Abstract Trevisan showed that many pseudorandom generator constructions give rise to constructionsof explicit extractors. We show how to use such constructions to obtain explicit lossless condensers. A lossless condenser is a probabilistic map using only O(log n) additional random bitsthat maps n bits strings to poly(log K) bit strings, such that any source with support size Kis mapped almost injectively to the smaller domain. Our construction remains the best lossless condenser to date.By composing our condenser with previous extractors, we obtain new, improved extractors. For small enough min-entropies our extractors can output all of the randomness with only O(log n) bits. We also obtain a new disperser that works for every entropy loss, uses an O(log n)bit seed, and has only O(log n) entropy loss. This is the best disperser construction to date,and yields other applications. Finally, our lossless condenser can be viewed as an unbalanced

We give an improved explicit construction of highly unbalanced bipartite expander graphs with expansion arbitrarily close to the degree (which is polylogarithmic in the number of vertices). Both the degree and the number of right-hand vertices are polynomially close to optimal, whereas the previous constructions of Ta-Shma, Umans, and Zuckerman (STOC ‘01) required at least one of these to be quasipolynomial in the optimal. Our expanders have a short and self-contained description and analysis, based on the ideas underlying the recent list-decodable errorcorrecting codes of Parvaresh and Vardy (FOCS ‘05). Our expanders can be interpreted as near-optimal “randomness condensers, ” that reduce the task of extracting randomness from sources of arbitrary min-entropy rate to extracting randomness from sources of min-entropy rate arbitrarily close to 1, which is a much easier task. Using this connection, we obtain a new construction of randomness extractors that is optimal up to constant factors, while being much simpler than the previous construction of Lu et al. (STOC ‘03) and improving upon it when the error parameter is small (e.g. 1/poly(n)).

We introduce the Bloomier filter, a data structure for compactly encoding a function with static support in order to support approximate evaluation queries. Our construction generalizes the classical Bloom filter, an ingenious hashing scheme heavily used in networks and databases, whose main attribute -- space efficiency -- is achieved at the expense of a tiny false-positive rate. Whereas Bloom filters can handle only set membership queries, our Bloomier filters can deal with arbitrary functions. We give several designs varying in simplicity and optimality, and we provide lower bounds to prove the (near) optimality of our constructions.

A pseudorandom generator G n : f0; 1g is hard for a propositional proof system P if (roughly speaking) P can not ef- ciently prove the statement G n (x 1 ; : : : ; x n ) 6= b for any string b 2 . We present a function (m 2 ) generator which is hard for Res( log n); here Res(k) is the propositional proof system that extends Resolution by allowing k-DNFs instead of clauses.

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This paper discusses the Slepian–Wolf problem of distributed near-lossless compression of correlated sources. We introduce practical new tools for communicating at all rates in the achievable region. The technique employs a simple “sourcesplitting” strategy that does not require common sources of randomness at the encoders and decoders. This approach allows for pipelined encoding and decoding so that the system operates with the complexity of a single user encoder and decoder. Moreover, when this splitting approach is used in conjunction with iterative decoding methods, it produces a significant simplification of the decoding process. We demonstrate this approach for synthetically generated data. Finally, we consider the Slepian–Wolf problem when linear codes are used as syndrome-formers and consider a linear programming relaxation to maximum-likelihood (ML) sequence decoding. We note that the fractional vertices of the relaxed polytope compete with the optimal solution in a manner analogous to that observed when the “min-sum ” iterative decoding algorithm is applied. This relaxation exhibits the ML-certificate property: if an integral solution is found, it is the ML solution. For symmetric binary joint distributions, we show that selecting easily constructable “expander”-style low-density parity check codes (LDPCs) as syndrome-formers admits a positive error exponent and therefore provably good performance.

We use expander graphs to provide efficient new constructions for two security applications: authentication of long digital streams over lossy networks and building scalable, robust overlay networks. Here is a summary of our contributions: (1) To authenticate long digital streams over lossy networks, we provide a construction with a provable lower bound on the ability to authenticate a packet -- and that lower bound is independent of the size of the graph. To achieve this, we present an authentication expander graph with constant degree. (Previous work, such as [MS01], used authentication graphs but required graphs with degree linear in the number of vertices.) (2) To build ecient, robust, and scalable overlay networks, we provide a construction using undirected expander graphs with a provable lower bound on the ability of a broadcast message to successfully reach any receiver. This also gives us a new, more efficient solution to the decentralized certificate revocation problem [WLM00].

Microarrays (DNA, protein, etc.) are massively parallel affinity-based biosensors capable of detecting and quantifying a large number of different genomic particles simultaneously. Among them, DNA microarrays comprising tens of thousands of probe spots are currently being employed to test multitude of targets in a single experiment. In conventional microarrays, each spot contains a large number of copies of a single probe designed to capture a single target, and, hence, collects only a single data point. This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. Typically, only a fraction of the total number of genes represented by the two samples is differentially expressed, and, thus, a vast number of probe spots may not provide any useful information. To this end, we propose an alternative design, the so-called compressed microarrays, wherein each spot contains copies of several different probes and the total number of spots is potentially much smaller than the number of targets being tested. Fewer spots directly translates to significantly lower costs due to cheaper array manufacturing, simpler image acquisition and processing, and smaller amount of genomic material needed for experiments. To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. For sparse measurement matrices, we propose an algorithm that has significantly lower computational complexity than the widely used linear-programming-based methods, and can also recover signals with less sparsity.

Abstract We prove two results about the value of Delsarte's linear program for binary codes.Our main result is a new lower bound on the value of the program, which, in particular, is nearly tight for low rate codes.We also give an easy proof of an upper bound, which coincides with the best known bound for a wide range of parameters. 0

We consider dictionary data structures based on expander graphs. We show that any one probe scheme with the properties of the previous data structure from [OP02] is indeed space optimal. We then construct four different dictionary data structures for various models of parallel external memory. All of them allows lookups using a single parallel probe. In the following n denotes the number of keys in the dictionary, and u the universe of possible keys. ∆opt denotes the space in bits required to store the n keys and their satellite data without any type of compression and d = O(log(u/n)). • A static dictionary data structure with error correcting codes using O(∆opt) bits of space, and one requiring O(ndlog d + ∆opt) bits of space without using error correcting codes. • A dynamic dictionary data structure for the parallel disk head model using O(ndlog n + ∆opt) bits of space, where updates take O(1) I/O’s amortized. • A dynamic dictionary data structure for the parallel disk model, with