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The approximation error is fixed at (varepsilon=0.01), and the average computational results (standard deviation) are obtained by running the algorithms ([19, 21] and ours) for 10 times.
We automatically processed several force-volume images by running the algorithms for analyzing all force curves recorded at the pixels constituting the image.
All experimental results in this section are obtained by running the algorithms on an Intel(R) Xeon(R) CPU 2.67 GHz with 24 GB of memory.
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A decision is made by running the Algorithm 1~3 at the ProSe Application Server.
By running the algorithm in this fashion, the learning accuracy is significantly improved.
By running the algorithm at different scales, the quality of the image is evaluated for different viewing distances.
Stability is usually calculated by running the algorithm several times, varying some parameters or adding noise to the input data, and then contrasting the perturbed replicas.
The elements of the adjoint matrix as given in the RHS of (15) can be found by running the algorithm described previously.
This value was empirically determined by running the algorithm on different simulated image sets (different object densities) and different colocalization extents (for details see Text S1 and Fig. S1).
Additionally, miRDeep2 calculates false-positive rates by running the algorithm on a set of "signatures" and secondary structures that are paired by random permutation.
However, this restriction is easily sidestepped by running the algorithm twice with nonoverlapping sets, at the minor cost of doubling the execution time.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com