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In conclusion, for the greatest part of the considered real life datasets, the proposed method guarantees competitive performance for a robust, fast, accurate and automated object alignment, consequently boosting the usability of high-end 3D scanners.
Typically only angiosperms with sequenced genomes are included in taxon sets for large eukaryotic tree of life datasets [ 52, 53].
Finally our approach has been validated only on simulated data but it should be applicable to real life datasets as well.
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We present the results of the application of AHERF over a real life dataset composed of 156,120 admission cases recorded between January 2013 and August 2015.
To demonstrate scalability, we also ran costscape on the full 100-taxa tree of life dataset (also using transfer and loss costs ranging from 0.5 to 2) and found that the median runtime remains <1 min. In this work, we have described new algorithms and tools for understanding the relationship between event costs and maximum parsimony reconciliations.
It has proven effective on a variety of real-life datasets.
However, many have observed that real-life datasets tend to have non-uniform distributions.
Computational results on randomly generated instances and real-life datasets are also presented.
We tested the effectiveness, efficiency, and scalability of our aligner for various standard and real-life datasets.
The results on synthetic datasets and real-life datasets demonstrate the rationality and effectiveness of the proposed algorithm.
Various simulation and real-life datasets are used to test the performance of the proposed OC-LSSVM.
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