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We simulated here for seed sets of sizes (n = 1 ldots 30) which are generated from different influence measures.
Suppose O 1,…,O n and P 1,…,P m are two 3-D rotation data sets of sizes n and m.
An average of 88% classification success rate is achieved using leave-one-out cross validation on five different well-chosen patient-control image sets of sizes from 15 to 27 subjects per disease class.
We simulated the information diffusion based on the IC model with time-respecting paths for seed sets of sizes (n = 1ldots 25) which are generated from different influence measures.
Moreover, several sets of sizes will be considered in the simulation, taking into account the SPV, inverter and number of batteries needed to achieve cost-effective, reliable and efficient performance in the optimisation process.
We randomly split the data into training and test sets of sizes 3500 and 1392, respectively, to evaluate the performance of our method.
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We investigate the existence of some large sets of size nine.
We analysed the performance of proposed architecture for different modulo sets of size up to ten.
The most outstanding result proved with this technique is a k-1 hardness result for the hitting set problem with sets of size k.
We employed congruent points sets of size N = 4.
From this, the number of non-isomorphic bond sets of a certain size can be calculated by a straightforward application of Pólya's Enumeration Theorem [33]: decalin has four non-isomorphic bond sets of size one, 182 non-isomorphic bond sets of size two, 47 non-isomorphic bond sets of size three, and 92 non-isomorphic bond sets of size four.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com