ashika

seems useful for the types of support we recently provided the saudis in yemen. this tech would lower the marginal cost of providing that type of support significantly. doubt taxpayers will claw any of that back, though :|

I feel you've said more or less the same thing the article is saying, just in more casually dismissive and incendiary terms. TFA refers to many of these factors as being driven by the second demographic transformation. Yes, feminism is a very important part of the SDT, but one needs to be careful with cause and effect. Did feminism cause the SDT? Unlikely. Did the SDT cause feminist ideas to become attractive to more people? That seems far more likely. If that's the case then I wouldn't necessarily lay this (more old people living/dying alone) at feminism's feet. Feminism may provide an ideological argument in favor of certain relationships, but we are the ones choosing to live and die alone, and when we choose we do so for myriad reasons.

Also, women haven't been liberated from the home [1].

[1] https://www.ft.com/content/0c9f068c-711f-11e9-bf5c-6eeb83756...

i am surprised that the physical aspect of digesting the processed vs unprocessed food doesn't seem to really matter for that study. i'd have naively thought that chewing and swallowing a chick pea would result in lower nutrient uptake than eating an equal amount of besan. instead it seems a calorie is a calorie and the weight difference is entirely due to less consumption. even if nutrient uptake is the same, i think it's clear the stomach and intestines are designed for dealing with unprocessed foods.

>There are all sorts of other cool stats that we could mine out of the data: greatest frags/minute, longest uninterrupted quake game, cruelest to newbies, etc, etc.

I agree with his use of a double etc there.

>By introducing a second layer of scheduling, the M-to-N model makes performance tuning to the model exceedingly difficult.

Reminds me of Simon Marlow's work[1] on tuning the Haskell runtime for a specific use at fb.

[1] https://simonmar.github.io/posts/2016-12-08-Haskell-in-the-d...

As other posters mention, no path in life is guaranteed to be fulfilling for all individuals. Given that, you shouldn't feel sorry for anyone who has had the opportunity to lead their life according to their own principles. And that is all "feminists" want, the ability for women/all people to be considered equal, responsible agents.

A little experience. Enough to say at this point it's probably overkill, but if you start writing more geo-aware features (e.g. search nearby, overlapping course search, etc) it may save you some time vs implementing in application code.

Are you storing the race paths using the PostGIS extension for postgres or something else?

Knoll's ReGeneration is the yin to the Mirra 2's yang.

The author is a very funny/insane guy. Exhibit A is his twitter account (https://twitter.com/bonus500). I wouldn't call it an inside joke, but rather a general feature of good comedy where it feels familiar yet distant.

This can't be serious. Quincy, Illinois?

This article is ... problematic;

The author apparently made no attempt to do an apples to apples comparison and drew a hasty conclusion. After realizing the error the only change to the article is a small update at the end saying everything he said before was wrong. This is diggbait at its worst.

dict(foo='bar') becomes {'foo': 'bar'}, removing the need for quotes around the key.

agreed. i sympathise with mongohq's position as a proprietary SAAS provider, but if they can find a way to push any of this back, they should. tools matter.

the fact you can download this gnu cheatsheet as xlsx is some delicious irony

i'd wager they thought they were operating in simulation

Seconded. I'd suggest just browsing around http://www.khanacademy.org/#finance and click on any links that seem interesting. Each video is quite accessible on its own.

If, however, OP's interest in learning about the market goes further than the academic, then the motley fool (www.fool.com) website has a decent amount of basic info geared towards individual investors, e.g. http://www.fool.com/investing/beginning/investing-strategies...

I think you're conflating computational finance with geographical arbitrage, and its pursuit by some traders. Computational finance is a general discipline that's more concerned with modeling and prediction of future asset prices, whereas the problem discussed in the article/paper has virtually nothing to do with prediction or modeling. It's much more about the pennies made when taking related product's prices to equilibrium in global markets, and a novel strategy of doing that.

I guess the article is optimistic in that sense. If you can engineer fiber that's 74% of c, or better yet, bore one of your mirror holes directly through the center of the earth, between nyc and shanghai, or something even crazier, you can make a lot of money.

The market participant who's able to collect, process and act on related sercurities' price discrepancies the fastest will always make money this way. Your example would simply raise the stakes- instead of continually slicing time into ever smaller intervals during which trading decisions can be made, there would be 8,640 global auction events, resulting in the exact same amount of arbitrage monies flowing into fewer hands.

Amazon EMR is an amazing value proposition for virtually any research need, and it's very cool to see wrapper frameworks targeting it directly. Still, for anyone managing their own compute clusters and wanting to do MR in python, I'd suggest checking out Disco.

Disco (http://discoproject.org) is a really elegant MR framework implemented in erlang and python, with additional support for jobs in C and Java. I've used it for a little over a year and am convinced it is the superior MR platform (Hadoop's terasort victories notwithstanding). New features are being integrated very quickly, the core platform is rock solid, management is simple and it's extremely flexible.

was the performance gain worth it in the end? In my experience numpy is pretty tight on its own, but I've seen some excellent speed gains from using cython + numpy.

No additional complexity was introduced into those two modules, aside from being a different language that's easily grasped by anyone who knows Python and C. LOC-wise, the modules were about the same as the Python versions.

I've done some integration of C code using ctypes, which works quite well, and offers the obvious speed boost, but feels less coherent and ultimately less maintainable, project-wise, than a well-coded Cython module. Writing a full-on CPython module from scratch would probably offer better performance than Cython if you know the quirks and are disciplined. But to someone who doesn't already drip CPython C modules, Cython is a godsend.

Ultimately, there's 5 commonly used ways (CPython, Boost::Python, SWIG, Cython, ctypes) to integrate C into Python, and right now you'd be crazy not to give Cython a shot, if that's your need. It's very easy to learn for anyone familiar with both C and Python.

I recently refactored some underperforming production code using Cython. After spending ~6 hours rewriting 2 modules in Cython's "python superset" syntax, I'm left with a 33% performance boost, and virtually no additional project complexity.

I've also been replacing some rather excessive struct.unpack usage in my code with Cython's C struct pointer casting syntax, and uncovering _massive_ performance gains. 45 seconds of parsing now takes 3 seconds.

I'm pretty much convinced there's no reason to learn CPython's C API, given Cython's maturity and PyPy's improbable, scintillating ascendancy. viz. RPython may be Python's performance future, but Cython is ready now.

Seconding interactive brokers for this type of experiment.

For a reasonably accessible, yet complete introduction to basic trade design, 'Pairs Trading' by Vidyamurthy is quite solid. The strategy may well be picked clean, but the book does a commendable job in exposing the general process of finding edge and minimizing slippage.

pretty close. They're talking about designated "market makers" (of which specialists are a subset) who get into a contractual obligation with an exchange to be in certain markets for specified percentages of the trading day. They've been part of the exchange landscape for a long time, and they've been largely displaced by HFT firms. To say that this changing landscape is somehow to blame for the flash crash is dubious, at best, however.

the recumbent bike thing seems a little douchey. doubly so given the guy's gut. Medical imaging workstations seem much more humane- put everyting on a hydraulic table so you can work comfortably sitting, standing or in a low crouch waiting to strike