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Experimental results on a few benchmarks with parameters extracted from industrial designs show that compared with a maze routing-based approach, our algorithm can achieve up to 72% wire length reduction.
However, combined with a traditional haplotype inference approach, our algorithm is able to infer haplotypes containing both rare and common SNPs, including SNPs that are unique to individuals.
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Compared to existing approaches, our algorithm can efficiently generate a wide variety of textures.
Moreover, unlike other approaches, our algorithm implements both the discovery and the coverage phase.
Differing with the existing local approaches, our algorithm divides the matching process into two steps, initial matching and disparity calibration.
In contrast to previous approaches, our algorithm is independent of index direction (symmetric), and also allows a selection of knot intervals to remain unaltered by the subdivision process.
In opposition to existing approaches, our algorithm, of distributed nature, is provided to find such a trade-off whatever the initial network configuration is.
Compared to previously presented island-based approaches, our algorithm is, to the best of our knowledge, the first island-based algorithm that aims at minimizing simultaneously CAPEX and OPEX while using an accurate QoT estimator incorporating all major linear and nonlinear physical-layer impairment effects during the design and dimensioning process.
In contrast to previous layered approaches, our algorithm uses two boundary layers and one reliable layer, performs image-based 3D warping only, and was generically implemented, that is, does not necessarily rely on 3D graphics support.
Among all five approaches, our algorithm always outperforms the others especially when the patch size is small (64 × 64 for instance) since it requires less data to estimate a discriminative model.
Like all other approaches, our algorithm also has limitations.
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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