### Table 3: Complexity of Processes This table contains data on the complexity of the 12 pos- sible combinations of concepts with technologies. The least complex case is the simplest concept (mini) com- bined with the most convenient technology (tool / improved tool). The most complex case, in turn, is the most elaborated concept (maxi) combined with the least sophisticated technology (no tool).

"... In PAGE 10: ... In-line views of these processes have between 50 activities at a maximum depth of nesting of 7 for mini and improved tool, and 655 activities at a maximum depth of nesting of 11 for maxi and no tool. Table3 contains the complexity statistics for all combinations of concepts and tech-... ..."

### Table 4 : Degrees of convenience

"... In PAGE 6: ... One is office text data files and the other is a constantly growing image file with few updates. Table4 shows the degrees of suitability of dynamic signauture file methods according to the criteria of storage structures. The SSF is a simple file of fixed- length sigiiatures 91.... In PAGE 6: ... It is called single-level signature file or signature f! le method with bit-string represen- tation. Table4 is based on the results of performance analysis of dynamic signature file methods based on analytic cost models and experiments. For this ap- plication such as the office text file, HS file is much better than any other dynamic signature file method.... ..."

### Table 17 for convenience.

"... In PAGE 18: ... Table17 : Summary of the results of the system on the presented sets of conjectures. The system that we have developed performs well on different sets of conjectures, particu- larly if the cost of the proofs of the conjectures is measured in the number of clauses processed by the prover.... ..."

### TABLE I. Number of triangles in a convenient set of lattice-commensurate triangles with longest side equal to l units. The cumulative total for all cases with longest side less than or equal to l determines the required storage capacity Tl. Note that, for a single image with 512 512 pixels, triangle size has been restricted in the text to l 64 to avoid storing more values of S3 than the total number of pixels in the original image.

1988

Cited by 2

### Table 1: Benchmarks Used and Compression Achieved with Popular Tools The column raw is the sum of the sizes of all the class files, as they are distributed. The column jar is the size of a Java archivea that contains only these classes; they are individually compressed. For the column jar0.gz, the classes are grouped uncompressed in a Java archive that is then compressed with gzip. The fifth and sixth columns show the compression achieved in these two cases as a percentage of the original size. a. The jar files were produced with zip, for speed and convenience.

### Table 5. Formula n 2;6, n even, for nding W(2; 6). Classes are named C1; C2 for convenience.

2005

"... In PAGE 5: ...1 Formulation Since we consider a case where k = 2, we reinterpret variables to remove some of the constraints shown in Table 3. The formulas we consider are described in Table5 . They use single index variables.... In PAGE 8: ... This is why we needed the tunnels in the rst place. With the above modi cations to the SAT solver, and the improved formu- lation shown in Table5 , a greatly improved bound for W(2; 6) was obtained. However, this was not the case if no tunnels had been added at the outset.... In PAGE 9: ... The rst family of tunneling constraints is shown in Table 6. By using nega- tive indices in Table5 these constraints remain xed as n grows. Otherwise, the tunnel would have to move with n.... ..."

Cited by 2

### Table 11. Requirements-based Test Cases for Example 1

"... In PAGE 31: ... This provides a view of the test cases and the source code in a convenient format. Inputs and expected observable outputs for the requirements-based test cases for example 1 are given in Table11 . Table 11.... ..."

### Table 4: Degree of convenience of the rules.

1999

Cited by 4

### Table 1: The lter output values (scaled into the interval [?128; +128]) for the rst salient points of the object in gure 2, the values of the histogram of the object h(MkjOn), the average histogram h(MkjAverage) and the calculated value p?(OnjMk) In many cases we obtain cluster of salient points (as e.g. in gure 2) so that it seems to be convenient to de ne a network of salient regions rather than a network of salient points. The concept for xation control described below can be easily extended to such a network of salient regions.

1996

Cited by 3

### Table 3. Formula n k;l for nding Van der Waerden numbers. Equivalence classes are named C1; C2; :::; Ck for convenience.

2005

"... In PAGE 4: ... Known bounds on van der Waerden numbers. The number W(k; l) can be found by determining whether solutions exist for certain formulas of a class of CNF formulas described in Table3 . We refer to a formula of this class, with parameters n; k; l, by n k;l.... In PAGE 5: ... 3 Formulation for W (2; 6) and the tunnels 3.1 Formulation Since we consider a case where k = 2, we reinterpret variables to remove some of the constraints shown in Table3 . The formulas we consider are described in Table 5.... ..."

Cited by 2