Invariant measures for random expanding on average Saussol maps

Several imaging algorithms including patch-based image denoising, image time series recovery, and convolutional neural networks can be thought of as methods that exploit the manifold structure of signals. While the empirical performance of these algorithms is impressive, the understanding of recovery of the signals and functions that live on manifold is less understood. In this paper, we focus on the recovery of signals that live on a union of surfaces. In particular, we consider signals living on a union of smooth band-limited surfaces in high dimensions. We show that an exponential mapping transforms the data to a union of low-dimensional subspaces. Using this relation, we introduce a sampling theoretical framework for the recovery of smooth surfaces from few samples and the learning of functions living on smooth surfaces. The low-rank property of the features is used to determine the number of measurements needed to recover the surface. Moreover, the low-rank property of the features also provides an efficient approach, which resembles a neural network, for the local representation of multidimensional functions on the surface. The direct representation of such a function in high dimensions often suffers from the curse of dimensionality; the large number of parameters would translate to the need for extensive training data. The low-rank property of the features can significantly reduce the number of parameters, which makes the computational structure attractive for learning and inference from limited labeled training data.

Over 100 years ago Harald Bohr identified a deep problem about the convergence of Dirichlet series, and introduced an ingenious idea relating Dirichlet series and holomorphic functions in high dimensions. Elaborating on this work, almost twnety years later Bohnenblust and Hille solved the problem posed by Bohr. In recent years there has been a substantial revival of interest in the research area opened up by these early contributions. This involves the intertwining of the classical work with modern functional analysis, harmonic analysis, infinite dimensional holomorphy and probability theory as well as analytic number theory. New challenging research problems have crystallized and been solved in recent decades. The goal of this book is to describe in detail some of the key elements of this new research area to a wide audience. The approach is based on three pillars: Dirichlet series, infinite dimensional holomorphy and harmonic analysis.

Errata to "Analysis and Applications of Holomorphic Functions in Higher Dimensions"

The fast escaping set of a transcendental entire function is the set of all points which tend to infinity under iteration as fast as compatible with the growth of the function. We study the analogous set for quasiregular mappings in higher dimensions and show, among other things, that various equivalent definitions of the fast escaping set for transcendental entire functions in the plane also coincide for quasiregular mappings. We also exhibit a class of quasiregular mappings for which the fast escaping set has the structure of a spider's web.

We study the boundary behavior of rational inner functions (RIFs) in dimensions three and higher from both analytic and geometric viewpoints. On the analytic side, we use the critical integrability of the derivative of a rational inner function of several variables to quantify the behavior of a RIF near its singularities, and on the geometric side we show that the unimodular level sets of a RIF convey information about its set of singularities. We then specialize to three-variable degree $(m,n,1)$ RIFs and conduct a detailed study of their derivative integrability, zero set and unimodular level set behavior, and non-tangential boundary values. Our results, coupled with constructions of non-trivial RIF examples, demonstrate that much of the nice behavior seen in the two-variable case is lost in higher dimensions.

A triangle statistic is proposed for testing the equality of two multivariate continuous distributions in high-dimensional settings based on sample interpoint distances. Given two independent p-dimensional random samples, a triangle can be formed by randomly selecting one observation from one sample and two observations from the other sample. The triangle statistic estimates the probability that the distance between the two observations from the same distribution is the largest, the middle or the smallest in the triangle being formed by these three observations. We show that the test based on the triangle statistic is asymptotically distribution-free under the null hypothesis of equal, but unknown continuous distribution functions. The triangle test is compared with other nonparametric tests through a simulation study. The triangle statistic is well defined when the number of variables p is larger than the number of observations m, and its computational complexity is independent of p, making it suitable for high-dimensional settings.

This study focuses on the key challenge of achieving high-precision guidance for spacecraft: real-time trajectory optimization. The highly efficient sequential convex programming (SCP) is a potential method to address this issue, but its convergence is difficult to guarantee. To enhance the convergence performance of SCP, this paper proposes a high-dimensional merit-function-based SCP method. The main contribution of this paper is threefold. Firstly, the traditional one-dimensional penalty function used to ensure the convergence of SCP is extended to a high-dimensional merit function, which avoids using the penalty parameter that is difficult to determine in advance. Secondly, to match the high-dimensional merit function, a feasibility-then-optimality convergence strategy is proposed, which is essentially an application of the iterative direction descent property in higher dimensions. Finally, based on the high-dimensional merit function and the feasibility-then-optimality strategy, an iterative algorithm is developed, and its convergence analysis is provided under certain assumptions. In numerical simulations, the effectiveness and robustness of the proposed algorithm are validated using the reentry trajectory optimization problem of a lunar capsule.

In this article we establish an alternative proof of the generalized Fueter method presented in a former paper [Qian, T. and Sommen, F., 2003, Deriving harmonic functions in higher dimensional spaces. Zeitschrift fur Analysis und ihre Anwendungen, 22(2), 275–288] leading to the construction of special harmonic and monogenic functions in higher dimensions. At the same time, we also obtain a generalization of this result. §Dedicated to Professor Guochun Wen on the occasion of his 75th birthday.

This research deals with the extended investigations on the concept of a chaos-driven evolutionary algorithm Differential Evolution (DE). This paper is aimed at the embedding of set of six discrete dissipative chaotic systems in the form of chaos pseudo random number generator for DE. Repeated simulations were performed on the set of two shifted benchmark test functions in higher dimensions. Finally, the obtained results are compared with canonical DE.

We consider co-rotational wave maps from Minkowski space in $d+1$ dimensions to the $d$-sphere. Recently, Bizo\'n and Biernat found explicit self-similar solutions for each dimension $d\geq 4$. We give a rigorous proof for the mode stability of these self-similar wave maps.

Nonuniform sampling and recovery of bandlimited functions in higher dimensions

We analyse the finite-time blow-up of solutions of the heat flow for k-corotational maps . For each dimension we construct a countable family of blow-up solutions via a method of matched asymptotics by glueing a re-scaled harmonic map to the singular self-similar solution: the equatorial map. We find that the blow-up rates of the constructed solutions are closely related to the eigenvalues of the self-similar solution. In the case of 1-corotational maps our solutions are stable and represent the generic blow-up.

This research deals with the hybridization of the two softcomputing fields, which are chaos theory and evolutionary computation. This paper aims on the investigations on the multi-chaos-driven evolutionary algorithm Differential Evolution (DE) concept. This paper is aimed at the embedding and alternating of set of two discrete dissipative chaotic systems in the form of chaos pseudo random number generators for the DE. In this paper the novel initial concept of DE/rand/1/bin strategy driven alternately by two chaotic maps (systems) is introduced. From the previous research, it follows that very promising results were obtained through the utilization of different chaotic maps, which have unique properties with connection to DE. The idea is then to connect these two different influences to the performance of DE into the one multi-chaotic concept. Repeated simulations were performed on the selected set of shifted benchmark functions in higher dimensions. Finally, the obtained results are compared with canonical DE.

In this letter, based on Hamiltonian identity we propose a generalized expression of the second hypervirial for an arbitrary central potential wave functions in higher dimensions D and then present a useful general recurrence relation among the off-diagonal multipolar matrix elements. We show that this new proposed recurrence formula is very powerful in deriving the recurrence relations among the hydrogenic matrix elements. Some important recurrence relations can be simply obtained. It is interesting to find that the selection rule is independent of the central potential V(r) for f = rk with k = 0, 2 and that the diagonal matrix elements of V′(r) are independent of the system eigenvalues for k = 1. The application of the general recurrence relation to the isotropic harmonic oscillator case is discussed briefly.

Hadamard type inequality for quasiconvex functions in higher dimensions