Note that the additional term (frac {1}{n}frac {ell ^{2}}{r}) that we have for the case when r<ℓ vanishes as n becomes large as compared to ℓ.
Here, we would like to note that the difference between the rate achieved by the partition code and the bound in (7) gets smaller as the parameter r becomes large as compared to the parameter ℓ.
In the asymptotical state, when the number of repetitions becomes large as compared to the number of variables, the performances of each method reflects how much they are affected by the variability in the neural data, producing the following ranking MDA>PCA>MGD≈ANN.
The difference disappears when the rate of switching becomes large as compared to production and degradation rates, that is in the adiabatic limit.
However, when the number of query becomes large, the difference of processing time becomes larger as well.
"Channelization" here refers to the fluid velocity component in the direction of the applied pressure gradient becoming larger as compared to the velocity component in the direction normal to the overall flow direction as the fluid becomes more shear-thinning.
These gains become larger as I becomes higher.
The fluctuations of distribution become larger as approximate to zero and the CFAR thresholds become larger (dashed lines).
This threshold becomes larger as the quality of the SR links improves as compared to the SD links, that is, as and get larger compared to and.
The blur index (Delta lambda ') for the IT images was remarkably low at all the speeds as compared with that for the NT images; it became larger as the speed of the circle dot increased.
