1,546 research outputs found
sj-pdf-1-smm-10.1177_09622802211060520 - Supplemental material for Censored quantile regression based on multiply robust propensity scores
No full textSupplemental material, sj-pdf-1-smm-10.1177_09622802211060520 for Censored quantile regression based on multiply robust propensity scores by Xiaorui Wang, Guoyou Qin, Xinyuan Song and Yanlin Tang in Statistical Methods in Medical Research</p
Supplemental Material - Experimental study of a heparin-coated venous stent fabricated by atomic layer deposition
No full textSupplemental Material for Experimental study of a heparin-coated venous stent fabricated by atomic layer deposition by Xiaoying Li, Kunpeng Zhang, Xiaorui Jiang, Lei Wang, Tao Zhang, Xiaoming Zhang and Haijie Che in Journal of Biomaterials Applications.</p
Data for: Mechanical properties of wood columns with rectangular hollow cross section made of Spruce-Pine-Fir lumbers under Compression
No full textexperimental data for column
Figure 2 from: Wang X-R, Li M, Spence JR, Zhao J-C, Mamtimin S (2021) Haplodontium altunense (Bryaceae, Bryopsida), a new moss species from Northwest China. PhytoKeys 183: 9-19. https://doi.org/10.3897/phytokeys.183.71642
No full textFigure 2 Light micrographs of Haplodontium altunenseA–C plants (dry) D Capsule (dry) E capsule (wet) F annulus growing on the capsule mouth G annulus falling off the capsule mouth H dorsal views of median peristome showing the large papillae along the horizontal and median vertical lines I dorsal views of distal peristome showing adherent endostomial material and exostome teeth J transverse section of stem K transverse section of midleaf L transverse section of costa M leaf apex N median laminal cells O basal laminal cells P leaves. Photographed by Xiaorui Wang from the holotype (HBNU!)
Figure 2 from: Wang X-R, Li M, Spence JR, Zhao J-C, Mamtimin S (2021) Haplodontium altunense (Bryaceae, Bryopsida), a new moss species from Northwest China. PhytoKeys 183: 9-19. https://doi.org/10.3897/phytokeys.183.71642
No full textFigure 2 Light micrographs of Haplodontium altunenseA–C plants (dry) D Capsule (dry) E capsule (wet) F annulus growing on the capsule mouth G annulus falling off the capsule mouth H dorsal views of median peristome showing the large papillae along the horizontal and median vertical lines I dorsal views of distal peristome showing adherent endostomial material and exostome teeth J transverse section of stem K transverse section of midleaf L transverse section of costa M leaf apex N median laminal cells O basal laminal cells P leaves. Photographed by Xiaorui Wang from the holotype (HBNU!)
The Coupling Effect of Manufacturing Tolerances and Rotor Eccentricity on Cogging Torque
No full text
Power Improvement of an Aviation Hybrid Excitation Generator with Anti-Short-Circuit Capability by Variable Inductance Design
No full textGaussian dynamic convolution for efficient single-image segmentation
No full textInteractive single-image segmentation is ubiquitous in the scientific and commercial imaging software. Lightweight neural network is one practical and effective way to accomplish the single-image segmentation task. This work focuses on the single-image segmentation problem only with some seeds suchas scribbles. Inspired by the dynamic receptive field in the human being’s visual system, we propose the Gaussian dynamic convolution (GDC) to fast and efficiently aggregate the contextual information for neural networks. The core idea is randomly selecting the spatial sampling area according to the Gaussiandistribution offsets. Our GDC can be easily used as a module to build lightweight or complex segmentation networks. We adopt the proposed GDC to address the typical single-image segmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation. Experiments demonstrate that the GDC outperforms other existing convolutions on three benchmark segmentation datasets including Pascal-Context, Pascal-VOC 2012, and Cityscapes. Additional experiments are also conducted to illustrate that the GDC can produce richer and more vivid features compared with other convolutions. In general, our GDC is conducive to the convolutional neural networks to form an overall impression of the image
- …
