A Phenomenological Expression for Deuteron Electromagnetic Form Factors Based on Perturbative QCD Predictions

For deuteron electromagnetic form factors,perturbative QCD(pQCD) predicts that $G^{+}_{00}$ becomes the dominate helicity amplitude and that $G^{+}_{+0}$ and $G^{+}_{+-}$ are suppressed by factors $\Lambda_{\rm QCD}/Q$ and $\Lambda_{\rm QCD}^2/Q^2$ at large $Q^2$,respectively. We try to discuss the higher order corrections beyond the pQCD asymptotic predictions by interpolating an analytical form to the intermediate energy region. From fitting the data,our results show that the helicity-zero to zero matrix element $G^{+}_{00}$ dominates the gross structure function $A(Q^2)$ in both of the large and intermediate energy regions; it is a good approximation for $G^{+}_{+-}$ to ignore the higher order contributions and the higher order corrections to $G^{+}_{+0}$ should be taken into account due to sizeable contributions in the intermediate energy region.Comment: 9 pages,3 figure

Patch-based Progressive 3D Point Set Upsampling

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3