### Table 1 Comparison of core/periphery fitness measures using Beck et al. (2003; ND) data

2004

"... In PAGE 5: ....P Boyd, W.J. Fitzgerald, R.J. Beck/Social Networks columns 4 and 5 of Table1 . Column 6 of Table 1 compares the results from the UCINET (Version 6.... In PAGE 5: ... For all 12 groups, all three of these algorithms matched the exhaustive search by consistently finding the global optimum from several starting configurations. [ Table1 about here] From the results in Table 1, the genetic algorithm in UCINET finds the global optimum in two out of our 12 cases. The UCINET fit statistic is among the five best for seven of the 12 cases, and among the ten best for nine of the 12 cases.... In PAGE 5: ... For all 12 groups, all three of these algorithms matched the exhaustive search by consistently finding the global optimum from several starting configurations. [Table 1 about here] From the results in Table1 , the genetic algorithm in UCINET finds the global optimum in two out of our 12 cases. The UCINET fit statistic is among the five best for seven of the 12 cases, and among the ten best for nine of the 12 cases.... In PAGE 7: ... A low probability along with an intuitively high observed fitness value suggests that the observed data may have a core/periphery structure. To illustrate this permutation test, we used Mathematica to program a random permutation generator based upon the observed within group distribution of messages for each of the 12 groups from Table1 . As with the observed data, diagonal cells were also ignored for these permutations.... In PAGE 7: ... For Group 1, for example, no random permutation in each of the 3 runs produced an optimal fitness value equal to or greater than the observed fitness value of 0.867 (see Table1 ). For Group 3, 43 of the random permutations in the first run produced optimal fitness values equal to or greater than the observed fitness value (0.... ..."

Cited by 1

### Table 2. Results of minimizing (1) with respect to n, r and k with 40 initial points.

### Table 1. Dependence of the average number of loops in the self-avoiding polymer on the number of monomers, and on the average number of branches. Data is from the cubic lattice, and the branching probability is fixed to be 0.1.

1979

### Table 3. Per-condition Coefficients of Correlation Between Subjective Scores and Objective Estimators

1998

"... In PAGE 23: ... The resulting coefficients of correlation are shown in Table 3. The correlation values in Table3 were calculated after averaging all available subjective scores for each condition to a single score for that condition. Similarly, for each condition, all available objective... In PAGE 27: ... A more advanced analysis technique, described in [37], recognizes the importance of the distributions of the objective estimates and the subjective scores for each condition, how they influence confidence intervals, and in turn, the final conclusions that one draws from objective and subjective tests. Table3 demonstrates the limitations of SNR, SNRseg, and PWSNRseg as estimators of perceived speech quality. CD and BSD tend to show higher correlations for tests 5, 6, and 7, which contain only conditions that tend to preserve waveforms.... In PAGE 27: ... Table 4 shows per-condition correlation values for L(AD) as calculated by the two MNB structures. Since L(ND) is used as a reference, that column from Table3 is repeated as column 2 of Table 4 to allow for easy comparisons. Two versions of the estimators were evaluated.... ..."

### Table 4. Test processes.

2005

"... In PAGE 8: ... In further research, we intend to implement a multi-resource feedback algorithm, measur- ing and taking these factors into consideration when choos- ing optimal frequencies. Table4 summarizes the results of the test cases. In the majority of cases, there is no difference between the execu-... ..."

Cited by 2

### Table 1 summarizes the 68 test cases; detailed results have been omitted for brevity.

2005

"... In PAGE 4: ... Table1... ..."

Cited by 7

### Table 1 summarizes the 68 test cases; detailed results have been omitted for brevity.

"... In PAGE 4: ... Table1... ..."

### Table 1: A Template for Incremental AC Algorithms. 1: Input: Randomized parameterized policy ( j ; ), Value function feature vector f(s). 2: Initialization: Policy parameters = 0, Value function weight vector v = v0, Step sizes = 0; = 0; = c 0.

"... In PAGE 5: ... 4 Actor-Critic Algorithms We present four new AC algorithms in this section. These algorithms are in the general form shown in Table1 . They update the policy parameters along the direction of the average reward gradient.... ..."

### Table 1: A Template for Incremental AC Algorithms. 1: Input: Randomized parameterized policy ( j ; ), Value function feature vector f(s). 2: Initialization: Policy parameters = 0, Value function weight vector v = v0, Step sizes = 0; = 0; = c 0.

"... In PAGE 5: ... 4 Actor-Critic Algorithms We present four new AC algorithms in this section. These algorithms are in the general form shown in Table1 . They update the policy parameters along the direction of the average reward gradient.... ..."