Personalized and graph genomes reveal missing signal in epigenomic data Cristian Groza, Tony Kwan, Nicole Soranzo, Tomi Pastinen & Guillaume Bourque Genome Biology volume 21, Article number: 124 (2020)
QT:{{ “Using permissive cutoffs, modified reference genomes are found to alter approximately 1% of peak calls while de novo assembled genomes alter up to 5% of peaks.” }}
A Case for Banning Facial Recognition
https://www.nytimes.com/2020/06/09/technology/facial-recognition-software.html
MIT contact tracing:
http://news.mit.edu/2020/bluetooth-covid-19-contact-tracing-0409
Cuomo mandated both rider and driver of uber+lyft to wear mask.
NY’s all-the-time mask law is the most restrictive in the nation; other states are catching up
endnote for gdoc
https://paperpile.com/features
There’s a paper on this topic that introduced the idea of “kind and wicked learning environments”:
https://pdfs.semanticscholar.org/5c5d/33b858eaf38f6a14b3f042202f1f44e04326.pdf
…in wicked environments it is difficult to do inference based on data. One solution seems to be to break down the problem in such a way that you can observe sub-problems in a kind environment.
The Two Settings of Kind and Wicked Learning Environments
Robin M. Hogarth1, Tomás Lejarraga2, and Emre Soyer3
Abstract
QT:{{” Inference involves two settings: In the first, information is acquired (learning); in the second, it is applied (predictions or choices). Kind learning environments involve close matches between the informational elements in the two settings and are a necessary condition for accurate inferences. Wicked learning environments involve mismatches. This conceptual framework facilitates identifying sources of inferential errors and can be used, among other things, to suggest how to target corrective procedures. For example, structuring learning environments to be kind improves probabilistic judgments. Potentially, it could also enable economic agents to exhibit maximizing behavior.
“}}
linkstream2 microblog 