### Table 1. Parameters in the general model.

"... In PAGE 7: ... (9) In the above formulas, Ei(x) denotes the set of all ingress-links to node x; similarly, Ee(x) denotes the set of all egress-links from node x; Qs in Eq. (8) equals the sum of all Qsj for all types j; Table1 summarizes all related notations. As in [13], the cost associated with an unassigned request is assumed to have a large enough value (virtually infinite value).... ..."

### TABLE III. Bivariate Analyses of BMD and OF at Wrist* Model II Model III Model IV Model V

### TABLE II. Bivariate Analyses of BMD and OF at Spine* Model I Model II Model III Model IV Model V (unrestricted) (rG = h2BMD = h2OF = 0) (rG = h2OF = 0) (rG = h2OF =rE = 0) (bweight = 0) BMD

### TABLE 2. Genetic Model Fitting Resultsa

### Table 2: Performance of General Model Coll.

"... In PAGE 10: ...mprovement is statistical significant (p-val lt;0.05) and **means very significant (p-val lt;0.001) To investigate the performance of the general model described in section 3, we compared the general model with both document expansion and query expansion alone. Table2 shows the results. Here UM is the unigram model, which does not perform any document/query expansion, and document model is smoothed using absolute discounting (Formula (11)).... ..."

### Table 2 Log-likelihood for five different models testing the effects of age, gender, and carrier status for N291S or 93T G:D9N haplotype for the quantitative traits and BMI in FCHL pedigrees

"... In PAGE 4: ... Table 1 shows the estimated means and their standard errors of these six categories for BMI and measured lipid parameters. Table2 shows log-likeli- hoods of the full model and the four submodels which provide a test for each of the abovementioned effects (age, gender, N291S and 93T G:D9N haplotype) on BMI and lipid variables. No significant effect of either of the mutations is found on BMI, which means that BMI cannot explain possible differences between carriers and non-carriers.... ..."

### Table 1. Examples of hydrological models and general modeling tools. Model name

2007

"... In PAGE 19: ... The data requirements for these models grow rapidly with the increasing complexity of the models and with the increasing spatial resolution (Rosbjerg and Madsen, 2005). Table1 lists some of the available hydrological models and their type of conceptualization and spatial description, according to the classification categories previously introduced. A comprehensive review of the existing hydrological models can be found in Singh and Woolhiser (2002).... ..."

### Table 2: General Linear Model

### Table 2. Models generalization performance

"... In PAGE 5: ... Model constructed using the selected set of genes as inputs achieves high generalization performance on test set: around 96% of accuracy. The performance of each model is presented in Table2 . Genes resulted, from each experiment, are used to build a model based on a neural network.... ..."

### Table 3 RMSE of General Model to Scaled Original Data and Secondary Data

"... In PAGE 6: ... Original subjective data sets one through six were part of the eleven data sets used to train the General model [3] (data set twelve was collected after the General model was finalized and hence was not used for training). Table3 identifies the ability of the General model to predict the original and secondary subjective data. The values listed in Table 3 are calculated as the root mean square error (RMSE) of a first order linear regression, run with the general model as the predictor variable (x-axis) and either the scaled original subjective data (i.... In PAGE 6: ... Table 3 identifies the ability of the General model to predict the original and secondary subjective data. The values listed in Table3 are calculated as the root mean square error (RMSE) of a first order linear regression, run with the general model as the predictor variable (x-axis) and either the scaled original subjective data (i.e.... ..."

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