Generalized residual vector quantization for large scale data

Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named \textit{residual vector quantization} (RVQ). Next, we propose \textit{generalized residual vector quantization} (GRVQ) to further improve over RVQ. Many vector quantization methods can be viewed as the special cases of our proposed framework. We evaluate GRVQ on several large scale benchmark datasets for large scale search, classification and object retrieval. We compared GRVQ with existing methods in detail. Extensive experiments demonstrate our GRVQ framework substantially outperforms existing methods in term of quantization accuracy and computation efficiency.Comment: published on International Conference on Multimedia and Expo 201

BILINGUALISM POLICY IN SINGAPORE ELITE SCHOOLS

The Singapore government has been promoting the mastery of the English language as well as the mother tongue since 1987 in the hope that Singaporeans can be fluent in both the working language and one related to their native roots. From then on, all Chinese schools are required to teach in the English language, and English is officially known as the first language of all students. This paper aims to study the policy’s background, specifically in the area of Mandarin, and find out whether this policy has managed to achieve its goals, how it has affected Singapore students’ language development in elite schools, how to improve the policy to benefit students in the future as well as provide some implications for enhancing Chinese teaching pedagogy.Keywords: Mother tongue,  the  English  Language,  elite school, Chinese teaching pedagogy

Bilingualism policy in Singapore elite schools

The Singapore government has been promoting the mastery of the English language as well as the mother tongue since 1987 in the hope that Singaporeans can be fluent in both the working language and one related to their native roots. From then on, all Chinese schools are required to teach in the English language, and English is officially known as the first language of all students. This paper aims to study the policy’s background, specifically in the area of Mandarin, and find out whether this policy has managed to achieve its goals, how it has affected Singapore students’ language development in elite schools, how to improve the policy to benefit students in the future as well as provide some implications for enhancing Chinese teaching pedagogy

MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals

Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to the lack of a unified method. To handle the above issues, we propose to study the self-supervised learning (SSL) framework for brain signals that can be applied to pre-train either SEEG or EEG data. Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain. Inspired by this, we propose MBrain to learn implicit spatial and temporal correlations between different channels (i.e., contacts of the electrode, corresponding to different brain areas) as the cornerstone for uniformly modeling different types of brain signals. Specifically, we represent the spatial correlation by a graph structure, which is built with proposed multi-channel CPC. We theoretically prove that optimizing the goal of multi-channel CPC can lead to a better predictive representation and apply the instantaneou-time-shift prediction task based on it. Then we capture the temporal correlation by designing the delayed-time-shift prediction task. Finally, replace-discriminative-learning task is proposed to preserve the characteristics of each channel. Extensive experiments of seizure detection on both EEG and SEEG large-scale real-world datasets demonstrate that our model outperforms several state-of-the-art time series SSL and unsupervised models, and has the ability to be deployed to clinical practice

INFLUENCING FACTORS OF THE DIABETES DISTRESS AMONG CHINESE PATIENTS WITH TYPE 2 DIABETES MELLITUS

Background: Patients with Diabetes Mellitus (DM) are required to have long-term treatment regimen and strict selfmanagement, which thus might lead to the Diabetes Distress (DD). Patients’ DD varies in different regions with different levels of medical conditions. For improving the treatment effect of the patients with the Type-2-Diabetes-Mellitus (T2DM), this study explores the influencing factors of the patients’ DD in the regions where the medical treatment are at low level. Subjects and methods: In this study, 167 adult patients with T2DM were selected from the People’s Hospital of Jinhua city, an A-grade hospital of a small-mid-sized city in Eastern China. Based on these samples, the Diabetes Distress Scale with 17 items (DDS17) was adopted to measure the degree of patients’ DD, and then regression analyses were carried out to investigate the influencing factors for their DD. Results: The T2DM patients with moderate and high levels of DD take up 54.5% of the samples investigated in this study. According to the Spearman correlation analysis, sleep time, physical exercise, diet control, treatment regimen, TG and HbA1c significantly affect the patients’ DD. Through the multivariate linear regression analysis, this study shows that (1) sleep time significantly influences the T2DM patients’ DD (????=-0.213, P=0.034); (2) sleep time also impacts emotional burden (????=-0.379, P=0.027); (3) physical exercise (????=-0.195, P=0.002), treatment regimen (????=0.158, P=0.026) and diet control (????=0.158, P=0.026) are the three major influencing factors for regimen-related distress. Conclusion: In the small-mid-sized city where the treatment regimen is not highly advanced, to alleviate the T2DM patients’ DD, the medical staff should suggest the patients to improve sleep quality and exercise more, help them positively understand the insulin infusion therapy and take proper diet control