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Experimental results demonstrate that READ consistently performs better than existing approaches in performance and reliability while achieving a comparable level of energy consumption.
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We evaluate our approach in performance from two aspects.
This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses.
Given the similarity of all the heuristic approaches in their performance, we argue that it is sufficient to compare the performance of only one heuristic approach with the exact solution.
This is what balanced approaches in school performance measurement are intended to capture.
(3) The CEB statistical model is selected to compare with the MB model to gauge the differences of the two approaches in dynamic performance.
The architecture presented focuses on the use of virtual resources as a management concept and identifies different approaches in the performance evaluation on edge computing devices.
We suggest that the uniform prioritization method based on seed degree, combined with statistical adjustment based on centrality, can be considered the "winner", since this approach is the only one that outperforms existing approaches in all performance metrics used.
We will use this approach in our performance evaluation.
The deterministic approach, in which performance in the event of link or node failure is designed to exceed a predetermined level, is considered.
We show on the simulated and real datasets that the proposed approaches outperform original approach in detection performance and report more statistically significant DMRs on real MeRIP-Seq data.
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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