284,651 research outputs found
Comment on "Mass and K Lambda coupling of N*(1535)"
It is argued in [1] that when the strong coupling to the K Lambda channel is
considered, Breit-Wigner mass of the lightest orbital excitation of the nucleon
N(1535) shifts to a lower value. The new value turned out to be smaller than
the mass of the lightest radial excitation N(1440), which effectively solved
the long-standing problem of conventional constituent quark models. In this
Comment we show that it is not the Breit-Wigner mass of N(1535) that is
decreased, but its bare mass.
[1] B. C. Liu and B. S. Zou, Phys. Rev. Lett. 96, 042002 (2006).Comment: 3 pages, comment on "Mass and K Lambda coupling of N*(1535)", B. C.
Liu and B. S. Zou, Phys. Rev. Lett. 96, 042002 (2006
WP - liu hua
An installation commenting on the tragic death of a Chinese student studying at Wimbledon College of Arts and the historical relationship between China and Europe, referencing Orientalism, Chinoiserie and the Willow Pattern design
Multispectral Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection is essential for around-the-clock
applications, e.g., surveillance and autonomous driving. We deeply analyze
Faster R-CNN for multispectral pedestrian detection task and then model it into
a convolutional network (ConvNet) fusion problem. Further, we discover that
ConvNet-based pedestrian detectors trained by color or thermal images
separately provide complementary information in discriminating human instances.
Thus there is a large potential to improve pedestrian detection by using color
and thermal images in DNNs simultaneously. We carefully design four ConvNet
fusion architectures that integrate two-branch ConvNets on different DNNs
stages, all of which yield better performance compared with the baseline
detector. Our experimental results on KAIST pedestrian benchmark show that the
Halfway Fusion model that performs fusion on the middle-level convolutional
features outperforms the baseline method by 11% and yields a missing rate 3.5%
lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora
Neutrino Flavor Ratio on Earth and at Astrophysical Sources
We present the reconstruction of neutrino flavor ratios at astrophysical
sources. For distinguishing the pion source and the muon-damped source to the
3 level, the neutrino flux ratios,
and
, need to be measured in accuracies better
than 10%.Comment: 3 pages, 8 figures. Talk presented by T.C. Liu in ERICE 2009, Sicily
One-Shot Learning for Semantic Segmentation
Low-shot learning methods for image classification support learning from
sparse data. We extend these techniques to support dense semantic image
segmentation. Specifically, we train a network that, given a small set of
annotated images, produces parameters for a Fully Convolutional Network (FCN).
We use this FCN to perform dense pixel-level prediction on a test image for the
new semantic class. Our architecture shows a 25% relative meanIoU improvement
compared to the best baseline methods for one-shot segmentation on unseen
classes in the PASCAL VOC 2012 dataset and is at least 3 times faster.Comment: To appear in the proceedings of the British Machine Vision Conference
(BMVC) 2017. The code is available at https://github.com/lzzcd001/OSLS
The Laplacian energy of random graphs
Gutman {\it et al.} introduced the concepts of energy \En(G) and Laplacian
energy \EnL(G) for a simple graph , and furthermore, they proposed a
conjecture that for every graph , \En(G) is not more than \EnL(G).
Unfortunately, the conjecture turns out to be incorrect since Liu {\it et al.}
and Stevanovi\'c {\it et al.} constructed counterexamples. However, So {\it et
al.} verified the conjecture for bipartite graphs. In the present paper, we
obtain, for a random graph, the lower and upper bounds of the Laplacian energy,
and show that the conjecture is true for almost all graphs.Comment: 14 page
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