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Figure4a,b,c shows the system sum throughput and per-user throughput when applying the cross-layer optimization defined in (18) for the MR algorithm.

The MR algorithm recovers low-frequency components of the reconstructed image at coarser resolutions in fewer iterations, reducing the number of iterations required at finer resolution to recover high-frequency components.

Furthermore, as expected, Figure3 shows that the MR algorithm achieves a higher sum throughput in comparison with the PF strategy, at the cost of unfair treatment of the arriving traffic flows.

As it can be observed, in all cases, the behavior of the simulation of the system under consideration with different scheduling algorithms, namely the MR algorithm (left) and the PF algorithm (right), is faithfully reproduced by our analytical PHY-MAC-DLC layer model.

Generally, the MR algorithm assigns a protein with the most frequent function terms among its direct interaction partners.

Assessing the MR algorithm on the same target dataset that we used in CSIDOP, MR made functional predictions with an accuracy of 59.50% (Table 1).

Finally, the MR algorithms are steadily improving.

(2) environment-adaptable algorithm: when choosing an optimal configuration in a large-scale RF-FPGA, the EAR-MR algorithm reflects communication quality changes due to interference in the field.

(3) the improved time-efficiency of EAF-MR optimization algorithm for a large-scale RF-FPGA: in the EAF-MR algorithm, the EAF optimization synergizes with the multi-resolution method in order to improve the time efficiency of the optimization process in a large-scale RF-FPGA.  .

Danny Crichton described the new algorithm-driven hybrid market in "Meet your New Boss, Mr. Algorithm".

In experiment 1, the calculation times of SPRINT and MR-DIDC are compared, to evaluate the time performance and test the scalability of the MR-DIDC algorithm.