Hong Kong Baptist University

About: Hong Kong Baptist University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: China & Population. The organization has 7811 authors who have published 18919 publications receiving 555274 citations. The organization is also known as: Hong Kong Baptist College & HKBU.

Luminescent organometallic poly(aryleneethynylene)s: functional properties towards implications in molecular optoelectronics

Abstract: This article highlights recent progress in the development of rigid-rod metallopolyyne polymers of late transition metals which can exhibit easily tunable luminescent and electronic properties. Considerable focus is placed on the design strategies towards the tuning capability of phosphorescence emission color and optical bandgap of this class of luminescent metallopolymers, and the evaluation of their suitability as emitters in light-emitting applications, optical power limiters for sensor protection and semiconductors in organometallic photovoltaic cells. An effective approach based on structural modification of the spacer group can be devised to achieve the full-color phosphorescence emission spanning almost the whole visible spectrum and to tune the material properties of these functional organometallic polymers. The major advances, ongoing challenges and future perspectives of this research frontier are also discussed.

Unsupervised Embedding Learning via Invariant and Spreading Instance Feature.

Abstract: This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories.