NougatRillettes

Nice. I wrote up another possible derivation here https://ngr.yt/blog/kalman/ in case anyone is interested.

This is very cool!

The likely implied thought here is that it would be linked to the Covid vaccines. However, I believe the most likely explanation is provided by this citation:

> To find out how many deaths actually occurred during the last two decades among FIFA players (2001-2020), we used Wikipedia - "List of association footballers who died while playing". To know how many cases occurred in 2021, we used the list collected by us in "Real-Time News" (which includes the cases noted in Wikipedia for 2021).

There is an obvious sampling bias, and they likely collected much more cases for 2021 than for other years. Looking at mortality statistics by cause at nation level would be much more rigorous.

My understanding of it after a quick read of the paper: you want to make a suction cup. The usual way is with a solid cup (think plastic) with a softer rubber-like ring around it. Near the ring, you will have atmospheric pressure (high) outside, and vacuum pressure (low) inside, so if the ring doesn't make perfect contact with the surface, some air is going to come in and ruin you vacuum. What they are doing is that they are rotating a bit of water in the suction cup, which because of centrifugal force will come close to the suction cup frontier in a ring-like shape. This water ring will -- thanks to fluid mechanics black magic -- have a different pressure at its exterior and its interior. Its interior pressure will necessarily be the same as the vacuum, and you can make it so that the pressure outside is the same as the atmospheric pressure, hence, according to this paper, if the rubber ring fails to make hermetic contact, air won't come in because at the frontier of the cup, the pressure is the same both outside (atmosphere) and inside (exterior of the water ring).

This has many of the red flags for "technological" development that won't withstand scientific scrutiny.

Thanks for your comments! I don't see how what is discussed here conflicts with the notation I introduced into the post, do you still believe there is a soundness issue in what I have written?

Thanks for your comment (and thanks to everyone who answered below). I have been way too many times in the same situation where someone says something is "trivial" or "easy" so I completely get your point and will try not to make the same mistake for the next posts.

Regarding your question, I think the answers you have had are on point!

I am neither a lawyer nor a US citizen, but I think the ruling was not on "whether or not discrimination took place", but rather on "if such discrimination had taken place, would the 1st Amendment of the constitution have allowed it as 'editorial freedom'".

Hi ! I'll try to answer your questions as I can :

Unfortunately the final version of the paper is not the one that is on hal -- which is an older one, and I must concede that being new to this whole publishing world, I don't know exactly how you can have access to it. I'll try to sort it out and get back to you. In the mean time, you can refer to the hal version.

  In section 4 of the abstract [1], you mentioned that "when the dimension increases... the PAC learning algorithm can leverage available prior knowledge...". Are you referring to the time dimension adding more clauses to the k-CNF?

I think this sentence is actually referring to what is presented in section 5.3 of the final (and hal version) paper.

  I'm having trouble reconciling the PAC term "h" with "model confidence" in section 5.2. Is this allowed because the PAC learning "delta" (probability) [2] parameter is dropped for the k-CNF adaptation?

I think there are two things here.

First the "delta" you are referring to is indeed taken to be equal to the "epsilon" and both are what we call "h" in section 2.1.

Second, the idea of the discussion in section 5.2 is the following: let's say you fix a number A of initial states and simulate B steps of for each of these states. You will have a given number of (de)activation samples for each (de)activation function. Then, accross all 2n (de)activation functions, take the minimum number of samples you got, and call it Lmin. Then using results from section 2.2, with S=(2n)^(k+1), you can find h so that Lmin=2h(S+log(h)). This h will be your "model confidence".

However, if we didn't have one "h" but one "delta" and one "epsilon" (wikipedia notations), I guess we would not have a given value but only a relation between the two (i.e. defining one would define the other).

I'm afraid I don't get your last point.

I'm new to HN so I hope this answer will be somewhat correctly formatted.

Very interesting topic indeed !

Me and two other researchers have published a paper[1] using Valiant's Probably Approximately Correct learning to learn regulatory Gene networks, which can be interesting if you want to dig deeper !

If anyone has questions on the topic, feel free to ask, I'll keep an eye on the thread.

[1]A. Carcano, F. Fages, and S. Soliman, “Probably Approximately Correct Learning of Regulatory Networks from Time-Series Data,” presented at the CMSB’17 - 15th International Conference on Computational Methods for Systems Biology, 2017, vol. Lecture Notes in Computer Science, pp. 74–90. https://hal.archives-ouvertes.fr/hal-01519826v2