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We derive and prove an explicit formula for the sum of the fractional parts of certain geometric series. Although the proof is straightforward, we have been unable to locate any reference to this result. This summation formula allows us to efficiently analyze the average behavior of certain common nonlinear dynamical systems, such as the angle-doubling map, x → 2x modulo 1. In particular, one can use this information to analyze how the behavior of individual orbits deviates from the global average (called fluctuations). More generally, the formula is valid in R m , where expanding maps give rise to so-called number systems. To illustrate the usefulness in this setting, we compute the fluctuations of a certain map on the plane.

We show that if (X, d) is a metric space which admits a consistent covex geodesic bicombing, then we can construct a conical bicombing on CB(X), the hyperspace of nonempty, closed, bounded, and convex subsets of X (with the Hausdorff metric). If X is a normed space, this same method produces a consistent convex bicombing on CB(X). We follow this by examining a geodesic bicombing on the nonempty compact subsets of X, assuming X is a proper metric space.

We study statistical properties of the truncated flat spot map f t (x) defined in Figure 1. In particular, we investigate whether for large n, the deviations n−1 i=0 f i t (x 0 ) − 1 2 upon rescaling satisfy a Q-Gaussian distribution if x 0 and t are both independently and uniformly distributed on the unit circle. This was motivated by the fact that if f t is the rotation by t, then [2] found that in this case the rescaled deviations are distributed as a Q-Gaussian with Q = 2 (a Cauchy distribution). This is the only case where a non-trivial (i.e. Q = 1) Q-Gaussian has been analytically established in a conservative dynamical system.In this note, we prove that for the family considered here, lim n S n /n converges to a random variable with a curious distribution which is clearly not a Q-Gaussian. However, the tail of the distribution is very reminiscent of a Q-Gaussian with Q ≈ 0.7.