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2,083,539
Least squares quantization in pcm
 IEEE Transactions on Information Theory
, 1982
"... AbstractIt has long been realized that in pulsecode modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as th ..."
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Cited by 1358 (0 self)
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as the number of quanta become large. The optimum quantization schemes for 26 quanta, b = 1,2, t,7, are given numerically for Gaussian and for Laplacian distribution of signal amplitudes. T I.
Exact Matrix Completion via Convex Optimization
, 2008
"... We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfe ..."
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Cited by 860 (27 self)
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perfectly recover most lowrank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys m ≥ C n 1.2 r log n for some positive numerical constant C, then with very high probability, most n × n matrices of rank r can be perfectly recovered
Investor psychology and security market under and overreactions
 Journal of Finance
, 1998
"... We propose a theory of securities market under and overreactions based on two wellknown psychological biases: investor overconfidence about the precision of private information; and biased selfattribution, which causes asymmetric shifts in investors ’ confidence as a function of their investment ..."
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Cited by 661 (38 self)
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We propose a theory of securities market under and overreactions based on two wellknown psychological biases: investor overconfidence about the precision of private information; and biased selfattribution, which causes asymmetric shifts in investors ’ confidence as a function of their investment outcomes. We show that overconfidence implies negative longlag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, publiceventbased return predictability. Biased selfattribution adds positive shortlag autocorrelations ~“momentum”!, shortrun earnings “drift, ” but negative correlation between future returns and longterm past stock market and accounting performance. The theory also offers several untested implications and implications for corporate financial policy. IN RECENT YEARS A BODY OF evidence on security returns has presented a sharp challenge to the traditional view that securities are rationally priced to ref lect all publicly available information. Some of the more pervasive anoma
Improved prediction of signal peptides  SignalP 3.0
 J. MOL. BIOL.
, 2004
"... We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cle ..."
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Cited by 655 (7 self)
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P version 2. In version 3, correctness of the cleavage site predictions have increased notably for all three organism groups, eukaryotes, Gramnegative and Grampositive bacteria. The accuracy of cleavage site prediction has increased in the range from 617 % over the previous version, whereas the signal
Dynamic Finegrained Localization in AdHoc Networks of Sensors
 PROCEEDINGS OF THE SEVENTH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING, MOBICOM 2001
, 2001
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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
Detection of Abrupt Changes: Theory and Application
 HTTP://PEOPLE.IRISA.FR/MICHELE.BASSEVILLE/KNIGA/
, 1993
"... ..."
A Signal Processing Approach To Fair Surface Design
, 1995
"... In this paper we describe a new tool for interactive freeform fair surface design. By generalizing classical discrete Fourier analysis to twodimensional discrete surface signals  functions defined on polyhedral surfaces of arbitrary topology , we reduce the problem of surface smoothing, or fai ..."
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Cited by 668 (15 self)
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In this paper we describe a new tool for interactive freeform fair surface design. By generalizing classical discrete Fourier analysis to twodimensional discrete surface signals  functions defined on polyhedral surfaces of arbitrary topology , we reduce the problem of surface smoothing
Results 1  10
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2,083,539