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Both approaches can be used separately or in combination depending on the size of the image.
Both approaches can be extremely expensive in terms of computational time.
Both approaches can be used for segment-wise architecture modeling in static software analysis approaches.
That matters, Apfelbaum told Poets & Quants, because while both approaches can be effective, each can be also be counterproductive.
Both approaches can be applied to detect and locate damage through acceleration data obtained from sensor arrays.
The above code is very explicit, but note that both approaches can be implemented in a more concise fashion using while loops.
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In principle, both the developed approaches can be applied to any natural circulation loop configuration.
While both these approaches can be used in the supervised setting in which paired training data is available from the source and target modalities, only the second approach is applicable to the unsupervised setting in which paired training data is not available.
Both general approaches can be used for cluster randomized trials [ 3, 4].
Further, both MAGI approaches can be applied to studies without replication.
(Amde et al. 2012, p. 5) Both these approaches can be considered as HPSR+A capacity assets within their own contexts.
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