A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collection

Unmanned aerial vehicles (UAVs) are promising for providing communication services due to their advantages in cost and mobility, especially in the context of the emerging Metaverse and Internet of Things (IoT). This paper considers a UAV-assisted Metaverse network, in which UAVs extend the coverage of the base station (BS) to collect the Metaverse data generated at roadside units (RSUs). Specifically, to improve the data collection efficiency, resource allocation and trajectory control are integrated into the system model. The time-dependent nature of the optimization problem makes it non-trivial to be solved by traditional convex optimization methods. Based on the proposed UAV-assisted Metaverse network system model, we design a hybrid framework with reinforcement learning and convex optimization to {cooperatively} solve the time-sequential optimization problem. Simulation results show that the proposed framework is able to reduce the mission completion time with a given transmission power resource.Comment: This paper appears in IEEE Network magazin

Tetra­kis(μ-naphthalene-1-acetato-κ2 O:O′)bis­[(N,N-dimethyl­formamide-κO)copper(II)]

The asymmetric unit of the title compound, [Cu2(C12H9O2)4(C3H7NO)2], contains two independent centrosymmetric dinuclear copper(II) complexes. The central paddle-wheel units are formed by four bridging bidentate naphthalene-1-acetate ligands with two dimethyl­formamide ligands in the axial positions. The unique CuII ions have slightly distorted square-pyramidal coordination geometries. One of the naphthalene rings is disordered over two sets of sites, with refined occpancies of 0.535 (4) and 0.465 (4)

Singularity Analysis for a 5-DoF FullySymmetrical Parallel Manipulator 5-RRR (RR

Abstract-A 5-DoF 3R2T (three dimensional rotation and two dimensional translation degrees of freedom) fully-symmetrical parallel manipulator can be adopted in many applications such as simulating the motion of spinal column. However, kinematics of this type parallel manipulator has not been studied enough because of short history. The study of kinematics of the manipulators leads inevitably to the problem of singular configuration. Singularity of a 5-DoF 3R2T fully-symmetrical parallel manipulator, 5-RRR(RR), is illustrated in this study. According to the singularity classification by Fang and Tsai, both limb singularity and actuation singularity are illustrated by screw theory and Grassmann geometry. The result of this study will be helpful for singularity analysis of 5-DoF 3R2T fully-symmetrical parallel manipulators because of their similar constraint property

Recovering the CMB Signal with Machine Learning

The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious foreground contaminations from several sources, such as galactic synchrotron and thermal dust emissions. Here, we build a deep convolutional neural network (CNN) to recover the tiny CMB signal from various huge foreground contaminations. Focusing on the CMB temperature fluctuations, we find that the CNN model can successfully recover the CMB temperature maps with high accuracy, and that the deviation of the recovered power spectrum $C_\ell$ is smaller than the cosmic variance at $\ell>10$. We then apply this method to the current Planck observation, and find that the recovered CMB is quite consistent with that disclosed by the Planck collaboration, which indicates that the CNN method can provide a promising approach to the component separation of CMB observations. Furthermore, we test the CNN method with simulated CMB polarization maps based on the CMB-S4 experiment. The result shows that both the EE and BB power spectra can be recovered with high accuracy. Therefore, this method will be helpful for the detection of primordial gravitational waves in current and future CMB experiments. The CNN is designed to analyze two-dimensional images, thus this method is not only able to process full-sky maps, but also partial-sky maps. Therefore, it can also be used for other similar experiments, such as radio surveys like the Square Kilometer Array.Comment: 19 pages, 25 figures, and 3 tables, ApJS, in press. The code repository is available at https://github.com/Guo-Jian-Wang/cmbNNC

Antitumor Cyclic Hexapeptides from Rubia Plants: History, Chemistry, and Mechanism (2005–2011)

Rubiaceae-type cyclopeptides (RAs), cyclic hexapeptides from Rubia plants, have shown potential antitumor activity in vitro and in vivo. Based on the review about plant cyclopeptides (Chem. Rev., 2006, 106: 840), this mini-review will highlight new progress on the discovery, synthesis, and mechanism of RAs isolated during 2005 to 2011, covering recent work in our group

The influence of net-quarks on the yields and rapidity spectra of identified hadrons

Within a quark combination model, we study systematically the yields and rapidity spectra of various hadrons in central Au+Au collisions at $\sqrt{s_{NN}}= 200$ GeV. We find that considering the difference in rapidity between net-quarks and newborn quarks, the data of multiplicities, rapidity distributions for $\pi^{\pm}$, $K^{\pm}$, $p(\bar{p})$ and, in particular the ratios of charged antihadron to hadron as a function of rapidity, can be well described. The effect of net-quarks on various hadrons is analysed, and the rapidity distributions for $K^{0}_{s}$, $\Lambda(\bar{\Lambda})$, $\Sigma^{+}(\bar{\Sigma}^{_-})$, $\mathrm{\Xi^{-}}$ ($\mathrm{\bar{\Xi}^{_+}}$) and $\mathrm{\Omega^{-}}(\mathrm{\bar{\Omega}}^{_+})$ are predicted. We discuss the rapidity distribution of net-baryon, and find that it reflects exactly the energy loss of colliding nuclei.Comment: 8 pages, 7 figure