Ciro Santilli @cirosantilli 35

This technique has managed to determine protein 3D structures for proteins that people were not able to crystallize for X-ray crystallography.

It is said however that cryoEM is even fiddlier than X-ray crystallography, so it is mostly attempted if crystallization attempts fail.

By looking at Figure 1. "A cryoEM image", you can easily understand the basics of cryoEM.

We just put a gazillion copies of our molecule of interest in a solution, and then image all of them in the frozen water.

Each one of them appears in the image in a random rotated view, so given enough of those point of view images, we can deduce the entire 3D structure of the molecule.

Ciro Santilli once watched a talk by Richard Henderson about cryoEM circa 2020, where he mentioned that he witnessed some students in the 1980's going to Germany, and coming into contact with early cryoEM. And when they came back, they just told their principal investigator: "I'm going to drop my PhD theme and focus exclusively on cryoEM". That's how hot the cryo thing was! So cool.

Basically the same remarks as for university, just 10 times more useless, see also: Section "Motivation".

Vs: image: the codomain is the set that the function might reach.

The image is the exact set that it actually reaches.

E.g. the function:could have:

Note that the definition of the codomain is somewhat arbitrary, e.g. $x^2$ could as well technically have codomain:even though it will obviously never reach any value in ${\mathbb{R}}^2$.

The exact image is in general therefore harder to characterize.

Obesity is an extremely serious disease that is very hard to cure, and has deep psychological implications.

Basically a mini-Constellation.

Specific type of Josephson junction. Probably can be made tiny and in huge numbers through photolithography.

Standard from 2011: abcnotation.com/wiki/abc:standard:v2.1

A decent way to write diatonic music as plaintext!

Conversion to MIDI with abcmidi.

No bend/vibratto/slides :-(

Multitrack volatile: abcnotation.com/wiki/abc:standard:v2.1#multiple_voices

Some of the earlier computers of the 20th centure were analog computers, not digital.

At some point analog died however, and "computer" basically by default started meaning just "digital computer".

As of the 2010's and forward, with the limit of Moore's law and the rise of machine learning, people have started looking again into analog computing as a possile way forward. A key insight is that huge floating point precision is not that crucial in many deep learning applications, e.g. many new digital designs have tried 16-bit floating point as opposed to the more traditional 32-bit minium. Some papers are even looking into 8-bit: dl.acm.org/doi/10.5555/3327757.3327866

As an example, the Lightmatter company was trying to implement silicon photonics-based matrix multiplication.

A general intuition behind this type of development is that the human brain, the holy grail of machine learning, is itself an analog computer.

A branch of mathematics that attempts to prove stuff about computers.

Unfortunately, all software engineers already know the answer to the useful theorems though (except perhaps notably for cryptography), e.g. all programmers obviously know that iehter P != NP or that this is unprovable or some other "for all practical purposes practice P != NP", even though they don't have proof.

And 99% of their time, software engineers are not dealing with mathematically formulatable problems anyways, which is sad.

The only useful "computer science" subset every programmer ever needs to know is:

Funnily, due to the formalization of mathematics, mathematics can be seen as a branch of computer science, just like computer science can be seen as a branch of Mathematics!