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Showing papers by "Sameep Mehta published in 2004"


Journal ArticleDOI
TL;DR: Sublingual misoprostol appears to be as effective as intravenous methylergometrine in the prevention of postpartum hemorrhage, however, larger randomized studies are needed to advocate its routine use.

41 citations


Proceedings ArticleDOI
10 Oct 2004
TL;DR: The methods of defect (feature) detection are at least as robust as those based on the exploration of electron density for Si systems, according to results obtained from both sources of data.
Abstract: In this article we explore techniques to detect and visualize features in data from molecular dynamics (MD) simulations Although the techniques proposed are general, we focus on silicon (Si) atomic systems The first set of methods use 3D location of atoms Defects are detected and categorized using local operators and statistical modeling Our second set of exploratory techniques employ electron density data This data is visualized to glean the defects We describe techniques to automatically detect the salient iso-values for iso-surface extraction and designing transfer functionsWe compare and contrast the results obtained from both sources of data Essentially, we find that the methods of defect (feature) detection are at least as robust as those based on the exploration of electron density for Si systems

26 citations


Proceedings Article
22 Aug 2004
TL;DR: This work examines contact maps generated from protein data in order to discover spatial relationships among the connected patterns contained in those maps and presents a method for finding relationships between approximate patterns in contact maps.
Abstract: We present a method for finding relationships between approximate patterns in contact maps. We examine contact maps generated from protein data in order to discover spatial relationships among the connected patterns contained in those maps. We discuss our criteria for determining whether two patterns are approximately equivalent as well as the motivation behind our work. Finally, we provide results that validate our efforts.

13 citations


Proceedings ArticleDOI
01 Nov 2004
TL;DR: A novel PCA-based unsupervised algorithm for the discretization of continuous attributes in multivariate datasets is presented, which leverages the underlying correlation structure in the dataset to obtain the discrete intervals, and ensures that the inherent correlations are preserved.
Abstract: Discretization is a crucial preprocessing primitive for a variety of data warehousing and mining tasks. In this article we present a novel PCA-based unsupervised algorithm for the discretization of continuous attributes in multivariate datasets. The algorithm leverages the underlying correlation structure in the dataset to obtain the discrete intervals, and ensures that the inherent correlations are preserved. The approach also extends easily to datasets containing missing values. We demonstrate the efficacy of the approach on real datasets and as a preprocessing step for both classification and frequent item set mining tasks. We also show that the intervals are meaningful and can uncover hidden patterns in data.

9 citations