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Compare with the random circuit matrix method, our approach uses less code length for evolutionary hardware description.
Compared with a previously proposed method, our approach requires a much less computing time to achieve designs with high statistical efficiencies.
Using Least Angle Regression (LARS) method, our approach can choose features adaptively, efficiently and as few as possible to guarantee generalization performance.
Employing the idea of selective pole shift based on LQR method, our approach provides a systematic framework to synthesize fully distributed consensus controllers which have low rank and non-conservative coupling strengths.
Finally, note that although designed for the VOF method, our approach could be easily adapted to other interface-capturing methods, such as the Level-Set, which may present similar workload imbalances.
Even though the imposition of boundary conditions is a key ingredient that is very often used to classify the fixed-grid method, our approach can be applied together with any technique to impose approximately boundary conditions, although we also describe the one we actually favor.
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Compared with traditional methods, our approach allows us to treat discrete systems of very high dimension.
Compared with contemporary methods, our approach identified 1.4-2.1 1.4-2.1ore sQtimesmore of which helped usQTLsribe manycular effects tofdisease-associated variants.
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.
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