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Unlike previous works, the algorithm is optimized to massive parallel processing on GPU.
To accomplish this, additional nodes in the network, but not in the query list, would be recruited in order to make the target subnetwork interconnected, while the algorithm is optimized towards a minimum list of the additional nodes.
Meanwhile, the algorithm is optimized for detecting fall incidents.
The tracker part of the algorithm is optimized and evaluated with the segmentation control parameters fixed.
The algorithm is optimized for integer q-bit operations (q= 8,16,32,…), allowing a direct implementation in almost any hardware platform, while avoiding rounding errors of floating-point operations.
We did notice, however, that this algorithm systematically detected deletions located in sub-telomeric regions for both tumors and controls, which indicates a systematic bias and suggests that the algorithm is optimized to detect duplications and amplifications but not deletions.
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So essentially the algorithm is optimizing for quality over quantity right now, but that should equalize as the natural language processing gets better over time.
Performance of the algorithm was optimized using a training sample of 10 trees to achieve human worker's pruning level.
The algorithm was optimized using a preset procedure and the subsequent macro was saved and applied to all slides.
The algorithm was optimized using a synthetic metagenome dataset generated by MetaSim, which consisted of 100 000 reads sampled from 19 microbial genomes, 3 viruses and 2 human chromosomes.
For small a max values this can only be achieved by removing completely some bulls from the analysis, where the algorithm was optimized to exclude as few bulls as possible.
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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