bitcoin 2017 ends with bigger bang!

hi all. bitcoin is going crazy at moment. its spiked rapidly in last few months and nearly $15k per coin as of this writing. talk about critical mass!

my 1st blog on bitcoin on this blog is early 2014. ahead of the curve™.

have mixed feelings on its success. feel it is still insecure/ vulnerable in a lot of ways. its not good for the price to spike so high in so short of a time. think “what goes up must go down”. it feels like a partial bubble to me. feel there will be some commensurate pain in the near future.

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collatz ad infinitum

threw this code together and it was unusually/ surprising timeconsuming to debug yet has rather little code/ logic. the difficulty is related to the early-discovered property of how skewed (nonlinear) a random sample of trajectory distributions is. nearly all are very short and it seems quite possible its in line with some power law (never tried to fit it, but do have the overwhelming urge to, and just go hunting for as many power-law properties in the data one can find, strongly suspect they might be quite widespread).

the idea is to try to bias (or unbias depending on pov) the sample of randomly chosen trajectories (based on initial density spread sample) so that they are nearly linearly distributed in length. was developing the test data to test the trajectory (meta function) calculation logic. to be more exact was partly working off of the matrix6 code and wanting to improve the aesthetic graph results with “more linearity” in the sample. did not end up with meta function calculation logic tied to it worth saving, but given how tricky it was, do want to save this sampling code for future reference! this generates 10 samples of 500 points over uniform density, and then finds the min and ¾ the max of trajectory lengths, and tries to sample linearly over that range (starting top down), and the result is quite smooth as in the graph (of trajectory length).

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collatz, loose ends

as mentioned in the last post, am zooming in on the “power iteration” algorithm. it is explained as, “if you want to find the dominant eigenvector, use the power iteration”. in my case, found it by discovering that “if you use the power iteration, it will lead to the dominant eigenvector”. kind of subtle right? maybe reminds me of that old saying “all paths lead to rome”. and then ofc, the classic, “rome wasnt built in a day”.

here is the code that compares the (normalized) current state vector to the dominant eigenvector, which apparently ruby organizes it as the leftmost column of the left eigen-decomposition matrix. it uses/ selects the 95th/100th density iteration which tends to lead to a longer trajectory. am in good company, as wikipedia notes the power iteration is used at the core of Google pagerank algorithm! 😀 😎 💡 ⭐ ❗ ❤

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cybersecurity 2017, epic saga, russian angle, hacking etc

hi all. this is now a complicated/ fast paced/ multidimensional subj to track. cybersecurity used to be a bit more arcane but the topic has expanded dramatically in only the last year based on russian hacking/ collusion accusations by our govt.

years ago read books on “infowarfare” or “cyberwarfare” predicting this exotic new aspect of international intrigue and interrelations. here it is now on our doorstep in 2017 and its having massive impact on politics and govt at this point.

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collatz perplexity

hi all, last month collatz installment made some progress, but unf have been a bit tied up with work, where a fiscal year transition/ boundary tends to lead to some crunch-like dynamics, leaving less time for one of my favorite side projects, namely this one, bummer/ ouch.

but, here is a small trickle/ dribble of some newer ideas, mainly benefitting from google searches and maybe a little algebra.

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