Fully open-flavor tetraquark states bcqˉsˉbc\bar{q}\bar{s} and scqˉbˉsc\bar{q}\bar{b} with JP=0+,1+J^{P}=0^{+},1^{+}

We have studied the masses for fully open-flavor tetraquark states $bc\bar{q}\bar{s}$ and $sc\bar{q}\bar{b}$ with quantum numbers $J^{P}=0^{+},1^{+}$. We systematically construct all diquark-antiquark interpolating currents and calculate the two-point correlation functions and spectral densities in the framework of QCD sum rule method. Our calculations show that the masses are about $7.1-7.2$ GeV for the $bc\bar{q}\bar{s}$ tetraquark states and $7.0-7.1$ GeV for the $sc\bar{q}\bar{b}$ tetraquarks. The masses of $bc\bar{q}\bar{s}$ tetraquarks are below the thresholds of $\bar{B}_{s}D$ and $\bar{B}_{s}^{*}D$ final states for the scalar and axial-vector channels respectively. The $sc\bar{q}\bar{b}$ tetraquark states with $J^{P}=1^{+}$ lie below the $B_{c}^{+}K^{*}$ and $B_{s}^{*}D$ thresholds. Such low masses for these possible tetraquark states indicate that they can only decay via weak interaction and thus are very narrow and stable.Comment: 17 pages, 4 figure

Design and Implementation of a FPGA and DSP Based MIMO Radar Imaging System

The work presented in this paper is aimed at the implementation of a real-time multiple-input multiple-output (MIMO) imaging radar used for area surveillance. In this radar, the equivalent virtual array method and time-division technique are applied to make 16 virtual elements synthesized from the MIMO antenna array. The chirp signal generater is based on a combination of direct digital synthesizer (DDS) and phase locked loop (PLL). A signal conditioning circuit is used to deal with the coupling effect within the array. The signal processing platform is based on an efficient field programmable gates array (FPGA) and digital signal processor (DSP) pipeline where a robust beamforming imaging algorithm is running on. The radar system was evaluated through a real field experiment. Imaging capability and real-time performance shown in the results demonstrate the practical feasibility of the implementation

Measurements of spin and orbital parameters in Cen X-3 by Insight-HXMT

We present a detailed temporal analysis for the eclipsing high-mass X-ray binary system Cen X-3 using the Insight-HXMT data in 2018 and 2020. Three consecutive and high statistic observations among data are used for the precise timing analysis. The pulse profiles are revealed to vary with energy and time. The pulse profiles for the 2018 observations showed a double peak in the low energy bands below 10 keV and evolved to a single peak in higher energies without the correlation between pulse fraction and flux, and profiles in low energies changed with time. But the pulse profile for the 2020 observation only showed a broad single-peaked pulse in all energy bands with a positive relation between pulse fraction and flux, which may indicate the transition of the emission patterns from a mixture of a pencil and a fan beam to a dominated pencil-like beam. With performing a binary orbital fitting of spin periods, we obtain an accurate value for the spin period and the orbital parameters. The intrinsic spin period of the neutron star is found to be $4.79920 \pm 0.00006$ s at MJD 58852.697, with the orbital period determined at $P_{\rm orb}=2.08695634\pm 0.00000001$ day, and its decay rate of -(1.7832 $\pm$ 0.0001) $\times$ 10$^{-6}$ yr$^{-1}$ for the binary.Comment: 12 pages in the authors' version, reference: Journal of High Energy Astrophysics, 38 (2023), 32-4

Learning user-specific latent influence and susceptibility from information cascades

Predicting cascade dynamics has important implications for understanding information propagation and launching viral marketing. Previous works mainly adopt a pair-wise manner, modeling the propagation probability between pairs of users using n^2 independent parameters for n users. Consequently, these models suffer from severe overfitting problem, specially for pairs of users without direct interactions, limiting their prediction accuracy. Here we propose to model the cascade dynamics by learning two low-dimensional user-specific vectors from observed cascades, capturing their influence and susceptibility respectively. This model requires much less parameters and thus could combat overfitting problem. Moreover, this model could naturally model context-dependent factors like cumulative effect in information propagation. Extensive experiments on synthetic dataset and a large-scale microblogging dataset demonstrate that this model outperforms the existing pair-wise models at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015