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Proving Boolean Combinations of Deterministic Properties
 In Proceedings of the Second Symposium on Logic in Computer Science
, 1987
"... This paper gives a method for proving that a program satisfies a temporal property that has been specified in terms of Buchi automata. The method permits extraction of proof obligations for a property formulated as the Boolean combination of properties, each of which is specified by a deterministic ..."
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Cited by 15 (0 self)
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This paper gives a method for proving that a program satisfies a temporal property that has been specified in terms of Buchi automata. The method permits extraction of proof obligations for a property formulated as the Boolean combination of properties, each of which is specified by a deterministic
Deciding the deterministic property for soliton graphs
"... Soliton automata are a mathematical model for electronic switching at the molecular level. In the design of soliton circuits, deterministic automata are of primary importance. The underlying graphs of such automata, called soliton grahs, are characterized in terms of generalized trees and graphs hav ..."
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Cited by 1 (0 self)
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Soliton automata are a mathematical model for electronic switching at the molecular level. In the design of soliton circuits, deterministic automata are of primary importance. The underlying graphs of such automata, called soliton grahs, are characterized in terms of generalized trees and graphs
New results in linear filtering and prediction theory
 TRANS. ASME, SER. D, J. BASIC ENG
, 1961
"... A nonlinear differential equation of the Riccati type is derived for the covariance matrix of the optimal filtering error. The solution of this "variance equation " completely specifies the optimal filter for either finite or infinite smoothing intervals and stationary or nonstationary sta ..."
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Cited by 581 (0 self)
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in this field. The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results. In several examples, the estimation problem and its dual are discussed sidebyside. Properties of the variance equation are of great interest
Lag length selection and the construction of unit root tests with good size and power
 Econometrica
, 2001
"... It is widely known that when there are errors with a movingaverage root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We conside ..."
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Cited by 532 (14 self)
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consider a class of Modified Information Criteria (MIC) with a penalty factor that is sample dependent. It takes into account the fact that the bias in the sum of the autoregressive coefficients is highly dependent on k and adapts to the type of deterministic components present. We use a local asymptotic
A simple parallel algorithm for the maximal independent set problem
 SIAM Journal on Computing
, 1986
"... Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial intel ..."
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Cited by 449 (9 self)
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Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial
Deterministic edgepreserving regularization in computed imaging
 IEEE Trans. Image Processing
, 1997
"... Abstract—Many image processing problems are ill posed and must be regularized. Usually, a roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of edges, which are very important attributes of the image. In this paper, we first give conditions for the design of such ..."
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Cited by 306 (27 self)
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. We propose a deterministic strategy, based on alternate minimizations on the image and the auxiliary variable. This leads to the definition of an original reconstruction algorithm, called ARTUR. Some theoretical properties of ARTUR are discussed. Experimental results illustrate the behavior
Wireless Network Information Flow: A Deterministic Approach
, 2009
"... In contrast to wireline networks, not much is known about the flow of information over wireless networks. The main barrier is the complexity of the signal interaction in wireless channels in addition to the noise in the channel. A widely accepted model is the the additive Gaussian channel model, and ..."
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Cited by 293 (43 self)
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the Gaussian model but still captures two key wireless channel properties of broadcast and superposition. We consider a model for a wireless relay network with nodes connected by such deterministic channels, and present an exact characterization of the endtoend capacity when there is a single source and one
Evaluation of Deterministic Property of Time Series by the Method of Surrogate Data and the Trajectory Parallel Measure Method
, 2000
"... this paper, we apply the TPM method and the method of surrogate data to test a chaotic time series and a random time series. We also examine whether a practical time series has a deterministic property or not. The results demonstrate that the TPM method is useful for judging whether the original and ..."
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Cited by 1 (0 self)
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this paper, we apply the TPM method and the method of surrogate data to test a chaotic time series and a random time series. We also examine whether a practical time series has a deterministic property or not. The results demonstrate that the TPM method is useful for judging whether the original
Pairwise data clustering by deterministic annealing
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... Partitioning a data set and extracting hidden structure from the data arises in different application areas of pattern recognition, speech and image processing. Pairwise data clustering is a combinatorial optimization method for data grouping which extracts hidden structure from proximity data. We d ..."
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Cited by 234 (26 self)
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describe a deterministic annealing approach to pairwise clustering which shares the robustness properties of maximum entropy inference. The resulting Gibbs probability distributions are estimated by meanfield approximation. A new structurepreserving algorithm to cluster dissimilarity data
Results 1  10
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285,714