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The results show that this method is able to achieve better results than a feature based approach in terms of accuracy and recall.
The goal of the tests consisted in evaluating the subjective difference between standard stereo recording technique and beamforming based approach in terms of stereophonic sound rendering.
Evaluation showed that the proposed utility based solution outperformed the existing heuristic based approach in terms of energy savings and minimizing SLAVs in both lightly loaded and more heavily loaded cloud data centers.
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This meshless approach presents significant advantages over grid based approaches in terms of being monolithic and in handling interaction with free solids.
The recombinant approach is certainly a powerful alternative to classical, animal based approaches, in terms of price, speed and ethics.
Using an augmented cost function and optimizing regularization results in better performance compared to pixel based and unregularized shape based approach measured in terms of MSE and spatial localization as measured using the Dice coefficient and Symmetric difference.
Under both scenarios, our approach outperforms the t-statistic based approach, either in terms of sensitivity or specificity.
Results provided by the new approach is compared with simulator-based approach in terms of performance and time efficiency.
As seen from the graphs, our method outperforms the Slepian-based approach in terms of both estimation error and robustness against increased Doppler frequencies.
The results demonstrated that the bundle of homology models significantly outperformed the crystalline-based approach in terms of MCC, which agrees with results from previous reports [14].
We note that although the local maxima-based approaches we described in this paper (including the ProportionalHill method suggested above) outperform the traditional threshold-based approach in terms of overcoming the giant-component problem and producing continuously overlapping modules, nevertheless they also have their own drawbacks.
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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