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673
Randomized Gossip Algorithms
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... Motivated by applications to sensor, peertopeer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join a ..."
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Cited by 532 (5 self)
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distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for an arbitrary network graph, and find that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly
Where the REALLY Hard Problems Are
 IN J. MYLOPOULOS AND R. REITER (EDS.), PROCEEDINGS OF 12TH INTERNATIONAL JOINT CONFERENCE ON AI (IJCAI91),VOLUME 1
, 1991
"... It is well known that for many NPcomplete problems, such as KSat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NPcomplete problems can be summarized by at least one "order parameter", and that the hard p ..."
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Cited by 683 (1 self)
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of a solution changes abruptly from near 0 to near 1. It is the high density of wellseparated almost solutions (local minima) at this boundary that cause search algorithms to "thrash". This boundary is a type of phase transition and we show that it is preserved under mappings between
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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. For each experimental run, we first gen erated random CPTs. We then sampled from the joint distribution defined by the network and clamped the observed nodes (all nodes in the bottom layer) to their sampled value. Given a structure and observations, we then ran three inference algorithms junction tree
The importance of being random: Statistical principles of iris recognition,”
 Pattern Recognition,
, 2003
"... Abstract The statistical variability that is the basis of iris recognition is analysed in this paper using new large databases. The principle underlying the recognition algorithm is the failure of a test of statistical independence on iris phase structure encoded by multiscale quadrature wavelets. ..."
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Cited by 193 (4 self)
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Abstract The statistical variability that is the basis of iris recognition is analysed in this paper using new large databases. The principle underlying the recognition algorithm is the failure of a test of statistical independence on iris phase structure encoded by multiscale quadrature wavelets
Spectra of random graphs with given expected degrees
, 2003
"... In the study of the spectra of power law graphs, there are basically two competing approaches. One is to prove analogues of Wigner’s semicircle law while the other predicts that the eigenvalues follow a power law distributions. Although the semicircle law and the power law have nothing in common, ..."
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Cited by 180 (19 self)
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power law graph obeys the power law. Our results are based on the analysis of random graphs with given expected degrees and their relations to several key invariants. Of interest are a number of (new) values for the exponent β where phase transitions for eigenvalue distributions occur. The spectrum
Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets
"... Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and/or storage space in a data warehouse environment. It is advantageous to have fast, a ..."
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Cited by 198 (3 self)
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and spaceefficient representation of the underlying multidimensional array, based upon a multiresolution wavelet decomposition. In the online phase, each aggregation query can generally be answered using the compact data cube in one I/O or a small number of I/Os, depending upon the desired accuracy. We
Random subgraphs of finite graphs. I. The scaling window under the triangle condition. Random Structures Algorithms,
, 2005
"... Abstract We study random subgraphs of an arbitrary finite connected transitive graph G obtained by independently deleting edges with probability 1 − p. Let V be the number of vertices in G, and let Ω be their degree. We define the critical threshold p c = p c (G, λ) to be the value of p for which t ..."
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Cited by 46 (15 self)
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the expected cluster size of a fixed vertex attains the value λV 1/3 , where λ is fixed and positive. We show that for any such model, there is a phase transition at p c analogous to the phase transition for the random graph, provided that a quantity called the triangle diagram is sufficiently small
2+p SAT: Relation of typicalcase complexity to the nature of the phase transition. Random Structures and Algorithms
, 1999
"... ABSTRACT: Heuristic methods for solution of problems in the NPcomplete class of decision problems often reach exact solutions, but fail badly at ‘‘phase boundaries,’ ’ across which the decision to be reached changes from almost always having one value to almost always having a different value. We r ..."
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Cited by 58 (2 self)
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of inputs per clause, exceeds 0.4. The cost of finding solutions also increases dramatically above this changeover. The nature of its ‘‘random firstorder’ ’ phase transition, seen at values of K large enough to make the computational cost of solving typical instances increase exponentially with problem
A Phase Transition for a Random Cluster Model on Phylogenetic Trees
, 2004
"... We investigate a simple model that generates random partitions of the leaf set of a tree. Of particular interest is the reconstruction question: what number k of independent samples (partitions) are required to correctly reconstruct the underlying tree (with high probability)? We demonstrate a phase ..."
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Cited by 26 (12 self)
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We investigate a simple model that generates random partitions of the leaf set of a tree. Of particular interest is the reconstruction question: what number k of independent samples (partitions) are required to correctly reconstruct the underlying tree (with high probability)? We demonstrate a
Reconstruction of Markov random fields from samples: Some easy observations and algorithms
, 2008
"... Markov random fields are used to model high dimensional distributions in a number of applied areas. Much recent interest has been devoted to the reconstruction of the dependency structure from independent samples from the Markov random fields. We analyze a simple algorithm for reconstructing the und ..."
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Cited by 54 (4 self)
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Markov random fields are used to model high dimensional distributions in a number of applied areas. Much recent interest has been devoted to the reconstruction of the dependency structure from independent samples from the Markov random fields. We analyze a simple algorithm for reconstructing
Results 1  10
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