Electric Character of Strange Stars

Using the Thomas-Fermi model, we investigated the electric characteristics of a static non-magnetized strange star without crust in this paper. The exact solutions of electron number density and electric field above the quark surface are obtained. These results are useful if we are concerned about physical processes near the quark matter surfaces of strange stars.Comment: 4 pages, 2 figures, LaTeX, Published in Chinese Physics Letters, Vol.16, p.77

Support theorem for stochastic variational inequalities

We prove a support theorem of the type of Stroock-Varadhan for solutions of stochastic variational inequalities

Half-lives of α\alpha-emitters approaching the N=Z line

The half-lives of newly observed $\alpha$-emitters $^{105}$Te and $^{109}$Xe [Seweryniak \textit{et al.}, Phys. Rev. C \textbf{73}, 061301(R) (2006); Liddick \textit{et al.}, Phys. Rev. Lett. \textbf{97}, 082501 (2006)] are investigated by the density-dependent cluster model. The half-lives of $\alpha$-emitters close to the N=Z line are also studied in a unified framework where the influence of the nuclear deformation is properly taken into account. Good agreement between model and data is obtained and some possible $\alpha$-emitters are suggested for future experiments.Comment: 9 pages, 3 figures, To appear in Phys. Rev.

Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in energy harvesting mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to the centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning. We prove the convergence of the proposed algorithm and analytically show that the learned policy has a simple monotone structure amenable to practical implementation. Our simulation results validate the efficacy of our algorithm, which significantly improves the edge computing performance compared to fixed or myopic optimization schemes and conventional reinforcement learning algorithms.Comment: arXiv admin note: text overlap with arXiv:1701.01090 by other author