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It is shown that minimizing the LQR performance limit of the multi-agent system under the distributed controller equals minimizing the sum performance of a single agent system.
Both Greening 1 and 3 have a very good fairness performance, but this comes with a price of greening, i.e., a decrease in normalized weighted data rate sum performance of 4 and 4.5%, respectively.
It can be seen that Greening 4A is the best in terms of rate sum performance, i.e., 50% of system transmit power is saved while still achieving 93% of full-power data rate performance.
The inclusion of fairness thus results in a fairer allocation of data rates and/or transmit powers, but the price of adding fairness is a reduced data rate sum performance compared to Greening 4A, as can be seen in Figure 8a.
In sum, performance is ideally defined broadly, provided that the set of performance measures remains comprehensible for providers.
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In sum, performance-based pay is worth considering in some contexts; but making it work well and sustainably is a formidable challenge (OECD [2012]; Rowland and Potemski [2009]).
Table 1 shows the rank sum performances of the five disparity estimation methods considered.
For applications that are less bandwidth bound, these primitives can help improve binary prefix sums performance by up to 24%, and binary reduction performance by up to 100%.
Table S3 in additional file 1 gives an overview on the deviation among summed performance scores for the different baseline methods which would lead to changing rankings.
This composite score is obtained by summing performance from three verbal working memory subtests: Backward Digit Recall, Counting Recall and Listening Recall.
The Phonological Loop Component Score is obtained by summing performance from four subtests: Digit Recall, Word List Matching, Word List Recall and Nonword List Recall.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com