cdavidcash | 10 years ago | on: The Moral Failure of Computer Scientists
cdavidcash's comments
cdavidcash | 13 years ago | on: NIST Selects Winner of Secure Hash Algorithm (SHA-3) Competition
Also, your explanation of the sponge structure omits the real difference between it and MD: It is a transform that turns a non-compressing function (f in that diagram) into a compressing function. MD, on the other hand, starts with a fixed-input-size compressing function and extends its domain.
By the way, what do you mean by "Furthermore, Keccak's Sponge design derives security by only allowing inputs to directly influence a subset of the internal state bits."? That's as true for an MD-type construction as it is for a sponge construction. In fact, it's a crucial fact that allows us to build a reduction from, say, the collision-resistance of MD[f] to the collision-resistance of f.
cdavidcash | 14 years ago | on: Finite Field Arithmetic and Reed-Solomon Coding
cdavidcash | 14 years ago | on: Don't use bcrypt
Is this a common opinion amongst practitioners? The opposite philosophy (e.g., that a random oracle is a "weaker object" than an ideal cipher) underlies some lines of work in the theoretical cryptography literature.
cdavidcash | 14 years ago | on: Xkcd Password Generator
cdavidcash | 15 years ago | on: The puzzle that started complexity theory.
(And we'd use a cryptographic hash function anyway.)
cdavidcash | 15 years ago | on: 25 Questions to Ask During an Information Security Interview
Not only are these just trying establish if you're in the club or not, but judging from his answers on the DH vs RSA questions (which are not-well formed to begin with), it seems like he's trying to establish if you're in the club that knows the names but has no knowledge of WTF is actually going on in crypto.
cdavidcash | 15 years ago | on: Understanding "randomness"
Under some reasonable assumptions, rand() + rand() mod 1 is twice as random! If both calls return independent samples, and at least one of the calls to rand() returns a uniform sample in [0,1], then the sum will be uniform on [0,1].
So if you assume you get at least one good shot and that the other is not specifically correlated to it, you'll be improving your randomness.
cdavidcash | 15 years ago | on: Understanding "randomness"
cdavidcash | 15 years ago | on: Mathematics for computer science (Fall 2010, MIT)
cdavidcash | 15 years ago | on: First improvement of fundamental algorithm in 10 years
cdavidcash | 15 years ago | on: First improvement of fundamental algorithm in 10 years
In general, taking theoretical approximation algorithms and shrinking the approximation factor to below some threshold where you "tell the difference" is a dead end. For instance, this algorithm's running time has a poly^{1/\epsilon} factor, so you pay a huge cost for it. It is better to think \epsilon = 1/3 or so.
cdavidcash | 15 years ago | on: Easy Ways to Fail a Ph.D.
Actually, I still find myself reading and learning unnecessary math papers most evenings as a hobby. I think it makes a huge difference.
Perhaps this is unique to mathematics, where theorem proving skills are portable. I certainly wouldn't use my situation to argue that PL grad students need to study French. But maybe the PL students should take an extra distributed systems course - and TA while they do it!
cdavidcash | 15 years ago | on: Issues in the Proof that P ≠ NP
But! All of this has put a spotlight on a theorem that I had somehow never appreciated. It is a beautiful theorem that is well within reach of a mathematically-literate computer scientist: P = FO(LFP) over finite ordered structures. If you start reading up on this, it's immediately clear that this is interesting, because FO(LFP) say nothing a priori about computational resources in the sense that P does.
Anyway, a sufficiently curious reader can look it up as theorem 4.10 in the following text:
http://www.amazon.com/Descriptive-Complexity-Texts-Computer-...
That book is pretty light on the basics of logic. I used this book:
http://www.amazon.com/Mathematical-Logic-Undergraduate-Texts...
cdavidcash | 15 years ago | on: P ≠ NP
Such proofs are said to relativize -- i.e., they are still valid relative to any oracle.
cdavidcash | 15 years ago | on: P ≠ NP
And besides, the question is over the entire proof strategy and not the specific techniques involved. It seems plausible that one could give a relativizing proof using some method of calculation from statistical mechanics, for example.
cdavidcash | 15 years ago | on: Two Envelopes Problem
cdavidcash | 15 years ago | on: Two Envelopes Problem
This is exactly wrong, at least in the mathematical formalization of this problem that everyone is assuming. The definition of "don't know how much money" must be taken to be "has a uniform distribution".
If you want to use some reasoning that treats the amount of money as an "unknown in an equation" then you are instead giving a solution that differs for each possible value of the money and does not capture the random choices we are trying to model.
By the way, no one said that uniform distributions on infinite sets don't exist. He said "infinite series (the list of possible monies)", a slightly mis-used term that clearly excludes [0,1], your chosen example.
cdavidcash | 15 years ago | on: Ask HN: What's your most interesting life goal currently?
http://3dpancakes.typepad.com/ernie/2005/03/re_phd_with_low....
My advice is think about what you really want to do, and why you want to do it. If you want to do good research then it makes sense to apply to highly ranked PhD programs because they will give you a support system to help you learn. If you just want to own a PhD from a specific school, then it will be harder to get in, harder to finish, and less meaningful when you're done.
My advice for your specific goal is to somehow get involved with a professor's research. Preferably someone who will be recognized by admission committees. Their letter will help a lot. This might involve getting an MS at the highest-ranked school you can get admitted to. Sadly, those programs are not usually funded (as opposed to American PhD programs).