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We have made our algorithm, called RASCAL, and several multi-exposure image sets available online.
"We made our algorithm mark accounts as Russian as soon as any Azbuka letter [from Russia's Cyrillic script] is detected in user media or biography.
Using a mixture of regressions based on the cluster-experiment error variances (see Methods and Additional file 5 Figure S3), the strategy of formalizing the notion of a clustering consensus among independent experiments made our algorithm robust against inter-study variation.
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This makes our algorithm more accurate and even faster.
This framework makes our algorithm easier to generalize, and also easier to compare against other methods related to elimination trees.
Some special properties of wrap-around L2-discrepancy can be used to make our algorithm much more efficient.
Since our one-step ALE finite volume scheme is based directly on a space time conservation formulation of the governing PDE system, the remapping stage is not needed, making our algorithm a so-called direct ALE method.
Our experiments show that the number of these switches is extremely low in practice, and this makes our algorithm outperform all the state-of-the-art methods that provide a guarantee of success.
To solve this problem and make our algorithm robust under multi-camera setting, we take full advantage of low-level features, attributes and inter-attribute correlations at the same time.
This characteristic makes our algorithm flexible and controllable.
This makes our algorithm very competitive in terms of complexity compared to other methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com