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The paper shows that the proposed method can drastically reduce a computational burden.
In data processing-oriented applications, the new method can drastically reduce the size of the test suite produced at the expense of a (possibly) more complex generation process.
The results show that the GM method can drastically reduce the needed number of samples to estimate the BER in order to reduce the required simulation run-time, even at very low BER.
The results show that the GM method can drastically reduce the needed number of samples to estimate the BER in order to reduce the simulation run-time even at very low BER.
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Since the mapping step only needs to be done once and the comparison step demands little computation, projection methods can drastically speed up structural comparison.
Here we present a structure-based computational method that can drastically redesign protein ligand-binding specificities.
However, the numerical behaviors of these methods can differ drastically in practice, and no systematic analysis and comparison have been reported previously for these methods.
In this research, a new design method is proposed which can drastically reduce computational costs and design parameters, while maintaining the performance of the targeted outcome.
We empirically demonstrate the effectiveness of our framework, showing that it eliminates the bias inherent in deterministic selection strategies (e.g. the pooling method) and that it can drastically reduce the number of judgments necessary compared to naive sampling approaches.
Depending on the disturbance frequency to be suppressed, the displacement control method in some cases can drastically increase the reaction forces.
Then, in Section 3, we show on both simulated and real data that this method can lead to a drastically reduced total number of MC samples.
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