### Table 1: Average Measurements over Several Random Populations Measurement Comm/Learning N/N Y/N Y/Y 6.31 11.63 59.65 _ 104

1991

"... In PAGE 22: ... We have observed quantitatively similar results in many experi- ments. Table1 (adapted from an earlier report[21]) shows average measurements from several experiments that di er only in initial pop- ulation.To better understand the asymptotic behavior of the evolutionary process, we have run several simulations for ten times as long as those previously described.... In PAGE 29: ....2.3 Other Observations In the course of these experiments we have made several observations that provide some insight into the evolution of communication. All of our experiments in which communication (and learning) is suppressed show a slight upward trend in tness (see Figure 2 and Table1 ). This is surprising, since in the absence of communication it would seem that there is no way to improve on guessing.... ..."

Cited by 99

### TABLE 1: Comparison of Exact and DSD-Determined Parameters for a Synthesized Time Signal Consisting of a Sum of Exponentially Decaying Oscillations, with Known Frequencies, Amplitudes, Phases, and Damping Constantsa

### TABLE I RANDOM PROCESS NOTATIONS

### Table 9: Testing for a single change-point in high-frequency volatility filters in the YN/US$ during the period 1986-1996 Change-point Statistics

"... In PAGE 19: ... In an approach to verify that there was indeed a structural change in the asset returns processes we examine the volatility character- istics of the series in alternative subsamples - prior and after the breaks. The results in Table9 report the estimated MLE parameters from a Normal GARCH(1,1). The varying estimated coefficients of volatility persistence and unconditional variance over the subsamples can be considered as further supportive empirical evidence that complements the change-point tests.... ..."

### Table 1: Random Walks and Diffusion Processes Case Operator Stochastic Process

2005

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### TABLE VI QUERY PROCESSING WITH RANDOM WALKS

2006

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### Table 1: Linear Correlation Between yn and n

1998

"... In PAGE 11: ... Having done so, we computed the least squares t yn(E) log NE (E)n + (E) for n = 1; 2; : : : 15, for each curve E, and then for the curves in each subset we computed the maximum, minimum, and average of and . The results are listed in Table1 , together with the number of curves in each subset and the correlation coe cient which measures how well yn(E) log NE is linearly approximated by n.... In PAGE 12: ...or some small gt; 0. Then a calculation similar to the proof of Proposition 5.1 shows that log N ? 2 1 X n=1 2 n (1 + ) log N = 1 ? log N : Of course, in practice the estimate (5) would only hold for small values of n, but this computation shows that the net e ect will be to decrease the contribution of the R1(Et; ) apos;s to the average rank. On the other hand, the fact that the apos;s in Table1 are positive means that at least the rst few yn apos;s are larger than the theoretical value of 2 n= log N. This will have the e ect of increasing the value of the R1(Et; ) apos;s, thereby making the average rank somewhat larger.... ..."

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### Table 2: Results on unsolved problems YN and SWV

"... In PAGE 7: ... The experiments with a stopping criterion according to (4) provided good approximations of the (previous) upper bounds within a relatively short run-time. Table2 shows our approximation within about 1% for the YN problems and 5% for the SWV benchmark problems, respectively. In the table, LB denotes the lower and UB the upper bounds taken from [6, 8, 20].... In PAGE 12: ... The algorithm returns simply to that temperature where the best solution so far has been found. The tempering strategy was used in runs that have been denoted in Table2 by the index t !1.... ..."

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### Table 4. Results on still unsolved problems YN and SWV

"... In PAGE 13: ... We have chosen the second cooling schedule to attack the larger 20 20 benchmark problems YN1 till YN4 and the 50 10 problems SWV11, SWV12, SWV13, and SWV15. Table4 displays that we improved and approached the known upper bounds (the values from the OR-Library, (? The results reported are the best ones obtained after 5 runs of the algorithm and the corresponding computation time is the average time over these ve runs.... ..."

### Table 7: Normality Test Results for Daily YN/US$ Standardized Returns based on various intraday sampling frequencies YN/US$

"... In PAGE 23: ... The nonlinear and linear dependence results for spot volatilities and XQV 1,k,lscript, especially for the DM/US$, provide evidence of weak and strong temporal dependence. The distributional properties of normalized returns are assessed in Table7 for the YN/US$... ..."