### Table 1. Environment Rules: Initial amp; Final Rule Sets.

"... In PAGE 3: ... Service Predictor and Alert System Modules of the Environment Agent. As explained above, details of user behaviour in the environment are logged; this information is processed by the AprioriAll algorithm in order to create a set of sequence rules, which re ect patterns in service invocation ( Table1 ). Each rule has an associated support and a con dence level dependent upon the number of examples in the log data supporting that rule.... In PAGE 4: ... To recommend services ef ciently RECO needs train- ing information which requires the system to be utilised for several weeks by several users. In the example presented here we have there- fore assumed that log data has been analyzed to generate the initial rule sets shown in Table1 and Figure 7. These rules form the basis for an initial environment service model.... In PAGE 5: ...the path to the Evening service, the system learns the change of be- haviour and starts to recommend that service. Table1 shows that the new rule ([restaurants]- gt;[evenings]) is also added into the environment model; however the support and con dence of the rule are initially not high enough to be considered for a new user. New User Sup.... ..."

### 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