes: 

Go over Socrative review questions. while loop, csv handler

=> datePython.py 

regular expression in Python

https://docs.python.org/3/howto/regex.html

https://github.com/AstunTechnology/python-basics/blob/master/regular-expressions.md

HCA AIDA pilot

19-year-old human stage, 21-year-old human stage, 22-year-old human stage, 23-year-old human stage, 24-year-old human stage, 25-year-old human stage, 26-year-old human stage, 27-year-old human stage, 28-year-old human stage, 29-year-old human stage, 30-year-old human stage, 31-year-old human stage, 32-year-old human stage, 33-year-old human stage, 34-year-old human stage, 35-year-old human stage, 36-year-old human stage, 37-year-old human stage, 38-year-old human stage, 39-year-old human stage, 40-year-old human stage, 41-year-old human stage, 42-year-old human stage, 43-year-old human stage, 44-year-old human stage, 45-year-old human stage, 46-year-old human stage, 47-year-old human stage, 48-year-old human stage, 49-year-old human stage, 50-year-old human stage, 51-year-old human stage, 52-year-old human stage, 53-year-old human stage, 54-year-old human stage, 55-year-old human stage, 56-year-old human stage, 57-year-old human stage, 58-year-old human stage, 59-year-old human stage, 60-year-old human stage, 61-year-old human stage, 62-year-old human stage, 63-year-old human stage, 64-year-old human stage, 65-year-old human stage, 66-year-old human stage, 68-year-old human stage, 71-year-old human stage, 73-year-old human stage

 

https://data.humancellatlas.org/explore/projects/f0f89c14-7460-4bab-9d42-22228a91f185

single cell aging mouse brain （error）

 37069 cells

https://singlecell.broadinstitute.org/single_cell/study/SCP263/aging-mouse-brain

455M is too big to download by browser. 

Tring to get certificate at local server

openssl s_client -showcerts -servername singlecell.broadinstitute.org/single_cell/api/v1 -connect server:443 > cacert.pem

https://singlecell.broadinstitute.org/

singlecell.broadinstitute.org/single_cell/api/v1

(base) hqin@CS313BQin q-sandbox % which openssl

/Users/hqin/opt/anaconda3/bin/openssl

(base) hqin@CS313BQin q-sandbox % openssl s_client -showcerts -servername https://singlecell.broadinstitute.org -connect server:443 > cacert.pem

4504972800:error:2008F002:BIO routines:BIO_lookup_ex:system lib:crypto/bio/b_addr.c:730:nodename nor servname provided, or not known

connect:errno=0

CURL download error

curl: (60) SSL certificate problem: unable to get local issuer certificate

More details here: https://curl.haxx.se/docs/sslcerts.html

design for inference : scRNA-seq, Aviv Regev

 

Schrodinger Equation

 i and h are constant

H hat is the Hamiltonian operator that includes the total energy. 

So, Schrodinger Equation indicates that change of a quantum system is ~ ih of the total energy?! 

quantum reading notes, QAOA

superposition is a linear combination of states

quantum gates are matrices

Hadamard gate creates superposition. 

H on |0> gives to sqrt(2)( |0> + |1> ), whereas H on |1> gives sqrt(2) ( |0> - |1> ) 

Controlled NOT gate is on two qubit systems: 

Quantum Approximate Optimization Algorithm (QAOA) can solve Max-Cut of graphs, an NP-complete problem. 

For n vertices and m edges, the vertices can be either k(i) = +1 or -1. A cut can be defined as k(i)xk(j)= -1. 

The n-vertices can implemented as n qubits. The 2^n assignments for the vertices will correspond to the 2^n dimenstional Hilbert space generated by n qubits. So, if <i,j> is a cut, the related qubit will land onto different bases. 

QIN: In the above implementation, each vertice is allowed either 1 or 0. So, expand this for my network aging model, I may be able to expand the vertice to sub-vertices, which allows for 1 or 0 separately. Can I? 

Reference: 

Quantgates training materials

greek symbols pronunciations

 

https://www.thoughtco.com/the-greek-alphabet-1705558

COVID variants B.1.1.529 Omicron

betacoronavirus

GR

B.1.1.529

2021-11-18

(NSP5_P132H,Spike_H69del,Spike_T95I,Spike_A67V,Spike_S373P,Spike_Q493R,Spike_H655Y,N_R203K,Spike_N969K,Spike_N856K,Spike_G142D,NSP3_A1892T,Spike_Q954H,N_P13L,NSP3_L1266I,Spike_N501Y,N_R32del,M_Q19E,Spike_N440K,NSP4_T492I,NSP6_L105del,Spike_N679K,Spike_N764K,Spike_L212I,Spike_Y505H,NSP6_G107del,NSP6_I189V,Spike_T547K,M_D3G,Spike_D796Y,N_G204R,Spike_T478K,Spike_V143del,M_A63T,Spike_G496S,NSP3_V1069I,Spike_K417N,NSP6_S106del,Spike_S371L,Spike_G339D,NSP3_S1265del,NSP14_I42V,Spike_P681H,Spike_Y144del,Spike_ins214EPE,N_S33del,Spike_S375F,Spike_Q498R,Spike_G446S,Spike_S477N,N_E31del,NSP3_K38R,Spike_N211del,Spike_E484A,E_T9I,Spike_V70del,Spike_L981F,NSP12_P323L,Spike_D614G,Spike_Y145del)

VOC Omicron GR/484A (B.1.1.529) first detected in Botswana/Hong Kong/South Africa

2021-11-11

Africa / Botswana / South East / Greater Gaborone / Gaborone

Human

2021-11-23

TRUE

0.0131383910524

0.379838874855

29684

 

B1.1.529 immuno comprosied person of origin

 

https://english.alarabiya.net/coronavirus/2021/11/26/Explainer-What-is-the-new-B-1-1-529-COVID-19-variant-

remote jupyter-notebook on ts117

Follow https://github.com/justinmclark/utc_lookout_remote_jupyter

I used 'grib' environment, and reinstalled jupyter in 'grib'. 

I then used a browser on local:8888 in ECS313 desktop, and it worked. 

 

CPSC 4180 data science

== pre-class to do: 

calendar email invitation: including guests;  done. 

socrative questions (midterm exam, questions on contents from last lecture ).  done

update Canvas course materials, update learning objectives. assignments as needed.   done

* make sure grades are NOT shown on Canvas. 

Test-run code:  ipynb. NA

learning objectives:  updated on canvas, NA

== In-class to do: 

clean up desktop space, calendars": 

ZOOM, live transcript (start video recording).  Turn the computer speaker on. 

Socrative sign in 

Give time for students to take course surveys

* ask students to share projects. Need to work on writing. 

* Show my ERA5 land weather data, PBS jobs, run errors and outputs. 

Application instruction for UTC Fall 2022 PhD program

 

To apply for Fall 2022 enrollment of PhD program, please apply at https://www.utc.edu/research/graduate-school/prospective-students/graduate-school-admissions

 

The deadline for fall 2022 application is February 1 (for students interested in graduate assistantship)

https://www.utc.edu/engineering-and-computer-science/academic-programs/electrical-engineering/graduate-curriculum/graduate-admission

 

Our graduate school application requires GRE and TOEFL/IELTS scores. 

 

Thanks again for your interest. Please let me know if I can answer some of your questions,

 

15 Apple counties not found in JHU county

 

There are  32  Apple counties not found in counties2/

Out[83]:

{'AguadillaMunicipio_PuertoRico_US_v2.csv',
 'AnchorageMunicipality_Alaska_US_v2.csv',
 'AreciboMunicipio_PuertoRico_US_v2.csv',
 'BayamónMunicipio_PuertoRico_US_v2.csv',
 'BoxElder_Utah_US_v2.csv',
 'CaguasMunicipio_PuertoRico_US_v2.csv',
 'CarolinaMunicipio_PuertoRico_US_v2.csv',
 'Carson_Nevada_US_v2.csv',
 'DoradoMunicipio_PuertoRico_US_v2.csv',
 'DoñaAna_NewMexico_US_v2.csv',
 'Dukes_Massachusetts_US_v2.csv',
 'FairbanksNorthStarBorough_Alaska_US_v2.csv',
 'Guam_Guam_US_v2.csv',
 'GuaynaboMunicipio_PuertoRico_US_v2.csv',
 'GuraboMunicipio_PuertoRico_US_v2.csv',
 'HumacaoMunicipio_PuertoRico_US_v2.csv',
 'James_Virginia_US_v2.csv',
 'Juab_Utah_US_v2.csv',
 'JuneauandBorough_Alaska_US_v2.csv',
 'KenaiPeninsulaBorough_Alaska_US_v2.csv',
 'Matanuska-SusitnaBorough_Alaska_US_v2.csv',
 'MayagüezMunicipio_PuertoRico_US_v2.csv',
 'Millard_Utah_US_v2.csv',
 'PonceMunicipio_PuertoRico_US_v2.csv',
 'RíoGrandeMunicipio_PuertoRico_US_v2.csv',
 'SanJuanMunicipio_PuertoRico_US_v2.csv',
 'Sevier_Utah_US_v2.csv',
 'St.CroixIsland_VirginIslands_US_v2.csv',
 'St.ThomasIsland_VirginIslands_US_v2.csv',
 'ToaBajaMunicipio_PuertoRico_US_v2.csv',
 'TrujilloAltoMunicipio_PuertoRico_US_v2.csv',
 'Weber_Utah_US_v2.csv'}

Sevier Utah is an unincoporated community. 

After remove some "and", "City", "Municipio", "Borough",  there are 18 left. 

There are  15  Apple counties not found in counties2/
Out[13]:
{'Bayamón_PuertoRico_US_v2.csv',
 'BoxElder_Utah_US_v2.csv',
 'Carson_Nevada_US_v2.csv',
 'DoñaAna_NewMexico_US_v2.csv',
 'Dukes_Massachusetts_US_v2.csv',
 'Guam_Guam_US_v2.csv',
 'James_Virginia_US_v2.csv',
 'Juab_Utah_US_v2.csv',
 'Mayagüez_PuertoRico_US_v2.csv',
 'Millard_Utah_US_v2.csv',
 'RíoGrande_PuertoRico_US_v2.csv',
 'Sevier_Utah_US_v2.csv',
 'St.CroixIsland_VirginIslands_US_v2.csv',
 'St.ThomasIsland_VirginIslands_US_v2.csv',
 'Weber_Utah_US_v2.csv'}

Dukes_Massachusetts_US_v2.csv is in Apple. 
In JHU, this seems to be DukesandNantucket_Massachusetts_US_v2.csv 

deepl

 

https://www.deepl.com/en/translator

Deviance information criterion DIC

From https://en.wikipedia.org/wiki/Deviance_information_criterion : 

There are two calculations in common usage for the effective number of parameters of the model. The first, as described in Spiegelhalter et al. (2002, p. 587), is ${\displaystyle p_{D}={\overline {D(\theta )}}-D({\bar {\theta }})}$, where ${\displaystyle {\bar {\theta }}}$ is the expectation of ${\displaystyle \theta }$. The second, as described in Gelman et al. (2004, p. 182), is ${\displaystyle p_{D}=p_{V}={\frac {1}{2}}{\overline {\operatorname {var} \left(D(\theta )\right)}}}$. The larger the effective number of parameters is, the easier it is for the model to fit the data, and so the deviance needs to be penalized.

The deviance information criterion is calculated as

${\displaystyle \mathrm {DIC} =p_{D}+{\overline {D(\theta )}},}$

or equivalently as

${\displaystyle \mathrm {DIC} =D({\bar {\theta }})+2p_{D}.}$

,

Metropolis–Hastings Algorithm

 

https://stephens999.github.io/fiveMinuteStats/MH-examples1.html

bayesian statistics, MCMC, UCSC, coursera

 

https://www.coursera.org/learn/mcmc-bayesian-statistics/home/week/3

R code 

4180 data science project discussion

   == pre-class to do: 

calendar email invitation: including guests;  done. 

socrative questions (midterm exam, questions on contents from last lecture ).  done

update Canvas course materials, update learning objectives. assignments as needed.   done

* make sure grades are NOT shown on Canvas. 

Test-run code:  ipynb. NA

learning objectives:  updated on canvas, NA

== In-class to do: 

clean up desktop space, calendars": 

ZOOM, live transcript (start video recording).  Turn the computer speaker on. 

Socrative sign in 

Give time for students to take course surveys

* ask students to share projects. Need to work on writing. 

* discussion covid19 transmission 

https://www.nature.com/articles/s41467-021-21358-2 

Easy MCMC

https://stephens999.github.io/fiveMinuteStats/MH-examples1.html 

quillbot

 quillbot

secure python CEDI

 Socrative

Slides

ask students to share their screen 

Last times, I let students worked on replacingExample1.py

=======lesson plan ==========

15 minutes: 

Go over Socrative review questions. 

10 minutes: 

whileExample1.py :: line.strip()

5 minutes: 

whileExample2.py :: line.split()

10 minutes: 

=> ask student to modify whileExample2 to process gleep2.  This is whileExample3.py

15 minutes 

csvHandler.py

20 minutes:

=> Lab exercise 1. datetime.datetime 

=> datePython.py

cpsc 4180 data science, project discussion

  == pre-class to do: 

calendar email invitation: including guests;  done. 

socrative questions (midterm exam, questions on contents from last lecture ).  done

update Canvas course materials, update learning objectives. assignments as needed.   done

* make sure grades are NOT shown on Canvas. 

Test-run code:  ipynb. NA

learning objectives:  updated on canvas, NA

== In-class to do: 

clean up desktop space, calendars": 

ZOOM, live transcript (start video recording).  Turn the computer speaker on. 

Socrative sign in 

Give time for students to take course surveys

* ask students to share projects. Need to work on writing. 

* Show my ERA5 land weather data

CAE showcase workshop

https://cyberedwiki.org/index.php?title=Welcome_to_CyberEd_Wiki  

https://www.caeresource.directory/home

parse ERA5 GRIB for JHU counties on ts117, Nov 14-17, 7 days runs

 The template pbs is: 

-bash-4.2$ cat ts_grib_job_template.pbs 

#!/bin/bash -l

#$ -S /bin/bash

#$ -N pygrib_NUMBER

#$ -cwd

. /etc/profile.d/modules.sh

module load anaconda

source activate grib 

python parse_grib_4JHU-debug.py STARTINDEX  ENDINDEX 

-bash-4.2$ 

Qin used a python code to generate pbs jobs, and then submit them using shell script. 

-bash-4.2$ cat generate_PBS_bash.py 

import os

import re

filebatchshell = "submit_PBS.sh"

fshell = open(filebatchshell, "w")

step = 15

for i in range( 0, 600, step):

    fin = open("ts_grib_job_template.pbs", "r")

    LinesIn = fin.readlines() 

    fin.close()

    LinesIn[2] = "#$ -N pygrib_" + str(i) + "\n"

    LinesIn[7] = "python parse_grib_4JHU.py " + str(i) + " " + str(i + step) + "  \n"

            

    fileout = "ts_" + str(i) + ".pbs"

    fout = open(fileout, "w")

    for line in LinesIn: 

        fout.write(line)

    fout.close()

    buffer = "qsub " + fileout + "\n"

    fshell.write(buffer)

fshell.close()

To parse the 2m-dew point, I used generate_PBS_bash_dewpoint.py

This is a 7-day run. The last t2m output is on Nov 20, and the last dewpoint file output is on Nov 21. 

On Nov 21, I use github to backup running logs, and output csv files 

ERA5 grib file parsing

grbs = pygrib.open('2020-2021NovT2m-dewpoint.grib') #does not work

grbs = pygrib.open('2019-2020June10.grib') #works

It would be strange if grib format changed. I need to redownload the grib file. again

6pm, Qin found out that remove the time selection, and the codes runs on 2021 GRIB file. 

DoD 8140 certification

 

8140 certification replace 8570 certification. 

https://www.comptia.org/blog/what-is-dod-8140-cybersecurity-certifications-and-requirements 

UTC admin evaluation results

 

https://webapp.utc.edu/common/faculty-ratings/index2.php

CS courses with video lectures

 

https://github.com/Developer-Y/cs-video-courses

data science journals, conferences

 https://www.computer.org/csdl/journal/bd

https://conferenceindex.org/disciplines

Springer: International Journal of Data Science and Analytics

ACM Transactions on Knowledge Discovery from Data

CPSC4900 software engineering

UTC course scheduling, time slots

Aquarium fish tank video with Yolo

https://github.com/Developer-Y/cs-video-courses#software-engineering

GitHub - CookingWithCale/MarkdownSoftwareEngineering: Software engineering markdown templates, including Requirements Analysis Document and Software Design Document.

GitHub - CookingWithCale/MarkdownSoftwareEngineering: Software engineering markdown templates, including Requirements Analysis Document and Software Design Document.

 

Rt batch run on ts

Generate PBS submission jobs

 * python generate_PBS_bash.py 

  * sh submit_PBS_small.sh 

-bash-4.2$ cat submit_PBS_small.sh 

qsub PBS/ts_1_2.pbs

qsub PBS/ts_2_3.pbs

qsub PBS/ts_3_4.pbs

qsub PBS/ts_4_5.pbs

qsub PBS/ts_5_6.pbs

qsub PBS/ts_6_7.pbs

qsub PBS/ts_7_8.pbs

qsub PBS/ts_8_9.pbs

qsub PBS/ts_9_10.pbs

qsub PBS/ts_10_11.pbs

These jobs were done within minutes. 

23:50 sh submit_PBS.sh  ==> 3342 jobs, one for each USA county. small test run. 

00:14 Use generate_PBS_bash.py  for time from Feb 1, 2020 to Nov 1, 2021. Sub 3342 jobs again. 

-bash-4.2$ qstat | head

job-ID  prior   name       user         state submit/start at     queue                          slots ja-task-ID 

-----------------------------------------------------------------------------------------------------------------

  40904 0.50500 epi_job1   hqin         r     11/10/2021 01:16:14 all.q@ts26.cm.cluster              1        

  40905 0.50500 epi_job2   hqin         r     11/10/2021 01:16:14 all.q@ts19.cm.cluster              1        

  40906 0.50500 epi_job3   hqin         r     11/10/2021 01:16:14 all.q@ts01.cm.cluster              1        

  40907 0.50500 epi_job4   hqin         r     11/10/2021 01:16:14 all.q@ts33.cm.cluster              1        

  40909 0.50500 epi_job6   hqin         r     11/10/2021 01:16:14 all.q@ts19.cm.cluster              1        

  40910 0.50500 epi_job7   hqin         r     11/10/2021 01:16:14 all.q@ts01.cm.cluster              1        

  40912 0.50500 epi_job9   hqin         r     11/10/2021 01:16:14 all.q@ts29.cm.cluster              1        

  40914 0.50500 epi_job11  hqin         r     11/10/2021 01:16:14 all.q@ts26.cm.cluster              1  

The last output file was generated on Nov 14, 2021 at 04:43. So the job run for more than 5 days. 

total 209792

-rw-r--r-- 1 hqin simctr 23771 Nov 14 04:43 Suffolk_NewYork_US_v2.csv

EpiNow2 runntime errors

This runntime error seems to be just a warning, because Rt was still reported.  

https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup

Logging threshold set at INFO for the EpiNow2 logger

Writing EpiNow2 logs to the console and: /var/folders/bw/k6_tkc2142v1wh5r_1yhkqhc0000gp/T//Rtmpb4xcT4/regional-epinow/2020-10-27.log

Logging threshold set at INFO for the EpiNow2.epinow logger

Writing EpiNow2.epinow logs to the console and: /var/folders/bw/k6_tkc2142v1wh5r_1yhkqhc0000gp/T//Rtmpb4xcT4/epinow/2020-10-27.log

WARN [2021-11-09 19:42:32] epinow: There were 2 divergent transitions after warmup. See

http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup

to find out why this is a problem and how to eliminate them. - 

WARN [2021-11-09 19:42:32] epinow: Examine the pairs() plot to diagnose sampling problems

data science, project discussion

  == pre-class to do: 

calendar email invitation: including guests;  done. 

socrative questions (midterm exam, questions on contents from last lecture ).  done

update Canvas course materials, update learning objectives. assignments as needed.   done

* make sure grades are NOT shown on Canvas. 

Test-run code:  ipynb. NA

learning objectives:  updated on canvas, NA

== In-class to do: 

clean up desktop space, calendars": 

ZOOM, live transcript (start video recording).  Turn the computer speaker on. 

Socrative sign in 

* two student presented: one on tweet sentiment and election; one on kaggle heart disease prediction. 

Rpubs stationary test

 

https://rpubs.com/richkt/269797

negative correlation tweet sentiment ~ Rt

 

VAR: vector autoregression model

 VAR model

https://en.wikipedia.org/wiki/Vector_autoregression

A VAR model include a set of $k$ variables. 

"VAR models are characterized by their order, which refers to the number of earlier time periods the model will use. Continuing the above example, a 5th-order VAR would model each year's wheat price as a linear combination of the last five years of wheat prices. A lag is the value of a variable in a previous time period. So in general a pth-order VAR refers to a VAR model which includes lags for the last p time periods. A pth-order VAR is denoted "VAR(p)" and sometimes called "a VAR with p lags".

"The process of choosing the maximum lag p in the VAR model requires special attention because inference is dependent on correctness of the selected lag order.^[2][3]"

cointegration with random signal

 With discussion with Prof W and his student M., I tried permutation to see how Rt ~ tweet sentiment co-integrate. To my surprise, after permutation, Johansen's test becomes more significant. So, HQ thought that cointegration of a signal with a random signal may likely to co-integrate. So, this means that significant co-integration of Rt versus some factors that we thought are significant maybe be good news. 

We need to demonstrate that both factors are not stationary first, and only their combination are stationary. 

According to https://en.wikipedia.org/wiki/Johansen_test , Johansen test seems to only consider of I(1) order of 1 co-integration. 

For the number of $k$ time series, "The null hypothesis for the trace test is that the number of cointegration vectors is r = r* < k, vs. the alternative that r = k. Testing proceeds sequentially for r* = 1,2, etc. and the first non-rejection of the null is taken as an estimate of r. The null hypothesis for the "maximum eigenvalue" test is as for the trace test but the alternative is r = r* + 1 and, again, testing proceeds sequentially for r* = 1,2,etc., with the first non-rejection used as an estimator for r."

A time series is integrated of order d if 

GitHub raw allow private CSV read into R

GitHub raw use token to allow private CSV read into R. 

 Read private GitHub csv into R 

This might be a security issue. 

top 30 security flaws

 

https://news.softpedia.com/news/top-30-critical-security-flaws-most-used-by-cybercriminals-533616.shtml

cpsc 4180 datascience, project discussion

 == pre-class to do: 

calendar email invitation: including guests;  done. 

socrative questions (midterm exam, questions on contents from last lecture ).  done

update Canvas course materials, update learning objectives. assignments as needed.   done

* make sure grades are NOT shown on Canvas. 

Test-run code:  ipynb. NA

learning objectives:  updated on canvas, NA

== In-class to do: 

clean up desktop space, calendars": 

ZOOM, live transcript (start video recording).  Turn the computer speaker on. 

Socrative sign in 

* three student volunteer, one on traffic accident, one on covid and entertainment industry, one on medical images. 

A student asked about life cycle of data analysis project. HQ answered that any project should have a life cycle, it has given amount of time. 

CT images

 

https://en.wikipedia.org/wiki/Hounsfield_scale 

UTC PhD computer science

program of study

Total 72 credit hours (24 courses), 24 in doctoral research and dissertation

2x3 credit hours from: CPSC 5210: algorithm, 5260: parallel algorithm,  5240 principle of data analytics, 5440: intro to machine learning. 

2x3 of math: Math 5210: linear algebra and matrix theory; MATH 5600 numeral analysis I, MATH 5610: numerical analysis II

For MS students, 24 hours credits are given. 

CEDI secure python unit 3

 Socrative

Slides

I mixed the Python loop syntax with R. 

cpsc 4180 data science, project discussion

== pre-class to do: 

calendar email invitation: including guests;  done. 

socrative questions (midterm exam, questions on contents from last lecture ).  done

update Canvas course materials, update learning objectives. assignments as needed.   done

* make sure grades are NOT shown on Canvas. 

Test-run code:  ipynb. NA

learning objectives:  updated on canvas, NA

== In-class to do: 

clean up desktop space, calendars": 

ZOOM, live transcript (start video recording).  Turn the computer speaker on. 

Socrative sign in 

* start breakroom, ask students to explain their final projects to others. After the breakout room discussion, ask a student to explain other students topic, what are the interesting part of them.