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We advocate the idea of the natural-language-driven(NLD) simulation to efficiently produce the object interactions between multiple objects in the virtual road scenes, for teaching and testing the autonomous driving systems that should take quick action to avoid collision with obstacles with unpredictable motions. The NLD simulation allows the brief natural-language description to control the object interactions, significantly reducing the human efforts for creating a large amount of interaction data. To facilitate the research of NLD simulation, we collect the Language-to-Interaction(L2I) benchmark dataset with 120,000 natural-language descriptions of object interactions in 6 common types of road topologies. Each description is associated with the programming code, which the graphic render can use to visually reconstruct the object interactions in the virtual scenes. As a methodology contribution, we design SimCopilot to translate the interaction descriptions to the renderable code. We use the L2I dataset to evaluate SimCopilot's abilities to control the object motions, generate complex interactions, and generalize interactions across road topologies. The L2I dataset and the evaluation results motivate the relevant research of the NLD simulation., Comment: 12 pages, 6 figures

Dirac materials have been proposed as a new class of electron-based detectors for light dark-matter (DM) scattering or absorption, with predicted sensitivities far exceeding superconductors and superfluid helium. The superiority of Dirac materials originates from a significantly reduced in-medium dielectric response winning over the suppression of DM scattering owing to the limited phase space at the point-like Fermi surface. Here we propose a new route to enhance significantly the DM detection efficiency via strongly correlated topological semimetals. Specifically, by considering a strongly correlated Weyl semimetal model system, we demonstrate that the strong correlation-induced flatband effects can amplify the coupling and detection sensitivity to light DM particles by expanding the scattering phase space, while maintaining a weak dielectric in-medium response., Comment: 5+ pages, 4 embedded figures, and supplemental material

Recently, metasurfaces have experienced revolutionary growth in the sensing and superresolution imaging field, due to their enabling of subwavelength manipulation of electromagnetic waves. However, the addition of metasurfaces multiplies the complexity of retrieving target information from the detected fields. Besides, although the deep learning method affords a compelling platform for a series of electromagnetic problems, many studies mainly concentrate on resolving one single function and limit the research's versatility. In this study, a multifunctional deep neural network is demonstrated to reconstruct target information in a metasurface targets interactive system. Firstly, the interactive scenario is confirmed to tolerate the system noises in a primary verification experiment. Then, fed with the electric field distributions, the multitask deep neural network can not only sense the quantity and permittivity of targets but also generate superresolution images with high precision. The deep learning method provides another way to recover targets' diverse information in metasurface based target detection, accelerating the progression of target reconstruction areas. This methodology may also hold promise for inverse reconstruction or forward prediction problems in other electromagnetic scenarios.

We construct a semiclassical theory for the transport of topological junctions starting from a microscopic Hamiltonian that comprehensively includes the interplay among the Majorana qubit, the Josephson phase, and the dissipation process. With the path integral approach, we derive a set of semiclassical equations of motion that can be used to calculate the time evolution of the Josephson phase and the Majorana qubit. In the equations we reveal rich dynamical phenomena such as the qubit induced charge pumping, the effective spin-orbit torque, and the Gilbert damping. We demonstrate the influence of these dynamical phenomena on the transport signatures of the junction. We apply the theory to study the Shapiro steps of the junction, and find the suppression of the first Shapiro step due to the dynamical feedback of the Majorana qubit., Comment: 6 pages, 3 figures

Recent experimental breakthrough in magnetic Weyl semimetals have inspired exploration on the novel effects of various magnetic structures in these materials. Here we focus on a domain wall structure which connects two uniform domains with different magnetization directions. We study the topological superconducting state in presence of an s-wave superconducting pairing potential. By tuning the chemical potential, we can reach a topological state, where a chiral Majorana mode or zero-energy Majorana bound state is localized at the edges of the domain walls. This property allows a convenient braiding operation of Majorana modes by controlling the dynamics of domain walls., Comment: 8 pages, 8 figures

The Higgs mode associated with amplitude fluctuations of the superconducting gap in uniform superconductors usually is heavy, which makes its excitation and detection difficult. We report on the existence of a gapless Higgs mode in the Fulde-Ferrell-Larkin-Ovchinnikov states. This feature is originated from the Goldstone mode associated with the translation symmetry breaking. The existence of the gapless Higgs mode is demonstrated by using both a phenomenological model and microscopic Bardeen-Cooper-Schrieffer (BCS) theory. The gapless Higgs mode can avoid the decay into other low energy excitations, which renders it stable and detectable., Comment: 11 pages, 3 figures, LA-UR-21-24876