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We implement and run the algorithm on a large multi-core system and perform an extensive experimental analysis on data generated from realistic probability distributions.
In summary at first we run the algorithm on the original weights W u, v) to compute W∗.
To assess the effect of this factor on BiMeta, we run the algorithm on samples from L1 to L6 and compare with MetaCluster 5.0 and AbundanceBin.
For a dataset of 1000 markers and 4000 haplotypes the proposed method took 14 min to construct the phylogenies and 13 min to run the algorithm on an Intel Xeon 3.40 GHz processor with 2 Gb of memory.
The memory footprint of the classifier is also significantly smaller, from 4 GB down to about 200 MB (a factor of 20), making it possible to run the algorithm on a wider variety of computational platforms.
One could, for example, run the algorithm on a coarse subset of the measurements and choose, based on the results, a specific region of interest where a finer grid of measurements is used to get a better estimate.
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However, it is challenging to run the algorithms on mobile devices since the process of aesthetic analysis is quite complicated and time-consuming, especially for large amount of images.
In other words, the number of matched landmarks is not necessarily the same, neither when running the algorithm on one-core, quad-core, or GPU nor when running it for different numbers of particles.
These times are attained when running the algorithm on one core of an eight-core Intel i7-3770 CPU at 3.40 Ghz.
For a less difficult dataset, we ran the algorithm on all clusters contained in more than one input tree.
When Yoon ran the algorithm on this dataset, she discovered more than 14,000 small, previously unreported earthquakes.
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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