Community dynamics generates complex epidemiology through self-induced amplification and suppression

The development of quantitative models of outbreaks is key to their eventual control, from human and computer viruses through to social (and antisocial) activities. Standard epidemiological models can reproduce many general features of outbreaks. Unfortunately, the large temporal fluctuations which often dominate real-world data are thought to require more complicated, system-specific models involving super-spreaders, specific social network topologies and rewirings, and birth-death processes. However we show here that these large fluctuations have a generic explanation in terms of underlying community dynamics. Communities increasing (or decreasing) in size, act as instantaneous amplifiers (or suppressors) yielding a complex temporal evolution whose features vary dramatically according to the relative timescales of the community dynamics. We uncover, and provide an analytic theory for, a novel epidemiological phase transition driven by the population's response to an outbreak. An imminent epidemic will be suppressed if individual communities start to break up more frequently or join together less frequently, but will be amplified if the reverse is true

Impact on Travelers Hedonic and Utilitarian Shopping Behavior by Adoption of Mobile Application: Results from a Quasi-experiment

The continuing development of mobile technology has led to an explosion of mobile applications, which have exposed a broader consumer base to mobile consumption. It is currently unclear how mobile apps using will affect travelers’ shopping behavior, particularly from the perspective of the hedonic and utilitarian shopping behavior of travelers. Using a special quasi-experiment launching by an airline, we collected the datasets of more than 10000 travelers and to investigate the impact of mobile app on the travelers’ shopping behavior. The results suggested that mobile apps adoption improved travelers’ hedonic shopping behavior (e.g., ancillary services purchasing), while the utilitarian shopping conduct (e.g. booking tickets in advance) decreased. It was also found that the mobile app adoption increased hedonic shopping in males but decreased hedonic and utilitarian shopping in frequent flyers and members. This investigation can help with the management of travelers’ purchasing habits and provide guidance for industrial decision makers

On the Optimal Lower and Upper Complexity Bounds for a Class of Composite Optimization Problems

We study the optimal lower and upper complexity bounds for finding approximate solutions to the composite problem $\min_x\ f(x)+h(Ax-b)$, where $f$ is smooth and $h$ is convex. Given access to the proximal operator of $h$, for strongly convex, convex, and nonconvex $f$, we design efficient first order algorithms with complexities $\tilde{O}\left(\kappa_A\sqrt{\kappa_f}\log\left(1/{\epsilon}\right)\right)$, $\tilde{O}\left(\kappa_A\sqrt{L_f}D/\sqrt{\epsilon}\right)$, and $\tilde{O}\left(\kappa_A L_f\Delta/\epsilon^2\right)$, respectively. Here, $\kappa_A$ is the condition number of the matrix $A$ in the composition, $L_f$ is the smoothness constant of $f$, and $\kappa_f$ is the condition number of $f$ in the strongly convex case. $D$ is the initial point distance and $\Delta$ is the initial function value gap. Tight lower complexity bounds for the three cases are also derived and they match the upper bounds up to logarithmic factors, thereby demonstrating the optimality of both the upper and lower bounds proposed in this paper

Hierarchical-level rain image generative model based on GAN

Autonomous vehicles are exposed to various weather during operation, which is likely to trigger the performance limitations of the perception system, leading to the safety of the intended functionality (SOTIF) problems. To efficiently generate data for testing the performance of visual perception algorithms under various weather conditions, a hierarchical-level rain image generative model, rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on the generative adversarial network (GAN) and can generate images of light, medium, and heavy rain. Different rain intensities are introduced as labels in conditional GAN (CGAN). Meanwhile, the model structure is optimized and the training strategy is adjusted to alleviate the problem of mode collapse. In addition, natural rain images of different intensities are collected and processed for model training and validation. Compared with the two baseline models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the structural similarity (SSIM) is improved by 18% and 8%, respectively. The ablation experiments are also carried out to validate the effectiveness of the model tuning

Using Non-Additive Measure for Optimization-Based Nonlinear Classification

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2 – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are a relatively small number of training cases available (). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered

Chinese herb medicine in augmented reality

Augmented reality becomes popular in education gradually, which provides a contextual and adaptive learning experience. Here, we develop a Chinese herb medicine AR platform based the 3dsMax and the Unity that allows users to visualize and interact with the herb model and learn the related information. The users use their mobile camera to scan the 2D herb picture to trigger the presentation of 3D AR model and corresponding text information on the screen in real-time. The system shows good performance and has high accuracy for the identification of herbal medicine after interference test and occlusion test. Users can interact with the herb AR model by rotating, scaling, and viewing transformation, which effectively enhances learners' interest in Chinese herb medicine

Surface chemistry and structure manipulation of graphene-related materials to address the challenges of electrochemical energy storage

Energy storage devices are important components in portable electronics, electric vehicles, and the electrical distribution grid. Batteries and supercapacitors have achieved great success as the spearhead of electrochemical energy storage devices, but need to be further developed in order to meet the ever-increasing energy demands, especially attaining higher power and energy density, and longer cycling life. Rational design of electrode materials plays a critical role in developing energy storage systems with higher performance. Graphene, the well-known 2D allotrope of carbon, with a unique structure and excellent properties has been considered a “magic” material with its high energy storage capability, which can not only aid in addressing the issues of the state-of-the-art lithium-ion batteries and supercapacitors, but also be crucial in the so-called post Li-ion battery era covering different technologies, e.g., sodium ion batteries, lithium-sulfur batteries, structural batteries, and hybrid supercapacitors. In this feature article, we provide a comprehensive overview of the strategies developed in our research to create graphene-based composite electrodes with better ionic conductivity, electron mobility, specific surface area, mechanical properties, and device performance than state-of-the-art electrodes. We summarize the strategies of structure manipulation and surface modification with specific focus on tackling the existing challenges in electrodes for batteries and supercapacitors by exploiting the unique properties of graphene-related materials