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The four amplitude samples are modeled as linearly dependent on the STO, using a first-order approximation around δ=0.
The flywheel inverted pendulum is an underactuated mechanical system with a nonlinear model but admitting a linear approximation around the unstable equilibrium point in the upper position.
During each coarse scale time step, we adopt a linear wave approximation around the interface, with the wave speed updated using the coarse grid information.
It is based on the early-late principle [18] and can be derived by using a few approximations and assumptions: The four amplitude samples are modeled as linearly dependent on the STO, using a first-order approximation around δ=0.
In fact, several case studies have shown that the approximation around support vectors is usually restricted in small regions, so that optimal predicted performances are very close to some actually simulated performance vectors.
A point to note here is that, as f goes far from f R (x0), both the bias and the noise increase in R o (f); the bias increases because the model of Eq. (1) is an asymptotic approximation around f R (x0) (see Appendix A for its comparison with the exact solution to Southwood's equation), so that it deviates from the true FLR solution far from f R (x0).
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Existing CCU models for control and optimization are either linear approximations around an operating point or very complex nonlinear ones, resulting in difficulties for real time applications.
With this procedure a set of function values is mapped onto a set of local approximations around every node.
We prove that this second heuristic has an approximation guarantee of around 0.866 under the same conditions as in the first algorithm.
Light scattering data of the first fraction did not give reliable results, but an approximation suggests that around 24 subunits (4 × 6-mers) are assembled.
qmin is underestimated by the binomial approximation only around the singularity (λ > 0.8), and thus the singularity is actually located at higher λ, which makes the singularity even less plausible.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com