Further studies on the exclusive productions of J/ψ+χcJJ/\psi+\chi_{cJ} (J=0,1,2J=0,1,2) via e+e−e^+e^- annihilation at the BB factories

By including the interference effect between the QCD and the QED diagrams, we carry out a complete analysis on the exclusive productions of $e^+e^- \to J/\psi+\chi_{cJ}$ ($J=0,1,2$) at the $B$ factories with $\sqrt{s}=10.6$ GeV at the next-to-leading-order (NLO) level in $\alpha_s$, within the nonrelativistic QCD framework. It is found that the $\mathcal O (\alpha^3\alpha_s)$-order terms that represent the tree-level interference are comparable with the usual NLO QCD corrections, especially for the $\chi_{c1}$ and $\chi_{c2}$ cases. To explore the effect of the higher-order terms, namely $\mathcal O (\alpha^3\alpha_s^2)$, we perform the QCD corrections to these $\mathcal O (\alpha^3\alpha_s)$-order terms for the first time, which are found to be able to significantly influence the $\mathcal O (\alpha^3\alpha_s)$-order results. In particular, in the case of $\chi_{c1}$ and $\chi_{c2}$, the newly calculated $\mathcal O (\alpha^3\alpha_s^2)$-order terms can to a large extent counteract the $\mathcal O (\alpha^3\alpha_s)$ contributions, evidently indicating the indispensability of the corrections. In addition, we find that, as the collision energy rises, the percentage of the interference effect in the total cross section will increase rapidly, especially for the $\chi_{c1}$ case.Comment: 10 pages, 4 figures. Accepted for publication in EPJ

A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the "policy layer", is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the "execution layer", is a short-term optimization-based controller that tracks the reference trajecotries given by the "policy layer" with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the "policy layer" to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the "policy layer" can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework