Production rates for hadrons, pentaquarks Θ+\Theta ^+ and Θ∗++\Theta ^{*++}, and di-baryon (ΩΩ)0+(\Omega\Omega)_{0^{+}} in relativistic heavy ion collisions by a quark combination model

The hadron production in relativistic heavy ion collisions is well described by the quark combination model. The mixed ratios for various hadrons and the transverse momentum spectra for long-life hadrons are predicted and agree with recent RHIC data. The production rates for the pentaquarks $\Theta ^+$, $\Theta ^{*++}$ and the di-baryon $(\Omega\Omega)_{0^{+}}$ are estimated, neglecting the effect from the transition amplitude for constituent quarks to form an exotic state.Comment: The difference between our model and other combination models is clarified. The scaled transverse momentum spectra for pions, kaons and protoms at both 130 AGeV and 200 AGeV are given, replacing the previous results in transverse momentum spectr

A Lite Distributed Semantic Communication System for Internet of Things

The rapid development of deep learning (DL) and widespread applications of Internet-of-Things (IoT) have made the devices smarter than before, and enabled them to perform more intelligent tasks. However, it is challenging for any IoT device to train and run DL models independently due to its limited computing capability. In this paper, we consider an IoT network where the cloud/edge platform performs the DL based semantic communication (DeepSC) model training and updating while IoT devices perform data collection and transmission based on the trained model. To make it affordable for IoT devices, we propose a lite distributed semantic communication system based on DL, named L-DeepSC, for text transmission with low complexity, where the data transmission from the IoT devices to the cloud/edge works at the semantic level to improve transmission efficiency. Particularly, by pruning the model redundancy and lowering the weight resolution, the L-DeepSC becomes affordable for IoT devices and the bandwidth required for model weight transmission between IoT devices and the cloud/edge is reduced significantly. Through analyzing the effects of fading channels in forward-propagation and back-propagation during the training of L-DeepSC, we develop a channel state information (CSI) aided training processing to decrease the effects of fading channels on transmission. Meanwhile, we tailor the semantic constellation to make it implementable on capacity-limited IoT devices. Simulation demonstrates that the proposed L-DeepSC achieves competitive performance compared with traditional methods, especially in the low signal-to-noise (SNR) region. In particular, while it can reach as large as 40x compression ratio without performance degradation.Comment: Accpeted by JSA

Compliance adaptation of an intrinsically soft ankle rehabilitation robot driven by pneumatic muscles

Pneumatic muscles (PMs)-driven robots become more and more popular in medical and rehabilitation field as the actuators are intrinsically complaint and thus are safer for patients than traditional rigid robots. This paper proposes a new compliance adaptation method of a soft ankle rehabilitation robot that is driven by four pneumatic muscles enabling three rotational movement degrees of freedom (DoFs). The stiffness of a PM is dominated by the nominal pressure. It is possible to control the robot joint compliance independently of the robot movement in task space. The controller is designed in joint space to regulate the compliance property of the soft robot by tuning the stiffness of each active link. Experiments in actual environment were conducted to verify the control scheme and results show that the robot compliance can be adjusted when provided changing nominal pressures and the robot assistance output can be regulated, which provides a feasible solution to implement the patient-cooperative training strategy