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Our method can improve the efficiency of data slicing, reduce the energy consumption of nodes, prolong the network life time and maintain a good privacy preservation level in the same time.
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When this is done, we have the tables of data slices according to distributions of DescriptorName_Pairs.
"1" means the data slice ID, and it represent the data between initial data slices of frequencies "1" and "2".
For slices of data sets slice i (i = 1, 2,···, k) in, level distribution ld can be expressed by formula ld i = slice i - slice i +1 where i = 1, 2,···, k - 1.
The capability and efficiency of this slicing algorithm are demonstrated by examples.
In the process of data mining, we construct an data slicing algorithm called discrete derivatives.
Google maintains a page dedicated to the energy efficiency of its data centers.
A downloads page provides access to a number of pre-generated data slices [134].
Secondly, the coding efficiency of the slice parallelism using OpenMP will be presented for the four-slice case.
Various parameters were used to measure the efficiency of the SLiCE reactions under each experimental condition.
Deep learning experts have proposed several ways to tackle the problem of data efficiency.
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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