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Started this implementation with Bing less than two months ago.
The second is the comparison of this implementation with a multiprocessor implementation and the implications of these comparisons.
From these evaluations some suggestions and modifications are proposed to improve the directed diffusion behavior according to this implementation with respect to these metrics.
This implementation achieves 39.6 fps which is a speedup of 503.9× compared to the OpenCV-based approach and 2× compared to this implementation with optimizations.
Because rating systems vary by region, Google has worked on this implementation with the help of the International Age Rating Coalition (IARC) and its participating bodies, including the Entertainment Software Rating Board (ESRB), Pan-European Game Information (PEGI), Australian Classification Board, Unterhaltungssoftware Selbstkontrolle (USK) and Classificação Indicativa (ClassInd).
As in the CUDA, we combined this implementation with the one developed using MPI thus to exploit cluster of multicore CPUs; again, the effort spent to integrate the codes was actually affordable.
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It is notable that this implementation works with top-down scheduling algorithms [27].
This implementation starts with a generic VHDL specification and then it is developed on FPGA architecture.
This implementation starts with a generic VHDL specification and then it is developed on an FPGA architecture.
This implementation starts with some compelling motivational narratives given by DI theorists.
Availability and implementation: The source code for this implementation together with instructions for installation is available at http://github.com/IlincaTudose/OntoQuery.
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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