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Mixing can be intuitively explained by the following example, first used by Gibbs in introducing the concept of mixing.
This can be intuitively explained by the difficulty that most clustering algorithms have of dealing with nominal values.
Furthermore, comparing Fig. 8a, b, we observe that (I_{max }^m) decreases with interest which can be intuitively explained by the fact that for our considered parameters the corresponding condition stated at the end of "Optimal control framework for information diffusion" is satisfied.
This can be intuitively explained by the fact that the spatial interpolation depends mainly on the spatial correlation among the sensor nodes, and a higher node density means a stronger spatial correlation among the data samples, thus a better estimation fidelity.
The resulting processing gain of the frequency domain-based approach can be intuitively explained: By processing only the subchannels bearing a pilot, half the noise power present at unoccupied subchannels is abandoned and not used in the metric, whereas in the time domain metric, no equivalent noise filtering takes place.
These results can be intuitively explained by interactive cryptic effects between the shapes of the contrasting markings and attributes of the background.
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This behavior is intuitively explained by looking in to the network topology.
This result is intuitively explained by the authors as follows: Too strong diffusion diminishes the stochasticity of dynamics and so impedes the transition, whereas too weak diffusion prevents replicators from encountering each other and, thus, from catalyzing each other's replication.
These phenomena can be intuitively explained as follows.
These limiting behaviors can be intuitively explained.
Can this be intuitively explained?" Author response: We tried to do this.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com