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Note that two proposed schemes in this paper achieve significantly improved performance compared to the scheme in [10].
Numerical examples are presented showing that the second to fourth order methods implemented in this paper achieve or exceed the expected accuracy and demonstrating the advantage of using our high order numerical methods.
From the results and observations in this paper, we would like to conclude that we are not able to show that random coding techniques and inner bounds in this paper achieve the capacity region due to lack of nontrivial outer bounds in all cases for this problem.
The systems described in this paper achieve a high level of performance through many shared characteristics, including the collection of a broad range of data elements and incorporation of robust quality assurance mechanisms, efficiently facilitated by electronic data collection at the point of care.
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(ii) In contrast to [42] and [43], this paper achieves both superlinear and global convergence.
However, the more complex ODE model that we present in this paper achieves this goal.
Examples show that the method in this paper achieves satisfying merging results.
The proposed method in this paper achieves good results and is beneficial to the standardization of the entire evaluation process.
Additionally, the best model of this paper achieves state-of-the-art performance on different types of data.
Therefore, the algorithm in this paper achieves the higher security at the cost of the declination of success rate of anonymity.
In the previous three experiments, we showed that the face recognition algorithm proposed in this paper achieved excellent results and outperformed the approaches of previous works.
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CEO of Professional Science Editing for Scientists @ prosciediting.com