High Throughput

In late 2011, I gave cheek swabs to National Geographic to trace my genetic genealogy. The samples looked at markers from my Y chromosome and mitochondrial DNA: the first was passed down from my father from his father from his father up through the generations, and the second from my mother from her mother from her mother etc. The markers would help me learn about those ancestral lines. It took a couple months for the results to come back, and this puzzled me.

A few years prior, I was at a center that was sequencing DNA at surprisingly fast rates, so I couldn’t understand why my sequences would take so long. I took follow-up tests with Family Tree DNA to better understand my genetic genealogy, but these were just as slow.

Meanwhile, the fast sequencing that I had observed was being commercialized by companies like Illumina, which provide “high throughput” sequencing technology. One well-known technique involves reading fragments of DNA and reassembling it into the original genome in a manner akin to solving a jigsaw puzzle. A couple nights ago, I was having dinner with a friend who worked at one of these high throughput sequencing companies, and I asked how “high throughput” these jigsaw-puzzle solvers are.

“The throughput is roughly one genome per day,” he responded. The entire genome? Yup. Even eukaryotes? Yup. And they handle multiple chromosomes? Yup. Wouldn’t my tests only take a tiny fraction of that time? Yup. Were the companies I sent this to using high throughput sequencing technology? Yup. Then what was going on?

My tests amounted to genotype matching, which my friend confirmed was a simpler process and would take less time; however, the technology requires samples to be prepped according to the type of test. While the prep work itself may only take a few hours, running a test on my sample simply isn’t cost-effective by itself. In order to achieve economies of scale, it would be best to batch my test in with similar ones.

“Why not just sequence the entire genome and do post-processing in the cloud?” This, it turned out, was the holy grail, and companies were working on new ways to improve the speed.

Were they simply building faster jigsaw-puzzle solvers? No. The algorithms for assembling the genomes were fairly mature, so the real gains have come from finding ways to set up the prep so that it can provide side information in the later assembly. In one such example, Illumina had acquired the start-up Moleculo, which had found a clever way to barcode the preps for faster assembly. Moleculo consisted of a mix of molecular biologists and computer scientists, with the innovations happening at the interface between the fields.

Posted in Biology | Leave a comment

Passing Notes in Class

I just got back from a four-day camping trip for the Fourth. While roasting marshmallows and hiking along trails, I managed to fall into a few puzzles. Justin posed a puzzle that we’d formulated a while ago: a multi-person variation on the unreliable postal service puzzle. At some later point, I brought up The League Problem. On the ride back, Amit mentioned the water jug puzzle and tried explaining his prior work on inaccessible cardinals to me.

I came home to good news about some of my prior work. Specifically, a puzzle that I like to call Passing Notes in Class had been posted while I was away: Alice and Bob want to pass notes to each other in class, but they don’t want the teacher to notice (or at the very least, not catch them very often). If Alice and Bob can both see how much the teacher is paying attention to them, and the rustling of notes between the two will make the teacher more likely to look their way in the future, what is the optimal tradeoff between passing as much information as possible between the two of them while limiting the number of times they get caught by the teacher?

Posted in Information Theory, Papers, Puzzle | Leave a comment

Fool Me Once

I recently started watching Penn and Teller’s Fool Me, a show in which magicians try to fool Penn and Teller for a shot to perform in Vegas. After one of the episodes, there was a trick that I couldn’t even begin to understand, so I went looking for it online. Instead of getting a definitive answer, I found a web page with a bunch of guesses that had degenerated into a flame war. The flame war itself reminded me of an xkcd comic, but one of the comments mentioned a separate trick that had fooled Penn and Teller, a supposed mathematical trick performed by Graham Jolley.

The comment didn’t describe Graham Jolley’s trick, but it was pretty easy to find and watch. After experimenting with a deck of cards along with a pencil and some scratch paper, I’ve come up with one possible implementation.

The Effect

The magician takes out a deck of cards and has two participants sit on each side. Participant A is asked to cut about a third of the way into the deck and look at the bottom card. Participant B is asked to cut about half way into the remaining deck and look at the bottom card. The magician then recollects the card stack from Participant A and Participant B, placing them on top of the deck, respectively.

The magician then spreads the deck face down to reveal two jokers facing up. The jokers are taken out, and the magician holds each one close, as if it were transmitting where in the deck to look. In fact, the magician states that the first card will be found A cards into the deck, and the other will be found B cards in. When the magician counts through the deck and sets aside the cards at positions A and B, they turn out to be the ones for Participant A and Participant B, respectively.

One Possible Method

The method I came up with assumes that Participant A will draw a card roughly 1/3 of the way into the deck, and Participant B draws a card roughly halfway into the remaining deck or roughly 2/3 of the way into the original deck.

Let’s start with a deck containing N cards (not counting the jokers) with the first joker between cards i and i+1, and the second between cards j and j+1:

1, \ldots, i, \|JOKER\|, i+1, \ldots, j, \|JOKER\|, j+1, \ldots, N

We’ll now proceed with some definitions and assumptions

N_A – the initial position of the card drawn by Participant A
N_B – the initial position of the card drawn by Participant B
i < N_A < j < N_B

This would mean that the arrangement of the deck would be as follows:

1, \ldots, i, \|JOKER\|, i+1, \ldots, N_A, \ldots, j, \|JOKER\|, j+1, \ldots, N_B, \ldots, N

The magician achieves this by having Participant A get a card from near 1/3 of the way into the deck and placing the first joker well before 1/3 of the way in. Likewise for the second joker (j = N/2 would be the safest choice).

When Participants A and B replace their stacks on the top of the deck, Participant A goes first, so the order upon this reshuffle looks as follows:

N_{A}+1, \ldots, j, \|JOKER\|, j+1, \ldots, N_B, 1, \ldots, i, \|JOKER\|, i+1, \ldots, N_A, N_{B}+1, \ldots, N

Note that the first group of cards before the joker

N_{A}+1, \ldots, j

contains j - N_A cards. The second group of cards (between the first and second joker)

j+1, \ldots, N_B, 1, \ldots, i,

contains Participant B’s card i positions from the end, and the third group

i+1, \ldots, N_A, N_{B}+1, \ldots, N

contains Participant A’s card N_A - i cards from the beginning.

Thus, by moving the third group between the first and third (this happens when removing the jokers), Participant A’s card lands j - i positions from the start, and Participant B’s lands i cards from the end.

For a standard deck, N = 52, let j = 26, i = 6, and Participant A’s card will land at position 20, and Participant B’s will land at position 46.

Posted in Puzzle | Leave a comment

Complexity and Asymptotes

A friend pointed out to me that the statement that the final divide-and-conquer Fibonacci algorithm from the previous post could run in O(\log N) time was a bit misleading. The objection was that I had assumed that the matrix multiplication would not depend on N, but that this in fact not the case at all. Namely, if the numbers being added get large, neither the addition nor the multiplication operations of the matrix multiplication cannot be assumed to be constant.

In fact, the earlier analysis of Fibonacci indicated that the value of \text{fib}(N) could be bounded above and below by 2^{N/2} < \text{fib}(N) < 2^{N}, so each of the roughly \log N matrix multiplication amounts to 10 multiplication operations and 4 addition operations, which we can bound above as the addition or multiplication on order-of- N -bit numbers. By contrast, the iterative algorithm, which I had claimed was linear-time O(N), requires only one addition operation at each step, resulting in an order of N addition operations on order-of- N -bit numbers.

If we consider the complexity of the arithmetic operations, the addition of two N bit numbers can be completed in O(N) time, so the revised complexity of the iterative algorithm becomes O(N^2).

For the divide-and-conquer algorithm, we have to consider the complexity of both multiplication operations and addition operations. Suppose we opt for the Schönhage–Strassen algorithm, which has complexity O(N \log N \log \log N). In this case, the multiplication operations dominate the addition ones, and the revised overall complexity of the algorithm becomes O(N \log^2 N \log \log N), which is still faster than the O(N^2) iterative algorithm asymptotically. However, I wonder if there is an intermediate region before that asymptotic behavior takes effect in which the iterative approach completes faster.

Posted in Programming | Leave a comment

Code Monkey

During my years as a student, I sometimes encountered a disdain in others for writing code. In some cases, the term “code monkey” would get used against someone who enjoyed writing code. Something never quite felt right about that term. Recently, I went to a talk by Donald Knuth, who said something that resonated with me. I am paraphrasing it here: “Some people say that you truly understand something when you can explain it to a child. I’d say that you truly understand something when you can explain it to a computer.”

I took a programming class in high school during which we had to write a program to compute the Fibonacci sequence: 1, 1, 2, 3, 5, 8, 13, etc. The way to get the next number in the sequence was to add the previous two numbers in the sequence. In my mind, one that had just digested the idea of recursion, this was relatively simple to explain to a computer:

int fib(int N) {
  if (N == 1) return 1;
  if (N == 2) return 2;
  return fib(N - 1) + fib(N - 2);
}

There was only one problem with my explanation: it would take a computer exponential time in N to run the code, denoted as O(2^N). One way to see it is to notice that the number of calls to the function doubles for every N > 2, resulting in a binary tree with the minimum depth to a leaf of \frac{N}{2} and a maximum depth of N. Another way to see it is to compile the code and use it to try to compute \text{fib}(50) and then wait… and wait… and wait.

The Fibonacci sequence grows exponentially, and a naive recursion effectively counts up one-by-one. Thus, such an algorithm grows exponentially, as well.

I got tired of waiting, and after studying computer science a while longer, I learned how to explain Fibonacci to a computer that would take linear time, i.e. O(N):

int fib(int N) {
  int a = 0;
  int b = 1;
  for (int i = 1; i < N; ++i) {
    int temp = b;
    b += a;
    a = temp;
  }
  return b;
}

Now I didn’t have to wait for \text{fib}(50), but I would for \text{fib}(2^{50}). This improved my understanding of the Fibonacci sequence, but at the time, my understanding was quite limited: I hadn’t taken a linear algebra class yet. Once I had that and some signal processing classes under my belt, I learned that the Fibonacci sequence could be solved in closed-form with a z-transform. Alternatively, one could arrive at the closed-form solution by recognizing that the Fibonacci sequence could be represented as the product of an exponentiated matrix with a vector, and all one had to do was decompose that matrix into its eigenvalues/eigenvectors:

\left(\begin{array}{c} \text{fib}(N+1) \\ \text{fib}(N) \end{array}\right) = \left(\begin{array}{cc} 1 & 1 \\ 1 & 0 \end{array}\right)^N \left(\begin{array}{c} 1 \\ 0 \end{array}\right)

There was still a problem, though. The eigenvalues of the Fibonacci matrix are irrational, and the closed-form solution contains irrational terms, so explaining this to a computer could result in floating point errors:

\text{fib}(N) = \frac{(\frac{1 + \sqrt{5}}{2})^N - (\frac{1 - \sqrt{5}}{2})^N}{\sqrt{5}}

By this time, I was well versed in MATLAB, and this wonderful vector-matrix notation could be fed right in, but perhaps I didn’t understand what was going on as well as I thought.

Some years went by before I took a closer look at the above expression and noticed something: the computation of the closed-form expression involves two quantities that need to be exponentiated, which can be done using a divide-and-conquer algorithm in O(\log N), i.e. logarithmic time. Is there a way to do this that doesn’t involve floating-point arithmetic?

It turns out the approach is pretty simple: one can apply the same divide-and-conquer technique for exponentiating a number to exponentiating a matrix. The divide step is easy to see when N is even:

\left(\begin{array}{c} \text{fib}(N+1) \\ \text{fib}(N) \end{array}\right) = \left(\left(\begin{array}{cc} 1 & 1 \\ 1 & 0 \end{array}\right)^2\right)^{N/2} \left(\begin{array}{c} 1 \\ 0 \end{array}\right)

If N is odd, the same idea holds:

\left(\begin{array}{c} \text{fib}(N+1) \\ \text{fib}(N) \end{array}\right) = \left(\begin{array}{cc} 1 & 1 \\ 1 & 0 \end{array}\right)\left(\left(\begin{array}{cc} 1 & 1 \\ 1 & 0 \end{array}\right)^2\right)^{(N-1)/2} \left(\begin{array}{c} 1 \\ 0 \end{array}\right)

Given this, it isn’t too hard to explain to a computer how to compute the Fibonacci sequence in O(\log N) time without resorting to floating-point arithmetic:

int fib(int N) {
  Matrix<int> m(1,1,1,0);
  Vector<int> v(1,0);
  Vector<int> v = fib_helper(N, m, v);
  return v[0];
}
Vector fib_helper(int N, Matrix<int> m, Vector<int> v) {
  // Base case
  if (N == 1) {
    // Multiples the matrix and the vector.
    return MultiplyMatrixVector(m, v);
  }
  // Even case
  if (N % 2 == 0) {
    // Squares the matrix.
    return fib_helper(N / 2, SquareMatrix(m), v);
  }
  // Odd case
  return MultiplyMatrixVector(
      m, fib_helper(N - 1, m, v));
}

Are there any cases in which programming has helped you understand a concept better? Are there any cases in which understanding a concept has made you a better programmer?

Posted in Programming, Signal Processing | 1 Comment

Voting Paradox

Inspired by the upcoming elections, I spent a little time yesterday trying to think up an example in which people could potentially have logically consistent beliefs individually but as a whole produce logically inconsistent outcomes. The result of that effort follows.

It’s election time, and there are three propositions on the ballot to spend a budget surplus. Proposition 1 is to increase funding for education. Proposition 2 is to increase funding for the healthcare. However, if both Propositions 1 and 2 pass, the tax rate needs to increase to 8% to avoid a budget shortfall. Proposition 3 is designed to do just this.

There are three voters in the town. Alice is for education only, so she supports Proposition 1 but not 2 or 3. Bob is for healthcare only, so he supports Proposition 2 but not 1 or 3. Cindy wants both education and healthcare, so she supports Propositions 1, 2, and 3. While everyone believes in something logically consistent, in this scenario, both Propositions 1 and 2 pass, but Proposition 3, the tax increase, is defeated, leading to a budget shortfall.

I posed the problem above to Justin Bledin, a graduate student in the Logic Group at UC Berkeley, to find out if the idea made sense or not.

“If you’d stumbled upon this ten years ago, it would have made a nice paper,” Justin responded before pointing me to the judgment aggregation paradox, something that he had come across in a decision theory seminar.

In 2002, Christian List and Philip Pettit‘s “Aggregating Sets of Judgments: An Impossibility Result” was published in Economics and Philosophy. The paper starts with an example similar in flavor to the one above and goes on to prove that a voting function will produce logically inconsistent output for certain logically consistent profile of inputs if the voting function satisfies the following three conditions:

  1. The voting function should accept any individual’s voting profile if it satisfies certain conditions for logical consistency.
  2. The output of the voting function should be the same for any permutation of the individual voting profiles.
  3. If two propositions have the same votes in favor, then their outcome should be the same.

The paper concludes with strategies that could produce one a consistent voting function if one of the rules were relaxed. One idea that comes out of the second theorem of the paper is a median-based voting method, so long as there is a way to order individual voting profiles. It would be interesting to think about how one might construct such voting systems in practice.

Posted in Papers, Puzzle | Leave a comment

Walking Downhill

The problem asks you to show that f(\vec{x}) \geq 0. If f(x_1,x_2) = x_1^2 + x_2^2, then the solution is quite easy. Set the gradient equal to zero and solve the system of equations for \vec{x}. Since the function is convex, this is the minimum point, and the answer is simply

f(\vec{x}) \geq f(0,0) = 0.

However, one can add a wrinkle to this problem to make it more of a challenge. If the f(x_1, x_2) = \log (1 + x_1^2 + x_2^2), then we no longer have a convex function. However, it can be shown that this function is minimized at \vec{x} = \vec{0}, and there is a surprisingly general argument that works and has been used to considerable effect in the literature.

The argument can be summarized intuitively as follows: show that from any point, there is a downward path to the minimum. For instance, in the above problem, one can define g(t) as follows:

g(t) = f(x_1 \cdot (1 - t), x_2 \cdot (1 - t)).

Then, it is straightforward to show that over the interval [0,1], g'(t) \leq 0 for either choice of f(x_1, x_2) above. Furthermore, g(0) = f(x_1, x_2) and g(1) = 0, so we can conclude that f(\vec{x}) \geq 0.

Entropy Power Inequality

As mentioned before, this type of argument has proved to be potent in papers. One of the first places I encountered the argument was in the proof of the entropy power inequality. There are several equivalent statements of the result (see e.g. Dembo et al. 1991), one of which is the following. Given two independent random variables X and Y with differential entropies h(X) and h(Y), respectively, then

h(X + Y) \geq h(\tilde{X} + \tilde{Y}),

where \tilde{X} and \tilde{Y} are independent Gaussian random variables with the same differential entropies as X and Y, respectively. The entropy power inequality has been used to prove converses for Gaussian broadcast channels and the quadratic Gaussian CEO problem.

The proof essentially involves transforming the distributions of X and Y to the distributions of \tilde{X} and \tilde{Y} along a path that does not increase the differential entropy of the sum.

Parameter Redundancy of the KT-Estimator

I’ll end with another use of the argument found in Willems, Shtarkov, and Tjalken’s “The Context-Tree Weighting Method: Basic Properties” from the May 1995 issue of the IEEE Transactions on Information Theory. Many of the results that follow from the paper make use of the Krichevski-Trofimov (KT) estimator, which approximates the probability distribution of a binary sequence based on the number of ones and zeroes. With the above argument, one can show that the parameter redundancy of a KT-estimator can be uniformly bounded. To state this mathematically, first let P_e (a, b) represent the KT-estimator for a sequence with a ones and b zeroes. Also note that for a Bernoulli sequence with parameter \theta, the probability the sequence has a ones and b zeroes is (1 - \theta)^a \cdot \theta^b. The result states that for all values of \theta,

\log \frac{(1 - \theta)^a \cdot \theta^b}{P_e (a, b)} \leq \frac{1}{2} \log (a + b) + 1.

Note that the upper bound does not depend on \theta. This result is a key component for the authors to show that the redundancy of the context-tree weighting method is small and thereby demonstrate they have a compelling strategy for universal source coding.

The uniform bound above follows from a lower bound on the KT-estimator:

P_e (a, b) \geq \frac{1}{2} \cdot \frac{1}{\sqrt{a + b}} \cdot (\frac{a}{a+b})^a \cdot (\frac{b}{a+b})^b.

To prove the result, the authors define

\Delta(a, b) = \frac{P_e (a, b)}{\frac{1}{\sqrt{a + b}} \cdot (\frac{a}{a+b})^a \cdot (\frac{b}{a+b})^b}

and find a downward path to show that \Delta(a, b) \geq \Delta(1,0) = \Delta(0,1) = \frac{1}{2}.

Posted in Information Theory, Papers | Leave a comment