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2,327
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
Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements
 CISS 2006 (40th Annual Conference on Information Sciences and Systems
, 2006
"... Abstract — This paper proves best known guarantees for exact reconstruction of a sparse signal f from few nonadaptive universal linear measurements. We consider Fourier measurements (random sample of frequencies of f) and random Gaussian measurements. The method for reconstruction that has recently ..."
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Cited by 108 (7 self)
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Abstract — This paper proves best known guarantees for exact reconstruction of a sparse signal f from few nonadaptive universal linear measurements. We consider Fourier measurements (random sample of frequencies of f) and random Gaussian measurements. The method for reconstruction that has
The Determinants of Credit Spread Changes.
 Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
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Cited by 422 (2 self)
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rates, r 10 t . To capture potential nonlinear effects due to convexity, we also include the squared level of the term structure, (r 10 t ) 2 . Slope of Yield Curve We define the slope of the yield curve as the difference between Datastream's 10year and 2year Benchmark Treasury yields, slope
On sparse reconstruction from Fourier and Gaussian measurements
 Communications on Pure and Applied Mathematics
, 2006
"... Abstract. This paper improves upon best known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the Sparse Approximation Theory is to relax this highly nonconvex problem ..."
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Cited by 262 (8 self)
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Abstract. This paper improves upon best known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the Sparse Approximation Theory is to relax this highly nonconvex problem
Application of hierarchical linear models to assessing change.
 Psychological Bulletin,
, 1987
"... Recent advances in the statistical theory of hierarchical linear models should enable important breakthroughs in the measurement of psychological change and the study of correlates of change. A twostage model of change is proposed here. At the first, or withinsubject stage, an individual's s ..."
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Cited by 207 (5 self)
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Recent advances in the statistical theory of hierarchical linear models should enable important breakthroughs in the measurement of psychological change and the study of correlates of change. A twostage model of change is proposed here. At the first, or withinsubject stage, an individual
Linear Controller Design: Limits of Performance Via Convex Optimization
, 1990
"... this paper, we first give a very brief overview of control engineering. The goal of control engineering is to improve, or in some cases ena ble, the performance of a system by the addition of sensors, which measure various signals in the system and external command signals, control processors, whic ..."
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Cited by 202 (25 self)
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this paper, we first give a very brief overview of control engineering. The goal of control engineering is to improve, or in some cases ena ble, the performance of a system by the addition of sensors, which measure various signals in the system and external command signals, control processors
Nonadaptive Group Testing: Explicit bounds and novel algorithms
, 2012
"... We present computationally efficient and provably correct algorithms with nearoptimal samplecomplexity for noisy nonadaptive group testing. Group testing involves grouping arbitrary subsets of items into pools. Each pool is then tested to identify the defective items, which are usually assumed to ..."
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Cited by 17 (4 self)
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We present computationally efficient and provably correct algorithms with nearoptimal samplecomplexity for noisy nonadaptive group testing. Group testing involves grouping arbitrary subsets of items into pools. Each pool is then tested to identify the defective items, which are usually assumed
A verifiable secret shuffle and its application to EVoting
, 2001
"... We present a mathematical construct which provides a cryptographic protocol to verifiably shuffle a sequence of k modular integers, and discuss its application to secure, universally verifiable, multiauthority election schemes. The output of the shuffle operation is another sequence of k modular in ..."
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Cited by 217 (0 self)
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is important because it provides a linear size proof of correctness for the output sequence (i.e. a proof that it is of the form claimed) that can be checked by an arbitrary verifiers. The complexity of the protocol improves on that of FurukawaSako[16] both measured by number of exponentiations and by overall
Nonadaptive Group Testing: Explicit bounds and novel algorithms
"... PAPER AWARD1. We present computationally efficient and provably correct algorithms with nearoptimal samplecomplexity for noisy nonadaptive group testing. Group testing involves grouping arbitrary subsets of items into pools. Each pool is then tested to identify the defective items, which are usua ..."
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PAPER AWARD1. We present computationally efficient and provably correct algorithms with nearoptimal samplecomplexity for noisy nonadaptive group testing. Group testing involves grouping arbitrary subsets of items into pools. Each pool is then tested to identify the defective items, which
Behavioral theories and the neurophysiology of reward,
 Annu. Rev. Psychol.
, 2006
"... ■ Abstract The functions of rewards are based primarily on their effects on behavior and are less directly governed by the physics and chemistry of input events as in sensory systems. Therefore, the investigation of neural mechanisms underlying reward functions requires behavioral theories that can ..."
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Cited by 187 (0 self)
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toward the current Annu. Rev. Psychol. 2006.57:87115. Downloaded from arjournals.annualreviews.org by HARVARD UNIVERSITY on 04/18/07. For personal use only. THEORY AND NEUROPHYSIOLOGY OF REWARD 89 optimum. This review describes some of the knowledge on brain mechanisms related to rewarding outcomes
Results 1  10
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