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So efficiently scheduling instances such that the number of powered servers is reduced is a very tough challenge.
Put simply, the chosen hyperplane separates the instances such that the margins between the closest points on each side, called support vectors, are maximised.
Using (mixed) integer linear programming, we are able to compute the theoretical optima for some of these instances such that the performance of the different algorithms can be compared with these optima.
In particular, for all the instances such that i+w=m, the probability that B j is decodable is close to 1 when q=256, while it is less than 0.4 when q=2.
One can make use of this aspect to reduce the total energy consumption by splitting on-chip memory into multiple instances such that frequently accessed segments of the address space reside in separate small memory instances.
This is a generalization of Support Vector Machines, a type of classifier which attempts to find a minimal-margin separator, which is a hyperplane in the space of instances such that one class is on one side of the hyperplane and the other class is on the other side, with the distance between each class's instances and the hyperplane being maximized.
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Instances such as that in Table 1 illustrate that the two procedures may give opposite results.
One may add more sensors (the open ended coaxial waveguide resonator sensor shown in Figure 2, for instance) such that an overdetermined non-linear system of equations is obtained.
Moreover, as the analysis window is strictly limited to the closed phase [17], another practical issue may arise: this time-frame might be too short (for high-pitched voices for instance) such that the analysis becomes ineffective [16].
The problem statement is defined as follows: Find an allocation α of memory instances and a mapping β that assigns each application profile to exactly one memory instance such that α and β yield the lowest power consumption of all possible allocations and mappings while satisfying area and user-defined constraints.
It remains to show how to transform the MTO instance such that the conflict graph of the new instance is identical to G c ′.
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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