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The memory foot-print required by our implementation is significantly lower than the one imposed by a direct implementation.
The main service used by our implementation is the CADET service, which offers pairwise authenticated end-to-end encryption between all participants.
We defined a threat model to analyze the GenePING privacy requirements and ensure that the level of security provided by our implementation is reasonable.
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Furthermore, our implementation is outperformed by a naïve implementation by a factor of approximately 20.
An important role in our implementation is played by the recently proposed empirical interpolation method, which allows approximating the non-affinely parameterized deformations with affinely parameterized ones.
Furthermore, our implementation is optimized by using inline assembly language in C programs, and hence it gives faster performance than the previous implementation results of [16] in the same scheme.
Our implementation was accomplished by using the CUDA programming paradigm.
Our implementation was inspired by the algorithm applied to solve traveling salesman problems.
The eigenvector ψ k (0 ) corresponds to population size fluctuations that in our implementation are controlled by a carrying capacity.
This method was originally developed by Meuwissen et al. [ 20] and our implementation was as in [ 10].
We validate the feasibility of RTS on blog sites and show that runtime performance incurred by our current implementation is negligible.
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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