The Experts below are selected from a list of 6300 Experts worldwide ranked by ideXlab platform

Shun’ya Mizoguchi - One of the best experts on this subject based on the ideXlab platform.

  • Colliding wave solutions, duality, and diagonal embedding of general relativity in two-dimensional heterotic String theory
    Nuclear Physics, 1996
    Co-Authors: Shun’ya Mizoguchi
    Abstract:

    The non-linear sigma model of the dimensionally reduced Einstein (-Maxwell) theory is diagonally Embedded into that of the two-dimensional heterotic String theory. Consequently, the Embedded String backgrounds satisfy the (electromagnetic) Ernst equation. In the pure Einstein theory, the Matzner-Misner SL(2,R) transformation can be viewed as a change of the conformal structure of the compactified flat two-torus, and in particular its integral subgroup SL(2,Z) acts as the modular transformation. The Ehlers SL(2,R) and SL(2,Z) similarly act on another torus whose conformal structure is induced through the Kramer-Neugebauer involution. Either of the Matzner-Misner and the Ehlers SL(2,Z) can be Embedded to a special T-duality, and if the former is chosen, then the Ehlers SL(2,Z) is shown to act as the S-duality on the four-dimensional sector. As an application we obtain some new colliding String wave solutions by using this embedding as well as the inverse scattering method.

Shun'ya Mizoguchi - One of the best experts on this subject based on the ideXlab platform.

  • Colliding wave solutions, duality, and diagonal embedding of general relativity in two-dimensional heterotic String theory
    Nuclear Physics B, 1996
    Co-Authors: Shun'ya Mizoguchi
    Abstract:

    The non-linear sigma model of the dimensionally reduced Einstein (-Maxwell) theory is diagonally Embedded into that of the two-dimensional heterotic String theory. Consequently, the Embedded String backgrounds satisfy the (electro-magnetic) Ernst equation. In the pure Einstein theory, the Matzner-Misner SL(2,{\bf R}) transformation can be viewed as a change of conformal structure of the compactified flat two-torus, and in particular its integral subgroup SL(2,{\bf Z}) acts as the modular transformation. The Ehlers SL(2,{\bf R}) and SL(2,{\bf Z}) similarly act on another torus whose conformal structure is induced through the Kramer-Neugebauer involution. Either of the Matzner-Misner and the Ehlers SL(2,{\bf Z}) can be Embedded to a special T-duality, and if the former is chosen, then the Ehlers SL(2,{\bf Z}) is shown to act as the S-duality on the four-dimensional sector. As an application we obtain some new colliding String wave solutions by using this embedding as well as the inverse scattering method.Comment: 32 pages, revte

Raymond Veldhuis - One of the best experts on this subject based on the ideXlab platform.

  • Embedding renewable cryptographic keys into noisy data
    International Journal of Information Security, 2010
    Co-Authors: Ileana Buhan, Jeroen Doumen, Pieter Hartel, Qian Tang, Raymond Veldhuis
    Abstract:

    A fuzzy extractor is a powerful but theoretical tool that can be used to extract uniform Strings from (discrete) noisy sources. However, when using a fuzzy extractor in practice, extra features are needed, such as the renewability of the extracted Strings and the ability to use the fuzzy extractor directly on continuous input data instead of discrete data. Our contribution is threefold. Firstly, we propose a fuzzy embedder as a generalization of the fuzzy extractor. A fuzzy embedder naturally supports renewability, as it allows a String to be Embedded instead of extracted. It also supports direct analysis of quantization effects, as it makes no limiting assumptions about the nature of the input source. Secondly, we give a general construction for fuzzy embedders based on the technique of quantization index modulation ( QIM ). We show that the performance measures of a QIM , as proposed by the watermarking community, translate directly to the security properties of the corresponding fuzzy embedder. Finally, we show that from the perspective of the length of the Embedded String, quantization in two dimensions is optimal. We present two practical constructions for a fuzzy embedder in two-dimensional space. The first construction is optimal from reliability perspective, and the second construction is optimal in the length of the Embedded String.

Ileana Buhan - One of the best experts on this subject based on the ideXlab platform.

  • Embedding renewable cryptographic keys into noisy data
    International Journal of Information Security, 2010
    Co-Authors: Ileana Buhan, Jeroen Doumen, Pieter Hartel, Qian Tang, Raymond Veldhuis
    Abstract:

    A fuzzy extractor is a powerful but theoretical tool that can be used to extract uniform Strings from (discrete) noisy sources. However, when using a fuzzy extractor in practice, extra features are needed, such as the renewability of the extracted Strings and the ability to use the fuzzy extractor directly on continuous input data instead of discrete data. Our contribution is threefold. Firstly, we propose a fuzzy embedder as a generalization of the fuzzy extractor. A fuzzy embedder naturally supports renewability, as it allows a String to be Embedded instead of extracted. It also supports direct analysis of quantization effects, as it makes no limiting assumptions about the nature of the input source. Secondly, we give a general construction for fuzzy embedders based on the technique of quantization index modulation ( QIM ). We show that the performance measures of a QIM , as proposed by the watermarking community, translate directly to the security properties of the corresponding fuzzy embedder. Finally, we show that from the perspective of the length of the Embedded String, quantization in two dimensions is optimal. We present two practical constructions for a fuzzy embedder in two-dimensional space. The first construction is optimal from reliability perspective, and the second construction is optimal in the length of the Embedded String.

  • ICICS - Embedding Renewable Cryptographic Keys into Continuous Noisy Data
    Information and Communications Security, 2008
    Co-Authors: Ileana Buhan, Jeroen Doumen, Pieter H. Hartel, Qiang Tang, Raymond N.j. Veldhuis
    Abstract:

    Fuzzy extractor is a powerful but theoretical tool to extract uniform Strings from discrete noisy data. Before it can be used in practice, many concerns need to be addressed in advance, such as making the extracted Strings renewable and dealing with continuous noisy data. We propose a primitive fuzzy embedderas a practical replacement for fuzzy extractor. Fuzzy embedder naturally supports renewability because it allows a randomly chosen String to be Embedded. Fuzzy embedder takes continuous noisy data as input and its performance directly links to the property of the input data. We give a general construction for fuzzy embedder based on the technique of Quantization Index Modulation ( QIM ) and derive the performance result in relation to that of the underlying QIM . In addition, we show that quantization in 2-dimensional space is optimal from the perspective of the length of the Embedded String. We also present a concrete construction for fuzzy embedder in 2-dimensional space and compare its performance with that obtained by the 4-square tiling method of Linnartz, et al.[13].

Horst Bunke - One of the best experts on this subject based on the ideXlab platform.

  • multiple classifier systems for Embedded String patterns
    Artificial Neural Networks in Pattern Recognition, 2006
    Co-Authors: Barbara Spillmann, Michel Neuhaus, Horst Bunke
    Abstract:

    Multiple classifier systems are a well proven and tested instrument for enhancing the recognition accuracy in statistical pattern recognition problems. However, there has been reported only little work on combining classifiers in structural pattern recognition. In this paper we describe a method for embedding Strings into real vector spaces based on prototype selection, in order to gain several vectorial descriptions of the String data. We present methods for combining multiple classifiers trained on various vectorial data representations. As base classifiers we use nearest neighbor methods and support vector machine. In our experiments we demonstrate that this approach can be used to significantly improve the classification accuracy of String patterns.

  • ANNPR - Multiple classifier systems for Embedded String patterns
    Artificial Neural Networks in Pattern Recognition, 2006
    Co-Authors: Barbara Spillmann, Michel Neuhaus, Horst Bunke
    Abstract:

    Multiple classifier systems are a well proven and tested instrument for enhancing the recognition accuracy in statistical pattern recognition problems. However, there has been reported only little work on combining classifiers in structural pattern recognition. In this paper we describe a method for embedding Strings into real vector spaces based on prototype selection, in order to gain several vectorial descriptions of the String data. We present methods for combining multiple classifiers trained on various vectorial data representations. As base classifiers we use nearest neighbor methods and support vector machine. In our experiments we demonstrate that this approach can be used to significantly improve the classification accuracy of String patterns.