High-efficiency generation of nanoscale single silicon vacancy defect array in silicon carbide

Color centers in silicon carbide have increasingly attracted attention in recent years owing to their excellent properties such as single photon emission, good photostability, and long spin coherence time even at room temperature. As compared to diamond which is widely used for holding Nitrogen-vacancy centers, SiC has the advantage in terms of large-scale, high-quality and low cost growth, as well as advanced fabrication technique in optoelectronics, leading to the prospects for large scale quantum engineering. In this paper, we report experimental demonstration of the generation of nanoscale $V_{Si}$ single defect array through ion implantation without the need of annealing. $V_{Si}$ defects are generated in pre-determined locations with resolution of tens of nanometers. This can help in integrating $V_{Si}$ defects with the photonic structures which, in turn, can improve the emission and collection efficiency of $V_{Si}$ defects when it is used in spin photonic quantum network. On the other hand, the defects are shallow and they are generated $\sim 40nm$ below the surface which can serve as critical resources in quantum sensing application

Redox condition and organic carbon accumulation mechanism in the Cryogenian Nanhua Basin, South China: Insights from iron chemistry and sulfur, carbon, oxygen isotopes of the Datangpo Formation

Global glaciation, oxidation event and eukaryotic expansion and diversification in the Neoproterozoic period are marked events that characterize the early evolution of the Earth, but how the interactions occurred among these events is not well understood. The organic matters preserved in the black shales of the Datangpo Formation (Cryogenian period) are sensitive to redox conditions, and thus its accumulation and preservation offer beneficial clues to unravel the early evolutional history of the Earth. This study presents new chemostratigraphic data of iron component, TOC content, sulfur isotope of pyrite, carbon and oxygen isotopes of carbonaceous shale of the Datangpo Formation (Cryogenian period) in the Datangpo section, South China. The analyzed results imply abundant nutrients existing in the ocean in the Early Cryogenian. The nutrients, such as phosphorus, resulting from neighbor volcanic eruptions, provided nutrients that enabled microbes to flourish during the Cryogenian interglacial gap. Iron components and sulfur isotopes indicated anoxic, euxinic deep water environments for the black shales in the lower portion of the Datangpo Formation. The anoxic setting was good for the preservation of organic matter, but terrigenous materials inputs, as revealed by the high Al2O3 contents, diluted the organic carbon content (TOC).Cited as: Wang, C., Shi, G. Redox condition and organic carbon accumulation mechanism in the Cryogenian Nanhua Basin, South China: Insights from iron chemistry and sulfur, carbon, oxygen isotopes of the Datangpo Formation. Advances in Geo-Energy Research, 2019, 3(1): 67-75, doi: 10.26804/ager.2019.01.0

PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector

Arbitrary-oriented object detection is a fundamental task in visual scenes involving aerial images and scene text. In this report, we present PP-YOLOE-R, an efficient anchor-free rotated object detector based on PP-YOLOE. We introduce a bag of useful tricks in PP-YOLOE-R to improve detection precision with marginal extra parameters and computational cost. As a result, PP-YOLOE-R-l and PP-YOLOE-R-x achieve 78.14 and 78.28 mAP respectively on DOTA 1.0 dataset with single-scale training and testing, which outperform almost all other rotated object detectors. With multi-scale training and testing, PP-YOLOE-R-l and PP-YOLOE-R-x further improve the detection precision to 80.02 and 80.73 mAP. In this case, PP-YOLOE-R-x surpasses all anchor-free methods and demonstrates competitive performance to state-of-the-art anchor-based two-stage models. Further, PP-YOLOE-R is deployment friendly and PP-YOLOE-R-s/m/l/x can reach 69.8/55.1/48.3/37.1 FPS respectively on RTX 2080 Ti with TensorRT and FP16-precision. Source code and pre-trained models are available at https://github.com/PaddlePaddle/PaddleDetection, which is powered by https://github.com/PaddlePaddle/Paddle.Comment: 6 pages, 2 figures, 3 table