10,608 research outputs found
Further studies on the exclusive productions of () via annihilation at the factories
By including the interference effect between the QCD and the QED diagrams, we
carry out a complete analysis on the exclusive productions of () at the factories with GeV at
the next-to-leading-order (NLO) level in , within the nonrelativistic
QCD framework. It is found that the -order terms
that represent the tree-level interference are comparable with the usual NLO
QCD corrections, especially for the and cases. To
explore the effect of the higher-order terms, namely , we perform the QCD corrections to these -order terms for the first time, which are found to be able
to significantly influence the -order results.
In particular, in the case of and , the newly calculated
-order terms can to a large extent counteract
the 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 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
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