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

Path integral formulation of Hodge duality on the brane

In the warped compactification with a single Randall-Sundrum brane, a puzzling claim has been made that scalar fields can be bound to the brane but their Hodge dual higher-rank anti-symmetric tensors cannot. By explicitly requiring the Hodge duality, a prescription to resolve this puzzle was recently proposed by Duff and Liu. In this note, we implement the Hodge duality via path integral formulation in the presence of the background gravity fields of warped compactifications. It is shown that the prescription of Duff and Liu can be naturally understood within this framework.Comment: 7 pages, LaTe

Towards symmetric scheme for superdense coding between multiparties

Recently Liu, Long, Tong and Li [Phys. Rev. A 65, 022304 (2002)] have proposed a scheme for superdense coding between multiparties. This scheme seems to be highly asymmetric in the sense that only one sender effectively exploits entanglement. We show that this scheme can be modified in order to allow more senders to benefit of the entanglement enhanced information transmission.Comment: 6 page

Quantifying Facial Age by Posterior of Age Comparisons

We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio

Construction and optical-electrical properties of inorganic/organic heterojunction nanostructures

We have designed and synthesized a series of ordered inorganic/organic hybrid aggregate nanostructures of by self-assembly and self-organizing technique. The process and mechanism of growing hybrid aggregate nanostructures have been studied. The ability to tune the size and morphologies of hybrid aggregate nanostructures has been achieved by controlling reaction conditions. The effects of morphologies and size dependent on electrical and optical properties have been demonstrated. These semiconductor molecular hybrid aggregate nanostructures exhibit interesting electrical, optical, and optoelectronic properties for use in next-generation electronic and optoelectronic devices. REFERENCES [1] Liu, H. B.; Zuo, Z. C.; Guo, Y. B.; Li, Y. J.; Li, Y. L. Angew. Chem. Int. Ed. 2010, 49, 2705. [2] Huang, C. S.; Li, Y. L.; Song, Y. L.; Li, Y. J.; Liu, H. B.; Zhu, D. B. Adv. Mater. 2010, 22, 3532. [3] Wang, K.; Yang, H.; Qian, X. M.; Xue, Z.; Li, Y. J.; Liu, H. B.; Li, Y. L. Dalton Trans. 2014, 43, 11542. [4] Liu, H. B.; Wang, K.; Zhang, L.; Qian, X. M.; Y. J.; Li, Y. L. Dalton Trans. 2014, 43, 432. [5] Guo, Y. B.; Xu, L.; Liu, H. B.; Li, Y. J.; Che, C.-M.; Li, Y. L. Adv. Mater. 2015, 27, 985

Holistic, Instance-Level Human Parsing

Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.Comment: Poster at BMVC 201