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Backward swimming can be generated by setting (omega_{1}=0) and solving in a similar fashion.
The OAM modes of |l|= 1,2,3 can be generated by setting the appropriate electric potential applied in the SAZs.
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Key generation: a secret key x is generated by setting its value as a random number (x in _{R} mathbb {Z}^_{q}).
A vibrato effect is generated by setting proper MIDI commands.
Optimal conditions were generated by setting the best sensory performance as a goal for each response.
Three diagenetic models, DR1, DR2 and DR2, were generated by setting a gradual deformation parameter of 0, 0.5 and 1, respectively.
Multiple collinear virtual microphones are generated by setting the virtual microphone interpolation parameter α to different values.
Annular dark field images were generated by setting the detector inner angle to 40 mrads outside of the probe semi-angle.
AdaBoostRBFSVM can be described as follows (Algorithm 1): Firstly, weak RBFSVM classifiers are generated by setting a large σ value, and the weights of training samples are initialized to the same value.
First, a Cross Recurrence Plot (CRP), denoted as c, is generated by setting its element c p,q to "1" when there exists recurrence between f i (p) and f j (q) and "0" otherwise.
Finally, initial conditions are generated by setting the plane to the (stable) homogeneous state and then adding the generated pattern, which represents increased activity like in neural avalanche, to the activator variable u, which represents the ionic imbalance most notably the extracellular potassium concentration.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com