Quantifying chaos: practical estimation of the correlation dimension

Be it a physical object or a mathematical model, a nonlinear dynamical system can display complicated aperiodic behavior, or "chaos." In many cases, this chaos is associated with motion on a strange attractor in the system's phase space. And the dimension of the strange attractor indicates the effective number of degrees of freedom in the dynamical system.

In this thesis, we investigate numerical issues involved with estimating the dimension of a strange attractor from a finite time series of measurements on the dynamical system.

Of the various definitions of dimension, we argue that the correlation dimension is the most efficiently calculable and we remark further that it is the most commonly calculated. We are concerned with the practical problems that arise in attempting to compute the correlation dimension. We deal with geometrical effects (due to the inexact self-similarity of the attractor), dynamical effects (due to the nonindependence of points generated by the dynamical system that defines the attractor), and statistical effects (due to the finite number of points that sample the attractor). We propose a modification of the standard algorithm, which eliminates a specific effect due to autocorrelation, and a new implementation of the correlation algorithm, which is computationally efficient.

Finally, we apply the algorithm to chaotic data from the Caltech tokamak and the Texas tokamak (TEXT); we conclude that plasma turbulence is not a low- dimensional phenomenon.

A biochemical and structural characterization of Drosophila neuroglian

A number of cell-cell interactions in the nervous system are mediated by immunoglobulin gene superfamily members. For example, neuroglian, a homophilic neural cell adhesion molecule in Drosophila, has an extracellular portion comprising six C- 2 type immunoglobulin-like domains followed by five fibronectin type III (FnIII) repeats. Neuroglian shares this domain organization and significant sequence identity with Ll, a murine neural adhesion molecule that could be a functional homologue. Here I report the crystal structure of a proteolytic fragment containing the first two FnIII repeats of neuroglian (NgFn 1,2) at 2.0Å. The interpretation of photomicrographs of rotary shadowed Ng, the entire extracellular portion of neuroglian, and NgFnl-5, the five neuroglian Fn III domains, is also discussed.

The structure of NgFn 1,2 consists of two roughly cylindrical β-barrel structural motifs arranged in a head-to-tail fashion with the domains meeting at an angle of ~120, as defined by the cylinder axes. The folding topology of each domain is identical to that previously observed for single FnIII domains from tenascin and fibronectin. The domains of NgFn1,2 are related by an approximate two fold screw axis that is nearly parallel to the longest dimension of the fragment. Assuming this relative orientation is a general property of tandem FnIII repeats, the multiple tandem FnIII domains in neuroglian and other proteins are modeled as thin straight rods with two domain zig-zag repeats. When combined with the dimensions of pairs of tandem immunoglobulin-like domains from CD4 and CD2, this model suggests that neuroglian is a long narrow molecule (20 - 30 Å in diameter) that extends up to 370Å from the cell surface.

In photomicrographs, rotary shadowed Ng and NgFn1-5 appear to be highly flexible rod-like molecules. NgFn 1-5 is observed to bend in at least two positions and has a mean total length consistent with models generated from the NgFn1,2 structure. Ng molecules have up to four bends and a mean total length of 392 Å, consistent with a head-to-tail packing of neuroglian's C2-type domains.

A generalized Hausdorff dimension for functions and sets

Let E be a compact subset of the n-dimensional unit cube, 1_n, and let C be a collection of convex bodies, all of positive n-dimensional Lebesgue measure, such that C contains bodies with arbitrarily small measure. The dimension of E with respect to the covering class C is defined to be the number

d_C(E) = sup(β:H_{β, C}(E) > 0),

where H_{β, C} is the outer measure

inf(Ʃm(C_i)^β:UC_i Ↄ E, C_i ϵ C) .

Only the one and two-dimensional cases are studied. Moreover, the covering classes considered are those consisting of intervals and rectangles, parallel to the coordinate axes, and those closed under translations. A covering class is identified with a set of points in the left-open portion, 1’_n, of 1_n, whose closure intersects 1_n - 1’_n. For n = 2, the outer measure H_{β, C} is adopted in place of the usual:

Inf(Ʃ(diam. (C_i))^β: UC_i Ↄ E, C_i ϵ C),

for the purpose of studying the influence of the shape of the covering sets on the dimension d_C(E).

If E is a closed set in 1₁, let M(E) be the class of all non-decreasing functions μ(x), supported on E with μ(x) = 0, x ≤ 0 and μ(x) = 1, x ≥ 1. Define for each μ ϵ M(E),

d_C(μ) = lim/c → inf/0 log ∆μ(c)/log c , (c ϵ C)

where ∆μ(c) = v/x (μ(x+c) – μ(x)). It is shown that

d_C(E) = sup (d_C(μ):μ ϵ M(E)).

This notion of dimension is extended to a certain class Ӻ of sub-additive functions, and the problem of studying the behavior of d_C(E) as a function of the covering class C is reduced to the study of d_C(f) where f ϵ Ӻ. Specifically, the set of points in 1₁,

(*) {d_B(F), d_C(f)): f ϵ Ӻ}

is characterized by a comparison of the relative positions of the points of B and C. A region of the form (*) is always closed and doubly-starred with respect to the points (0, 0) and (1, 1). Conversely, given any closed region in 1₂, doubly-starred with respect to (0, 0) and (1, 1), there are covering classes B and C such that (*) is exactly that region. All of the results are shown to apply to the dimension of closed sets E. Similar results can be obtained when a finite number of covering classes are considered.

In two dimensions, the notion of dimension is extended to the class M, of functions f(x, y), non-decreasing in x and y, supported on 1₂ with f(x, y) = 0 for x · y = 0 and f(1, 1) = 1, by the formula

d_C(f) = lim/s · t → inf/0 log ∆f(s, t)/log s · t , (s, t) ϵ C

where

∆f(s, t) = V/x, y (f(x+s, y+t) – f(x+s, y) – f(x, y+t) + f(x, t)).

A characterization of the equivalence d_C1(f) = d_C2(f) for all f ϵ M, is given by comparison of the gaps in the sets of products s · t and quotients s/t, (s, t) ϵ C_i (I = 1, 2).