### Table 2 This table shows the number of iterations, the total time T , the preprocessing time Tpre,and the time to select the independent set Tis as a function of the number p of processors and the target size k of the independent sets.

1999

"... In PAGE 9: ... 5 Summary of Experimental Results We now summarize our experimental results. The results reported in Table2 are for runs with no limit on the number of nodes touched and no limit on the degrees of nodes included in the independent sets. These parameters maximize parallelism at the possible expense of the quality of the ordering.... ..."

Cited by 5

### Table 2 This table shows the number of iterations, the total time T, the preprocessing time Tpre, and the time to select the independent set Tis as a function of the number p of processors and the target size k of the independent sets.

"... In PAGE 9: ... 5 Summary of Experimental Results We now summarize our experimental results. The results reported in Table2 are for runs with no limit on the number of nodes touched and no limit on the degrees of nodes included in the independent sets. These parameters maximize parallelism at the possible expense of the quality of the ordering.... ..."

### Table 3.4: This table shows the number of iterations, the total time T , the preprocessing time Tpre, and the time to select the independent set Tis as a function of the number p of processors and the target size k of the independent sets.

2001

### Table 1: Summary of Notation entry events in [t; t + A), the joint distribution of their times in [t; t + A) is identical to that of k independent [t; t + A] uniform random variables (Ross 1983, pg. 37). Choosing one of these k uniformly at random, the probability that its arrival message lies outside of [t; t + A) is given by PrfArrival message time for service entry event gt; t + A j S = kg = Z t+A

"... In PAGE 8: ...ess than A. Let ai;k denote the mean fraction of generation i messages that have rank k. Letting ~ fk(s) be the density function for an Erlang-k conditioned on being less than A, we approximate the arrival time density function of an arbitrary generation i message as the mixture t + P1 k=2 ai;k ~ fk(s). Table1 summarizes our notation. All random quantities are LP-oriented, rather than system-oriented.... ..."

### Table 1: Polywheels and graph homology

"... In PAGE 13: ... For k = 1, 2, and 3 this gives us k independent equations in k unknowns (the Chern numbers) which we can invert. Then according to the relations in Table1 , all the Rozansky-Witten invariants may be determined from this information. When k = 4 we get four independent equations in ve unknowns, and hence we cannot determine all of the Chern numbers, let alone the Rozansky-Witten invariants, from what we know thus far.... In PAGE 13: ... For = , c is a linear expression in k (as proved in [12]), and using our calculations for k = 1, 2, and 3 we can determine this expression precisely. Substituting into Equation (5) gives us the following results b k(S[k]) = 12k(k + 3)k (9) b k(Kk) = 12k(k + 1)k+1: (10) From Table1 we can see that b 4 is a characteristic number. Therefore when k = 4 we get a fth equation for the Chern numbers which we can combine with the four independent equations we already have, and this system can then be solved to give all of the Chern numbers.... ..."

### Table 2: Technology Mapping results

"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."

### Table 4: Review of classes of graphs optimally solvable in polynomial time. (n denotes the number of nodes in the graph, m the number of edges and d its maximal degree)

2002

"... In PAGE 6: ... Recall that NP-completeness results do not rule out the existence of efficient algorithms to get optimal layouts on particular classes of graphs. Table4 summarizes... ..."

Cited by 11

### Table 2. Optimal Newman polynomials.

Cited by 1

### Table 2. Speedup in Worst-Case Execution Time for Optimized Virtual Table Algorithm

"... In PAGE 5: ... However, for the OVTA, the optimiza- tion over VTA depends completely on the characteristics of the generator polynomial chosen. Table2 shows the improvement over the VTA for several different polyno- mials (refer to Section 4 for a description of CRC32sub8 and CRC32sub16) . Note that for the particular CRC24 and CRC32 polynomials we used for our experiments, the OVTA has no improvement at all over the VTA.... ..."

### Table 2. Number of land cover types retained at each level of aggregation for seven landscapes.

"... In PAGE 5: ... These re- sults (Table 1) apply only if k is independently and randomly distributed on the landscape. Although this condition will seldom be satisfied exactly, the general pattern holds for a variety of actual land- scapes ( Table2 ). At increasingly coarser scales of resolution, cover types with a small P,(l) dis- appear.... In PAGE 7: ... The dominance and contagion indices both de- pend on a maximum value, which is determined by m, the number of land cover types present. The number of land cover types observed decreases as resolution becomes increasingly more coarse and rare cover types disappear ( Table2 ). Indices D and C are sensitive to loss of cover types, and break points occur when the indices are plotted against grain (Fig.... ..."