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135,575
A Nearly Optimal Deterministic Parallel Voronoi Diagram Algorithm
, 1996
"... We describe an nprocessor, O(log(n) log log(n))time CRCW algorithm to construct the Voronoi diagram for a set of n pointsites in the plane. ..."
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Cited by 1 (0 self)
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We describe an nprocessor, O(log(n) log log(n))time CRCW algorithm to construct the Voronoi diagram for a set of n pointsites in the plane.
Nearly Optimal Deterministic Algorithm for Sparse WalshHadamard Transform
"... For every fixed constant α> 0, we design an algorithm for computing the ksparse WalshHadamard transform of an Ndimensional vector x ∈ RN in time k1+α(logN)O(1). Specifically, the algorithm is given query access to x and computes a ksparse x ̃ ∈ RN satisfying ‖x̃ − x̂‖1 ≤ c‖x̂−Hk(x̂)‖1, for a ..."
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use is a construction of nearly optimal and linear lossless condensers which is a careful instantiation of the GUV condenser (Guruswami, Umans, Vadhan, JACM 2009). Moreover, we design a deterministic and nonadaptive `1/`1 compressed sensing scheme based on general lossless condensers that is equipped
Nearoptimal deterministic algorithms for volume computation and lattice problems via mellipsoids
, 2012
"... We give a deterministic 2 O(n) algorithm for computing an Mellipsoid of a convex body, matching a known lower bound. This has several interesting consequences including improved deterministic algorithms for volume estimation of convex bodies and for the shortest and closest lattice vector problems ..."
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Cited by 3 (3 self)
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We give a deterministic 2 O(n) algorithm for computing an Mellipsoid of a convex body, matching a known lower bound. This has several interesting consequences including improved deterministic algorithms for volume estimation of convex bodies and for the shortest and closest lattice vector problems
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a powerlaw (or if the coefficient sequence of f in a fixed basis decays like a powerlaw), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball as the class F of those elements whose entries obey the power decay law f  (n) ≤ C · n −1/p. We take measurements 〈f, Xk〉, k = 1,..., K, where the Xk are Ndimensional Gaussian
Splitters and nearoptimal derandomization
"... We present a fairly general method for finding deterministic constructions obeying what we call krestrictions; this yields structures of size not much larger than the probabilistic bound. The structures constructed by our method include (n; k)universal sets (a collection of binary vectors of lengt ..."
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Cited by 64 (1 self)
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of length n such that for any subset of size k of the indices, all 2k configurations appear) and families of perfect hash functions. The nearoptimal constructions of these objects imply the very efficient derandomization of algorithms in learning, of fixedsubgraph finding algorithms, and of near optimal
Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding
 IEEE TRANS. ON INFORMATION THEORY
, 1999
"... We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing informationembedding rate, mini ..."
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Cited by 495 (15 self)
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distortionrobustness tradeoffs than currently popular spreadspectrum and lowbit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DCQIM is optimal (capacityachieving) and regular QIM is nearoptimal. These include both additive white Gaussian noise
Fuzzy extractors: How to generate strong keys from biometrics and other noisy data. Technical Report 2003/235, Cryptology ePrint archive, http://eprint.iacr.org, 2006. Previous version appeared at EUROCRYPT 2004
 34 [DRS07] [DS05] [EHMS00] [FJ01] Yevgeniy Dodis, Leonid Reyzin, and Adam
, 2004
"... We provide formal definitions and efficient secure techniques for • turning noisy information into keys usable for any cryptographic application, and, in particular, • reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying mater ..."
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Cited by 532 (38 self)
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, it can be used to reliably reproduce errorprone biometric inputs without incurring the security risk inherent in storing them. We define the primitives to be both formally secure and versatile, generalizing much prior work. In addition, we provide nearly optimal constructions of both primitives
A Fast and Elitist MultiObjective Genetic Algorithm: NSGAII
, 2000
"... Multiobjective evolutionary algorithms which use nondominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) nonelitism approach, and (iii) the need for specifying a sharing param ..."
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Cited by 1707 (58 self)
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, is able to find much better spread of solutions and better convergence near the true Paretooptimal front compared to PAES and SPEA  two other elitist multiobjective EAs which pay special attention towards creating a diverse Paretooptimal front. Moreover, we modify the definition of dominance in order
Mean shift, mode seeking, and clustering
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... AbstractMean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a modeseeki ..."
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Cited by 620 (0 self)
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seeking process on a surface constructed with a “shadow ” kernel. For Gaussian kernels, mean shift is a gradient mapping. Convergence is studied for mean shift iterations. Cluster analysis is treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data. Applications
The Cyclical Behavior of Equilibrium Unemployment and Vacancies
 American Economic Review
, 2005
"... This paper argues that a broad class of search models cannot generate the observed businesscyclefrequency fluctuations in unemployment and job vacancies in response to shocks of a plausible magnitude. In the U.S., the vacancyunemployment ratio is 20 times as volatile as average labor productivity ..."
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Cited by 839 (20 self)
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productivity, while under weak assumptions, search models predict that the vacancyunemployment ratio and labor productivity have nearly the same variance. I establish this claim both using analytical comparative statics in a very general deterministic search model and using simulations of a stochastic version
Results 1  10
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