Abstract. We propose a shape analysis that adapts to some of the complex composite data structures found in industrial systems-level programs. Examples of such data structures include “cyclic doubly-linked lists of acyclic singly-linked lists”, “singly-linked lists of cyclic doublylinked lists

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Abstract. We propose a shape analysis that adapts to some of the complex composite data structures found in industrial systems-level programs. Examples of such data structures include “cyclic doubly-linked lists of acyclic singly-linked lists”, “singly-linked lists of cyclic doublylinked lists

, and robotics. There is a greater emphasis on region data (i.e., two-dimensional shapes) and to a lesser extent on point, curvilinear, and threedimensional data. A number of operations in which such data structures find use are examined in greater detail.

In this paper, we propose a new method for index-ing large amounts of point and spatial data in high-dimensional space. An analysis shows that index structures such as the R*-tree are not adequate for indexing high-dimensional data sets. The major problem of R-tree-based index structures

the intrinsic clustering structure accurately. We introduce a new algorithm for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster

SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence

. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and shape of these structures. Deformable models are capable of accommodating

The central proposal of this article is that verbal reports are data. Accounting for verbal reports, as for other kinds of data, requires explication of the mech-anisms by which the reports are generated, and the ways in which they are sensitive to experimental factors (instructions, tasks, etc

boundary surfaces are presented. Independence of shading and classification calculations insures an undistorted visualization of 3-D shape. Non-binary classification operators insure that small or poorly defined features are not IosL The resulting colors and opacities am composited from back to front along