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The best subgraph, which maximizes its accuracy while minimizing its CPU cost, is extracted to build the smart query.
It uses aggressive filtering, smart query routing, and semantic caching to dramatically reduce network bandwidth utilization and improve query response times.
Then, all these phonetic transcriptions are compiled in a graph of phonemes where each path represents a pronunciation variant, as illustrated in the Figure 2. Figure 2 From the requested term to the smart query.
For this question, we offer a smart query with separate input fields for genes and cell lines.
The Smart Query tool helps Toolkit users find measures using keywords or concepts and traverses the entire Toolkit to provide relevant measures for consideration.
It provides a smart query user interface.
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Finally, we evaluate MLP-based filtering, with ASAP pruning and smart querying (A + SR + MLP).
Then, smart querying is added to the previous filtering system (A+SR-GMM).
Acoustic filtering and smart querying are calibrated on the ESTER training materials.
Smart querying does not impact significantly on the filtering rates, but provides a strong CPU-time saving, with the filtering time being reduced by a factor of two.
In order to be sure that the speech segment contains the full targeted speech utterance even if only a part of the phonetic string is spotted (due to smart queries), we enlarge the segment before and after the spotted area.
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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