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For certain classes of systems, the new algorithm attains optimality by expressing the Sparse Resultant as a single determinant.
It turns out that the proposed algorithm attains better design solution.
Numerical results show that the algorithm attains near optimal performance while having low computational complexity.
The chosen content-relocation algorithm attains content-availability improvements of up to 500% when a mobile user performs a request and compared against other existing solutions.
The outcome of the proposed algorithm attains the clustering accuracy of 89.6% for both Iris and Wine databases which ensures the better clustering performance.
The algorithm attains an attractive compression performance (Compression Ratio = 122.24, Percent Root Mean Square Difference = 0.02%, Cross Correlation Coefficient = 0.998845, etc).
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As depicted in Fig. 8, for each CPE configuration the algorithm attained similar lookup rates regardless of the prefix dataset.
Further, we execute the Bloomfwd in the 7250 processor, and the algorithm attained an additional speedup of about 2.4 × as compared to the execution in 7120P and a throughput of up to 169.65 Mlps.
The algorithm attained a maximum E that increased urine osmolality and inner medullary concentrating capability by 37.5% and 80.2%, respectively, above base-case values; the corresponding urine flow rate and the concentrations of NaCl and urea were all within or near reported experimental ranges.
We demonstrate that aggregation-based algorithms attain higher accuracies than exist- ing retrieval-based approaches, while scaling better with increasing numbers of worker segmentations.
Using similar computational techniques for the kinetic and continuum solvers, and employing intelligent domain decomposition algorithms attain the efficiency and numerical stability of the UFS.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com