Ciro Santilli @cirosantilli 37

Not a quantum computing pure-play, they also do sensing.

Vaporware?

Theses places give out free food all the time.

The first time Ciro Santilli went to one was when an Indian friend of his took him to the one in the North of Paris when they were living there in the first half of the 2010's, the Gurdwara Singh Sabha France.

Much like Islam's Ramadan, Ciro Santilli appreciates this a lot due to due to Ciro Santilli's self perceived compassionate personality and Ciro Santilli's cheapness.

Instead of just talking, those people really go out, and put food on the plate for anyone who needs it (or even for those that don't really need it! Although who would be so souless to eat for free and not donate a few bucks if they can afford to???). There's a beauty to that.

Writing this also remembered Ciro of non-religious groups that would give out free food to the poor at

The half-life of radioactive decay, which as discovered a few years before quantum mechanics was discovered and matured, was a major mystery. Why do some nuclei fission in apparently random fashion, while others don't? How is the state of different nuclei different from one another? This is mentioned in Inward Bound by Abraham Pais (1988) Chapter 6.e Why a half-life?

The term also sees use in other areas, notably biology, where e.g. RNAs spontaneously decay as part of the cell's control system, see e.g. mentions in E. Coli Whole Cell Model by Covert Lab.

The voltage changes perpendicular to the current when magnetic field is applied.

An intuitive video is:

The key formula for it is:where:

Applications:

Other more precise non-classical versions:

Bibliography:

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

Head of the theoretical division at the Los Alamos Laboratory during the Manhattan Project.

Richard Feynman was working under him there, and was promoted to team lead by him because Richard impressed Hans.

He was also the person under which Freeman Dyson was originally under when he moved from the United Kingdom to the United States.

And Hans also impressed Feynman, both were problem solvers, and liked solving mental arithmetic and numerical analysis.

This relationship is what brought Feynman to Cornell University after World War II, Hans' institution, which is where Feynman did the main part of his Nobel prize winning work on quantum electrodynamics.

Hans must have been the perfect PhD advisor. He's always smiling, and he seemed so approachable. And he was incredibly capable, notably in his calculation skills, which were much more important in those pre-computer days.

Applications:

The complex number analogue of a symmetric bilinear form.

The prototypical example of it is the complex dot product.

Note that this form is neither strictly symmetric, it satisfies:where the over bar indicates the complex conjugate, nor is it linear for complex scalar multiplication on the second argument.

Bibliography:

Initially there were mathematical reasons why people suspected that all boson needed to have 0 mass as is the case for photons a gluons, see Goldstone's theorem.

However, experiments showed that the W boson and the Z boson both has large non-zero masses.

So people started theorizing some hack that would fix up the equations, and they came up with the higgs mechanism.

Movies that are very expensive to make tend to be bad, because they have to make returns and thus appeal to a large amorphous population without any specialization, i.e. the lowest common denominator but in TV Tropes terminology rather than mathematics: tvtropes.org/pmwiki/pmwiki.php/Main/LowestCommonDenominator.

See e.g.:

Looking down the largest flops of all time list didn't help much, only Heaven's gate appears reasonable from the top 20.

On TV Tropes: it's Popular Now It Sucks.

As of 2020, basically means "liquid nitrogen temperature", which is much cheaper than liquid helium.

The dream of course being room temperature and pressure superconductor.

In 1962 Brian Josephson published his inaugural paper predicting the effect as Section "Possible new effects in superconductive tunnelling".

In 1963 Philip W. Anderson and John M. Rowell published their paper that first observed the effect as Section "Possible new effects in superconductive tunnelling".

Some golden notes can be found at True Genius: The Life and Science of John Bardeen page 224 and around. Philip W. Anderson commented:

As part of the course Anderson had introduced the concept of broken symmetry in superconductors. Josephson "was fascinated by the idea of broken symmetry, and wondered whether there could be any way of observing it experimentally."

MRI is using NMR to image inside peoples bodies!

Microbit simulator using some Microsoft framework.

TODO the Python code from there does not seem to run on the microbit via uflash, because it is not MicroPython.

support.microbit.org/support/solutions/articles/19000111744-makecode-python-and-micropython explains.

forum.makecode.com/t/help-understanding-local-build-options/6130 asks how to compile locally and suggests it is possible. Seems to require Yotta, so presumably compiles?

Presumably this is because Microsoft ported their MakeCode thing to the MicroBit, and the Micro Bit foundation accepted them.

E.g. there toggling a LED:but the code that works locally is a completely differently named API set_pixel:Microsoft going all in on adopt extend extinguish from an early age!