Two-stage acceleration of interstellar ions driven by high-energy lepton plasma flows

We present the particle-in-cell (PIC) simulation results of the interaction of a high-energy lepton plasma flow with background electron-proton plasma and focus on the acceleration processes of the protons. It is found that the acceleration follows a two-stage process. In the first stage, protons are significantly accelerated transversely (perpendicular to the lepton flow) by the turbulent magnetic field "islands" generated via the strong Weibel-type instabilities. The accelerated protons shows a perfect inverse-power energy spectrum. As the interaction continues, a shockwave structure forms and the protons in front of the shockwave are reflected at twice of the shock speed, resulting in a quasi-monoenergetic peak located near 200 MeV under the simulation parameters. The presented scenario of ion acceleration may be relevant to cosmic-ray generation in some astrophysical environments

Plate-Like Structure Damage Acoustic Emission Beamforming Array Technique and Probability-Based Diagnostic Imaging Method

A novel beamforming array technique and probability-based diagnostic imaging method are proposed to determine the acoustic emission (AE) source in plate-like structures. The technique that differs from common beamforming array techniques, in particular a sensor network, is used instead of a linear sensor array, to highlight information on the AE source location in one coordinate system as energy distribution. To reduce the uncertainty, avoid the boundary reflection effect, and ensure the rationality of the signal superposition, a Hilbert transform-based signal processing is applied before the delay-and-sum algorithm and a probability-based diagnostic imaging method is developed for AE source localization. The finite element numerical simulation method and the pencil-lead-broken experiment on aluminum plate are also conducted, and a thin-walled cylinder pipe-like structure is also tested by the pencil-lead-broken experiment to develop the application of the proposed method in various fields. The results indicate that this method is efficient and capable of visually showing the localization results highlighted in the probability images

Understanding Expressivity of GNN in Rule Learning

Rule learning is critical to improving knowledge graph (KG) reasoning due to their ability to provide logical and interpretable explanations. Recently, Graph Neural Networks (GNNs) with tail entity scoring achieve the state-of-the-art performance on KG reasoning. However, the theoretical understandings for these GNNs are either lacking or focusing on single-relational graphs, leaving what the kind of rules these GNNs can learn an open problem. We propose to fill the above gap in this paper. Specifically, GNNs with tail entity scoring are unified into a common framework. Then, we analyze their expressivity by formally describing the rule structures they can learn and theoretically demonstrating their superiority. These results further inspire us to propose a novel labeling strategy to learn more rules in KG reasoning. Experimental results are consistent with our theoretical findings and verify the effectiveness of our proposed method. The code is publicly available at https://github.com/LARS-research/Rule-learning-expressivity.Comment: 24 pages, 6 figure