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

EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer

Style transfer has been an important topic both in computer vision and graphics. Since the seminal work of Gatys et al. first demonstrates the power of stylization through optimization in the deep feature space, quite a few approaches have achieved real-time arbitrary style transfer with straightforward statistic matching techniques. In this work, our key observation is that only considering features in the input style image for the global deep feature statistic matching or local patch swap may not always ensure a satisfactory style transfer; see e.g., Figure 1. Instead, we propose a novel transfer framework, EFANet, that aims to jointly analyze and better align exchangeable features extracted from content and style image pair. In this way, the style features from the style image seek for the best compatibility with the content information in the content image, leading to more structured stylization results. In addition, a new whitening loss is developed for purifying the computed content features and better fusion with styles in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.Comment: Accepted by AAAI 202

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

Newton's method and its hybrid with machine learning for Navier-Stokes Darcy Models discretized by mixed element methods

This paper focuses on discussing Newton's method and its hybrid with machine learning for the steady state Navier-Stokes Darcy model discretized by mixed element methods. First, a Newton iterative method is introduced for solving the relative discretized problem. It is proved technically that this method converges quadratically with the convergence rate independent of the finite element mesh size, under certain standard conditions. Later on, a deep learning algorithm is proposed for solving this nonlinear coupled problem. Following the ideas of an earlier work by Huang, Wang and Yang (2020), an Int-Deep algorithm is constructed by combining the previous two methods so as to further improve the computational efficiency and robustness. A series of numerical examples are reported to show the numerical performance of the proposed methods