Daily Archives: December 24, 2012

Linkage (technical)

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Here’s a roundup of some interesting posts/pages on technical things.

Over at Larry Wasserman’s blog, Rob Tibshirani suggests 9 Great Statistics papers published after 1970. You know, in case you were looking for some light reading over winter break.

Videos from the DIMACS Differential Privacy Workshop are up.

All of these ads for jobs this year want someone who works on Big Data. But… do you really have big data? Or, as I like to ask, “how big is big, anyway?”

Speaking of big data, this talk by Peter Bartlett looks cool. (h/t Andrew Gelman)

Max Raginsky and Igal Sason have a tutorial on measure concentration. Log Sobolev inequalities are a dish best served cold.

I’ll probably do an ArXiV roundup sometime soon — trying to catch up on a backlog of reading and thinking lately.

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Video : “Matrices and their singular values” (1976)

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Via Allie Fletcher, here is an awesome video on the SVD from Los Alamos National Lab in 1976:

From the caption by Cleve Moler (who also blogs):

This film about the matrix singular value decomposition was made in 1976 at the Los Alamos National Laboratory. Today the SVD is widely used in scientific and engineering computation, but in 1976 the SVD was relatively unknown. A practical algorithm for its computation had been developed only a few years earlier and the LINPACK project was in the early stages of its implementation. The 3-D computer graphics involved hidden line computations. The computer output was 16mm celluloid film.

The graphics are awesome. Moler blogged about some of the history of the film. Those who are particularly “attentive” may note that the SVD movie seems familiar:

The first Star Trek movie came out in 1979. The producers had asked Los Alamos for computer graphics to run on the displays on the bridge of the Enterprise. They chose our SVD movie to run on the science officer’s display. So, if you look over Spock’s shoulder as the Enterprise enters the nebula in search of Viger, you can glimpse a matrix being diagonalized by Givens transformations and the QR iteration.