Figure 1. Two-dimensional visualization of a cube by minimization of E 1.

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Figure 2. The visualization of the hypercube in Figure 2 lacks symmetry. Symmetry is restored when the algorithm is applied for three dimensional visualizations. The visualizations obtained show point-symmetry relative to the center of the Q i -structure. Figure 3.

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Figure 4. Consider the smallest squares on the surface of the hypercube. B n, n—2 , where B denotes the binomial value. For the j-th smallest square, the k-th distance between edge points is denoted ds jk. Similar distances can be determined also for the images Q i of the edge points P i in the visualization. With these notations, two additional error functions can be defined:.

Error function E 2 measures how well smallest squares in a hypercube are mapped on squares in the visualization. E 3 measures how well the visualization preserves distance relations for smallest cubes. Three-dimensional visualizations are shown in Figures Figure 5. Two visualizations for cubes obtained by minimization of E 2. Figure 6. Two-dimensional visualization of a hypercube for minimization of E 2. Figure 7. Two-dimensional visualization of a hypercube for minimization of E 3. Figure 8. Figure 9. Figure Angular variables corresponding to a branching process. In the previous section, the search algorithm varied Cartesian coordinates of edge points Q i.

For a different choice of variables, the points Q i can be obtained by a spatial branching process consisting of n subsequent bifurcations. At the first stage of the bifurcation process, two branches with starting point in the origin are defined. The endpoint of a branch is the starting point for two new branches at the next level, until, at stage n, 2 n endpoints are generated. The angles f and q can be adapted by the stepwise algorithm of the previous section, for any of the functions E 1 , E 2 or E 3.

## Retinal Vascular Branching in Healthy and Diabetic Subjects | IOVS | ARVO Journals

This yields a system with 2. In the examples in Figures , branches were given length inversely decreasing with the branching level. The configuration to which the branching system converges lacks symmetry, but the endpoints of the branches have point-symmetry. The algorithm can be run for symmetrically constrained branching systems.

In this case, half of the tree is constructed as a point-mirror of the other half, and the total number of parameters is divided by 2. Then, solutions with higher E 1 -values are obtained. This is illustrated in Figures , where symmetry was imposed, resulting in E 1 -values The illustrations are for branches of constant length except for the first two branches, which were given shorter length. These visualizations of hypercubes have a special aesthetic property.

The tree-structure in itself has no clear interpretation to an observer not informed about its purpose. In Figures , the visualization of the hypercube, when drawn apart from the underlying tree-structure, is also of limited appeal since the E 1 -value is relatively high, the fact that a hypercube is being visualized is not apparent.

But by matching the tree-structure with the with the visualization of the hypercube, each type of three-dimensional structure can be interpreted with help of the other. Two examples for angular variables and with branches of decreasing length. Visualization of the five-dimensional hypercube for angular variables. Visualizations of hypercubes with point symmetry imposed. The branching algorithm can be run in a sequential way.

## On The Exact Number Of Bifurcation Branches In A Square And In A Cube

This process is continued. At the k-th step, the angles associated with the 2 k new branches are allowed to vary. This results in a fast algorithm when compared to a non-sequential procedure. Such an algorithm was run several times. Branching structures with systematicity were never found, in the sense that angles between branches at later stages were not related in any systematic way to angles between branches at previous stages.

This was not helped if different dimensions were given different weight in the metric function.

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As a consequence, each additional dimension led to a smaller deformation of the metric relations obtained at a previous stage. The lack of systematicity limits the aesthetics of the branching forms. Structural properties appear to be hard to reconcile with metric constraints, and this difficulty increases as n increases. For values of n higher than 6, representations which are visually accessible, or which have aesthetic symmetry, can still be obtained if the metric procedure is replaced by structural considerations.

Structural visualizations of high-dimensional discrete spaces. The spatial branching process used in section 3 can be generalized into an alternative visualization procedure based on structural considerations. As a point of departure, a rectangular parallelepiped is constructed with sides b.

This structure is turned into a visualization of four-dimensional hypercube by splitting the smallest side-surfaces into two smaller rectangles with sides b. Then, the new rectangles split again in two smaller rectangles to yield a representation of a five-dimensional hypercube, and so on. For splitting processes with appropriate structural properties, aesthetic representations result. Splitting the upper side-surface of a rectangular parallelepiped.

An instance of a splitting process is defined as follows. We confine the definition to the splitting process at the top rectangle of the cube so that half of the visualization of the hypercube is generated; the other half is obtained by mirroring the resulting structure relative to the horizontal plane. The first three binary dimensions of the n-hypercube are associated with the edges of the parallelepiped.

The curves are defined as successions of t line segments. The starting point of the curves is the middle of the upper rectangle of the parallelepiped. The new rectangles are attached on top of these curves. They are obtained by scaling the top-surface of the parallelepiped, and are orthogonal to the segment on top of which they are drawn. After the rectangles corresponding to the fourth dimension are drawn, each of the curves splits into two new curves.

This process is continued until k equals n. In order to prevent the branching structure from curling too strongly onto itself, the parameter k is defined as a decreasing function of k. Figure 19 was drawn with continuous circular contours in order to ease visual track of the bifurcation process.

Upper part of a structural visualization of the thirteen dimensional hypercube. Example of an underlying bifurcating curve set. The aesthetics of bifurcating curve sets can be studied in its own right.

## Conserved properties of dendritic trees in four cortical interneuron subtypes

For different parameters, and for a constant value of k , a binary tree corresponding to ten bifurcations is shown in Figure The end-points of this structure are associated with a binary, ten-dimensional space. The procedure can be defined for p-ary spaces. The nature of the curve sets generated can be made more general also by giving the rules for d j and d q a more general form. If f and g are well chosen, end-points of branches develop in non-co-planar ways and with aesthetic spatial symmetry. The parameters and functions in these rules can be tuned toward a visually accessible representation of a p-ary space, or toward representations of aesthetic interest.

Form associated with a space of ten binary dimensions. Form corresponding to an eight-dimensional ternary space. In Figures , the curve-structure was enveloped with non-circular volumetric elements. For each curve, circles were drawn around the end-points of all line segments, and orthogonal to the curve. In case of a ternary space, for each line segment, there are two corresponding segments on alternative curve-continuations and which belong to the same stage in the bifurcation process. An enveloping circle was elongated in the direction of these alternative continuations.

The resulting contours were connected by lines. Transforms constructed as weighted vertex-based functions. The previous section aimed to obtain visualizations for high-dimensional spaces by exploration of the structural instead of the metric domain. This section returns to the metric visualizations of section 2. Turbulent fluids for those painters is always something with a scale idea in it.

I truly do want to know how to describe clouds. Somewhere the business of writing down partial differential equations is not to have done the work on the problem. For the exposition of the logistic equation, cf. He says cf. Davies, The Cosmic Blueprint , p. The significance of these numbers lies not in their values but in the fact that they crop up again and again in completely different contexts.

Evidently they represent a fundamental property of certain chaotic systems.