Bulk-Fermi-Arc Transition Induced Large Photogalvanic Effect in Weyl Semimetals

The surface Fermi arc, as a hallmark of Weyl semimetals (WSMs), has been well known in current research, but it remains a challenge to unveil novel phenomena associated with the Fermi arc. Here, we predict a heretofore unrecognized process in WSMs, namely, the photoinduced transition between the bulk states and the Fermi arc. We find this process is significant and can lead to a large effective three-dimensional shift current on the boundaries with the Fermi arc in wide terahertz range. Moreover, due to the low symmetry of the boundaries, the surface photogalvanic effect predicted here can appear in a large class of WSMs that do not have bulk shift current. Hence, our work not only unveils a hidden photogalvanic effect in WSMs but also suggests that all the WSMs are promising material candidates for developing efficient terahertz photodetectors.Comment: 6 pages, 5 figure

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U

Analysis of nonlinear suspension power harvest potential

Because the power consumption of a controlled suspension is huge, the power harvest potential of a nonlinear controlled suspension is analyzed. Instead of simplifying the suspension to a linear model or adopting some control strategies to solve the problem, this paper investigates the effect of the nonlinear characteristics on the power harvesting potential. A mathematic model is introduced to calculate the nonlinear vibration, and the amount of harvested power was obtained using the multi-scale method. A numerical validation is carried out at the end of this study. The results show that the investigated mechanical parameters affect both the vibration amplitude and the induced current, while the electric parameters only affect the induced current. The power harvesting potential of the nonlinear suspension is generally greater than the linear suspension because the frequency band of the actual pavement also contains bandwidth surrounding the body resonance point. The only exception occurs if the vehicle travels on a road with a particular profile, e.g. a sine curve. To optimize harvested power, it is better to consider the nonlinear characteristics rather than simplifying the suspension to a linear model

Planar Hall effect in topological Weyl and nodal line semimetals

Using symmetry analysis and semiclassical Boltzmann equation, we theoretically explore the planar Hall effect (PHE) in three-dimensional materials. We demonstrate that PHE is a general phenomenon that can occur in various systems regardless of band topology. Both the Lorentz force and Berry curvature effects can induce significant PHE, and the leading contributions of both effects linearly depend on the electric and magnetic fields. The Lorentz force and Berry curvature PHE coefficient possess only antisymmetric and symmetric parts, respectively. Both contributions respect the same crystalline symmetry constraints but differ under time-reversal symmetry. Remarkably, for topological Weyl semimetal, the Berry curvature PHE coefficient is a constant that does not depends on the Fermi energy, while the Lorentz force contribution linearly increases with the Fermi energy, resulting from the linear dispersion of the Weyl point. Furthermore, we find that the PHE in topological nodal line semimetals is mainly induced by the Lorentz force, as the Berry curvature in these systems vanishes near the nodal line. Our study not only highlights the significance of the Lorentz force in PHE, but also reveals its unique characteristics, which will be beneficial for determining the Lorentz force contribution experimentally.Comment: 9 pages, 5 figure

5-Arc transitive cubic Cayley graphs on finite simple groups

AbstractIn this paper, we determine all connected 5-arc transitive cubic Cayley graphs on the alternating group A47; there are only two such graphs (up to isomorphism). By earlier work of the authors, these are the only two non-normal connected cubic arc-transitive Cayley graphs for finite nonabelian simple groups, and so this paper completes the classification of such non-normal Cayley graphs

GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis tool, GNNLens, to assist model developers and users in understanding and analyzing GNNs. Specifically, Parallel Sets View and Projection View enable users to quickly identify and validate error patterns in the set of wrong predictions; Graph View and Feature Matrix View offer a detailed analysis of individual nodes to assist users in forming hypotheses about the error patterns. Since GNNs jointly model the graph structure and the node features, we reveal the relative influences of the two types of information by comparing the predictions of three models: GNN, Multi-Layer Perceptron (MLP), and GNN Without Using Features (GNNWUF). Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.Comment: 15 page