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Figure 4 shows the distribution of data and test points.
Today's complex applications must face the distribution of data and code among different network nodes.
The IEL is a light-weight software integrator designed for managing the distribution of data and memory, coordinates communication among parallel processes, and directs execution of a set of loosely coupled numerical and physics tasks or modules.
Through its design, the distributed ASTRA toolbox allows implementing algorithms in Python using a high-level syntax that is close to the formal mathematical algorithm description, while performing the distribution of data and computation in a way that is almost hidden from the user.
We used the KS-test since it makes no assumption about the distribution of data and it is a non-parametric statistical test.
This clustering method makes no assumptions about the distribution of data and chooses the number of clusters automatically.
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We checked the distributions of data and performed tests of normality using the Kolmogorov-Smirnov test.
We propose to explore the distribution of data classes and optimize both the composition and the number of base learners to design an effective and compact code matrix.
The home network enables the distribution of data, voice, and video among consumer devices.
Further, this training is made without imposing any condition on the distribution of data (nonparametric) and under nonlinear conditions.
Different possibilities for the distribution of data are investigated, and the efficiencies determined.
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