The crazy title up there means “recovering a 3D model of a molecule, using multiple 2D images/projections”. This was a talk at MIT by Yoel Shkolnisky. The talk was really neat, but the bottom line is this. From a CS point of view he was solving the following question. We have n points that (we know) come from some finite metric. We are given the pairwise distances (+ noise) between points that are within each other’s vicinity (in that metric). The goal is to reconstruct the metric. I’ve phrased this problem a little more generally then the form in which it is addressed in Yoel’s work.

The cool thing is that Yoel et al. solve this problem using a very natural appeal to graph Laplacians. This is found in a technical report on his page. Amit Singer (a collaborator) additionally has a paper on using these techniques to recover the global metric of sensor networks, based on local distance information. This is found in the form of a paper on Amit’s webpage.

There might be an opportunity for a new paper lurking in there. In particular, it is interesting to ask under what noise conditions (and noise can be correlated in real-world examples) is the recovery good. Having heard the talk, I believe that the answer to this question will also depend on the type of metric being recovered, i.e. it may depend on its doubling dimension e.g.

Oh, I should also mention that Yoel seems to have some cool papers involving the discrete Radon transform. This is probably a good place to read about it, while avoiding the continuous lingo.

I’d like to spell out a conjecture of Feige (which is pretty elegant and simple to state) if for no better reason so that I don’t forget it:

Let be independent random non-negative variables with (and arbitrary variance). Then .

The conjecture is a little more general than that, but I’ve purified it for simplicity and, in fact, the above statement is the crux of the conjecture. Feige already proved the conjecture when is replaced by using a, well, horrendous case analysis. So the goal here is really to get the constant and hopefully with a more natural argument.

This conjecture appears in On the sum of nonnegative independent random variables with unbounded variance, Feige’03. I believe that solving implies a better approximation constant for a certain sub-modular optimization problem.

I’ve been obsessing over the following conjecture. And to be honest I haven’t been able to find any counterexamples, nor make any significant progress towards proving it. So, here goes:

If are positive semidefinite and , then .

In fact, it seems that if this is true for the -norm, it probably is true for all norms of the form . Any ideas or references would be greatly appreciated.

I will reward a $10 prize to the first person who (dis)proves the following simple conjecture:

Let be uniformly distributed on the ball . Then, for every the variance of is independent of , i.e. is a universal constant.

If you are wondering why I am posting simple conjectures for prize money — it is because I would have hated to spend the time (which I am short on) to discover they are false, while I would become immediately excited about them (and their consequences) if they are true

I will post the first correct solution as soon as it arrives, so if you are not seeing a solution below, get cracking. As a bonus for resolving this problem in the positive, I will share with you why it is important. I do require a complete formal argument!

(Prize has been awarded, see below!)

In fact, a slight variation on Alex’ argument (below) gives:

No distribution on has the property that depends only and non-trivially on .

Here’s why. Write , and . Expand and using and combine to get . Substitute the latter back into to obtain . The latter, combined with the requirement , holds only when or thus giving a contradiction.

Vershynin gave a talk at MIT today on a beautiful recent result. Recall that the condition number of a matrix is . Where is the norm of defined as . Generalizing a long line of prior work Vershynin and Rudelson show that

For a random matrix , and with high probability (approaching 1). Here, the matrix is random in the sense that its entries are i.i.d. random variables, where the only restriction (if I recall correctly) is that the distribution of the matrix entries has a 4-th moment.

The pivotal technical theorem that leads to this result is yet again a generalization of a line of prior results, which informally goes like this

Let be given, and let be randomly chosen signs. If , then can be “embedded” (with small error) in an arithmetic progression of size at most .

This theorem is the key ingredient to proving anti-concentration results – the opposite of what we usually do in Probability Theory – you want to prove that measure is spread out, not focused.

The talk was a very nice walk through the connection between a few fields: geometry, functional analysis, probability and combinatorics. What was interesting to me was how crisp-and-clear the connection between continuous objects (i.e. random real matrices) and additive combinatorics (i.e. arithmetic progression business) becomes after listening to this result.

Traditionally compressed sensing has been based on linear transformations with the Restricted Isometry Property (RIP). In a recent note Kashin and Temlyakov point out that a different matrix property, the width property (WP), also suffices for LP decoding. It is also known that RIP matrices have the WP (up to a small polylog difference in the isometry inequalities). However, it is unclear whether WP suffices for recovering in the presence of noise. The details of this topic are found in my notes for Piotr Indyk’s class on Streaming Algorithms.

First, let me say that I didn’t attend, but from reading others’ blogs, I gathered these must-reads:

• Fast Dimension Reduction Using Rademacher Series on Dual BCH Codes, Ailon, Liberty, SODA’08: I started reading this. It’s really nice. It improves Chazelle & Ailon’s FJLT by noticing that the worst-case is unlikely. Then it proceeds to use elegant techniques from Banach theory and coding
• Clustering for Metric and Non-Metric distance measures, Ackermann, Blomer, Sohler, SODA’08: This was recommended by The Geomblog
• Declaring Independence via Sketching of Sketches, Indyk, McGregor, SODA’08: This one I’ve actually read. This paper scratches the surface of a new subject of sensing statistics of matrix-like objects. The main open question (rephrased) of this paper is this:
  

  If a matrix is streamed (as element-wise updates), can you approximate , where ?

  On a related note, I will mention another open question (pertaining to streaming algorithms on matrices) without motivating it much (feel free to ask me for details): Can one “take the square root of a desired unimodal origin-symmetric distribution W”. I.e. is there some magical distribution Q, so that if X~Q and Y~Q then X*Y~W? E.g. when W is taken to be some p-stable distribution, one can use its (convolutional) square root Q to approximate -norms of the singular values vector of a streaming matrix in the rotated space. I am mentioning this question because no one seems to know the answer or have any intuition about it. Yet the question is natural and of a classical type (in Harmonic Analysis)

• Finally, Mitzenmacher’s blog reports that Diaconis’ talk on the relationship between carry, shuffling and Young tableux is quite fascinating

At this point I open the stage for you guys to tell me about other great picks.