Guillaume 🦗 @guillaume@aleph.land

I should try topology optimisation

But not sure how to deal with multi-objective optimisation there?

I spend so much time repairing and improving my old #recumbent #bicycle from 1999 I bought on Blocket that I'm thinking of just designing a new bike from scratch. But then I realise I know almost nothing about solid mechanics and there are a lot of important details, like how much pedalling power the frame absorbs by bending: http://www.recumbents.com/wisil/brown/bicycleframedesign.htm

This is really fun

#recumbentbikes #recumbent #bicycle

On my way to maybe buy an old #recumbent #bike

"On top of everything else, politicians are actually really bad at gerrymandering"

https://www.youtube.com/watch?v=Lq-Y7crQo44

How to come up with good technical names, like the Unscented Transform, by Jeffrey Uhlmann:

"Initially I only referred to it as the “new filter.” Needing a more specific name, people in my lab began referring to it as the “Uhlmann filter,” which obviously isn’t a name that I could use, so I had to come up with an official term. One evening everyone else in the lab was at the Royal Opera House, and as I was working I noticed someone’s deodorant on a desk. The word “unscented” caught my eye as the perfect technical term."

http://ethw.org/First-Hand:The_Unscented_Transform#What.E2.80.99s_with_the_Name_.E2.80.9CUnscented.E2.80.9D.3F

Zeynep Tufekci: Checking Facts Even If One Can't

https://www.theinsight.org/p/checking-facts-even-if-one-cant

#covid19 #science #conspiracies #facts

On a 60000×60000 sparse matrix, my code went from about 4.5 to 1.5 seconds to build it like that.

A trick to form large sparse matrices quickly in #python / #numpy. It should maybe have been obvious, but it took me a while to find it.

You could do
>>> m = scipy.sparse.lil_matrix(n, n)
and then assign values one by one like
>>> m[i, j] = v
but this is still kinda slow.

It's better to do:
>>> rows = [...] # list/array of row indices
>>> cols = [...] # list/array of column indices
>>> vals = [...] # list/array of values
>>> m = scipy.sparse.coo_matrix((vals, (rows, cols)), shape=(n, n))

It's kind of working now but not as well as I was hoping for.

I'm making Python segfault now. Maybe it's time to go to bed.

Oh I found out why. I'm dumb. Anyway I always wondered if I should try to sell those pictures of bugs. Maybe someone would like them on their living room wall.

I'm confused with this one: somehow I get a discontinuous function as a sum of continuous ones.

#debugging is fun

Oh interesting, apparently #chitosan can be made from #fungi: https://www.sciencedirect.com/science/article/pii/B9780128179703000146

(but it's not widely available yet).