Exploration of nonlocalities in ensembles consisting of bipartite quantum states

It is revealed that ensembles consisting of multipartite quantum states can exhibit different kinds of nonlocalities. An operational measure is introduced to quantify nonlocalities in ensembles consisting of bipartite quantum states. Various upper and lower bounds for the measure are estimated and the exact values for ensembles consisting of mutually orthogonal maximally entangled bipartite states are evaluated.Comment: The title and some contents changed, 4 pages, no figure

Bearing angle based cooperative source localization

© 2014 IEEE. This paper deals with the cooperative source localization problem with the goal of having an accurate estimate of the coordinate of the source cooperatively by a group of unicycle-type mobile agents. Neither absolute positioning information nor a common sense of direction is shared by the agents. Each agent gets its estimate about the source's coordinate in its own local frame based on the bearing measurements about its neighbors (that may or may not include the source) together with its own linear and angular speed information. A continuous time estimation scheme and a distributed fusion scheme are proposed for this goal such that the source's relative coordinate can be estimated at any time by each agent no matter whether it can directly detect the source or not. The globally asymptotic convergence of the estimation scheme and the fusion scheme is rigorously analyzed. Simulation results are also provided to verify the effectiveness of the proposed algorithms

A unified analysis of stochastic momentum methods for deep learning

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Stochastic momentum methods have been widely adopted in training deep neural networks. However, their theoretical analysis of convergence of the training objective and the generalization error for prediction is still under-explored. This paper aims to bridge the gap between practice and theory by analyzing the stochastic gradient (SG) method, and the stochastic momentum methods including two famous variants, i.e., the stochastic heavy-ball (SHB) method and the stochastic variant of Nesterov's accelerated gradient (SNAG) method. We propose a framework that unifies the three variants. We then derive the convergence rates of the norm of gradient for the non-convex optimization problem, and analyze the generalization performance through the uniform stability approach. Particularly, the convergence analysis of the training objective exhibits that SHB and SNAG have no advantage over SG. However, the stability analysis shows that the momentum term can improve the stability of the learned model and hence improve the generalization performance. These theoretical insights verify the common wisdom and are also corroborated by our empirical analysis on deep learning

A Cartesian cut-cell based multiphase flow model for large-eddy simulation of three-dimensional wave-structure interaction

A multiphase flow numerical approach for performing large-eddy simulations of three-dimensional (3D) wave-structure interaction is presented in this study. The approach combines a volume-of-fluid method to capture the air-water interface and a Cartesian cut-cell method to deal with complex geometries. The filtered Navier–Stokes equations are discretised by the finite volume method with the PISO algorithm for velocity-pressure coupling and the dynamic Smagorinsky subgrid-scale model is used to compute the unresolved (subgrid) scales of turbulence. The versatility and robustness of the presented numerical approach are illustrated by applying it to solve various three-dimensional wave-structure interaction problems featuring complex geometries, such as a 3D travelling wave in a closed channel, a 3D solitary wave interacting with a vertical circular cylinder, a 3D solitary wave interacting with a horizontal thin plate, and a 3D focusing wave impacting on an FPSO-like structure. For all cases, convincing agreement between the numerical predictions and the corresponding experimental data and/or analytical or numerical solutions is obtained. In addition, for all cases, water surface profiles and turbulent vortical structures are presented and discussed

The Impact of Geopolitical Risks on Tourism Supply in Developing Economies: The Moderating Role of Social Globalization

Capital investment is vital for sustainable tourism growth, particularly in times of geopolitical turmoil. This study examines how tourism investment was influenced by geopolitical risks considering social globalization as a moderating factor. Data were collected from 18 developing economies between 1995 and 2018. The results from the fixed effects and the least squares dummy variable–corrected methods show that the geopolitical risks negatively affect capital investment in tourism, with social globalization playing a moderating role in alleviating the adverse effect. The results were robust to different measures and analyses. The study advances our understanding of sustainable tourism growth amid geopolitical turmoil. Policymakers, especially those from developing economies, are suggested to be vigilant about the media atmosphere of geopolitics and enhancing social globalization as a countermeasure against politically turbulent times. The study also provides implications for alleviating the impact of the global pandemic on tourism investment

The impact of consumer skepticism on blockchain-enabled sustainability disclosure in a supply chain

The growing recognition of sustainable supply chain practices is indisputable. Nevertheless, consumer skepticism regarding the credibility of product sustainability information, which includes environmental impact and social responsibility, poses a significant challenge. Blockchain-enabled disclosure has surfaced as a promising approach to address this skepticism. In this paper, a game-theoretical model is developed to investigate the investment strategy in blockchain-enabled disclosure within a supply chain composed of one retailer and two manufacturers, each selling products with varying levels of sustainability. Considering consumer skepticism, we assume that consumers who trust sustainability information are willing to pay a premium for sustainable products, while skeptical consumers are not. Our analysis suggests that blockchain-enabled disclosure can effectively increase consumer trust in sustainability information and promote sustainable practices. However, our findings reveal a potential pitfall: intensified market competition between manufacturers, leading to reduced profits for both, while the retailer persistently benefits from blockchain-enabled disclosure. Furthermore, we find non-monotonic effects of consumer skepticism on retailer and manufacturer profits, with certain conditions resulting in a decreased likelihood of investing in blockchain-enabled disclosure as skepticism increases. Lastly, we examine the government-mandated disclosure policy, illustrating that such policy can generate a win–win situation for society and the environment by improving social welfare and environmental performance