elliptic

< A lot of industrial Haskell sadly still uses string interpolation for SQL synthesis, or fall back on libraries that use TemplateHaskell to unsafely lock a specific build to a snapshot of a database at compile-time.

This sounds kinda off-putting, but I'm not totally sure what it means. Are the SQL libraries for haskell comparable to e.g knex, or psycopg, or sqlalchemy? The bit about string interpolation makes me think that prepared statements aren't used?

Do you have a reference for this? While Greece was strongly influenced by the cultures of the Near East and Egypt, it entirely misstates the situation to say that "most of their culture was handed to them." Edit - Walter Burkert has an excellent, short book on this - The Orientalizing Revolution

I noticed that, but haven't any idea what the purpose is?

Perhaps the elegance would be better illustrated if the planets were shown to move in elliptical rather than circular orbits - this of course was the primary historical difficulty in understanding planetary motion

I agree with the main point of the article, but I'm somewhat disturbed by the statistical errors and misconceptions.

Few websites actually get enough traffic for their >audiences to even out into a nice pretty bell curve. If >you get less than a million visitors a month your >audience won’t be identically distributed and, even >then, it can be unlikely.

What is the author trying to say here? Has he thought hard about what it means for "an audience" to be identically distributed?

Likewise, the things that matter to you on your website, >like order values, are not normally distributed Why do they need to be?

Statistical power is simply the likelihood that the >difference you’ve detected during your experiment >actually reflects a difference in the real world. Simply googling the term would reveal this is incorrect.

How is that a 'devil's bargain'? Just seems like an everyday deal to me.

Perhaps your assessment says more about you than it does the actual facts of the case

That is precisely what happened with PDT, correct? It was spin out from MS

Nonsense. Tao is using perfectly idiomatic language here - "The sample space will be a probability space (once we have endowed it with some additional structure)".

Regarding d) - how much do you think the researcher, much of whose work was presumably funded by taxpayer dollars, should get?

Can someone explain the following comments, for someone with some knowledge of ML but none of NLP? "First, it's really much better to use Averaged Perceptron, or some other method which can be trained in an error-driven way. You don't want to do batch learning. Batch learning makes it difficult to train from negative examples effectively, and this makes a very big difference to accuracy" I thought that it was typical for suitably regularized batch methods to modestly outperform or at least match (in terms of accuracy) online methods, whose main advantage is their speed.

Is this claim correct? "You do four of these sequential scans, and in each you write sequentially to one of 256 locations. Four passes, no random access. This is great. I don't even see a log n there, do you?"

You're writing to one of 256 (presumably far apart) memory locations - why isn't this considered random access?

Out of curiosity, how much better, typically, is the 'optimal' blending rule than blending with some random or 'reasonable' weights? Never really thought to ask myself that question...

4-5x extremely minuscule is still minuscule.

Can someone summarize or translate (for non-Britons) the paragraph dealing with the educational background of Classics students?

They take place in the 15th century, and are fictional (although they chronicle many historical events), but I cannot recommend highly enough the 'House of Niccolo' series by Dorothy Dunnett to anyone who is interested in the Mediterranean (and to a lesser extend, Scotland, the Baltic, etc) of that era.

How was theoretical physics key to the war?

Your point is well taken, but with some training most healthy men can deadlift 400 pounds.

The GP article seems like self-congratulatory fluff. So, just to be clear, what you're saying is that you're brilliant and this is a problem?

I remember I was incredibly excited about the release; it was some years after QFG IV, right? But I didn't feel it was quite as good as I or (especially) II. Still, even the weaker III and IV had such intellectual variety and even wonderful emotional range...Humor to pathos, and everywhere in between.

The number-one sign appears to be: You're like the author

Is this not how most 'modern' (read 90s) relational db's work?

Spark the platform seems awesome. I'm somewhat less convinced by mllib - I'm not sure there are as many use cases for distributed machine learning as people seem to think (and I would bet that a good deal of companies that use distributed ML don't really need it). I've seen a lot of tasks that could be handled by simpler, faster algos on large workstations (you can get 250 GB RAM from AWS for like $4.00/hr). I'd love to hear counterarguments, though!

"Now as a European coming from a well developed city with millennia of history, I am rarely impressed by any US city, but man was Houston bad"

Europeans are just so charming, aren't they?

I've spent most of my life in Alaska. I don't really know anyone who would hunt brown bear with that cartridge; it would possibly suffice for self-defense. Many "smaller" rounds are fine - plenty of locals hunt with 30-06, or even 270 Winchesters, but those rounds are much more heavily loaded. Also, the comment in TFA article about the "tumbling" being key against bears sounds off; the hard part is getting through the protective mass of fur/bone/muscle/fat.

That said, bears (and especially wolves) surely represent minimal risk as compared to the elements and more generally being in got-damn space. Funny thing to worry about.

Not all of the reviews are horrible, and not a few of the books are classics only in the sense that they've been successfully crammed into the American high school curriculum.

Some of the reviews are dead on - Salinger, Vonnegut, Lee, Mitchell. Okay, maybe most are.

Periclean Athens, Elizabethan London, and Quattroceno Florence all had astonishing concentrations of artistic vitality; none of those were in economic decline.

Has anyone had experience training deep nets for domains in which examples are large heterogenous collections (as opposed to speech, or text, or images), like say transactional or click-stream data?

Only in the sense that a team of 11 Ford pickups would be the NFL champion.

"What else falls when you drop it?" A duck!