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While ultrasonic waveform tomography has succeeded in detecting such features, recovery is difficult because it requires computationally expensive high-frequency numerical wave simulations and an accurate understanding of large-scale background variations of the engineered structure.
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The main idea here is to avoid the disparity map stage because it requires extremely computationally intensive operations and cannot suitably estimate the high-resolution depth maps in the video sequence applications.
This kind of optimization method is computationally efficient because it requires fewer function evaluations though it is capable of getting stuck in a local optimum.
While the SML approach is straightforward, it is computationally expensive because it requires a large number of simulations of the stochastic model.
Finally, with millions of genotyped animals, SSMi may become computationally infeasible because it requires full inversion of two very dense matrices for the genotyped animals.
Harrell's C index could theoretically be estimated with a weighted approach, but this can be computationally difficult because it requires weighting each pair by the pairwise sampling probabilities, i.e., using a square matrix of size N'(N'-1), where N' is the size of the case-cohort sample.
The estimation of profiles according to an assignment of sequences to functional categories is a computationally expensive task because it requires the comparison of all protein sequences from a genome with a usually large database of annotated sequences or sequence families.
The developed framework provides system operators with a computationally efficient analysis tool because it requires only local information related to the congested line, such as marginal cost and GSF.
However, GMYC analysis is currently computationally more difficult to implement, especially because it requires a detour via branch length optimization and computation of an ultrametric tree and algorithms, which are prone to error if branch lengths are zero.
One drawback of this algorithm is that it requires extremely computationally intensive operations.
Because the choice of shrinkage parameter is semi-automatic, our method does not require computationally intensive CV.
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CEO of Professional Science Editing for Scientists @ prosciediting.com