### Table 1. Comparison of results between grids with and without diagonals. New results

1994

"... In PAGE 2: ... For two-dimensional n n meshes without diagonals 1-1 problems have been studied for more than twenty years. The so far fastest solutions for 1-1 problems and for h-h problems with small h 9 are summarized in Table1 . In that table we also present our new results on grids with diagonals and compare them with those for grids without diagonals.... ..."

Cited by 11

### Table 1{Performance bounds for zero propagation delay algorithms Class of Scheduling Range of Property P3 Property P2 Property P1 Algorithms Throughput k N k

1997

"... In PAGE 13: ...3 For gt; 12, S 6, and n 3, no scheduling algorithm in the class CONTIN- UOUS STATIC has any property P1{P4. Table1 summarizes the throughput and delay characteristics of the scheduling algorithms pre- sented in this and the previous section. The last three columns list the upper bounds for k N k,... ..."

Cited by 45

### 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 l Parameter values fitted by the optimization algorithms for the test problem*

1976

### Table 4.19: Comparison of the Pnc 2 with the Rise algorithm

### Table 7. Estimate Breaks in Variance by the ICSS, and Apply to the Absolute Stock Returns

1999

"... In PAGE 21: ... To avoid this problem, we use the ICSS method to identify breaks in variances of stock returns by using the model (19) - (21). Table7 reports the number of sudden changes in variance as identified by the ICSS algorithm for stock returns. Periods 3 and 7 have 17 break points and period 9 has only 4 change points and so on.... In PAGE 21: ... The significant changes in variance are a little bit more than those in level of absolute returns. The 3rd panel of Table7 shows the results of fitting breaks that correspond to the points of breaks in variance to the level of absolute stock returns. When breaks in variance are introduced, the evidence is somewhat mixed.... ..."

Cited by 19

### Table 8. Estimate Breaks in Variance by the ICSS, and Estimate d and LM statistics of the Squared Stock Return, Break Process and Squared Residuals

1999

"... In PAGE 22: ...negative estimates of d in the residuals are obtained, so there is some possibility of overdifference as pointed out in section 5. As an additional analysis, we also examined long memory in the squared stock returns in Table8 . As occasional breaks are incorporated directly into return series, the existence of long memory in volatility is mixed, too.... ..."

Cited by 19

### Table 1. Execution time, number of visited states and improvement wrt initial state

"... In PAGE 8: ... 4.3 Experimental results In order to validate our method, we implemented the proposed algorithms in C++ and experimented on the variation of measures like time (we present it in Table1 as the volume of visited states), volume of processed rows, improvement and quality of the proposed workflow. We have used a simple cost model taking into consideration only the number of processed rows based on simple formulae [17] and assigned selectivities for the involved activities.... In PAGE 9: ...consequently, for medium and large cases we compare (quality of solution) the best solution of HS and HS-Greedy to the best solution that ES has produced when it stopped (Table 2 and Figure 9). Table1 depicts the number of visited states for each algorithm, and the percentage of improvement for each algorithm compared with the cost of the initial state. We note that for small workflows, HS provides the optimal solution according to ES.... ..."

### Table 1. Execution time, number of visited states and improvement wrt initial state

"... In PAGE 8: ... 4.3 Experimental results In order to validate our method, we implemented the proposed algorithms in C++ and experimented on the variation of measures like time (we present it in Table1 as the volume of visited states), volume of processed rows, improvement and quality of the proposed workflow. We have used a simple cost model taking into consideration only the number of processed rows based on simple formulae [17] and assigned selectivities for the involved activities.... In PAGE 9: ...consequently, for medium and large cases we compare (quality of solution) the best solution of HS and HS-Greedy to the best solution that ES has produced when it stopped (Table 2 and Figure 9). Table1 depicts the number of visited states for each algorithm, and the percentage of improvement for each algorithm compared with the cost of the initial state. We note that for small workflows, HS provides the optimal solution according to ES.... ..."