### Table 1: Kinematics and gauges considered in other one-loop studies. None of these results is valid for arbitrary dimension n.

"... In PAGE 3: ... The quark-gluon vertex functions may also serve as a basis for modeling the photon-nucleon vertices [18] and the quark-Reggeon vertex [19]. From Table1 , one can see that, even if we consider the results in (or around) four dimensions, there are still several \white spots quot;. The aim of the present paper is to cover all such remaining spots.... ..."

### Table 3 shows a comparison of layouts using both prescribed and arbitrary dimensions. Table 4 shows several solutions obtained using arbitrary dimensions which are all better in quality than the solution obtained using prescribed dimensions. From this table we can see that designers are given a variety of solutions each different in terms of component dimensions and placement from which to choose from.

"... In PAGE 6: ... The by the dark component eight of the the height of important to awn to scale scale with d packing n used. The ith smaller comparisons ry, designers t areas a 1 13 4 sions) Table3 - Comparison of Fitness Values Optimal Layout (Prescribed Dimensions) Comparing Figures 6 and 7, it is easy to see wh level for the run with arbitrary dimensions is h wasted area in this layout (3330 sq. ft.... In PAGE 6: ... RESULTS Results are shown for two cases: first for prescribed dimensions (dimensions not allowed to change) and second, for arbitrary dimensions (dimensions allowed to change). The packed height, wasted area, and fitness level for the two cases are summarized in Table3 and the optimal layouts are shown in Fig.... ..."

### Table I. Timings using floating-point double precision, fi lib in- terval arithmetic and MPFI arbitrary precision interval arithmetic on Gaussian elimination on a M-matrix of dimension 300.

2002

Cited by 16

### Table 1: Execution time (in seconds) for di erent number of dimensions and quantizations m.

1999

"... In PAGE 15: ... Using hash-based strategy, FindOut can work with high dimensions with arbitrary quantization (See [23]). Table1 shows the timing requirements of FindOut with di erent number of dimensions and quantizations. The size of the testing dataset is... ..."

Cited by 13

### Table 4.8: Performance results, 17368 nodes subregion, exchanging intermediate results with other processors where necessary. The number of messages as well as their length depends on the chosen decomposition strategy. A well suited decomposition strategy can be easily provided by the user based on the multi-dimensional nite element grid. Our implementation supports arbitrary block decomposition in all three dimensions. For each new decomposition some preparatory steps (c.f. Subsection 4.2.4) including recompilation of the application have to be performed prior to the parallel execution.

### Table 1: Summary of results for problems on n moving objects in IRd, d 2. (As is customary, we assume throughout that d is a constant.) Each object moves with constant velocity from t = 0 to t = 1. The velocities are either the same for all objects, or each object has a possibly di erent velocity. The trajectories of the objects may come from c di erent directions, two (or d) orthogonal directions, or may be arbitrary. All bounds are \big-oh quot; and worst-case. (The constant factors depend on the dimension d.) The problems that are indicated are collision detection, computing the closest distance, and minimum L2-diameter over all times t 0. The line segments have arbitrary directions, but each one moves along its supporting line.

1996

"... In PAGE 2: ... The challenge is to do signi cantly better, which makes the solutions interesting and non-trivial. Table1 summarizes our results. We note that throughout the paper, the dimension d is assumed to be a constant.... ..."

Cited by 18

### Table 1. A summary of the results in this paper. The parameter quot; gt;1isany arbitrary

2002

"... In PAGE 6: ... 2.3 Synopsis of Results All our results are summarized in Table1 , except the result that GTile problem is NP-hard in two or more dimensions. Most of our algorithms use simple data structures (such as a double-ended queue) and are easy to implement.... ..."

Cited by 3

### Table 3: IQ scores sij of the 7 QCAs. Scores are partly inferred from the informal description in Section 1.2. Completeness and amount are not contained since they depend on the speci c user query. source selection see [NFS98]. The DEA method avoids the problems of scaling and weighting by de ning an e ciency frontier as the convex hull of the unscaled and unweighted vector space of IQ dimensions. Fig- ure 4 shows this vector space for two arbitrary IQ di- mensions. Sources on the hull are de ned as \good quot;, sources below the hull as \non-good quot;.

1999

"... In PAGE 6: ... To describe each step in detail we use the example of Section 2. Table3 gives IQ scores for each QCA and each criterion. We are aware of the di - culties of numerically expressing certain criteria, but since not the absolute IQ scores are of importance but rather their relative values, we believe that our ap- proach is reasonable.... ..."

Cited by 71

### Table 3: IQ scores sij of the 7 QCAs. Scores are partly inferred from the informal description in Section 1.2. Completeness and amount are not contained since they depend on the speci c user query. source selection see [NFS98]. The DEA method avoids the problems of scaling and weighting by de ning an e ciency frontier as the convex hull of the unscaled and unweighted vector space of IQ dimensions. Fig- ure 4 shows this vector space for two arbitrary IQ di- mensions. Sources on the hull are de ned as \good quot;, sources below the hull as \non-good quot;.

1999

"... In PAGE 6: ... To describe each step in detail we use the example of Section 2. Table3 gives IQ scores for each QCA and each criterion. We are aware of the di - culties of numerically expressing certain criteria, but since not the absolute IQ scores are of importance but rather their relative values, we believe that our ap- proach is reasonable.... ..."

Cited by 71

### Table 3: IQ scores sij of the 7 QCAs. Scores are partly inferred from the informal description in Section 1.2. Completeness and amount are not contained since they depend on the speci c user query. source selection see [NFS98]. The DEA method avoids the problems of scaling and weighting by de ning an e ciency frontier as the convex hull of the unscaled and unweighted vector space of IQ dimensions. Fig- ure 4 shows this vector space for two arbitrary IQ di- mensions. Sources on the hull are de ned as \good quot;, sources below the hull as \non-good quot;.

1999

"... In PAGE 6: ... To describe each step in detail we use the example of Section 2. Table3 gives IQ scores for each QCA and each criterion. We are aware of the di - culties of numerically expressing certain criteria, but since not the absolute IQ scores are of importance but rather their relative values, we believe that our ap- proach is reasonable.... ..."

Cited by 71