Kalman filter versus smoothing splines

 

https://stats.stackexchange.com/questions/15070/kalman-filter-vs-smoothing-splines

beautiful soup

 Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.

https://www.crummy.com/software/BeautifulSoup/bs4/doc/

rvest, websracpping

 

https://blog.rstudio.com/2014/11/24/rvest-easy-web-scraping-with-r/

color in hex values

 

https://www.colorhexa.com/

notes, NSF AI institute meeting


Dear Panelists: Please be advised that you are in Listen Mode Only. Thank you.
Link to FAQ: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123
Link to FAQ: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123



Link to FAQ: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123


From MEM AV TECH-Angel Ntumy to All panelists and other attendees: (15:47)


https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123
https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123



https://www.captionedtext.com/client/event.aspx?EventID=4573120&CustomerID=321



Dear attendees, more information of the Theme 5 - AI Institute in Dynamic Systems Webinar is available at https://www.nsf.gov/events/event_summ.jsp?cntn_id=301241&WT.mc_id=USNSF_13&WT.mc_ev=click


The DOD NDSEG Fellowship program

The DOD NDSEG Fellowship program is now open for applications. Please pass this opportunity along to your eligible students! https://ndseg.sysplus.com/NDSEG/About/

 

The fellowship lasts for 3 years and pays for full tuition and all mandatory fees; a monthly stipend ($38,400 annually); a $5,000 travel budget over the Fellow’s tenure for professional development; and up to $1,200 a year in health insurance. Applications are due November 2, 2020, and students pursuing any graduate degree in an appropriate field may apply (the field are what you might expect—math, physics, CS, pretty much any kind of engineering).

 

Eligibility

• The NDSEG Fellowship Program is open only to U.S. citizens or U.S. nationals. Dual citizens may apply.
• Eligible applicants are required to be enrolled in their final year of undergraduate studies through the second year of a traditional PhD program. See below for a breakout of eligibility criteria:
• Undergraduate Students (degree must be received prior to the Fellowship Start date)
• Recent Bachelor's Degree Recipients
• Graduate Students (1st year, 2nd year)
• Recent Master's Degree Recipients
• Pre-Doctoral Students (1st year, 2nd year)
• Dual MD-PHD Students
• Dual PSYD-PHD Students
• Applicants must plan to enroll in a full-time program at an accredited U.S. institution of higher education leading to graduate degrees in fields specified in the DoD services Broad Agency Announcements (BAAs) research and development discipline

 

tidyverse, ggplot, FAQ

 

# Count the number of full duplicates

sum(duplicated(bike_share_rides))

# Remove duplicates

bike_share_rides_unique <- distinct(bike_share_rides)

# Count the full duplicates in bike_share_rides_unique

sum(duplicated(bike_share_rides_unique))

# Find duplicated ride_ids

bike_share_rides %>% 

  count(ride_id) %>% 

  filter(n > 1)

# Remove full and partial duplicates

bike_share_rides_unique <- bike_share_rides %>%

  # Only based on ride_id instead of all cols

  distinct(ride_id, .keep_all = TRUE)

bike_share_rides %>%

  # Group by ride_id and date

  group_by(ride_id, date) %>%

  # Add duration_min_avg column

  mutate(duration_min_avg = mean(duration_min)) %>%

  # Remove duplicates based on ride_id and date, keep all cols

  distinct(ride_id, date, .keep_all = TRUE) %>%

  # Remove duration_min column

  select(-duration_min)

# Find bad dest_size rows

sfo_survey %>% 

  # Join with dest_sizes data frame to get bad dest_size rows

  anti_join(dest_sizes, by = "dest_size") %>%

  # Select id, airline, destination, and dest_size cols

  select(id, airline, destination, dest_size)

# Add new columns to sfo_survey

sfo_survey <- sfo_survey %>%

  # dest_size_trimmed: dest_size without whitespace

  mutate(dest_size_trimmed = str_trim(dest_size),

         # cleanliness_lower: cleanliness converted to lowercase

         cleanliness_lower = str_to_lower(cleanliness))

# Count values of dest_size_trimmed

sfo_survey %>%

  count(dest_size_trimmed)

# Count values of cleanliness_lower

sfo_survey %>%

  count(cleanliness_lower)

# Count categories of dest_region

sfo_survey %>%

  count(dest_region)

# Categories to map to Europe

europe_categories <- c("EU", "eur", "Europ")

# Add a new col dest_region_collapsed

sfo_survey %>%

  # Map all categories in europe_categories to Europe

  mutate(dest_region_collapsed = fct_collapse(dest_region, 

                                              Europe = europe_categories)) %>%

  # Count categories of dest_region_collapsed

  count(dest_region_collapsed)

sfo_survey %>%

  filter(str_detect(phone, "-"))

Outside Interest Disclosure Form, UTC

 

Outside Interest Disclosure Form, UTC

The form and instructions can be found here: https://new.utc.edu/finance-and-administration/human-resources/employment-services/conflict-of-interest

*** government response stringency index, COVID19

 university of oxford

COVID19 

https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker

https://github.com/OxCGRT/covid-policy-tracker/raw/master/data/timeseries/OxCGRT_timeseries_all.xlsx

https://github.com/OxCGRT/covid-policy-tracker

this is related to Twitter sentiment analysis

cell vision .org

 yeast data resource

https://thecellvision.org/

17 Eqs

 

mass spec data base

MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets

x

https://www.nature.com/articles/s41592-020-0955-0

resampling white-box to examine deep learning

 

Deep learning for gravitational-wave data analysis: A resampling white-box approach
Manuel D. Morales, Javier M. Antelis, Claudia Moreno, Alexander I. Nesterov
x

https://arxiv.org/abs/2009.04088?fbclid=IwAR3INBZBVRi7JL0tyeoHc2yM2W0LhSRssaErdODwhfA664ht-vGS9OZGM-4

Calculation of R0, Rt

 

Rt live: https://github.com/rtcovidlive/covid-model

zhang genome wide SARS- cov2 protein structure modeling

 

https://zhanglab.ccmb.med.umich.edu/COVID-19/