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The optimal distribution of vectors in three-dimensional space is not a trivial problem and resembles the task of equally distributing points on a sphere [30].
We begin distributing points along the limit cycle and then apply an instantaneous kick taking v ↦ v + ε, where ε = − 2.0, leaving w unchanged.
The thickness/clearance of the objects is visualized by distributing points on the visible surfaces of the objects and painting them with a unique color corresponding to the thickness/clearance values at those points.
For example, the Trees toolbox [10] creates dendrites by first distributing points in 3D space according to the density obtained by overlapping many examples of a given cell type, and then uses a minimal spanning tree to connect the points in a stochastic way that yet reproduces the connectivity and appearance of various neuron classes.
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The exponential distribution can be used to describe the length of intervals between uniformly distributed points.
The clouds of vertically distributed points correspond to debris fragments from the Cosmos-Iridium collision.
A square block array with the 4 corner blocks deleted, each block with uniform distributed points.
In addition, we develop algorithms that evenly distribute points to the extent possible while maintaining spatial locality.
We present an estimation method of fully-resolved/coarse-grained gradients from randomly distributed points in turbulence.
Thus, a possible application of sensor networks, in a field with distributed points, is the tracking problem of a dynamic object movement between these distributed points on the field.
Inverted generational distance (IGD) Let (P^*) be a set of uniformly distributed points on the Pareto front.
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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