### Table 4. Effective capacitance with and without regularity exploitation and chaining.

1995

Cited by 7

### Table 7.11 shows the mean naming times for the different conditions. The standard frequency by regularity interaction was only partially observed in the model. The effect is twofold: the first is that frequency only affects the naming speed of exception items, not regulars. This effect was obtained. However, another aspect of the standard effect is that both low and high frequency regulars, and high frequency exceptions all have very similar naming times; the low frequency exceptions are the different items. This effect was not seen in the model, which shows exaggerated effects of exceptionhood. I will return to the importance of the extended naming times for the low frequency exceptions in the discussion of the imageability effect.

1998

### TABLE 1 Effectivity indices in Examples 6.1 and 6.2 computed by the equilibrated residual method, by the method of hypercircle and by the combined method on regular triangulations with Ntri triangles

2004

### Table 3: Table showing number of effective iterations required to optimize a sequence of values for C for the parsing task, using the method described in Section 7.1.2. The column C shows the sequence of decreasing regularizer constants. Iterations shows the number of effective iterations over the training set required to optimize each value of C. Total iterations shows the cumulative value of Iterations, and Accuracy shows the validation accuracy obtained for every C value. It can be seen that the optimal accuracy is reached at C = 13.841.

"... In PAGE 33: ...tants, as in Section 7.1.2. Table3 shows results for a sequence of regularization constants. 8.... ..."

### Table 2: Table showing number of effective iterations required to optimize a sequence of values for C for the MNIST task, using the method described in Section 7.1.2. The column C shows the sequence of decreasing regularizer constants. Iterations shows the number of effective iterations over the training set required to optimized each value of C. Total iterations shows the cumulative value of Iterations, and Error shows the validation error obtained for every C value. It can be seen that the optimal error is reached at C = 1.62841.

### Table 1. Comparison of Regular and Microiteration Optimizations.a

"... In PAGE 6: ... The room temperature isolation of singlet carbenes is an important milestone in the study of these important reactive intermediates.53 This has been achieved through a combination of electronic and steric effects, as illustrated in the final example in Table1 . Again, microiterations improve the performance of the optimizations.... ..."

### Table I. A comparison of spreads on a swap with a BB credit trigger and regular swap spreads as studied above. The credit trigger has a significant effect in the case of a constant gener- ator, little effect when using the affine and logit generator due to the calibration shifting probability mass from downgrade transitions to direct default transitions.

1998

Cited by 5

### TABLE 2 The Mean Daily Return, Standard Deviation, Number of Trading Days, and Number of Significant Box-Ljung Statistics By Month From the Period 1885 Through 1962 (Total Number of Observations = 22,474)

### Table 3 Odds ratios (OR) for adolescent and young adult alcohol use in relation to number of regular drinkers they were exposed to OR 95% CI n

"... In PAGE 9: ... We used this index to compute the odds ratios for regular drinking in relation the number of regular drinkers the participants were exposed to. Table3 shows that participants were of higher risk for regular drinking if they were exposed to more regular drinkers. Results did not show significant interaction effects with sex and age (odds ratios ranging from .... ..."

### Table 1. Datapath extraction results were already explicitly preplaced regularly, be pulled into the reg- ularly placed area because of the wire-length reduction performed by placement tool. Finally, in the simplified layout model we used, subsequent placement is assumedto be row-based with only one single row of standard cells per bit slice. Clearly, in case of a very large number of datapath stages compared to the number of slices, the aspect ra- tio of the datapath matrix may become unfavorable with respect to the global floorplan of the chip. Allowing 2 or 3 rows per slice can greatly alleviate this effect while hardly affecting the advantages of regular placement generation. Alternatively, the datapath can be folded.

1996

"... In PAGE 6: ... Lacking a general way to quantify the success of regularity ex- traction of circuits that are not completelyregular, we usedindirect metrics indicating the usefulnessof the results, namelythe percent- age of regularcircuitry. Table1 presents some results of the extrac- tion algorithm on a number of examples. These times were mea- sured on a HP9000/735 workstation.... ..."

Cited by 6