Guilherme Duarte

78 Followers
309 Following
70 Posts

Very interesting interview with an applied topologist. Just as John Cook (the interviewer) I had no idea applied topology was a thing. Would not have thought homology can help with planning cellular network coverage. Apparently it does. #mathematics

https://www.johndcook.com/blog/2010/09/13/applied-topology-and-dante-an-interview-with-robert-ghrist/

Interview with Robert Ghrist

An interview with Robert Ghrist, an applied topologist. In addition to his mathematical work, we discuss his interest in literature, especially Dante.

John D. Cook | Applied Mathematics Consulting
Happy new year
Over at the @denverpost, I have a piece up eulogizing not Twitter, but the intellectual community that Twitter once hosted. The piece is currently paywalled but here's a brief summary (1/4):
https://www.denverpost.com/2022/12/29/why-leave-twitter-seth-masket-rip-twitter/
Opinion: RIP Twitter’s intellectual community

I left Twitter in early November, shortly after Elon Musk endorsed a party right before an election and promoted conspiracy theories about the attack on Paul Pelosi.

The Denver Post

RT @[email protected]

I find that we are reaching the break even point and mastodon (@kordinglab) is offering about equal quality of discussions as this place (despite a factor 9 smaller followership). They are just more engaged

🐦🔗: https://twitter.com/KordingLab/status/1608470393475694592

KordingLab 🦖 on Twitter

“I find that we are reaching the break even point and mastodon (@[email protected]) is offering about equal quality of discussions as this place (despite a factor 9 smaller followership). They are just more engaged”

Twitter

Vivienne Westwood, 81, Dies; Brought Provocative Punk Style to High Fashion - The London shop she ran with Malcolm McLaren defined an era. “I don’t think punk would have happened,” Chrissie Hynde said, “without Vivienne and Malcolm.” #nytimes

https://www.nytimes.com/2022/12/29/fashion/vivienne-westwood-dead.html

Vivienne Westwood, 81, Dies; Brought Provocative Punk Style to High Fashion

The London shop she ran with Malcolm McLaren defined an era. “I don’t think punk would have happened,” Chrissie Hynde said, “without Vivienne and Malcolm.”

Quote toot (click on image).
actually a very interesting rhetorical theoretical move http://bactra.org/weblog/942.html
Pareto at Melos

RT @ZinhoFFz
For me it was this @analisereal paper. Simple, concise and very easy to understand. I've been reviewing content on causal inference for my monograph and there's no better article to start understanding bad controls and the causal graph approach to causal inference than this paper https://twitter.com/instrumenthull/status/1606142852202913793
Peter Hull on Twitter

“what's the best paper you read this year”

Twitter

If you're reading a proof that seems too good to be true, you should quickly get a sense of the whole argument, then focus on the parts that seem likely to be wrong.

You shouldn't take a portion of the proof that's correct, and optimize it. But that's what I've just done.

This new claimed proof of the 4-color theorem, just 6 pages long, uses a formula for counting rooted planar maps. But there's a simpler formula that would greatly simplify some of the arguments!

(1/n)

#combinatorics

Plus, many expensive refutations now run in parallel, making them much faster (Thanks Andreas Stöffelbauer and Amey Verhade!) and as of DoWhy 0.9.1, DoWhy has cleaned up dependencies and supports python 3.10

See more in our release notes: https://github.com/py-why/dowhy/releases

If you want to see your favorite algorithm or feature in the next version of DoWhy, we have a contributor's guide (thanks to Michael Marien!) https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst

Open for discussions at our Discord and in GitHub issues

@causal

Releases · py-why/dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic...

GitHub