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It is found that the diversity of ultrafast dynamics originates from multiple parameter dependence.
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A neural net was used to perform a multiple parameter logistic regression while allowing for non-linear (usually sigmoidal) dependence on input parameters.
Bohm's theory involves parameter dependence and thus controllable probabilistic dependence: The probabilities of distant outcomes depend on the setting of the nearby apparatus.
In such models, parameter dependence is not a necessary condition for controllable probabilistic dependence.
Parameter dependence is a violation of this invariance condition.
A multiple parameter optimization viz.
Figure 3 shows the parameter dependence of the mean distance.
We therefore do not plot this parameter dependence.
Fig. 11 Poloidal/toroidal flow RMS ratio parameter dependence.
The analytical representations show the full parameter dependence.
At least some of the current modal interpretations seem to involve no parameter dependence.
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