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When compared to state-of-the-art methods, our solutions obtain competitive, if not superior, performance.
"They take so much knowledge with them of our methods, our solutions, our client base, knowledge of the industry," she said.
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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.
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.
In contrast to previously published methods, our algorithm computes provably optimal solutions without computationally demanding parameter optimization usually necessary in heuristic approaches.
We have solved the model equations using the spectral relaxation method, and to benchmark our solutions, we compared our results with some limiting cases from the literature.
We were challenged to prototype a solution to a human rights challenge of our choosing using the design thinking method, and then present our solutions to each other at the capstone Innovation Village, where two winning teams were selected.
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.
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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