383,485 research outputs found
Topology Discovery of Sparse Random Graphs With Few Participants
We consider the task of topology discovery of sparse random graphs using
end-to-end random measurements (e.g., delay) between a subset of nodes,
referred to as the participants. The rest of the nodes are hidden, and do not
provide any information for topology discovery. We consider topology discovery
under two routing models: (a) the participants exchange messages along the
shortest paths and obtain end-to-end measurements, and (b) additionally, the
participants exchange messages along the second shortest path. For scenario
(a), our proposed algorithm results in a sub-linear edit-distance guarantee
using a sub-linear number of uniformly selected participants. For scenario (b),
we obtain a much stronger result, and show that we can achieve consistent
reconstruction when a sub-linear number of uniformly selected nodes
participate. This implies that accurate discovery of sparse random graphs is
tractable using an extremely small number of participants. We finally obtain a
lower bound on the number of participants required by any algorithm to
reconstruct the original random graph up to a given edit distance. We also
demonstrate that while consistent discovery is tractable for sparse random
graphs using a small number of participants, in general, there are graphs which
cannot be discovered by any algorithm even with a significant number of
participants, and with the availability of end-to-end information along all the
paths between the participants.Comment: A shorter version appears in ACM SIGMETRICS 2011. This version is
scheduled to appear in J. on Random Structures and Algorithm
Derandomized Parallel Repetition via Structured PCPs
A PCP is a proof system for NP in which the proof can be checked by a
probabilistic verifier. The verifier is only allowed to read a very small
portion of the proof, and in return is allowed to err with some bounded
probability. The probability that the verifier accepts a false proof is called
the soundness error, and is an important parameter of a PCP system that one
seeks to minimize. Constructing PCPs with sub-constant soundness error and, at
the same time, a minimal number of queries into the proof (namely two) is
especially important due to applications for inapproximability.
In this work we construct such PCP verifiers, i.e., PCPs that make only two
queries and have sub-constant soundness error. Our construction can be viewed
as a combinatorial alternative to the "manifold vs. point" construction, which
is the only construction in the literature for this parameter range. The
"manifold vs. point" PCP is based on a low degree test, while our construction
is based on a direct product test. We also extend our construction to yield a
decodable PCP (dPCP) with the same parameters. By plugging in this dPCP into
the scheme of Dinur and Harsha (FOCS 2009) one gets an alternative construction
of the result of Moshkovitz and Raz (FOCS 2008), namely: a construction of
two-query PCPs with small soundness error and small alphabet size.
Our construction of a PCP is based on extending the derandomized direct
product test of Impagliazzo, Kabanets and Wigderson (STOC 09) to a derandomized
parallel repetition theorem. More accurately, our PCP construction is obtained
in two steps. We first prove a derandomized parallel repetition theorem for
specially structured PCPs. Then, we show that any PCP can be transformed into
one that has the required structure, by embedding it on a de-Bruijn graph
Computing a k-sparse n-length Discrete Fourier Transform using at most 4k samples and O(k log k) complexity
Given an -length input signal \mbf{x}, it is well known that its
Discrete Fourier Transform (DFT), \mbf{X}, can be computed in
complexity using a Fast Fourier Transform (FFT). If the spectrum \mbf{X} is
exactly -sparse (where ), can we do better? We show that
asymptotically in and , when is sub-linear in (precisely, where ), and the support of the non-zero DFT
coefficients is uniformly random, we can exploit this sparsity in two
fundamental ways (i) {\bf {sample complexity}}: we need only
deterministically chosen samples of the input signal \mbf{x} (where
when ); and (ii) {\bf {computational complexity}}: we can
reliably compute the DFT \mbf{X} using operations, where the
constants in the big Oh are small and are related to the constants involved in
computing a small number of DFTs of length approximately equal to the sparsity
parameter . Our algorithm succeeds with high probability, with the
probability of failure vanishing to zero asymptotically in the number of
samples acquired, .Comment: 36 pages, 15 figures. To be presented at ISIT-2013, Istanbul Turke
Pairwise tests of purchasing power parity
Given nominal exchange rates and price data on N + 1 countries indexed by i = 0,1,2,âŚ, N, the standard procedure for testing purchasing power parity (PPP) is to apply unit root or stationarity tests to N real exchange rates all measured relative to a base country, 0, often taken to be the U.S. Such a procedure is sensitive to the choice of base country, ignores the information in all the other cross-rates and is subject to a high degree of cross-section dependence which has adverse effects on estimation and inference. In this article, we conduct a variety of unit root tests on all possible N(N + 1)/2 real rates between pairs of the N + 1 countries and estimate the proportion of the pairs that are stationary. This proportion can be consistently estimated even in the presence of cross-section dependence. We estimate this proportion using quarterly data on the real exchange rate for 50 countries over the period 1957-2001. The main substantive conclusion is that to reject the null of no adjustment to PPP requires sufficiently large disequilibria to move the real rate out of the band of inaction set by trade costs. In such cases, one can reject the null of no adjustment to PPP up to 90% of the time as compared to around 40% in the whole sample using a linear alternative and almost 60% using a nonlinear alternative
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