The experimental results also show that there exists an optimal pressure difference between the two channels for overall cell performance.
Next we tested whether beta amplification is more marked the longer optimal phase differences between the STN and globus pallidus for beta amplification are sustained.
As the optimal free energy difference between conformations increases (Figure 3A), the behavior of the free energy changes from symmetric multiwell and wide minima, as in the ΔHU mutant, through asymmetric, typical of the wild type system, to symmetric with high amplitude oscillations (curve not shown), typical of "single-conformation" systems.
The difference between optimal individual and optimal population performance increases with density, because it is competition among individuals that creates and exacerbates the difference between individual and group selection.
If the plants in an agricultural field (without weeds) are so far apart that there is no competition among them, then there is no difference between optimal individual and optimal population behavior: the genotype that gives the highest individual yield gives also the highest population yield.
To do this, we defined the optimality degree, ξ Δ v k, in a target environment characterized by Δv∗ k and different from the optimal environment as the difference between Sexp evaluated in an environment containing Δv k = 0 (i.e., fitness in the optimal condition) and that evaluated in the target environment containing Δv∗ k.
Value of information is defined as the absolute difference between optimal value before experiment and the expected optimal value after experiment, or, the expected improvement in the optimum after experiment.
To reduce the feedback burden, the receiver broadcasts the optimal transmit power (optimal received power divided by channel gain, p i ∗ g i ) and carrier phase shift (difference between optimal carrier phase and estimated carrier phase, Δ θ i ∗ = θ i ∗ − θ ̂ i ) to the multiple transmitters.
But for testing samples, at this point, the classification model may not be optimal because of the difference between the training samples and the testing samples; the optimal classification model will lag in appearance (see Figure 1).
We emphasize that our analysis provides an estimate of the lower bound on the difference between optimal and heuristic solution, since alignments generated by MaxPairs are not always optimal (in the strict sense).
