@rw
Nothing like that out will charge your watch while its on your wrist
@tursiops
I saw that, wont charge your watch while on your wrist though
@marathon sorry that was not meant to be public or a reply to the post i made it on. I deleted and redrafted
@academicalnerd I see the same, thanks
@academicalnerd No the latex went wonky in the last update that added markdown support, not sure why. I'll be fixing in at the next upgrade hopefully which very very soon, just need to finish up some real life stuff first.
@justin LOL good to know its not just me.
Honestly I think the article confused two similar but different conditions.. a hair splinter (which is common and what the guy has in his foot), and " cutaneous pili migrans" which is where an ingrown hair creeps along the subcutaneous layer and creates a long hair under the skin... At least, thats what I could gather from reading up on it a bit.
Just so everyone is aware, Snow is still a horrible human being, be on the lookout for accounts on QOTO posting this nonsense and please report.
I literally had this exact thing happen to me about a week ago yet the article claims there has only been 26 cases in over 60 years. I call bullshit. I think it happens all the time its just people notice it, pull the hair, and it never gets a case study done.
I have, in my life, probably had this happen in 20 different places other than my foot, my foot was only once. Every time its obviously a hair with a sharp end, and when i notice the hair i pull it and im fine.}
inb4 its not an ingrown hair or anything as it always happens in places where hairs dont grow like my finger tips (most common) or as was the case last week the bottom of my foot.
Neat video of salt crystal formation at the atomic level (includes atom-thick carbon nanohorn - who knew?) (via HN)
https://www.u-tokyo.ac.jp/focus/en/press/z0508_00161.html
#science
Hey Freemo, I'm alive again xD The bisection search method is pretty clever to reduce your current computation time, but wouldn't that need to be repeated regularly?
Have you considered using some dimensionality reduction techniques to help limit your feature space to the most impactful variables to reduce your search space? It would be a rather long up-front computation more than likely to do the initial clustering, but then you could likely ignore/discard some of the variables that have a smaller impact on your prediction (or have relatively high correlation to other variables/combinations of variables) to make it faster in the future.
Eventually the search routine will get more advanced. For now I need to understand the properties of the search space a bit better before i consider how or where it can or should be improved. For the moment there are only 7 variables and I suspect most of them are fairly insignificant and wont need any optimization of any kind.
Its also a matter of implementation time priorities, the haalving of the search space one variable at a time was the simplest to design in a relative short period of time which is important as I have bigger things to work on right now in terms of debugging a few minor remaining issues (there appears to be some sort of locking missing somewhere that I cant grok that is a top priority and is preventing me from getting reliable error values anyway).
@awethon Because accounts setup with the purpose of spamming/advertising get deleted.
Threw together a parameter optimization algorithm for my stock trading algorithm last night. Nothing too ground breaking but hell-a useful and cool all the same.
There are about a dozen variables, some of which have some limited dependence on each other. I suspect the search space however is relatively simple based on my expiernce os optimizing it by hand with trial and error.
The challenge is the possible valid range of these variables is huge, most bounded at 0 but with a max that is in the hundreds of thousands.
The other challenge is that each test takes a few minutes to perform (simulated over a years worth of data one minute per data point). So even with some extreme multi-core usage and optimization it is too slow for a naive search algorithm to just do a random walk across the problem space.
So the search pattern had to be an exponential one that pulls back on overshoot in a decaying exponential form as well. Identifies the boundaries and then halves each side of the boundary until the boundaries are narrow enough to find a solution. Then it moves onto the next attribute and repeats, after it optimizes them all it does another run through and another (to account for interdependence) until none of the parameters are significantly adjusted.
All this takes several hours to run at least. Still cool to see its working as it drops the error rate significantly and gets to the point where it makes fairly good predictions about how a stock is likely to change in the near future (the value measuring the error against)... in fact i'm amazed just how powerful an accurate this algorithm is at predicting stocks right now.
#Stocks #Stockmarket #investment #quant #Quantitative #math #Science #MachineLearning #AI #ML @Science
@pganssle Just an FYI, now that I am past some of the multiprocessing headaches and found some easier workarounds that isnt as convoluted as my earlier attempts I can say that while still not my favorite WRT multiprocessing it is a lot more pleasant than my early experiences.
@0x0f Sure why not if you like them?
@admin The thing is long term trends are easier to predict for us humans because they tend to be based on actual company performance, short term trends are effected by impulsive human behavior to news or events.
As such while short term trading will always show you the greatest ROI (or the fastest loss), for more people long term trading, since they can predict it better, will tend to cause them to gain money slowly where short term trading will cause them to lose money quickly. A big part of the reason is that very impulsivity you just described and my algorithm is the cool collected logical one at the other side ready to make a win on your panic :)
Jeffrey Phillips Freeman
Innovator & Entrepreneur in Machine Learning, Evolutionary Computing & Big Data. Avid SCUBA diver, Open-source developer, HAM radio operator, astrophotographer, and anything nerdy.
Born and raised in Philadelphia, PA, USA, currently living in Utrecht, Netherlands, USA, and Thailand. Was also living in Israel, but left.
Pronouns: Sir / Mister
(Above pronouns are not intended to mock, i will respect any persons pronouns and only wish pronouns to show respect be used with me as well. These are called neopronouns, see an example of the word "frog" used as a neopronoun here: http://tinyurl.com/44hhej89 )
A proud member of the Penobscot Native American tribe, as well as a Mayflower passenger descendant. I sometimes post about my genealogical history.
My stance on various issues:
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Welfare: Yes, no one should starve
UBI: No, use welfare
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Guns: Shall not be infringed
LGBT+/minorities: Support
Pronouns: Will respect
Trump: Moron, evil
Biden: Senile, racist
Police: ACAB
Drugs: Fully legal, no prescriptions needed
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