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  1. Free, publicly-accessible full text available September 1, 2023
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  5. Breaking the time-reversal symmetry on the surface of a topological insulator can open a gap for the linear dispersion and make the Dirac fermions massive. This can be achieved by either doping a topological insulator with magnetic elements or proximity-coupling it to magnetic insulators. While the exchange gap can be directly imaged in the former case, measuring it at the buried magnetic insulator/topological insulator interface remains to be challenging. Here, we report the observation of a large nonlinear Hall effect in iron garnet/Bi2Se3 heterostructures. Besides illuminating its magnetic origin, we also show that this nonlinear Hall effect can be utilizedmore »to measure the size of the exchange gap and the magnetic-proximity onset temperature. Our results demonstrate the nonlinear Hall effect as a spectroscopic tool to probe the modified band structure at magnetic insulator/topological insulator interfaces.« less
    Free, publicly-accessible full text available March 12, 2023
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  7. Graphs/Networks are common in real-world applications where data have rich content and complex relationships. The increasing popularity also motivates many network learning algorithms, such as community detection, clustering, classification, and embedding learning, etc.. In reality, the large network volumes often hider a direct use of learning algorithms to the graphs. As a result, it is desirable to have the flexibility to condense a network to an arbitrary size, with well-preserved network topology and node content information. In this paper, we propose a graph compression network (GEN) to achieve network compression and embedding at the same time. Our theme is tomore »leverage the network topology to find node mappings, such that densely connected nodes, including their node content, are compressed as a new node, with a latent vector (i.e. embedding) being learned to represent the compressed node. In addition to compression learning, we also develop a novel encoding-decoding framework, using feature diffusion process, to "decompress" the condensed network. Different from traditional graph convolution which uses direct-neighbor message passing, our decompression advocates high-order message passing within compressed nodes to learning feature representation for all nodes in the network. A unique strength of GEN is that it leverages the graph neural network principle to learn mapping automatically, so one can compress a network to an arbitrary size, and also decompress it to the original node space with minimum information loss. Experiments and comparisons confirm that GEN can automatically find clusters and communities, and compress them as new nodes. Results also show that GEN achieves improved performance for numerous tasks, including graph classification and node clustering.« less
    Free, publicly-accessible full text available December 1, 2022
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  9. The Dirac B-spline R-matrix (DBSR) method is employed to treat low-energy electron collisions with thallium atoms. Special emphasis is placed on spin polarization phenomena that are investigated through calculations of the differential cross-section and the spin asymmetry function. Overall, good agreement between the present calculations and the available experimental measurements is found. The contributions of electron exchange to the spin asymmetry cannot be ignored at low impact energies, while the spin–orbit interaction plays an increasingly significant role as the impact energy rises.
    Free, publicly-accessible full text available December 1, 2022
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