Comparison of NˉN\bar{\hbox{N}}\hbox{N} optical models

We compare the strong part of the $\bar{\hbox{N}}\hbox{N}$ interaction obtained by the Nijmegen partial wave analysis and the results of some of the most popular $\bar{\hbox{N}}\hbox{N}$ optical potentials in configuration space. We have found severe discrepancies in most of the partial waves, especially above $p_{Lab}$=400 MeV/c where the partial wave analysis displays a resonant-like structure in the $^{31}$S$_0$ and $^{33}$P$_0$ waves. Some theoretical difficulties to interpret this behaviour in terms of dynamical resonances are pointed pout and an alternative explanation is suggested. A much better stability is observed in the low energy parameters, apart from some discrepancies due to the presence of near-threshold quasi-bound states in particular waves. Large deviations have also been found between the corresponding potentials, at short and medium-range ($r\gtrsim 1$ fm) distances

Epitaxy of SrTiO3 on Silicon: The Knitting Machine Strategy

Saint-Girons, Guillaume et al.SrTiO3 (STO) crystalline layers grown on Si open unique perspectives for the monolithic integration of functional oxides in silicon-based devices, but their fabrication by molecular beam epitaxy (MBE) is challenging due to unwanted interfacial reactions. Here we show that the formation of single-crystal STO layers on Si by MBE at the moderate growth temperature imposed by these interface reactions results from the crystallization of a partially separated amorphous mixture of SrO and TiO2 activated by an excess of Sr. We identify the atomic pathway of this mechanism and show that it leads to an antiphase domain morphology. On the basis of these results, we suggest and test alternative STO growth strategies to avoid antiphase boundary formation and significantly improve the STO structural quality. The understanding provided by these results offers promising prospects to crystallize perovskite oxides on semiconductors at moderate temperature and circumvent the issue of parasitic interface reactions.This work was partly supported by the European projects SITOGA (STREP FP7, grant number 619456) and TIPS (ICT H2020, grant number 107347) as well as by the French ANR programs HIRIS and DIAMWAFEL.Peer reviewe

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding. In this paper, we propose the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42). The code and pre-trained LayoutLM models are publicly available at \url{https://aka.ms/layoutlm}.Comment: KDD 202