The Chinese University of Hong Kong

About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.

Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in hong kong

Abstract: The purpose of this study is to (1) identify addiction symptoms that are uniquely associated with mobile phone use among adolescents in Hong Kong; (2) examine how demographics and psychological attributes (such as leisure boredom, sensation seeking, and self‐esteem) of individuals are related to the addiction symptoms; and (3) explore how these attributes, mobile phone addiction symptoms, and social capital can predict improper use of the mobile phone. Data were gathered from a probability sample of 402 teenagers and young adults aged 14–20 in Hong Kong. Exploratory factor analysis identified four addiction symptoms: “losing control and receiving complaints,” “anxiety and craving,” “withdrawal/escape,” and “productivity loss.” Results show that the higher one scored on leisure boredom and sensation seeking, the higher the likelihood one was addicted. Conversely, subjects who scored high on self‐esteem demonstrated less of such tendency. As hypothesized, subjects who scored low on self‐esteem but high on s...

HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis

Abstract: Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems. Despite that the convolutional neural networks are remarkable in learning discriminative features from images, the learning of comprehensive features of pedestrians for fine-grained tasks remains an open problem. In this study, we propose a new attentionbased deep neural network, named as HydraPlus-Net (HPnet), that multi-directionally feeds the multi-level attention maps to different feature layers. The attentive deep features learned from the proposed HP-net bring unique advantages: (1) the model is capable of capturing multiple attentions from low-level to semantic-level, and (2) it explores the multi-scale selectiveness of attentive features to enrich the final feature representations for a pedestrian image. We demonstrate the effectiveness and generality of the proposed HP-net for pedestrian analysis on two tasks, i.e. pedestrian attribute recognition and person reidentification. Intensive experimental results have been provided to prove that the HP-net outperforms the state-of-theart methods on various datasets.

0D/2D Heterojunctions of Vanadate Quantum Dots/Graphitic Carbon Nitride Nanosheets for Enhanced Visible-Light-Driven Photocatalysis.

Abstract: 0D/2D heterojunctions, especially quantum dots (QDs)/nanosheets (NSs) have attracted significant attention for use of photoexcited electrons/holes due to their high charge mobility. Herein, unprecedent heterojunctions of vanadate (AgVO3 , BiVO4 , InVO4 and CuV2 O6 ) QDs/graphitic carbon nitride (g-C3 N4 ) NSs exhibiting multiple unique advances beyond traditional 0D/2D composites have been developed. The photoactive contribution, up-conversion absorption, and nitrogen coordinating sites of g-C3 N4 NSs, highly dispersed vanadate nanocrystals, as well as the strong coupling and band alignment between them lead to superior visible-light-driven photoelectrochemical (PEC) and photocatalytic performance, competing with the best reported photocatalysts. This work is expected to provide a new concept to construct multifunctional 0D/2D nanocomposites for a large variety of opto-electronic applications, not limited in photocatalysis.

Room temperature high-detectivity mid-infrared photodetectors based on black arsenic phosphorus

Abstract: The mid-infrared (MIR) spectral range, pertaining to important applications, such as molecular "fingerprint" imaging, remote sensing, free space telecommunication, and optical radar, is of particular scientific interest and technological importance. However, state-of-the-art materials for MIR detection are limited by intrinsic noise and inconvenient fabrication processes, resulting in high-cost photodetectors requiring cryogenic operation. We report black arsenic phosphorus-based long-wavelength IR photodetectors, with room temperature operation up to 8.2 μm, entering the second MIR atmospheric transmission window. Combined with a van der Waals heterojunction, room temperature-specific detectivity higher than 4.9 × 109 Jones was obtained in the 3- to 5-μm range. The photodetector works in a zero-bias photovoltaic mode, enabling fast photoresponse and low dark noise. Our van der Waals heterojunction photodetectors not only exemplify black arsenic phosphorus as a promising candidate for MIR optoelectronic applications but also pave the way for a general strategy to suppress 1/f noise in photonic devices.

The Measurement of Relatedness: An Application to Corporate Diversification

Abstract: Employing commodity flow data from input-output (IO) tables, we construct two IO-based relatedness measures to capture inter-industry and inter-segment vertical relatedness and complementarity. At the industry level, we demonstrate that the new IO-based measures outperform traditional measures based on SIC codes in describing relatedness. At the firm level, we report that firms increase their degree of vertical relatedness and complementarity over time. The increasing pattern is robust; it is not sensitive to accounting changes in segment definition, different weighting methods and different input-output data employed to construct the relatedness measures. As an application to corporate diversification, we find that vertical relatedness is associated with lower firm value. Complementarity increased firm value only in the 1970s and early 1980s. Its effect has been neutral in more recent periods. We further document that the valuation effects of relatedness can be attributed to firms with more than three business segments, suggesting that relatedness is more relevant to firms pursuing wide diversification strategies.