Distorted-wave born approximation calculations for turbulence scattering in an upward-refracting atmosphere

Weiner and Keast observed that in an upward-refracting atmosphere, the relative sound pressure level versus range follows a characteristic 'step' function. The observed step function has recently been predicted qualitatively and quantitatively by including the effects of small-scale turbulence in a parabolic equation (PE) calculation. (Gilbert et al., J. Acoust. Soc. Am. 87, 2428-2437 (1990)). The PE results to single-scattering calculations based on the distorted-wave Born approximation (DWBA) are compared. The purpose is to obtain a better understanding of the physical mechanisms that produce the step-function. The PE calculations and DWBA calculations are compared to each other and to the data of Weiner and Keast for upwind propagation (strong upward refraction) and crosswind propagation (weak upward refraction) at frequencies of 424 Hz and 848 Hz. The DWBA calculations, which include only single scattering from turbulence, agree with the PE calculations and with the data in all cases except for upwind propagation at 848 Hz. Consequently, it appears that in all cases except one, the observed step function can be understood in terms of single scattering from an upward-refracted 'skywave' into the refractive shadow zone. For upwind propagation at 848 Hz, the DWBA calculation gives levels in the shadow zone that are much below both the PE and the data

A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning

In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints. To solve the problem, we first convert it to a dual problem and then propose a general primal-dual algorithm to optimize the primal and dual variables iteratively. In each optimization iteration, we employ a proximal operator to search optimal solution in the primal space. We prove convergence of the proposed algorithm and show its non-asymptotic convergence rate. By utilizing the proposed primal-dual optimization technique, we propose a novel metric learning algorithm which learns an optimal feature transformation matrix in the Riemannian space of positive definite matrices. Preliminary experimental results on an optimal fund selection problem in fund of funds (FOF) management for quantitative investment showed its efficacy.Comment: 8 pages, 2 figures, published as a conference paper in 2019 International Joint Conference on Neural Networks (IJCNN

A Graph Regularized Point Process Model For Event Propagation Sequence

Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph, where the event sequence propagates in a directed weighted graph whose nodes represent event marks (e.g., event types). Most existing works have only considered encoding sequential event history into event representation and ignored the information from the latent graph structure. Besides they also suffer from poor model explainability, i.e., failing to uncover causal influence across a wide variety of nodes. To address these problems, we propose a Graph Regularized Point Process (GRPP) that can be decomposed into: 1) a graph propagation model that characterizes the event interactions across nodes with neighbors and inductively learns node representations; 2) a temporal attentive intensity model, whose excitation and time decay factors of past events on the current event are constructed via the contextualization of the node embedding. Moreover, by applying a graph regularization method, GRPP provides model interpretability by uncovering influence strengths between nodes. Numerical experiments on various datasets show that GRPP outperforms existing models on both the propagation time and node prediction by notable margins.Comment: IJCNN 202

Online Mixed Discrete and Continuous Optimization: Algorithms, Regret Analysis and Applications

We study an online mixed discrete and continuous optimization problem where a decision maker interacts with an unknown environment for a number of $T$ rounds. At each round, the decision maker needs to first jointly choose a discrete and a continuous actions and then receives a reward associated with the chosen actions. The goal for the decision maker is to maximize the accumulative reward after $T$ rounds. We propose algorithms to solve the online mixed discrete and continuous optimization problem and prove that the algorithms yield sublinear regret in $T$. We show that a wide range of applications in practice fit into the framework of the online mixed discrete and continuous optimization problem, and apply the proposed algorithms to solve these applications with regret guarantees. We validate our theoretical results with numerical experiments

Similarity Principle and its Acoustical Verification

This study finds a similarity principle the waves emanated from the same source are similar to each other as long as two wave receivers are close enough to each other the closer to each other the wave receivers are the more similar to each other the received waves are We define the similarity mathematically and verify the similarity principle by acoustical experiment