Electron-phonon driven charge density wave in CuTe

The compound CuTe (vulcanite) undergoes a quasi one dimensional charge density wave (CDW) at $T< T_{\mathrm{CDW}}=335$ K with a $5\times1\times2$ periodicity. The mechanism at its origin is debated. Several theoretical works claimed that semilocal functionals are unable to describe its occurrence and ascribed its formation only to strong electron-electron interaction. Moreover, the possible role of quantum anharmonicity has not been addressed. Here, by performing quantum anharmonic calculations, we show that semilocal functionals correctly describe the occurrence of a CDW in CuTe if ultradense electron momentum grids allowing for small electronic temperatures are used. The distortion is driven by the perfect nesting among 1D Fermi surface sheets extending in the $k_y$ direction. Quantum anharmonic effects are important and tend to suppress both the distortion and $T_{\mathrm{CDW}}$. The quantum anharmonic structural minimization of the CDW phase in the generalized gradient approximation leads, however, to distorted Te-Te bond lengths in the low temperature phase that are $21\%$ of the experimental ones at $T=20$ K. This suggests that, even if the electron-electron interaction is not crucial for the mechanism of CDW formation, it is relevant to accurately describe the structural data for the low-T phase. We assess the effect of correlation on the CDW by using the DFT+U+V approximation with parameters calculated from first principles. We find that correlation enhances the Te-Te distortion, T$_{CDW}$ and the total energy gain by the distortion.Comment: 9 pages, 8 figures, to appear on Phys. Rev.

Metro systems : Construction, operation and impacts

Peer reviewedPublisher PD

Food-delivery behavior under crowd sourcing mobility services

The rapid development of the online food-delivery industry, has led to not only increases in the number of the crowd-sourced shared food-delivery service drivers on our roads, but also growing urban traffic safety management concerns. This study investigates the decision-making behaviors that exist between delivery drivers, their food-delivery platform and their potential impact on traffic safety. Using the evolutionary game theory, stakeholder decision-making behaviors involving traffic safety within the food-delivery industry were analyzed. From our analysis, several behavioral influencers were identified, including penalties for traffic violations, the opportunity cost of delivery drivers complying with traffic rules, the costs associated with risk and strict management approaches, reputation incentives, costs related to the delivery platform being punished, the probability of compliance with traffic rules, and the probability of adopting a strict management approach by the delivery platform. Our study demonstrates that stabilization strategies used by the food service industry differ when the types of government control measures also differ. When the government takes a more aggressive approach to regulation and control, compliance with the traffic rules and the adoption of strict enforcement measures by management are the only evolutionary stability strategies available to food-delivery platforms. As part of a strict management strategy, appropriate compensation or incentive measures should be provided by the distribution platform. Furthermore, the fines given for traffic violations should be increased to create a safer road environment that has fewer traffic accidents involving food-delivery drivers

RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models

The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. Our work leverages existing diffusion models to guide changes in the designated 3D content. Specifically, we semantically select the target object and a pre-trained diffusion model will guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF. Experiment results show that our algorithm is effective for editing 3D objects in NeRF under different text prompts, including editing appearance, shape, and more. We validate our method on both real-world datasets and synthetic-world datasets for these editing tasks. Please visit https://repaintnerf.github.io for a better view of our results.Comment: IJCAI 2023 Accepted (Main Track