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For our first implementation we used some simple approximations based solely on gradients that give us good results.
Thus, by selecting respondents that were most likely to yield useful information about the key factors influencing the pathway implementation, we used a purposeful sampling strategy [ 32] and considered our sample size as appropriate to achieve saturation [ 33].
(In our implementation we used ψ = 1).
In our implementation, we used K-means clustering.
For PSN implementation, we used stacks with dynamic sizes.
In our implementation, we used Paxos [51] as the consensus algorithm.
In our implementation, we used relative discounting strategy to clip the local histograms.
In our implementation, we used the Theano [103] library in Python.
In our implementation, we used the inverse depth initialization method [19].
In our implementation, we used FAST detector (features from accelerated segment test) [9].
A good description of the GBM implementation we used can be found in [9].
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CEO of Professional Science Editing for Scientists @ prosciediting.com