Omid Etesami

Raptor codes on symmetric channels

This paper extends the construction and analysis of Raptor codes originally designed in A. Shokrollahi (2004) for the erasure channel to general symmetric channels. We explicitly calculate the asymptotic fraction of output nodes of degree one and two for capacity-achieving Raptor codes, and discuss techniques to optimize the output degree distribution.

Mafia: A theoretical study of players and coalitions in a partial information environment

In this paper, we study a game called “Mafia,” in which different players have different types of information, communication and functionality. The players communicate and function in a way that resembles some real-life situations. We consider two types of operations. First, there are operations that follow an open democratic discussion. Second, some subgroups of players who may have different interests make decisions based on their own group interest. A key ingredient here is that the identity of each subgroup is known only to the members of that group. In this paper, we are interested in the best strategies for the different groups in such scenarios and in evaluating their relative power. The main focus of the paper is the question: How large and strong should a subgroup be in order to dominate the game? The concrete model studied here is based on the popular game “Mafia.” In this game, there are three groups of players: Mafia, detectives and ordinary citizens. Initially, each player is given only his/her own identity, except the mafia, who are given the identities of all mafia members. At each “open” round, a vote is made to determine which player to eliminate. Additionally, there are collective decisions made by the mafia where they decide to eliminate a citizen. Finally, each detective accumulates data on the mafia/citizen status of players. The citizens win if they eliminate all mafia members. Otherwise, the mafia wins. We first find a randomized strategy that is optimal in the absence of detectives. This leads to a stochastic asymptotic analysis where it is

Irregular product codes

We introduce irregular product codes, a class of codes where each codeword is represented by a matrix and the entries in each row (column) of the matrix come from a component row (column) code. As opposed to standard product codes, we do not require that all component row codes nor all component column codes be the same. Relaxing this requirement can provide some additional attractive features such as allowing some regions of the codeword to be more error-resilient, providing a more refined spectrum of rates for finite lengths, and improved performance for some of these rates. We study these codes over erasure channels and prove that for any 0 <; ε <; 1, for many rate distributions on component row codes, there is a matching rate distribution on component column codes such that an irregular product code based on MDS codes with those rate distributions on the component codes has asymptotic rate 1 - ε and can decode on erasure channels having erasure probability <; ε (and having alphabet size equal to the alphabet size of the component MDS codes).

On the One-Way Function Candidate Proposed by Goldreich

Goldreich [2000] proposed a candidate one-way function based on a bipartite graph of small right-degree d, where the vertices on the left (resp. right) represent input (resp. output) bits of the function. Each output bit is computed by evaluating a fixed d-ary binary predicate on the input bits adjacent to that output bit. We study this function when the predicate is random or depends linearly on many of its input bits. We assume that the graph is a random balanced bipartite graph with right-degree d. Inverting this function as a one-way function by definition means finding an element in the preimage of output of this function for a random input. We bound the expected size of this preimage. Next, using the preceding bound, we prove that two restricted types of backtracking algorithms called myopic and drunk backtracking algorithms with high probability take exponential time to invert the function, even if we allow the algorithms to use DPLL elimination rules. (For drunk algorithms, a similar result was proved by Itsykson [2010].) We also ran a SAT solver on the satisfiability problem equivalent to the problem of inverting the function, and experimentally observed an exponential increase in running time as a function of the input length.

Maximal Rank Correlation

Based on the notion of maximal correlation, we introduce a new measure of correlation between two different rankings of the same group of items. Our measure captures various types of correlation detected in previous measures of rank correlation like the Spearman correlation and the Kendall tau correlation. We show that the maximal rank correlation satisfies the data processing and tensorization properties (that make ordinary maximal correlation applicable to problems in information theory). Furthermore, MRC is shown to be intimately related to the FKG inequality. Finally, we pose the problem of the complexity of the computation of this new measure. We make partial progress by giving a simple but exponential-time algorithm for it.

Transversals in long rectangular arrays

In this paper it is shown that every mxn array in which each symbol appears at most (mn-1)/(m-1) times has a transversal, when n>=2m^3.

Relations between belief propagation on erasure and symmetric channels

An upper bound on the performance of the belief propagation algorithm in decoding a code over a binary-input output-symmetric channel in terms of the decoding threshold of the code over the erasure channel is presented in this paper. Using this upper bound, we obtain the overhead of fountain codes on the erasure channel, provided that they are capacity-achieving on a symmetric channel. The upper bound is similar to a lower bound proved by Khandekar. The lower bound will be used to bound from above the reception overhead of fountain codes on symmetric channels.

Deterministic Randomness Extraction from Generalized and Distributed Santha--Vazirani Sources

A Santha--Vazirani (SV) source is a sequence of random bits where the conditional distribution of each bit, given the previous bits, can be partially controlled by an adversary. Santha and Vazirani show that deterministic randomness extraction from these sources is impossible. In this paper, we study the generalization of SV sources for nonbinary sequences. We show that unlike the binary setup of Santha and Vazirani, deterministic randomness extraction in the generalized case is sometimes possible. In particular, if the adversary has access to

Deterministic Randomness Extraction from Generalized and Distributed Santha-Vazirani Sources

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The value of information-theoretic content of help bits for computation

“nondegenerate” dice that are