Honestly fair. I am the exact person that uses my Libby holds as my TBR. I had no idea the cost was so high for digital licenses. Especially embarrassing because my library is 2 blocks away
Hey Peter, whats your checklist or just intuition for when a company is ready to support H1B visas? Is there some clear point between seed-round/current-YC-batch and public-mega-corp?
The bar is low. It's presumed that the sponsoring company will be able to pay the sponsored H-1B worker so the focus actually isn't on funding but the existence of basic corporate requirements - that is, the company must be incorporated, have an FEIN, have physical commercially zoned office space, and be authorized to do business where it operates. So, the long and short of it is that new small companies can sponsor H-1B workers.
My Aunt has done a wonderful job at preserving the letters and diary entries between my grandfather + grandmother during WWII. My immediate thought is how our children and grandchildren will not have the same joy!
Ha, I’m not sure it’s quite the same. If my understanding of most couples is correct, this would be more akin to preserving all of their sticky notes e.g. “Pick up milk on the way home”, “You’re getting the kids, right?”, “see you in 20”, etc.
Yet I suppose there’s a certain charm to that, so I hope I don’t sound like too much of a wet blanket.
I actually needed this at work once! We needed to fuzz peoples address in a mapped view for analytics, without revealing PII. It ended up never being shipped, but we needed to fuzz geographic data and the thinking was like:
1. Truncate your lat longs to some arbitrary decimal place (this is very very stupid, you end up with grid lines [1])
2. The above method ^^ but everyone tries basically doing like random angle + random length along angle, which doesn't generate uniform results in a circle as the article mentions[2]. So then you try generating points in a box that encloses your circle, and rejecting anything outside the circle, but that smells bad. So you do some googling and find method 6 listed in the article (Good! and Fast!)
3. Realize that fuzzing points is stupid, what you really want is to view points in aggregate anyways, so you try heat maps
[1]: Ok you always end up with grid lines, but truncating to like 1-6 decimal places produces very obvious grid lines to the human eye
[2]: Try this in pyplot! You'll see right away with ~100 points its not uniform in the way you expect
I confess I've never used this in a situation where time complexity was at all relevant. For one thing, it is possible that a result only needs to be computed once for each N (and per shape, since this approach works on a wider range of shapes than just spheres).
The rejection method smells pretty good to me, in the sense that it should be pretty obvious to anybody with, like, middle school level math I think (right?).
It might fail for higher dimensions, but lots of programs only run on a 3D sphere of a planet, haha!
Fail is an understatement, the ratio between the two volumes is basically (n/2)!(4/pi)^(n/2). Which is also the expected number of tries you'll need. The time doesn't merely grow exponentially it grows faster than exponential.
I don't actually know of any useful algorithms with worse asymptomatic behaviour.
When I saw "n" in the title I assumed they wanted something that worked reasonably for large n. Rejection is no good because of the notorious "curse of dimensionality". So my idea was to choose a suitable distribution on the radius, then draw from it and choose the angles at random (not sure what those angles are called). You might have to delete the point at the center for that to work.
The article explicitly discusses both. Versions for low dimensions that have favorable properties, and those for high dimensions.
The method you propose is not used in high dimensions in practice as it would involve evaluating order n^2 trigonometric functions and is also far harder to implement than the methods discussed in the article.
Quibble: I don’t think “fail” is an understatement, it is as strong a word as anything else for something that doesn’t work.
Actually, it is an overstatement I think, or something orthogonal; the algorithm is still correct just wildly inefficient. Fail should be reserved for algorithms that produce the wrong result, not ones that produce the right result very slowly, however slowly.
Long time ago, I had the same problem while ray tracing using Monte Carlo techniques. Using Mersenne Twister fixed the clustering and grid like randomization.
https://en.m.wikipedia.org/wiki/Mersenne_Twister
I should think after defining a uniform distribution of points, you could cluster them as needed to form larger lower-resolution chunks according to population size. Binning everyone in that region to say the central most point. Which could then be adaptive as the populations change.
This is the correct answer. You need an aggregation threshold that restricts precision when population count is too low. In this case, the cell size needs to increase until there are at least N users.
Another user here mentioned Uber's h3, which is actually what I used. You end up being able to anonymize over arbitrarily large geographic areas using something like a tile, rather than a point
Assign everyone a random offset, that doesn't change, that is large enough to obscure the address with some reasonable radius but small enough to not drastically skew the heat map at a lower zoom level
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