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Speedup of our Spark implementation as a function of the number of cores on the cluster node, using a sample of 1,000 (blue) and 5,000 (orange) images.
As a preliminary but illustrative result, we show in Fig. 7 the speedup (the ratio between the execution time on multiple cores and the execution time on single core) of the image classification algorithm in our Spark implementation, as a function of the number of cores within a single node when 1,000 (blue), and 5,000 (orange) images were processed in parallel.
Measuring the execution time of our implementation as a function of n and m and performing non-linear least-squares model fitting leads to F = 3.98 · 10-9, α = 2.94 and β = 1.04.
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The accuracy achieved in thousandths was in a Supermartingale sense and implementation performed as a function of recursive estimation.
Our data suggest that the reason why some super users foster implementation success more effectively than others lies in the behaviors that they use to support implementation, which varied as a function of their role engagement.
This seems to be connected to the proper implementation of the reactance as a function of bias flow Mach number.
In addition, it finds that sustainable success in these initiatives is as much a function of the technology as it is of the change management function that must accompany the system implementation.
This was particularly true for difficult projects (for easily achievable goals, there was no improvement as a function of implementation intentions).
Thus, the resulting complexity differs as a function of implementation algorithms.
However, there is a precedent for studies examining implementation and sustainment of multiple EBPs as a function of large scale policy initiatives (Lau & Brookman-Frazee, 2016).
Figure 6 shows the average energy (left) and KL-divergence (right) over the 50 random data instances for parallel, sequential, and SGM-initialized-parallel mean-field approximation implementations, in addition to Graph Cuts, as a function of computation time.
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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