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It should not be surprising that Apple's mapping solution is nowhere near as good as Google's at this stage of its development.
CarPlay's only mapping solution is Apple Maps.
Specifically, the node mapping solution is { a→C,b→B,c→E,d→F} and the link mapping solution is { (a,b)→(C,B), (a,c)→(C,E), (c,d)→(E,F), (d,b)→(F,B)}.
The experiments report that an average clock cycles decrease of 60.7%, relative to an all fine-grain mapping solution, is achieved using the developed framework for the considered applications.
It's not just that Apple's current transit mapping solution is inelegant; it's that its solution is slow.
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The final mapping solution was generated by bundle adjustment mentioned in Functional model for reality based 3D mapping section.
where Ω(f) = -ln p(f) and so the Maximum A Posteriori (MAP) solution is expressed as the minimizer of this criterion which has two parts: the first part is due to the likelihood and the second part is due to the prior: Thus, depending on the choice of the prior we obtain different expressions for Ω(f).
By contrast, the 2-phase-mapping solution is solved for optimization, resulting in minimal cost.
Using a rational mapping function, each solution is obtained as a closed form.
Using a rational mapping function, the each solution is obtained as a closed form.
For IMRT fluence map optimization, the solution is the vector of beamlet intensities x at each beam (gantry/couch pair) that is used in the solution.
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