Ying, M 2001, Topology in process calculus : approximate correctness and infinite evolution of concurrent programs, Springer, New York.
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Rohde, K & Rohde, PP 2001, 'Fuzzy chaos: Reduced chaos in the combined dynamics of several independently chaotic populations', American Naturalist, vol. 158, no. 5, pp. 553-556.
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Ying, M 2001, 'Fuzzy Topology Based On Residuated Lattice-Valued Logic', Acta Mathematica Sinica-English Series, vol. 17, no. 1, pp. 89-102.
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We use a semantical method of complete residuated lattice-valued logic to give a generalization of fuzzy topology as a partial answer to a problem by Rosser and Turquette.
Ying, M & Wirsing, M 2001, 'Recursive Equations In Higher-Order Process Calculi', Theoretical Computer Science, vol. 266, no. 1-Feb, pp. 839-852.
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Regarding behaviour equivalence in higher-order process calculi, Sangiorgi (Inform. and Comput. 131 (1996) 141) and Thomsen (Inform. and Comput. 116 (1995) 38) introduced context and higher-order bisimulations, respectively. In this paper, uniqueness of
Zhang, S, Feng, Y, Sun, X & Ying, M 2001, 'Upper Bound For The Success Probability Of Unambiguous Discrimination Among Quantum States', Physical Review A, vol. 64, no. 6, pp. 1-3.
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One strategy to the discrimination problem is to identify the state with certainty, leaving a possibility of undecidability. This paper gives an upper bound for the maximal success probability of unambiguous discrimination among n states. This bound coin
Zhang, S, Feng, Y, Sun, X & Ying, M 2001, 'Upper bound for the success probability of unambiguous discrimination among quantum states', Physical Review A. Atomic, Molecular, and Optical Physics, vol. 64, no. 6.
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The general prediction of n quantum states was examined. An upper bound for success probability of unambiguous discrimination was established. The bound coincides with the known extended IDP limit when two states are concerned.
Blumenstein, M & Verma, B 2001, 'Analysis of segmentation performance on the CEDAR benchmark database', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 1142-1146.
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© 2001 IEEE. The purpose of this paper is to analyse the performance of our improved segmentation algorithm tested on the CEDAR benchmark database. Segmentation is achieved through the extraction of a wide range of information adjacent to or surrounding suspicious segmentation points. Initially, a heuristic technique is employed to search for structural features and to over-segment each word. For each segmentation point that is located, the left character (preceding the segmentation point), and centre character (centred on the segmentation point) are extracted along with other features from the segmentation area. The aforementioned features are presented to trained character and segmentation point validation neural networks to evaluate a number of confidence values. Finally, the confidence values are fused to obtain the final segmentation decision. Based on a detailed analysis, it was observed that the left and centre character networks increased the accuracy of the segmentation algorithm.