The Experts below are selected from a list of 29916 Experts worldwide ranked by ideXlab platform
Kouji Nakamura - One of the best experts on this subject based on the ideXlab platform.
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Recursive Structure in the definitions of gauge invariant variables for any order perturbations
arXiv: General Relativity and Quantum Cosmology, 2014Co-Authors: Kouji NakamuraAbstract:The construction of gauge-invariant variables for any order perturbations is discussed. Explicit constructions of the gauge-invariant variables for perturbations to 4th order are shown. From these explicit construction, the Recursive Structure in the definitions of gauge-invariant variables for any order perturbations is found. Through this Recursive Structure, the correspondence with the fully non-linear exact perturbations is briefly discussed.
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Recursive Structure in the definitions of gauge invariant variables for any order perturbations
Classical and Quantum Gravity, 2014Co-Authors: Kouji NakamuraAbstract:The construction of gauge-invariant variables for any order perturbations is discussed. Explicit constructions of the gauge-invariant variables for perturbations to the forth order are shown. From these explicit constructions, the Recursive Structure in the definitions of gauge-invariant variables for any order perturbations is found. Through this Recursive Structure, the correspondence with the fully non-linear exact perturbations is briefly discussed.
Shigeki Matsubara - One of the best experts on this subject based on the ideXlab platform.
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sentence compression by removing Recursive Structure from parse tree
Pacific Rim International Conference on Artificial Intelligence, 2008Co-Authors: Seiji Egawa, Yoshihide Kato, Shigeki MatsubaraAbstract:Sentence compression is a task of generating a grammatical short sentence from an original sentence, retaining the most important information. The existing methods of removing the constituents in the parse tree of an original sentence cannot deal with Recursive Structures which appear in the parse tree. This paper proposes a method to remove such Structure and generate a grammatical short sentence. Compression experiments have shown the method to provide an ability to sentence compression comparable to the existing methods and generate good compressed sentences for sentences including Recursive Structures, which the previous methods failed to compress.
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sentence compression by structural conversion of parse tree
International Conference on Digital Information Management, 2008Co-Authors: Seiji Egawa, Yoshihide Kato, Shigeki MatsubaraAbstract:Sentence compression is the task of generating a grammatical short sentence from an original sentence, retaining important information. The existing methods of only removing the constituents in the parse tree of an original sentence cannot emulate human compression that changes Structures of the parse tree. This paper proposes a method to remove Recursive Structures, one example of such structural conversions, and generate a grammatical short sentence. In order to remove a Recursive Structure, our method detects the constituents forming the Structure and removes them as a unit. Compression experiments have shown that our method generates more grammatical compressed sentences than the previous method.
Yannick Deville - One of the best experts on this subject based on the ideXlab platform.
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self adaptive separation of convolutively mixed signals with a Recursive Structure part ii theoretical extensions and application to synthetic and real signals
Signal Processing, 1999Co-Authors: Nabil Charkani, Yannick DevilleAbstract:Abstract This paper deals with the separation of two convolutively mixed signals. The proposed approach uses a recurrent Structure adapted by generic rules involving arbitrary separating functions. While the basic versions of this approach were defined and analyzed in our companion paper (Charkani and Deville, 1999), two extensions are considered here. The first one is intended for possibly colored signals. In addition, the second one may be used even when the probability density functions of the sources are unknown. We first analyze the convergence properties of these extended approaches at the separating state, i.e. we derive their equilibrium and stability conditions and their asymptotic error variance. We then determine the separating functions which minimize this error variance. We also report experimental results obtained in various conditions, ranging from synthetic data to mixtures of speech signals measured in real situations. These results confirm the validity of the proposed approaches and show that they significantly outperform classical source separation methods in the considered conditions.
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self adaptive separation of convolutively mixed signals with a Recursive Structure part i stability analysis and optimization of asymptotic behavior
Signal Processing, 1999Co-Authors: Nabil Charkani, Yannick DevilleAbstract:Abstract In this paper, we investigate the self-adaptive source separation problem for convolutively mixed signals. The proposed approach uses a recurrent Structure adapted by a generic rule involving arbitrary separating functions. A stability analysis of this algorithm is first performed. It especially applies to some classical rules for instantaneous and convolutive mixtures that were proposed in the literature but only partly analysed. The expression of the asymptotic error variance is then determined for strictly causal mixtures. This enables to derive the optimum separating functions that minimize this error variance. They are shown to be only related to the probability density functions of the sources. To perform this error minimization, two normalization procedures that improve the algorithm properties are proposed. Their stability conditions and their asymptotic behaviour are analysed.
Seiji Egawa - One of the best experts on this subject based on the ideXlab platform.
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sentence compression by removing Recursive Structure from parse tree
Pacific Rim International Conference on Artificial Intelligence, 2008Co-Authors: Seiji Egawa, Yoshihide Kato, Shigeki MatsubaraAbstract:Sentence compression is a task of generating a grammatical short sentence from an original sentence, retaining the most important information. The existing methods of removing the constituents in the parse tree of an original sentence cannot deal with Recursive Structures which appear in the parse tree. This paper proposes a method to remove such Structure and generate a grammatical short sentence. Compression experiments have shown the method to provide an ability to sentence compression comparable to the existing methods and generate good compressed sentences for sentences including Recursive Structures, which the previous methods failed to compress.
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sentence compression by structural conversion of parse tree
International Conference on Digital Information Management, 2008Co-Authors: Seiji Egawa, Yoshihide Kato, Shigeki MatsubaraAbstract:Sentence compression is the task of generating a grammatical short sentence from an original sentence, retaining important information. The existing methods of only removing the constituents in the parse tree of an original sentence cannot emulate human compression that changes Structures of the parse tree. This paper proposes a method to remove Recursive Structures, one example of such structural conversions, and generate a grammatical short sentence. In order to remove a Recursive Structure, our method detects the constituents forming the Structure and removes them as a unit. Compression experiments have shown that our method generates more grammatical compressed sentences than the previous method.
Nabil Charkani - One of the best experts on this subject based on the ideXlab platform.
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self adaptive separation of convolutively mixed signals with a Recursive Structure part ii theoretical extensions and application to synthetic and real signals
Signal Processing, 1999Co-Authors: Nabil Charkani, Yannick DevilleAbstract:Abstract This paper deals with the separation of two convolutively mixed signals. The proposed approach uses a recurrent Structure adapted by generic rules involving arbitrary separating functions. While the basic versions of this approach were defined and analyzed in our companion paper (Charkani and Deville, 1999), two extensions are considered here. The first one is intended for possibly colored signals. In addition, the second one may be used even when the probability density functions of the sources are unknown. We first analyze the convergence properties of these extended approaches at the separating state, i.e. we derive their equilibrium and stability conditions and their asymptotic error variance. We then determine the separating functions which minimize this error variance. We also report experimental results obtained in various conditions, ranging from synthetic data to mixtures of speech signals measured in real situations. These results confirm the validity of the proposed approaches and show that they significantly outperform classical source separation methods in the considered conditions.
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self adaptive separation of convolutively mixed signals with a Recursive Structure part i stability analysis and optimization of asymptotic behavior
Signal Processing, 1999Co-Authors: Nabil Charkani, Yannick DevilleAbstract:Abstract In this paper, we investigate the self-adaptive source separation problem for convolutively mixed signals. The proposed approach uses a recurrent Structure adapted by a generic rule involving arbitrary separating functions. A stability analysis of this algorithm is first performed. It especially applies to some classical rules for instantaneous and convolutive mixtures that were proposed in the literature but only partly analysed. The expression of the asymptotic error variance is then determined for strictly causal mixtures. This enables to derive the optimum separating functions that minimize this error variance. They are shown to be only related to the probability density functions of the sources. To perform this error minimization, two normalization procedures that improve the algorithm properties are proposed. Their stability conditions and their asymptotic behaviour are analysed.
