### Table 1. Platelet inhibition at various time intervals for individuals taking the soluble, chewed, and whole aspirin formulations.

"... In PAGE 4: ... Repeated measures analysis of variance was used to compare the salicylic acid concentrations required to inhibit platelet aggregation for the three aspirin formulations. Results Table1 shows the inhibition of platelet aggregation at various time intervals for individuals taking the soluble, chewed, and whole aspirin formulations. Four individuals (16.... ..."

### Table 2: Optimal stationary strategy. computation method is chosen due to the small size of the model. E[Yi(1)] is used as a criterion of optimization. The resulting strategy is summarized in Tab. 2. It applies for the whole interval of considered service rates. If one compares the stationary strategies in Tab. 2 with the transient strategies in Fig. 4(left), it can be seen how they relate to each other. In the long run

1997

Cited by 4

### Table 2: Optimal stationary strategy. computation method is chosen due to the small size of the model. E[Yi(1)] is used as a criterion of optimization. The resulting strategy is summarized in Tab. 2. It applies for the whole interval of considered service rates. If one compares the stationary strategies in Tab. 2 with the transient strategies in Fig. 4(left), it can be seen how they relate to each other. In the long run

### Table 3. Relative improvements of Past-Days, Fixed-Episode- Interval and Adaptive-Episode-Interval over Whole-History Category Level Level 1 Level 2 Level 3 Level 4

2002

"... In PAGE 5: ... The four methods are compared using relative improvement as defined by equation 2, using Whole-History as the baseline method. Table3 shows the results. Table 3.... ..."

Cited by 7

### Tables 2 through 5 display the error for u for the case = . Table 2 shows the L1-norm of the error for the whole time interval. Table 3 shows the L1-norm of the error for the transient stage time interval. Table 4 does the same for the stable stage time interval. Table 5 shows the error of the last value.

### TABLE I The minimum, maximum and mean irradiance values for the SMM/ACRIM I and Nim- bus-7/ERB irradiance values are listed together with the mean standard deviation and number of data points for the whole observing time intervals as well as for the quiet- and active-Sun periods.

### TABLE IV THE CLASSIFICATIONS OF HOSTNAMES BASED ON REVERSE-DNS LOOKUPS OF THE IP ADDRESSES OF CODE-RED INFECTED HOSTS BETWEEN AUGUST 1 AND AUGUST 8, 2001. SHOWN HERE ARE THE AVERAGE NUMBER OF ACTIVE HOSTS IN EACH TWO HOUR INTERVAL AND THE OVERALL PERCENTAGE OF EACH TYPE OF HOST ACROSS THE WHOLE SEVEN DAY INTERVAL. UNKNOWN HOSTS HAD NO REVERSE DNS RECORDS.

2002

Cited by 164

### Table 2: The classifications of hostnames based on reverse-DNS lookups of the IP addresses of Code-Red infected hosts. Shown here are the average number of active hosts in each two hour interval and the overall percentage of each type of host across the whole seven day interval. Unknown hosts had no reverse DNS records.

"... In PAGE 9: ... Computers without reverse DNS records are less likely to be running major services (such as those demonstrated in the other host types). Broadband and dial-up services represented the vast majority of identifiable hosts, as shown in Table2 . Further- more, we measured large diurnal variations in the number of infected hosts suggest that these machines are unlikely to be running production web servers of any kind, a surprising result given that the worm attacks a vulnerability in web servers.... ..."

### Table 1. Options for AlgebraicIntervalSolve. AlgebraicIntervalSolve uses the precision speci ed by the option WorkingPrecision in the computational process. For the purpose of debugging one can ask AlgebraicInterval Solve to give the whole list of approximations obtained at each step of the iterative process by using the option IterativeList. Table 1 gives the options for AlgebraicIntervalSolve and their default values.

1996

Cited by 2

### Table 1. Frequencies of distinct words truncated at different levels and their Zipf constants for both whole corpus and interval between 0.1% and10% of ranks. Note that words are partially sorted with respect to their frequencies in descending order and then, rank number is assigned in ascending order for each row starting from top. Finally Zipf constant is computed by averaging A(r)=p(r)*r values of each row, where r and p(r) indicate rank and probability of that rank, respectively.

"... In PAGE 3: ... 4. Discussion In Table1 , frequencies of truncated terms and their Zipf constants are given. We see that in regard to Zipf constant value the truncated terms around average word length can be treated equally because maximum change factor of Zipf constant in that group is about 18%, i.... ..."