### Table 13 Example of Random Variables for the Full-Depth AC Replacement, Double- Lane, Layer Profile B, Stochastic Analysis

2001

"... In PAGE 13: ...ersus Double-Lane Rehabilitation....................................................................................... 67 Table13 Example of Random Variables for the Full-Depth AC Replacement, Double-Lane, Layer Profile B, Stochastic Analysis.... In PAGE 82: ....2.2 Stochastic Analysis For the Full-Depth AC Replacement strategy, a stochastic analysis was completed and the results were compared with the results of the deterministic analysis. Table13 shows an example of the random variables used for the Full-Depth Double-Lane Layer Profile B case and their corresponding distribution types and probability distribution functions (PDF). Similar to the stochastic analysis for the CSOL case, the distribution types were realistically assumed using reference information from AC field data and the concrete case study with the I-10 project.... ..."

### TABLE 4 More Genetic Random Variable Definitions

"... In PAGE 8: ...8 In TABLE4 we add new random variables to those already introduced in TABLE 3: 3.2 Revised Selective Mating Hypothesis Let , for .... In PAGE 9: ...3 A Model with Reduced Penetrance, Depending on Y Genetic models with reduced penetrance related to the Y chromosome FIGURE 2 Tree diagram showing genotypes, phenotypes, and breeding acceptability (A) for males 3.3 Additive Genetic and Environmental Factors for the Full Model In the notation of TABLE 3 and TABLE4 , the PAP score of the sire can be written: , where the residual is as in (1), and . This can be abbreviated to .... ..."

### Table 1: Error in determining correspondence as a function of additive noise. Noise is

1992

"... In PAGE 22: ... In practice the number of iterations applied for both algorithms is generally a function of sensor noise 5 . Results are shown in Table1 for 4 di#0Berent additive noise levels. Random variables are sampled from a standardized normal distribution, N#280; 1#29, and scaled by 0#25, 1.... ..."

Cited by 9

### Table 1: Error in determining correspondence as a function of additive noise. Noise is expressed as a percentage of the maximum range value (50 pixels). The error indicated is RMS, units = pixels.

1992

"... In PAGE 22: ... In practice the number of iterations applied for both algorithms is generally a function of sensor noise5. Results are shown in Table1 for 4 di erent additive noise levels. Random variables are sampled from a standardized normal distribution, N(0; 1), and scaled by 0%, 1.... ..."

Cited by 9

### Table 7: Robustness of Estimation Results of Instrumental Variable Random Effects Tobit

2005

"... In PAGE 28: ... Both parametric and semiparametric results did not change by much. Table7 demonstrates that estimated coefficients and elasticities are fairly robust to inclusion of additional covariates. The results change little after including a quartic in age, year effects and state fixed effects to the base specification consisting of Net wage, virtual full income, young children and health.... ..."

### Table 8.1]. In fading channels, the instantaneous SNR Eb=N0 will be replaced by = , where := Eb=N0 now denotes average bit SNR, and is a random variable with unit mean. Supposing that the PDF of satisfies the conditions of Propositions 1 and 3, we can apply the same technique used in the proof of Proposition 3, with some additional properties of Laplace transforms, to obtain the following expression for the high- SNR average BER (derivation details are omitted due to lack of space):

2003

Cited by 16

### Table 1: (Top) The model. N(0; 2) denotes a random variable with a normal distribution that has no bias (mean=0) and a standard deviation of . (Bottom) The variables. The given default values are derived from real data (see main text), with the exception of g which is oriented to what can realistically be achieved (see results). The magnitude of the additive measurement error depends on several factors, including the technology platform, and a ects the results only indirectly through the signal to noise ratio.

"... In PAGE 3: ... Our distribution overestimates the within-class variability, since it is augmented by the dif- ferential expression between the classes. While the model of technical errors ( Table1 , Equation 1) seems to be a reasonably good approximation to reality, the biological assumption (Table 1, Equation 2) has to be taken with caution.... In PAGE 3: ... Our distribution overestimates the within-class variability, since it is augmented by the dif- ferential expression between the classes. While the model of technical errors (Table 1, Equation 1) seems to be a reasonably good approximation to reality, the biological assumption ( Table1 , Equation 2) has to be taken with caution.... ..."

### Table 2 presents the results from fairly watermark- ing formulas with known solutions by deleting literals. We first create a formula with exactly k solutions over n variables, then we generate 100 random messages of the same length and embed them into the original for- mula. Finally we check how many solutions can meet these additional constraints imposed by the signature.

"... In PAGE 5: ... Table2 : Watermarking formulas with known solutions by deleting literals.... ..."

### Table 2 presents the results from fairly watermark- ing formulas with known solutions by deleting literals. We first create a formula with exactly k solutions over n variables, then we generate 100 random messages of the same length and embed them into the original for- mula. Finally we check how many solutions can meet these additional constraints imposed by the signature.

"... In PAGE 5: ... Table2 : Watermarking formulas with known solutions by deleting literals.... ..."

### Table 2 presents the results from fairly watermark- ing formulas with known solutions by deleting literals. We first create a formula with exactly k solutions over n variables, then we generate 100 random messages of the same length and embed them into the original for- mula. Finally we check how many solutions can meet these additional constraints imposed by the signature.

"... In PAGE 5: ... Table2 : Watermarking formulas with known solutions by deleting literals.... ..."