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Abstract: We consider the estimation problem of conditional quantile when multi-dimensional covariates are involved. To overcome the “curse of dimensionality” yet retain model flexibility, we propose two partially linear models for conditional quantiles: partially linear single-index models (QPLSIM) and partially linear additive models (QPLAM). The unknown univariate functions are estimated by penalized splines. An approximate iteratively reweighted penalized least square algorithm is developed. To facilitate model comparisons, we develop effective model degrees of freedom for penalized spline conditional quantiles. Two smoothing parameter selection criteria, Generalized Approximate Cross-validation (GACV) and Schwartz-type Information Criterion (SIC) are studied. Some asymptotic properties are established. Finite sample properties are investigated through simulation studies. Application to the Boston Housing data demonstrates the success of the proposed approach. Both simulations and real applications show encouraging results of the proposed estimators. [Copyright &y& Elsevier]

The vehicle routing problem with time windows (VRPTW) is critical in the fields of operations research and combinatorial optimization. To promote research on the multiobjective VRPTW, a time-dependent green VRPTW (TDGVRPTW) is introduced in this study. Subsequently, a Q-learning-based multiobjective evolutionary algorithm (QMOEA) is proposed to solve the TDGVRPTW, where three objectives are simultaneously considered: total duration of vehicles, energy consumption, and customer satisfaction. In QMOEA, a hybrid initial method is devised that includes four problem-specific heuristics, to generate initial solutions with a high level of quality and diversity. Then, considering the problem features, two Pareto-front-based crossover strategies are designed to learn from the approximate Pareto front to explore the search space and accelerate the convergence process. Moreover, five local search operators are selected by a Q-learning agent at the decision point, to enhance local search abilities. Finally, a set of instances based on a realistic logistic system is presented to verify the effectiveness and superiority of QMOEA. [ABSTRACT FROM AUTHOR]

• This paper propose a PRE based trusted authorization scheme for nodes on CloudIoT. • It can update the authorization assuredly when some nodes are hijacked. • In this scheme, cloud servers are fully used than the other work. • The analysis shows it has better efficiency than other works with the same properties. In CloudIoT platform, the data is collected and shared by different nodes of Internet of Things (IoT), and data is processed and stored based on cloud servers. It has increased the abilities of IoT on information computation. Meanwhile, it also has enriched the resource in cloud and improved integration of the Internet and human world. All of this offer advantages as well as the new challenges of information security and privacy protection. As the energy limitation of the nodes in IoT, they are particularly vulnerable. It is much easier to hijack the nodes than to attack the data center for hackers. Thus, it is a crucial and urgent issue to realize the trusted update of authorization of nodes. When some nodes are hijacked, both of the behaviors to upload data to servers and to download information from servers should be forbidden. Otherwise, it might cause the serious damage to the sensitive data and privacy of servers. In order to solve this problem, we proposed a Proxy Re-encryption based Trusted Authorization scheme for nodes on CloudIoT (PRTA). PRTA is based on the proxy re-encryption (PRE), and the cloud server will play the roles of data storing and re-encrypting, which would reach the full potential of cloud computing and reduce the cost of nodes. The node's status is taken as one of the parameters for data re-encryption and it is under the authorization servers' control, which could ensure the security and reliability of the data and be beneficial for the privacy protection in CloudIoT. Also, the authorization servers are divided into the downloading and uploading kinds, which will make the application range much wider. [ABSTRACT FROM AUTHOR]

We present the phase transition analysis of binary Bose–Einstein condensates (BECs) with spin–orbit (SO) and Rabi couplings in quasi-two-dimensional system under rotation. In particular, we investigate the superfluid properties induced by rotation and SO coupling within hydrodynamic theory which can explain the emergence of the domain wall. By calculating the evolution of angular momentum with respect to rotation frequency, we discover first-order phase transitions where the domain wall changes into the wall-vortex complex. On the other hand, the angular momentum changes continuously with SO coupling strength accompanying by the elongation of the domain wall along x -direction. When the Rabi coupling strength exceeds a critical value, we observe a transition from the domain wall to vortex lattice resulting from a significant change of angular momentum. A phase diagram demonstrating the boundary regime in the plane of SO and Rabi coupling strengths is obtained. Our result is not only motivated by the search for novel states of matter inaccessible to existing experiments, but also by the need to identify situations where the phase transitions can be benchmarked systematically. • Monitoring the ground-state solution of Gross–Pitaevskii equations. • Investigating the superfluid properties within hydrodynamic theory. • Discovering first-order phase transitions through calculating the evolution of angular momentum. • Finding the critical Rabi frequency where the domain wall collapse into a single vortex. [ABSTRACT FROM AUTHOR]

Abstract: The functional coefficient regression models assume that the regression coefficients vary with some “threshold” variable, providing appreciable flexibility in capturing the underlying dynamics in data and avoiding the so-called “curse of dimensionality” in multivariate nonparametric estimation. We first investigate the estimation, inference, and forecasting for the functional coefficient regression models with dependent observations via penalized splines. The P-spline approach, as a direct ridge regression shrinkage type global smoothing method, is computationally efficient and stable. With established fixed-knot asymptotics, inference is readily available. Exact inference can be obtained for fixed smoothing parameter , which is most appealing for finite samples. Our penalized spline approach gives an explicit model expression, which also enables multi-step-ahead forecasting via simulations. Furthermore, we examine different methods of choosing the important smoothing parameter : modified multi-fold cross-validation (MCV), generalized cross-validation (GCV), and an extension of empirical bias bandwidth selection (EBBS) to P-splines. In addition, we implement smoothing parameter selection using mixed model framework through restricted maximum likelihood (REML) for P-spline functional coefficient regression models with independent observations. The P-spline approach also easily allows different smoothness for different functional coefficients, which is enabled by assigning different penalty accordingly. We demonstrate the proposed approach by both simulation examples and a real data application. [Copyright &y& Elsevier]