University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Computer science & Population. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

RefineDNet: A Weakly Supervised Refinement Framework for Single Image Dehazing

Abstract: Haze-free images are the prerequisites of many vision systems and algorithms, and thus single image dehazing is of paramount importance in computer vision. In this field, prior-based methods have achieved initial success. However, they often introduce annoying artifacts to outputs because their priors can hardly fit all situations. By contrast, learning-based methods can generate more natural results. Nonetheless, due to the lack of paired foggy and clear outdoor images of the same scenes as training samples, their haze removal abilities are limited. In this work, we attempt to merge the merits of prior-based and learning-based approaches by dividing the dehazing task into two sub-tasks, i.e., visibility restoration and realness improvement. Specifically, we propose a two-stage weakly supervised dehazing framework, RefineDNet. In the first stage, RefineDNet adopts the dark channel prior to restore visibility. Then, in the second stage, it refines preliminary dehazing results of the first stage to improve realness via adversarial learning with unpaired foggy and clear images. To get more qualified results, we also propose an effective perceptual fusion strategy to blend different dehazing outputs. Extensive experiments corroborate that RefineDNet with the perceptual fusion has an outstanding haze removal capability and can also produce visually pleasing results. Even implemented with basic backbone networks, RefineDNet can outperform supervised dehazing approaches as well as other state-of-the-art methods on indoor and outdoor datasets. To make our results reproducible, relevant code and data are available at https://github.com/xiaofeng94/RefineDNet-for-dehazing .

Inhibition of the Ras/Raf interaction and repression of renal cancer xenografts in vivo by an enantiomeric iridium(iii) metal-based compound.

Abstract: Targeting protein–protein interactions (PPIs) offers tantalizing opportunities for therapeutic intervention for the treatment of human diseases. Modulating PPI interfaces with organic small molecules has been found to be exceptionally challenging, and few candidates have been successfully developed into clinical drugs. Meanwhile, the striking array of distinctive properties exhibited by metal compounds renders them attractive scaffolds for the development of bioactive leads. Here, we report the identification of iridium(III) compounds as inhibitors of the H-Ras/Raf-1 PPI. The lead iridium(III) compound 1 exhibited potent inhibitory activity against the H-Ras/Raf-1 interaction and its signaling pathway in vitro and in vivo, and also directly engaged both H-Ras and Raf-1-RBD in cell lysates. Moreover, 1 repressed tumor growth in a mouse renal xenograft tumor model. Intriguingly, the Δ-enantiomer of 1 showed superior potency in the biological assays compared to Λ-1 or racemic 1. These compounds could potentially be used as starting scaffolds for the development of more potent Ras/Raf PPI inhibitors for the treatment of kidney cancer or other proliferative diseases.

Factors that Affect the Levels of Tourists' Satisfaction and Loyalty towards Food Festivals: a Case Study of Macau

Abstract: The present study investigates the factors that affect tourists' satisfaction towards food festivals and explores whether tourists intend to revisit and/or recommend food festivals to others. Based on personal interviews with 40 study subjects, eight factors that affect their satisfaction levels were identified: location and accessibility, food, venue facility, environment/ambiance, service, entertainment, timing and festival size. The majority of tourists were satisfied with the Macau Food Festival and intended to revisit and recommend it to others. The presented findings have practical implications for food festival organizers to further attract food tourists. © 2011 John Wiley & Sons, Ltd.

Real-Time System Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification

Abstract: In this article, a novel Bayesian real-time system identification algorithm using response measurement is proposed for dynamical systems. In contrast to most existing structural identification methods which focus solely on parametric identification, the proposed algorithm emphasizes also model class selection. By embedding the novel model class selection component into the extended Kalman filter, the proposed algorithm is applicable to simultaneous model class selection and parametric identification in the real-time manner. Furthermore, parametric identification using the proposed algorithm is based on multiple model classes. Examples are presented with application to damage detection for degrading structures using noisy dynamic response measurement.