I just couldn’t wait for a physical copy of The First Astronomers: How Indigenous Elders Read the Stars by Duane Hamacher, Ghillar Michael Anderson, Ron Day, Segar Passi, Alo Tapim, David Bosun and John Barsa (Allen & Unwin, 2022) to arrive in the US, so I immediately downloaded a copy of the e-book version.

@AllenAndUnwin @AboriginalAstro

Bookmarked Lecture Notes by Arun DebrayArun Debray (web.ma.utexas.edu)
I LATEXed up lecture notes for many of the classes I have taken; feel free to read through them or use them to review. If you find a mistake or typo, please let me know. If you want to look over the .tex source for any of these notes, please send me an email.
A great set of LaTeXed notes from a variety of coursework.

via Rama Kunapuli.

Read I re-read Surely You’re Joking, Mr. Feynman! by Jason McIntoshJason McIntosh (Fogknife)
Revisited this collection of Richard Feynman's eclectic adventures, and found them more inspiring than ever -- though parts demand a charitable eye
I’ve been tempted to read this. Thanks for the thoughtful review! This is some great writing Jason.
Liked a tweet by Tai-Danae Bradley (@math3ma) (Twitter)
Bookmarked At the Interface of Algebra and Statistics by Tai-Danae Bradley (arXiv.org)
This thesis takes inspiration from quantum physics to investigate mathematical structure that lies at the interface of algebra and statistics. The starting point is a passage from classical probability theory to quantum probability theory. The quantum version of a probability distribution is a density operator, the quantum version of marginalizing is an operation called the partial trace, and the quantum version of a marginal probability distribution is a reduced density operator. Every joint probability distribution on a finite set can be modeled as a rank one density operator. By applying the partial trace, we obtain reduced density operators whose diagonals recover classical marginal probabilities. In general, these reduced densities will have rank higher than one, and their eigenvalues and eigenvectors will contain extra information that encodes subsystem interactions governed by statistics. We decode this information, and show it is akin to conditional probability, and then investigate the extent to which the eigenvectors capture "concepts" inherent in the original joint distribution. The theory is then illustrated with an experiment that exploits these ideas. Turning to a more theoretical application, we also discuss a preliminary framework for modeling entailment and concept hierarchy in natural language, namely, by representing expressions in the language as densities. Finally, initial inspiration for this thesis comes from formal concept analysis, which finds many striking parallels with the linear algebra. The parallels are not coincidental, and a common blueprint is found in category theory. We close with an exposition on free (co)completions and how the free-forgetful adjunctions in which they arise strongly suggest that in certain categorical contexts, the "fixed points" of a morphism with its adjoint encode interesting information.
Read Freeman Dyson, Math Genius Turned Technological Visionary, Dies at 96 (nytimes.com)
After an early breakthrough on light and matter, he became a writer who challenged climate science and pondered space exploration and nuclear warfare.
How did I miss this when it came out?

Bookmarked on March 21, 2020 at 02:39PM

Read Dord (Wikipedia)
The word dord is a dictionary error in lexicography. It was accidentally created, as a ghost word, by the staff of G. and C. Merriam Company (now part of Merriam-Webster) in the New International Dictionary, second edition (1934). That dictionary defined the term a synonym for density used in physics and chemistry in the following way: "dord (dôrd), n. Physics & Chem. Density."