### Table 1: Load and dump times for varying design sizes (in minutes:seconds)

"... In PAGE 16: ... We evaluated the load time and dump time for four di erent designs that represent varying degrees of complexity. These results are presented in Table1 , which shows the designs, the size of the text les that contain their parasitic information, the number of terminals, the number of signals and the corresponding load and dump times. From these results, we observe that the times taken for the transfer of data in either direction are relatively small compared to the overall time taken for all the four stages of interconnect analysis, which is typically in the order of several hours.... ..."

### Table 1: Characteristics of neural network survival analysis methods.

2005

"... In PAGE 11: ... Again, no extension is presented to deal with time-varying inputs. Discussion Table1 presents an overview of the characteristics of the neural network based methods for survival analysis discussed in the previous subsections. From the literature review above, it becomes clear that for large scale data sets, the approaches of Faraggi, Mani and... In PAGE 21: ... 3rd most imp. insurance premium insurance premium frequency paid Table1 0: Predicting default in first 12 months on oversampled data set. gives the results for loan default between 12 and 24 months.... In PAGE 21: ... Note that when comparing Tables 10 and 11 with Tables 8 and 9, it becomes clear that the oversampling allowed to correctly detect a higher proportion of bads as bad. Analogous to the previous subsection, we Actual Logit Cox NN G-predicted G 2015 1753 1744 1757 G-predicted B 0 262 271 258 B-predicted G 0 262 271 258 B-predicted B 394 132 123 136 Table1 1: Predicting default 12-24 months on oversampled data set. can also generate 3D surface plots from the neural network outputs in order to present a general view of the sensitivity of the survival probabilities with respect to the continuous inputs.... ..."

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### Table 19: Mean Execution Times for Varying Production and Sam- pling Intervals for the String PROJFWD Section on Eight Proces- sors (seconds)

"... In PAGE 12: ... This combination ensured that the execution of each parallel section consisted of one sampling phase and one production phase. Table19 presents the execution times for the PROJFWD section running on eight processors for several combinations of target sampling and production intervals. As ex- pected for a section with dramatic efficiency differences between the versions, the performance increases with increases in the tar- get production interval and decreases with increases in the target... ..."

### TABLE 2. Model Time-Varying Inputs (cont.)

2007

"... In PAGE 32: ...TABLE2 . Model Time-Varying Inputs Tax rates Openness Per US Relative Capita Foreign Year Dividends Pro ts ROW US Size US Debt Shares 1960 .... ..."

### Table 4 Time-Varying Market Price of Currency Risk

"... In PAGE 25: ... To the extent that #20 and #20 #03 are time-varying, or that the correlation #1A zz #03 is time-varying, the sign of the currency risk premium may also be time-varying. Table4 allows the market price of currency risk to depend on the level of the exchange rate #28#20 t = #20 0 + #20 1 e t , model B#29, the interest rate di#0Berential #28#20 t = #20 0 + #20 2 #28r t , r t #03 #29, model C#29, or the volatility of the exchange rate #28#20 t = #20 0 + #20 3 v t , model D#29. Each line in the table presents only estimates of #20 0 , #20 1 , and #20 2 , along with the resulting log likelihood of the model.... In PAGE 25: ... However, when we let the market price of currency risk depend on both the level and the volatility of the exchange rate #28model E#29, only the dependence on the volatility remains signi#0Ccant. Plot A of Figure 5 shows a decomposition of the exchange rate drift with a time-varying market price of currency risk #28model D in Table4 #29. The solid line is the interest rate di#0Berential, the dashed line is the currency risk premium, and the dotted line is the interest rate risk premium.... In PAGE 26: ... Studies by Baillie and Bollerslev #281989,1990#29, Bekaert and Hodrick #281993#29, and Domowitz and Hakkio #281985#29, #0Cnd only weak support for the inclusion of the conditional exchange rate volatility in the exchange rate drift. The evidence presented in Table4 and in Figure 5 is much stronger for two reasons. We impose an economic model, which implies a speci#0Cc functional form for the drift, and we observe the instantaneous volatility of the exchange rate, rather than infer it with error from observed changes in the exchange rate.... In PAGE 28: ... 4.3 Implications for Currency Markets With time-varying market price of currency risk #28model D in Table4 #29 and time-varying correlation between innovations to the log exchange rate and innovations to its volatility #28model B in Table 6#29, our estimated model is: dr t =0:240 , 0:034 , r t #01 dt +0:047 p r t dW t ; dr t #03 =1:069 , 0:070 , r t #03 #01 dt +0:093 p r t #03 dW t #03 ; #2842#29 de t = h , r t , r t #03 #01 + #10 , 4:063 , 29:817v t #01 + , , 0:230 #01, , 0:194 #01 p r t #11 v t , 1 2 v t 2 i dt + v t dX t ; dv t =4:073 , 0:102 , v t #01 dt +0:305 p v t dY t ; where Corr 2 6 6 6 6 4 dW t dW t #03 dX t dY t 3 7 7 7 7 5 = 2 6 6 6 6 4 1:000 ,0:205 1:000 ,0:230 0:056 1:000 0:059 ,0:006 #1A xy 1:000 3 7 7 7 7 5 #2843#29 and #1A xy =2 exp #08 1:573 , 3:217e t #09 1 + exp #08 1:573 , 3:217e t #09 , 1: #2844#29 This model has some interesting implications for the currency spot and options markets. 4.... ..."

### Table 1: Algebraic Laws of Time-Varying Relations

1995

"... In PAGE 13: ... In particular, lt; satis es all of the criteria (1) through (6) outlined in section 1 for temporal algebras, and some other criteria of McKenzie and Snodgrass [26]. Table1 brie y summarizes some of the algebraic laws involving pointwise and temporal operators [27]. The distributive laws reinforce the fact that operators like ~ \ are pointwise.... ..."

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### Table A6: Fixed Effects Specifications with Time-Varying Covariates

### Table 1: Time-varying parameters of the model in simula-

"... In PAGE 2: ... 1955, Fant 1960, Kent 1972, Kent et al. 1972n29 suggests that the signin0ccant model pa- rameters are those listed in Table1 , and that their temporal variation may take the form of a sigmoidal function.... ..."