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We report a new and more precise Sellmeier equation obtained by using the analysis of quasi-phase-matching curves of the optical parametric generation (OPG) in 1D periodically poled LiTaO3 (1D-PPLT) of different grating periods. [ABSTRACT FROM AUTHOR]

Traditional outlier mining methods identify outliers from a global point of view. These methods are inefficient to find locally biased data points (outliers) in low dimensional subspaces. Constrained concept lattices can be used as an effective formal tool for data analysis because constrained concept lattices have the characteristics of high constructing efficiency, practicability and pertinency. In this paper, we propose an outlier mining algorithm that treats the intent of any constrained concept lattice node as a subspace. We introduce sparsity and density coefficients to measure outliers in low dimensional subspaces. The intent of any constrained concept lattice node is regarded as a subspace, and sparsity subspaces are searched by traversing the constrained concept lattice according to a sparsity coefficient threshold. If the intent of any father node of the sparsity subspace is a density subspace according to a density coefficient threshold, then objects contained in the extent of the sparsity subspace node are considered as bias data points or outliers. Our experimental results show that the proposed algorithm performs very well for high red-shift spectral data sets. [ABSTRACT FROM AUTHOR]

Abstract: Traditional outlier mining methods identify outliers from a global point of view. It is usually difficult to find deviated data points in low-dimensional subspaces using these methods. The concept lattice, due to its straight-forwardness, conciseness and completeness in knowledge expression, has become an effective tool for data analysis and knowledge discovery. In this paper, a concept lattice based outlier mining algorithm (CLOM) for low-dimensional subspaces is proposed, which treats the intent of every concept lattice node as a subspace. First, sparsity and density coefficients, which measure outliers in low-dimensional subspaces, are defined and discussed. Second, the intent of a concept lattice node is regarded as a subspace, and sparsity subspaces are identified based on a predefined sparsity coefficient threshold. At this stage, whether the intent of any ancestor node of a sparsity subspace is a density subspace is identified based on a predefined density coefficient threshold. If it is a density subspace, then the objects in the extent of the node whose intent is a sparsity subspace are defined as outliers. Experimental results on a star spectral database show that CLOM is effective in mining outliers in low-dimensional subspaces. The accuracy of the results is also greatly improved. [Copyright &y& Elsevier]