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Exact(42)
As opposed to existing methods, our approach provides efficient incremental maintenance of the compressed graph directly.
Compared with traditional methods, our approach allows us to treat discrete systems of very high dimension.
Different from other methods, our approach can detect most kinds of tables with high precision even when it is skewed.
Unlike other methods, our approach evaluates habitat quality by analyzing land use cover in conjunction with habitat threats.
Compared with other lossless compression methods, our approach can achieve a higher compression ratio with a comparable compression time.
Comparing with the previous fMRI methods, our approach makes better use of signal information so that the qualities of reconstructed images can be highly increased.
Similar(18)
Unlike other methods, our approaches focus on the changes of EEGs from different brain regions and the correlation between them.
Unlike the baseline method, our approach performs almost equally well at all three imaging qualities.
Compared with the fully manual method, our approach detected subtler polarity shifting as we extracted far more polarity shifting patterns.
Compare with the random circuit matrix method, our approach uses less code length for evolutionary hardware description.
In its use of a master-slave method, our approach is different from the split-node technique (Andrews, 1999) that has been used in fault modeling.
More suggestions(15)
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