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Although this algorithm produces uniform random bits, it is slow.
Although this algorithm requires more memory than other algorithms, it is often the fastest BP algorithm.
Although this algorithm yields the optimal and unique solution for, the algorithm converges to a local optimal solution for.
Although this algorithm works well on the highway which has a few vehicles but it cannot manage the occlusion of vehicles reliably.
Although this algorithm has a higher computational complexity than the existing (SSSRFinder) in practice it was found to be several factors faster (Figure 8).
Although this algorithm can be easily extended to reconstruct other non-Cartesian sampled raw data, we only implement it based on PROPELLER.
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Although in this algorithm, there is still a problem of the loss of color information under complicated condition, this method has highly complicated computations.
Although suboptimal, this algorithm can be easily extended to tackle the approximate case efficiently.
Although theoretically this algorithm provides a 1/2-approximation guarantee for any input, under some statistical conditions the we note, with high probability the correct partition distance is returned.
Although we propose this algorithm for peak detection in MALDI-MS data, we believe that it can also be used for data generated by other types of MS instruments such as LC-MS and SELDI-MS.
Although lattices reconstructed by this algorithm have a similar topology to the time-aligned lattice, the scores attached to each node and link have different meanings, with scores identified as likelihoods in this algorithm and posterior probabilities in the time-aligned lattice.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com