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Due to the characteristic of network coding, these schemes cannot work well when the data flows change frequently.
However, the existing partitioning algorithms [10, 11] (static) do not work well when the data access pattern changes and also do not model real world e-commerce application scenario.
Since robustness is the ability to perform well when the data obey the assumed model and to not provide completely useless results when the observations do not exactly follow it, moreover, estimators in SAR signal and image processing are always used in various robustness algorithms, thus, the robustness is of highest importance.
It performs well when the data fit with the parametric assumptions, but it is not reliable when data deviate significantly from the assumptions.
Many dual-space methods perform at least as well when the data are first expanded to the nominal space group P1 (Sheldrick & Gould, 1995 ▶).
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All of these algorithms perform well when the source data and target data are in a very similar domain.
Although the least-square error has been used successfully to match the observed production data, it does not work well when the seismic data are history matched at the same time.
All the routing protocols also operate well when the flow data rate is 512 Kbits/s.
The geometrical approaches do not work well when the observed data are highly mixed, because there are not enough vectors in simplex facets.
However, the approaches do not work as well when the trait data are not smooth, and they do not take account of the correlation among time points.
These approaches are fast to compute, work well when the trait data are smooth, and provide results that are easily interpreted.
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CEO of Professional Science Editing for Scientists @ prosciediting.com