Ciro Santilli @cirosantilli 37

I've started rewatching The Water Margin, it is just so good. I'm taking some reasonable notes this time however, because due to Ciro Santilli's bad old event memory I'll forget the details again otherwise.

That type of rebellion symbology could also be useful against the Chinese government. It is interesting that Mao Zedong loved the novel.

Ciro Santilli has sometimes wasted time with low impact projects such as those listed at Ciro Santilli's minor projects instead of doing higher impact projects such as those mentioned at: Section "The most important projects Ciro Santilli wants to do".

But maybe "Everything you did brought you where you are now." applies, maybe it is during the "low impact activities" that one gets the inspiration and experience required for the "high impact ones".

If you are going to live, you might as well chase one of them.

You might not achieve them in your lifetime, but you never know. At some point, the pieces just "fall into place", and they happen.

And they will all come from deep tech.

Ciro Santilli would like to contribute to them. but this is a bit less realistic than software projects.

But who knows? Maybe he can code some stuff in those areas.

And one can at least have some fun by learning deeply about those subjects.

The good:

The bad:

Beautifully argued at: Can't get you out of my head by Adam Curtis (2021).

Notable examples:

Fixed quantum angular momentum in a given direction.

Can range between $\pm l$.

E.g. consider gallium which is 1s2 2s2 2p6 3s2 3p6 4s2 3d10 4p1:

The z component of the quantum angular momentum is simply:so e.g. again for gallium:

Note that this direction is arbitrary, since for a fixed azimuthal quantum number (and therefore fixed total angular momentum), we can only know one direction for sure. $z$ is normally used by convention.

Vaporware?

Kind of works! Notably, has the amazing cycling database offline for you, if you fall within the 6 area downloads. It is worth supporting these people beyond the 6 free downloads however.

Given a bunch of points in $n$ dimensions, PCA maps those points to a new $p$ dimensional space with $p\le n$.

$p$ is a hyperparameter, $p=1$ and $p=2$ are common choices when doing dataset exploration, as they can be easily visualized on a planar plot.

The mapping is done by projecting all points to a $p$ dimensional hyperplane. PCA is an algorithm for choosing this hyperplane and the coordinate system within this hyperplane.

The hyperplane choice is done as follows:

www.sartorius.com/en/knowledge/science-snippets/what-is-principal-component-analysis-pca-and-how-it-is-used-507186 provides an OK-ish example with a concrete context. In there, each point is a country, and the input data is the consumption of different kinds of foods per year, e.g.:so in this example, we would have input points in 4D.

The question is then: we want to be able to identify the country by what they eat.

Suppose that every country consumes the same amount of flour every year. Then, that number doesn't tell us much about which country each point represents (has the least variance), and the first PCA axes would basically never point anywhere near that direction.

Another cool thing is that PCA seems to automatically account for linear dependencies in the data, so it skips selecting highly correlated axes multiple times. For example, suppose that dry codfish and olive oil consumption are very high in Portugal and Spain, but very low in Germany and Poland. Therefore, the variation is very high in those two parameters, and contains a lot of information.

However, suppose that dry codfish consumption is also directly proportional to olive oil consumption. Because of this, it would be kind of wasteful if we selected:since the information about codfish already tells us the olive oil. PCA apparently recognizes this, and instead picks the first axis at a 45 degree angle to both dry codfish and olive oil, and then moves on to something else for the second axis.

We can see that much like the rest of machine learning, PCA can be seen as a form of compression.

This game is quite detailed: www.youtube.com/watch?v=w4Jmqp8a_bU