Production of doubly heavy baryons via Higgs boson decays

We systematically analyzed the production of semi-inclusive doubly heavy baryons ($\Xi_{cc}$, $\Xi_{bc}$ and $\Xi_{bb}$) for the process $H^0 \rightarrow \Xi_{QQ'}+ \bar {Q'} + \bar {Q}$ through four main Higgs decay channels within the framework of non-relativistic QCD. The contributions from the intermediate diquark states, $\langle cc\rangle[^{1}S_{0}]_{\mathbf{6}}$, $\langle cc\rangle[^{3}S_{1}]_{\mathbf{\bar 3}}$, $\langle bc\rangle[^{3}S_{1}]_{\mathbf{\bar 3}/ \mathbf{6}}$, $\langle bc\rangle[^{1}S_{0}]_{\mathbf{\bar 3}/ \mathbf{6}}$, $\langle bb\rangle[^{1}S_{0}]_{\mathbf{6}}$ and $\langle bb\rangle[^{3}S_{1}]_{\mathbf{\bar 3}}$, have been taken into consideration. The differential distributions and three main sources of the theoretical uncertainties have been discussed. At the High Luminosity Large Hadron Collider, there will be about 0.43$\times10^4$ events of $\Xi_{cc}$, 6.32$\times10^4$ events of $\Xi_{bc}$ and 0.28$\times10^4$ events of $\Xi_{bb}$ produced per year. There are fewer events produced at the Circular Electron Positron Collider and the International Linear Collider, about $0.26\times 10^{2}$ events of $\Xi_{cc}$, $3.83\times 10^{2}$ events of $\Xi_{bc}$ and $0.17\times 10^{2}$ events of $\Xi_{bb}$ in operation.Comment: 15 pages, 3 figures, 7 table

Scheduling for Optimal Rate Allocation in Ad Hoc Networks With Heterogeneous Delay Constraints

This paper studies the problem of scheduling in single-hop wireless networks with real-time traffic, where every packet arrival has an associated deadline and a minimum fraction of packets must be transmitted before the end of the deadline. Using optimization and stochastic network theory we propose a framework to model the quality of service (QoS) requirements under delay constraints. The model allows for fairly general arrival models with heterogeneous constraints. The framework results in an optimal scheduling algorithm which fairly allocates data rates to all flows while meeting long-term delay demands. We also prove that under a simplified scenario our solution translates into a greedy strategy that makes optimal decisions with low complexity

A Fast Radio Burst Search Method for VLBI Observation

We introduce the cross spectrum based FRB (Fast Radio Burst) search method for VLBI observation. This method optimizes the fringe fitting scheme in geodetic VLBI data post processing, which fully utilizes the cross spectrum fringe phase information and therefore maximizes the power of single pulse signals. Working with cross spectrum greatly reduces the effect of radio frequency interference (RFI) compared with using auto spectrum. Single pulse detection confidence increases by cross identifying detections from multiple baselines. By combining the power of multiple baselines, we may improve the detection sensitivity. Our method is similar to that of coherent beam forming, but without the computational expense to form a great number of beams to cover the whole field of view of our telescopes. The data processing pipeline designed for this method is easy to implement and parallelize, which can be deployed in various kinds of VLBI observations. In particular, we point out that VGOS observations are very suitable for FRB search.Comment: Accepted for publication in A

SI-GAT: A method based on improved Graph Attention Network for sonar image classification

The existing sonar image classification methods based on deep learning are often analyzed in Euclidean space, only considering the local image features. For this reason, this paper presents a sonar classification method based on improved Graph Attention Network (GAT), namely SI-GAT, which is applicable to multiple types imaging sonar. This method quantifies the correlation relationship between nodes based on the joint calculation of color proximity and spatial proximity that represent the sonar characteristics in non-Euclidean space, then the KNN (K-Nearest Neighbor) algorithm is used to determine the neighborhood range and adjacency matrix in the graph attention mechanism, which are jointly considered with the attention coefficient matrix to construct the key part of the SI-GAT. This SI-GAT is superior to several CNN (Convolutional Neural Network) methods based on Euclidean space through validation of real data.Comment: 5 pages, 4 figure