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$\sigma$ level, the neutrino flux ratios, $R\equiv\phi(\nu_\mu)/(\phi(\nu_e)+\phi(\nu_\tau))$ and $S\equiv\phi(\nu_e)/\phi(\nu_\tau)$, 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 $G$, and furthermore, they proposed a conjecture that for every graph $G$, \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