13,588 research outputs found
The Moebius geometry of Wintgen ideal submanifolds
Wintgen ideal submanifolds in space forms are those ones attaining equality
pointwise in the so-called DDVV inequality which relates the scalar curvature,
the mean curvature and the scalar normal curvature. They are Moebius invariant
objects. The mean curvature sphere defines a conformal Gauss map into a
Grassmann manifold. We show that any Wintgen ideal submanifold has a Riemannian
submersion structure over a Riemann surface with the fibers being round
spheres. Then the conformal Gauss map is shown to be a super-conformal and
harmonic map from the underlying Riemann surface. Some of our previous results
are surveyed in the final part.Comment: This is a survey of our recent work on the Moebius geometry of
Wintgen ideal submanifolds, which also include two new important results.
Submitted to the the conference "ICM 2014 Satellite Conference on Real and
Complex Submanifolds
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with a position priori. Further, we employ
adversarial training to eliminate over-fitting due to the small sizes of
mammogram datasets. Multi-scale FCN is employed to improve the segmentation
performance. Experimental results on two public datasets, INbreast and
DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
than state-of-the-art approaches.
\footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with
arXiv:1612.0597
Implication the observed for studying the process
We study the charmonium reaction using
effective lagrangian approach where the contributions from well established
states are considered, and all parameters are fixed in the process of
at center of mass energy GeV.
The experimental data on the line shape of the mass distribution of the can be well reproduced. Based on the studying of , the total and differential cross sections of the reaction are predicted. At the same time we evaluated
also the cross sections of the reaction. It is
shown that the contribution of nucleon pole to this reaction is largest close
to the reaction threshold. However, the interference between nucleon pole and
the other nucleon resonance can still change the angle distributions
significantly. Those theoretical results may be test by the future experiments
at \overline{\mbox{P}}ANDA.Comment: 8 pages, 10 figures, and 4 tables. More discussions added and typos
corrected. Accepted by Eur. Phys. J.
Jet-dominated quiescent states in black hole X-ray binaries: the case of V404 Cyg
The dynamical and radiative properties of the quiescent state (X-ray
luminosity ) of black hole X-ray transients
(BHXTs) remains unclear, mainly because of low-luminosity and poor data
quantity. We demonstrate that, the simultaneous multi-wavelength (including
radio, optical, ultraviolet and X-ray bands) spectrum of V404 Cyg in its bright
quiescent state can be well described by the radiation from the companion star
and more importantly, the compact jet. Neither the outer thin disc nor the
inner hot accretion flow is important in the total spectrum. Together with
several additional recent findings, i.e. the power-law X-ray spectrum and the
constant X-ray spectral shape (or constant photon index) in contrast to the
dramatic change in the X-ray luminosity, we argue the quiescent state spectrum
of BHXTs is actually jet-dominated. Observational features consistent with this
jet model are also discussed as supporting evidences.Comment: accepted for the publication in MNRAS Letters, 5 pages, 2 figure
Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation
Mixture of Softmaxes (MoS) has been shown to be effective at addressing the
expressiveness limitation of Softmax-based models. Despite the known advantage,
MoS is practically sealed by its large consumption of memory and computational
time due to the need of computing multiple Softmaxes. In this work, we set out
to unleash the power of MoS in practical applications by investigating improved
word coding schemes, which could effectively reduce the vocabulary size and
hence relieve the memory and computation burden. We show both BPE and our
proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve
the time and memory consumption of MoS without performance losses. With MoS, we
achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English
corpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, on
the larger WMT 2014 machine translation dataset, our MoS-boosted Transformer
yields 29.5 BLEU score for English-to-German and 42.1 BLEU score for
English-to-French, outperforming the single-Softmax Transformer by 0.8 and 0.4
BLEU scores respectively and achieving the state-of-the-art result on WMT 2014
English-to-German task
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