We show that the inner distance inside a bounded planar domain is at most the one-dimensional Hausdorff measure of the boundary of the domain. We prove this sharp result by establishing an improved Painlevé length estimate for connected sets and by using the metric removability of totally disconnected sets, proven by Kalmykov, Kovalev, and Rajala. We also give a totally disconnected example showing that for general sets the Painlevé length bound ϰ(E)≤πH^1(E)is sharp.

In this paper we present a new variant of the Zhuang-Zi algorithm, which solves multivariate polynomial equations over a finite field by converting it into a single variable problem over a large extension field. The improvement is based on the newly developed concept of mutant in solving multivariate equations.

A sensor pixel integrates optical intensity across its extent, and we explore the role that this integration plays in phase space tomography. The literature is inconsistent in its treatment of this integration—some approaches model this integration explicitly, some approaches are ambiguous about whether this integration is taken into account, and still some approaches assume pixel values to be point samples of the optical intensity. We show that making a point-sample assumption results in apodization of and thus systematic error in the recovered ambiguity function, leading to underestimating the overall degree of coherence. We explore the severity of this effect using a Gaussian Schell-model source and discuss when this effect, as opposed to noise, is the dominant source of error in the retrieved state of coherence.

Anderson mixing (AM) is a powerful acceleration method for fixed-point iterations, but its computation requires storing many historical iterations. The extra memory footprint can be prohibitive when solving high-dimensional problems in a resource-limited machine. To reduce the memory overhead, we propose a novel class of short-term recurrence AM methods (ST-AM). The ST-AM methods only store two previous iterations with cheap corrections. We prove that the basic version of ST-AM is equivalent to the full-memory AM in strongly convex quadratic optimization, and with minor changes it has local linear convergence for solving general nonlinear fixed-point problems. We further analyze the convergence properties of the regularized ST-AM for nonconvex (stochastic) optimization. Finally, we apply ST-AM to several applications including solving root-finding problems and training neural networks. Experimental results show that ST-AM is competitive with the long-memory AM and outperforms many existing optimizers.

We propose a new basic trapdoor ℓIC (ℓ-Invertible Cycles) of the mixed field type for Multivariate Quadratic public key cryptosystems. This is the first new basic trapdoor since the invention of Unbalanced Oil and Vinegar in 1997. ℓIC can be considered an extended form of the well-known Matsumoto-Imai Scheme A (also MIA or C^∗), and share some features of stagewise triangular systems. However ℓIC has very distinctive properties of its own. In practice, ℓIC is much faster than MIA, and can even match the speed of single-field MQ schemes.