Media and information praxis of young activists in the Umbrella Movement

Young people were key participants in the Umbrella Movement in Hong Kong and the media also played an important role in this protest. This study examines how Hong Kong’s young activists developed communication strategies and media practices to mobilize this social movement. A framework termed “media and information praxis of social movements” is proposed for the analysis. The findings showed that in their praxis, the young activists used their media and information literacy skills to initiate, organize, and mobilize collective actions. They not only used social media and mobile networks but also traditional mass media and street booths in a holistic and integrated approach to receive and disseminate information. Hence, these young activists served as agents of mediatization. The results also indicated that the young activists moved away from the traditional movement mode which just tried to motivate a large number of people to protest in the streets. They actively engaged in the new movement mode, which emphasizes the media and information power game. Their praxis in the Umbrella Movement reflects the trend toward the mediatization of social movements in Hong Kong.     

Social media and Umbrella Movement: insurgent public sphere in formation

The study examines the role of social media during the Umbrella Movement in Hong Kong that lasted from September to December 2014. By interviewing a random sample of 1011 respondents over the telephone before the end of the Umbrella Movement, it was found that social media had become an insurgent public sphere (IPS) in the protest movement. Data showed that acquisition of political news through social media was related positively to support for the Umbrella Movement and adversely with satisfaction and trust of established political authorities, including the Hong Kong Special Administrative Region government, the Hong Kong police, and the Chinese central government. The insurgent public sphere role of social media, its implications, and likely development vis-à-vis the state and the market are discussed.     

Unbalanced data classification using support vector machines with active learning on scleroderma lung disease patterns

Unbalanced data classification has been a long-standing issue in the field of medical vision science. We introduced the methods of support vector machines (SVM) with active learning (AL) to improve prediction of unbalanced classes in the medical imaging field. A standard SVM algorithm with four different AL approaches are proposed: (1) The first one uses random sampling to select the initial pool with AL algorithm; (2) the second doubles the training instances of the rare category to reduce the unbalanced ratio before the AL algorithm; (3) the third uses a balanced pool with equal number from each category; and (4) the fourth uses a balanced pool and implements balanced sampling throughout the AL algorithm. Grid pixel data of two scleroderma lung disease patterns, lung fibrosis (LF), and honeycomb (HC) were extracted from computed tomography images of 71 patients to produce a training set of 348 HC and 3009 LF instances and a test set of 291 HC and 2665 LF. From our research, SVM with AL using balanced sampling compared to random sampling increased the test sensitivity of HC by 56% (17.5% vs. 73.5%) and 47% (23% vs. 70%) for the original and denoised dataset, respectively. SVM with AL with balanced sampling can improve the classification performances of unbalanced data.     

Multivariate generalized Birnbaum—Saunders kernel density estimators

In this article, we first propose the classical multivariate generalized Birnbaum–Saunders kernel estimator for probability density function estimation in the context of multivariate non negative data. Then, we apply two multiplicative bias correction (MBC) techniques for multivariate kernel density estimator. Some properties (bias, variance, and mean integrated squared error) of the corresponding estimators are also investigated. Finally, the performances of the classical and MBC estimators based on family of generalized Birnbaum–Saunders kernels are illustrated by a simulation study.     

How might sovereign bond yields in Asia Pacific react to US monetary normalisation under turbulent market conditions?*

This paper examines the potential impact of US monetary normalisation on sovereign bond yields in Asia Pacific. We apply the quantile vector autoregressive model with principal component analysis to the assessment of tail risk of sovereign debt, which may not be detectable using traditional OLS-based analysis. Our empirical evidence suggests that US Treasury bond yields can have a significant impact on sovereign bond yields in the region, an important channel through which monetary normalisation by the Fed can affect Asia-Pacific economies. Increases in sovereign bond yields will not only compromise the ability of the sovereigns in the region to service their debt but also translate into higher costs of borrowing for the rest of economy. The results show how much the outsized impact could potentially be if US monetary normalisation somehow turns out to be much more disorderly than expected.     

Economic statistical design of X‾-control charts: Modified version of Rahim and Banerjee (1993) model

Rahim and Banerjee considered a constant integral of the hazard function for all sampling intervals. This led the sampling intervals to depend on the extended first sampling interval (h₁). Since this limitation might not lead to an optimal situation, we first showed that elimination of the mentioned restriction did not cause any significant change in the average quality cycle cost. So if one is looking for an ideal cost and the simplicity of the process, the approach taken in Rahim and Banerjee’s study is the best procedure to adopt. Moreover, in many cases of non-uniform sampling method the first sampling interval becomes so large and this can sometimes lead the production system to the out-of-control state due to unexpected failures that might happen during that time. Therefore, we proposed a new model of uniform and non-uniform sampling intervals combination that allows us to confine the value of h₁ without undergoing high costs. The proposed model showed that the quality cycle cost of the proposed model is lower than Rahim and Banerjee’s model in the economic-statistical state. For more illustration, we conducted sensitivity analysis and gave numerical examples.     

The Analysis of Methadone Clinic Data Using Marginal and Conditional Logistic Models with Mixture or Random Effects

The paper develops methods for the statistical analysis of outcomes of methadone maintenance treatment (MMT). Subjects for this study were a cohort of patients entering MMT in Sydney in 1986. Urine drug tests on these subjects were performed weekly during MMT, and were reported as either positive or negative for morphine, the marker of recent heroin use. To allow correlation between the repeated binary measurements, a marginal logistic model was fitted using the generalized estimating equation (GEE) approach and the alternating logistic regression approach. Conditional logistic models are also considered. Results of separate fitting to each patient and score tests suggest that there is substantial between-patient variation in response to MMT. To account for the population heterogeneity and to facilitate subject-specific inference, the conditional logistic model is extended by introducing random intercepts. The two, three and four group mixture models are also investigated. The model of best fit is a three group mixture model, in which about a quarter of the subjects have a poor response to MMT, with continued heroin use independent of daily dose of methadone; about a quarter of the subjects have a very good response, with little or no heroin use, again independent of dose; and about half the subjects responded in a dose-dependent fashion, with reduced heroin use while receiving higher doses of methadone. These findings are consistent with clinical experience. There is also an association between reduced drug use and increased duration in treatment. The mixture model is recommended since it is quite tractable in terms of estimation and model selection as well as being supported by clinical experience.