General, Strong Impurity-Strength Dependence of Quasiparticle Interference

Quasiparticle interference (QPI) patterns in momentum space are often assumed to be independent of the strength of the impurity potential when compared with other quantities, such as the joint density of states. Here, using the $T$-matrix theory, we show that this assumption breaks down completely even in the simplest case of a single-site impurity on the square lattice with an $s$ orbital per site. Then, we predict from first-principles, a very rich, impurity-strength-dependent structure in the QPI pattern of TaAs, an archetype Weyl semimetal. This study thus demonstrates that the consideration of the details of the scattering impurity including the impurity strength is essential for interpreting Fourier-transform scanning tunneling spectroscopy experiments in general.Comment: main manuscript: 8 pages, 6 figures, Supplementary Information: 3 pages, 6 figure

High-fidelity 3D Human Digitization from Single 2K Resolution Images

High-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2K human dataset and infers 3D human models from 2K resolution images. The proposed method separately recovers the global shape of a human and its details. The low-resolution depth network predicts the global structure from a low-resolution image, and the part-wise image-to-normal network predicts the details of the 3D human body structure. The high-resolution depth network merges the global 3D shape and the detailed structures to infer the high-resolution front and back side depth maps. Finally, an off-the-shelf mesh generator reconstructs the full 3D human model, which are available at https://github.com/SangHunHan92/2K2K. In addition, we also provide 2,050 3D human models, including texture maps, 3D joints, and SMPL parameters for research purposes. In experiments, we demonstrate competitive performance over the recent works on various datasets.Comment: code page : https://github.com/SangHunHan92/2K2K, Accepted to CVPR 2023 (Highlight

One-dimensional hexagonal boron nitride conducting channel

Hexagonal boron nitride (hBN) is an insulating two-dimensional (2D) material with a large bandgap. Although known for its interfacing with other 2D materials and structural similarities to graphene, the potential use of hBN in 2D electronics is limited by its insulating nature. Here, we report atomically sharp twin boundaries at AA???/AB stacking boundaries in chemical vapor deposition???synthesized few-layer hBN. We find that the twin boundary is composed of a 6???6??? configuration, showing conducting feature with a zero bandgap. Furthermore, the formation mechanism of the atomically sharp twin boundaries is suggested by an analogy with stacking combinations of AA???/AB based on the observations of extended Klein edges at the layer boundaries of ABstacked hBN. The atomically sharp AA???/AB stacking boundary is promising as an ultimate 1D electron channel embedded in insulating pristine hBN. This study will provide insights into the fabrication of single-hBN electronic devices

MulGuisin, a Topological Network Finder and its Performance on Galaxy Clustering

We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and network-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm find networks most efficiently and it defines galaxy networks in a way that most closely resembles human vision.Comment: 15 pages,12 figure