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Unlike other methods, our approaches focus on the changes of EEGs from different brain regions and the correlation between them.
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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.
In contrast to earlier methods, our approach breaks the global dependence of compact methods by using explicit finite-difference methods at block interfaces and is fully conservative.
Unlike previous construction methods, our approach employs only a few n/2-variable affine subfunctions in the design, resulting in a more favourable algebraic structure.
Compared with previous methods, our approach avoids a wide range of preprocessing or postprocessing steps, reducing the impact of subjective factors.
Compared with the existing 3D reconstruction methods, our approach can obtain both geometrical information and semantic information of each part of shafts from 2D drawings.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com