### Table 1. Shape Analysis of the Convex Cylindrical Surface Standard Standard

"... In PAGE 8: ... Figure 10 shows the surface fit of the data and the control points of the surface. The means and standard deviations of estimated S and R and the estimated radius are shown in Table1 . For the cylinder (radius 6.... ..."

### Table 4: Experimental results for two tetrahedral meshes Convex Polyhedron U-shaped Object level NTET

1995

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### Table 6: Improved-quality meshes for two polyhedral regions Convex Polyhedron U-shaped Object level NTET

1995

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### Table 1 shows impressively that real cartography objects are only roughly approximated by BBs. In this table, fa is the average false area; min and max denote the minimum and the maximum false area in the map, respectively. This investigation was the starting point to look for other approximations that have better quality than the BB. Additionally, they should be simple to provide a fast filter. These requirements are fulfilled by convex shapes with straight-line borders. Therefore, we tested the rotated bounding box (RBB),theconvex hull (CH), the minimum enclosing convex 4-corner (4-C),andthe5-corner (5-C).

"... In PAGE 14: ...64 0.89 Table1 : False area of the BB... In PAGE 19: ... Certain measures are supported to maintain that each data object is totally included in a subspace. Table1 groups various index structures according to the techniques used to handle non-zero sized spatial objects. Each of the above approaches has its own strengths and weaknesses, which directly affect the performance of indexes using it.... In PAGE 20: ... DOT [FaR91] 1991 Figure 1: Evolution of spatial index structures. _ ___________________________________________________________________________ _ __________________________________________________________________________ Object Mapping _ _____________________________ k-d to 2k-d k-d to 1-d Object Duplication/Clipping Object Bounding _ __________________________________________________________________________ _ __________________________________________________________________________ Grid files locational keys Grid files multi-level grid files Twin grid files Z-ordering EXCELL R-files BANG files DOT mkd-trees PLOP-hashing GGF R+-trees skd-trees EXCELL Cell trees GBD-trees K-D-B-trees BANG files R-trees 4d-trees Packed R-trees hB-trees R*-trees LSD-trees Buddy-trees _ __________________________________________________________________________ ___________________________________________________________________________ Table1 : Classification of spatial index structures.... ..."

### Table 2: CPU time per grid point on the Convex during 40 time steps for the mo- mentum equations for the L-shaped driven cavity problem.

1993

"... In PAGE 15: ...slower rate of convergence, although on the vector machine they compete better than on the scalar machine. This is true for all test problems and is illustrated with the momentum equations of the L-shaped driven cavity problem in Table2 . Note that for a low Reynolds number Method 2 to 5 are comparable, but for a high Reynolds number Method 3 and 5 are superior to Method 2 and 4.... ..."

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### Table 8 shows the final outcomes of negotiations involving a Buyer and Seller with asymmetric preferences and value profiles 1, while in Fig. 7 these outcomes are plotted w.r.t the Pareto-optimal frontier. The notation is: 1..3 denotes the number of attributes shared and NG/G denotes whether guessing is used or not. The Pareto frontier in Fig. 7 is the same as in Fig. 6, just scaled between different values. In fact, the outcome reached in Fig. 6 appears as point 1G in Figure 7. The irregular, non- convex shape of the Pareto-efficient frontier (computed according to [20]) is typical for real-life domains, where some attributes take discrete values and only some are continuous.

2001

"... In PAGE 24: ...914 0.90166 Table8 : Distribution of final outcomes for the negotiations between a Buyer with a stronger preference for the attributes Drawing Hook and Airco, and a Seller with a stronger preference for CD player and Extra Speakers. From the above test set we can already make some observations.... ..."

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### Table 8 shows the final outcomes of negotiations involving a Buyer and Seller with asymmetric preferences and value profiles 1, while in Fig. 7 these outcomes are plotted w.r.t the Pareto-optimal frontier. The notation is: 1..3 denotes the number of attributes shared and NG/G denotes whether guessing is used or not. The Pareto frontier in Fig. 7 is the same as in Fig. 6, just scaled between different values. In fact, the outcome reached in Fig. 6 appears as point 1G in Figure 7. The irregular, non- convex shape of the Pareto-efficient frontier (computed according to [26]) is typical for real-life domains, where some attributes take discrete values and only some are continuous.

2001

"... In PAGE 24: ...914 0.90166 Table8 : Distribution of final outcomes for the negotiations between a Buyer with a stronger preference for the attributes Drawing Hook and Airco, and a Seller with a stronger preference for CD player and Extra Speakers. ... ..."

Cited by 20

### Table 4: A syntagmatic analysis of all segments in Keren, in terms of two segmental viewpoints: set of all pitch classes and shape.

"... In PAGE 12: ...Table4 , a representation of the whole piece in terms of segment numbers and their pitch class sets is displayed. 3.... In PAGE 12: ... The one-element patterns [convex] and [concave] were also important, with 10 and 8 occurrences respectively. Table4 shows a classification of all segments according to melodic shape. The longest pattern that was reported was [descending, concave, convex, descending, convex], occurring at segments 4 and 26.... ..."

### Table 2. Relative L2 and energy error norms for the patch test on convex polygonal meshes

in Summary

"... In PAGE 28: ... For the Laplace shape functions, numerical integration is done on the reference element (Figure 18b), whereas for all other interpolation schemes, the physical element (Figure 18a) is used in the numerical integration. The relative L2 and energy error norms for the convex polygonal elements are presented in Table2 . The Laplace interpolant provides the most... ..."

### Table 2 for the exact bounds.

1997

"... In PAGE 9: ... Table2 : Summary of arc shooting results Computing a depth order. Let K be a set of n non-intersecting convex simply- shaped (not necessarily horizontal) at objects in 3-space, and assume that the xy-projections of the objects in K are -fat.... ..."

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