Beyond public health : the cultural politics of tobacco control in Hong Kong

This work provides cultural and political explanations on how and why cigarette smoking has increasingly become an object of intolerance and control in Hong Kong. Since the 1980s, the smoking population has been falling. Smoking behavior, sales and promotion of cigarette products have been under close surveillance by the government, medical experts and society at large. Cigarette smoking, as well as smokers, has increasingly been rejected and demonized in the public discourse. What are the conditions that make the growing intolerant discourses and practices against cigarette smoking possible and dominant? Why and how has the tobacco control campaign become prevalent as a governmentalist project, which is strong enough to tear down the alliance of tobacco industry giants? Why is tobacco singled out from other legal but harmful substances, such as alcohol, as an imperative object of intolerance and control? This work tackles these questions by adopting a Foucauldian discursive approach and the theory of articulation developed in cultural studies. By considering tobacco control as a historical and contextual practice, it traces the specific trajectory of tobacco control in Hong Kong, maps the cultural and political contexts that make it possible, and considers its consequence regarding the complex relationship among control, construction of risk, identity and freedom in society

Insights of biosurfactant producing Serratia marcescens strain W2.3 isolated from diseased tilapia fish: a draft genome analysis

Background Serratia marcescens is an opportunistic bacterial pathogen with broad range of host ranging from vertebrates, invertebrates and plants. S. marcescens strain W2.3 was isolated from a diseased tilapia fish and it was suspected to be the causal agent for the fish disease as virulence genes were found within its genome. In this study, for the first time, the genome sequences of S. marcescens strain W2.3 were sequenced using the Illumina MiSeq platform. Result Several virulent factors of S. marcescens such as serrawettin, a biosurfactant, has been reported to be regulated by N-acyl homoserine lactone (AHL)-based quorum sensing (QS). In our previous studies, an unusual AHL with long acyl side chain was detected from this isolate suggesting the possibility of novel virulence factors regulation. This evokes our interest in the genome of this bacterial strain and hereby we present the draft genome of S. marcescens W2.3, which carries the serrawettin production gene, swrA and the AHL-based QS transcriptional regulator gene, luxR which is an orphan luxR. Conclusion With the availability of the whole genome sequences of S. marcescens W2.3, this will pave the way for the study of the QS-mediated genes expression in this bacterium

Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on low-dimensional distributional assumptions and thus suffer from the high dimensionality of latent features. Existing approaches tend to focus on uncertainty on discrete classification probabilities, which leads to poor generalizability to uncertainty estimation for other tasks. Moreover, most of the literature requires seeing the out-of-distribution (OOD) data in the training for better estimation of uncertainty, which limits the uncertainty estimation performance in practice because the OOD data are typically unseen. To overcome these limitations, we propose a new framework using data-adaptive high-dimensional hypothesis testing for uncertainty estimation, which leverages the statistical properties of the feature representations. Our method directly operates on latent representations and thus does not require retraining the feature encoder under a modified objective. The test statistic relaxes the feature distribution assumptions to high dimensionality, and it is more discriminative to uncertainties in the latent representations. We demonstrate that encoding features with Bayesian neural networks can enhance testing performance and lead to more accurate uncertainty estimation. We further introduce a family-wise testing procedure to determine the optimal threshold of OOD detection, which minimizes the false discovery rate (FDR). Extensive experiments validate the satisfactory performance of our framework on uncertainty estimation and task-specific prediction over a variety of competitors. The experiments on the OOD detection task also show satisfactory performance of our method when the OOD data are unseen in the training. Codes are available at https://github.com/HKU-MedAI/bnn_uncertainty.Comment: NeurIPS 202

Lexicon-phonology relationships in Cantonese-speaking children a cross-sectional and longitudinal investigation

"A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, April 30, 2003."Thesis (B.Sc.)--University of Hong Kong, 2003.Also available in print.published_or_final_versionSpeech and Hearing SciencesBachelorBachelor of Science in Speech and Hearing Science

Landfill extension developments in Hong Kong : a study of agenda setting and policy dynamics

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio