Research on Tomography by Using Seismic Reflection Wave in Laneway

AbstractAs a necessary step and an integral part of works in laneway, the geological prediction is an important means of reducing disaster losses and geological disasters in the works in laneway. This paper mainly discusses tunnel reflection tomography by using seismic reflection wave in laneway. The speed of elastic wave in front of tunnel face and the three-dimensional images can be figured out when the detector check the reflection of elastic wave from the focal points on the tunnel face. The location, size and depth of cave can be ascertained. According to the forecasts of laneway works in the Iron Mine Xishimen, tunnel reflection tomography by using seismic reflection wave can well forecast engineering geology and hydro-geological conditions in front of tunnel face. It will help to supply positive guidance for working plan and construction measures in laneway. Therefore, the construction safety and speed can be ensured, helping to lead to great practical significance and significant economic benefits

Probing the XYZXYZ states through radiative decays

In this work, we have adopted the spin rearrangement scheme in the heavy quark limit and extensively investigated three classes of the radiative decays: $\mathfrak{M}\to (b\bar{b})+\gamma$, $(b\bar{b})\to \mathfrak{M}+\gamma$, $\mathfrak{M} \to \mathfrak{M}^\prime+\gamma$, corresponding to the electromagnetic transitions between one molecular state and bottomonium, one bottomonium and molecular state, and two molecular states respectively. We also extend the same formalism to study the radiative decays of the molecular states with hidden charm. We have derived some model independent ratios when the initial or final states belong to the same spin flavor multiplet. Future experimental measurement of these ratios will test the molecular picture and explore the underlying structures of the $XYZ$ states.Comment: 21 pages, 10 tables Accepted by Phys.Rev.

PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud

The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging to fully explore. Existing works craft point operator under a single axis and reuse the crafted operator in all parts of 3D models. This overlooks the opportunity to better combine point interactions and dimensions by exploiting varying geometry/density of 3D point clouds. In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data. We establish a large search space to jointly consider versatile point interactions and point dimensions. This supports point operators with various geometry/density considerations. The enlarged search space with heterogeneous search components calls for a better ranking of candidate models. To achieve this, we improve the search space exploration by leveraging predictor-based Neural Architecture Search (NAS), and enhance the quality of prediction by assigning unique encoding to heterogeneous search components based on their priors. We thoroughly evaluate the networks crafted by PIDS on two semantic segmentation benchmarks, showing ~1% mIOU improvement on SemanticKITTI and S3DIS over state-of-the-art 3D models.Comment: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023: 1298-130