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Extensive experiments show that, comparing to traditional methods, our solution can assist the popular deep CNNs to achieve better performance.
Unlike existing methods, our solution is both precise (i.e., it computes this probability without error) and it scales to large networks.
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"They take so much knowledge with them of our methods, our solutions, our client base, knowledge of the industry," she said.
When compared to state-of-the-art methods, our solutions obtain competitive, if not superior, performance.
Compared to the other state-of-the-art MLC methods, our proposed solution DRABAL improves the F1Score significantly by about 22% in absolute measures, on average.
As with many strongly-coupled immersed-boundary methods, our method requires the solution of a nonlinear algebraic system at each time step.
Finally, we discuss some problems occurring when recovering the packets dropped during a handoff by the buffering method, and propose our solution.
Finally, the computational results show that for certain types of instances, our solution method outperforms existing methods proposed in the literature.
In contrast to previously published methods, our algorithm computes provably optimal solutions without computationally demanding parameter optimization usually necessary in heuristic approaches.
To illustrate our findings and validate our solution methods, we provide numerical results on some sample networks.
We consider several simplified geodynamic problems with a viscosity contrast to demonstrate the robustness and scalability of our solution methods.
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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