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We call (N_{pm }(E /int _{D(E)} d{r}) normalized population.
Figure 1 shows a map of normalized population densities.
The two rounds of hybridization would generate a normalized population of tester specific cDNAs with different adaptors on each end.
It is noted that we generally assume all training and testing samples are chosen from a normalized population data for the convenience of diagnosis in our context, which avoids possible renormalization and classifier retraining overhead for the following diagnosis.
Although we assume training and testing samples are picked from a normalized population in our context, our method can still work well provide the testing samples are not normalized or normalized with a different approach as the training ones.
Figure 2B shows the distribution of preferred orientation relative to the grating stimulus orientation (which was horizontal; labelled as 0° in Fig. 2B). Figure 2C shows the normalized population firing rate for the different contrasts and the two stimuli.
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Normalized populations (histogram) as a function of ψ i (a) at positive and (b) at negative temperatures are plotted in Figure 3. Figure 3 Populations for the equilibrium distributions (a) at positive and (b) at negative temperature are plotted as a function of energy possessed by each point vortex ψ i.
These fusion models were the superposition of normalized populations.
The training process leads to a robust selection of the cells with those values of the genetic diversity parameter m u that allow the normalized population-wide GFP response f(x) to roughly track the positive class data distribution.
Final weight-normalized population parameter estimates (obtained by normalizing the final population mean estimates with the average weight) are shown in Table 3.
The number of transcripts generated from the normalized library with either Viridiplant or Arabidopsis homologues was almost three times more than from the non-normalized populations (Table 1).
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