X(1576) and the Final State Interaction Effect

We study whether the broad peak X(1576) observed by BES Collaboration arises from the final state interaction effect of $\rho(1450,1700)$ decays. The interference effect could produce an enhancement around 1540 MeV in the $K^+K^-$ spectrum with typical interference phases. However, the branching ratio $B[J/\psi\to \pi^{0}\rho(1450,1700)]\cdot B[\rho(1450,1700)\to K^{+}K^{-}]$ from the final state interaction effect is far less than the experimental data.Comment: 6 pages, 4 figures. Some typos corrected, more discussion and references adde

The two-body open charm decays of Z+(4430)Z^+(4430)

The two-body open charm decays $Z^+(4430)\to D^{+}\bar{D}^{*0}, D^{*+}\bar{D}^{0}, D^{*+}\bar{D}^{*0}$ occur through the re-scattering mechanism and their branching ratios are strongly suppressed if $Z^+(4430)$ is a $D_1\bar D^*$ molecular state. In contrast, $Z^+(4430)$ falls apart into these modes easily with large phase space and they become the main decay modes if $Z^+(4430)$ is a tetraquark state. Experimental search of these two-body open charm modes and the hidden charm mode $\chi_{cJ}\rho$ will help distinguish different theoretical schemes.Comment: 6 pages, 3 figures, 1 tabl

Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning

With the Deep Neural Networks (DNNs) as a powerful function approximator, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks. Compared to DNNs with vanilla artificial neurons, the biologically plausible Spiking Neural Network (SNN) contains a diverse population of spiking neurons, making it naturally powerful on state representation with spatial and temporal information. Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding. For input coding, we apply population coding with dynamically receptive fields to directly encode each input state component. For neuronal coding, we propose different types of dynamic-neurons (containing 1st-order and 2nd-order neuronal dynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN is trained in conjunction with deep critic networks using the Twin Delayed Deep Deterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental results show that our TD3-PDSAN model achieves better performance than state-of-the-art models on four OpenAI gym benchmark tasks. It is an important attempt to improve RL with SNN towards the effective computation satisfying biological plausibility.Comment: 27 pages, 11 figures, accepted by Journal of Neural Network

Cell-Wall Mechanical Properties of Bamboo Investigated by In-Situ Imaging Nanoindentation

A novel in-situ imaging nanoindentation technique was used to investigate the cell-wall mechanical properties of bamboo fibers and parenchyma cells. In-situ imaging confirmed neither "piling up" nor "sinking in" occurred around the indentations in the cell walls. The load-displacement curves revealed different deformation mechanisms of the cell walls when indented, respectively, in the longitudinal and transverse direction of bamboo fibers. There existed significant differences in MOE between longitudinal (16.1 GPa) and transverse direction (5.91 GPa) for the cell walls of bamboo fibers, while no differences were significant in hardness. Furthermore, the measured longitudinal MOE and hardness of parenchyma cell walls were 5.8 GPa and 0.23 GPa. This corresponds to 33% and 63% of the corresponding value of bamboo fibers. It was found that the longitudinal MOE of the cells of bamboo fibers remained almost constant from the outer portion to the inner portion of bamboo culms, while hardness showed a decreasing tendency. It was concluded that the nanoindentation technique was capable of effectively characterizing the mechanical properties of bamboo at the cellular level, though it might underestimate the real longitudinal MOE of the cell walls. The results highlighted the extreme importance of locating indentations at the nano scale for the mechanical characterization of complicated natural biomaterials such as wood and bamboo