106 research outputs found
Comparison of optical models
We compare the strong part of the interaction
obtained by the Nijmegen partial wave analysis and the results of some of the
most popular optical potentials in configuration
space. We have found severe discrepancies in most of the partial waves,
especially above =400 MeV/c where the partial wave analysis displays a
resonant-like structure in the S and P 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 ( 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
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