Leading Causes of Death in Vietnam

Vietnam is currently facing a public health crisis. Rates of chronic and preventable diseases are climbing, in addition to mortality rates from these diseases. If nothing is done to halt these rising rates, the health of the Vietnamese people will only continue to decline. Although there may be many factors contributing to these high death rates due to chronic diseases, risky health behaviors, such as smoking, and the state of the healthcare system can be considered two main contributors to the leading causes of death in Vietnam. The high smoking rates and high costs of healthcare are hindering the health of Vietnam, and may be related to the top causes of death, including stroke, ischemic heart disease, chronic obstructive pulmonary disease (COPD), and lower respiratory infections (World Health Organization and UN partners, 2015). Implementing government programs, including smoking cessation, smoking education, tobacco taxes, healthcare education, and continued work toward universal healthcare coverage, will hopefully help decrease the rising rates of chronic diseases and the high mortality rates they cause.https://jdc.jefferson.edu/cwicposters/1033/thumbnail.jp

Pinching of the First Eigenvalue of the Laplacian and almost-Einstein Hypersurfaces of the Euclidean Space

In this paper, we prove new pinching theorems for the first eigenvalue of the Laplacian on compact hypersurfaces of the Euclidean space. These pinching results are associated with the upper bound for the first eigenvalue in terms of higher order mean curvatures. We show that under a suitable pinching condition, the hypersurface is diffeomorpic and almost isometric to a standard sphere. Moreover, as a corollary, we show that a hypersurface of the Euclidean space which is almost Einstein is diffeomorpic and almost isometric to a standard sphere.Comment: 18 page

Follow-up of referrals to the Social Service Department from the Out-Patient Department at the Boston State Hospital during 1950.

Thesis (M.S.)--Boston Universit

Learning to Resolve Natural Language Ambiguities: A Unified Approach

We analyze a few of the commonly used statistics based and machine learning algorithms for natural language disambiguation tasks and observe that they can be re-cast as learning linear separators in the feature space. Each of the methods makes a priori assumptions, which it employs, given the data, when searching for its hypothesis. Nevertheless, as we show, it searches a space that is as rich as the space of all linear separators. We use this to build an argument for a data driven approach which merely searches for a good linear separator in the feature space, without further assumptions on the domain or a specific problem. We present such an approach - a sparse network of linear separators, utilizing the Winnow learning algorithm - and show how to use it in a variety of ambiguity resolution problems. The learning approach presented is attribute-efficient and, therefore, appropriate for domains having very large number of attributes. In particular, we present an extensive experimental comparison of our approach with other methods on several well studied lexical disambiguation tasks such as context-sensitive spelling correction, prepositional phrase attachment and part of speech tagging. In all cases we show that our approach either outperforms other methods tried for these tasks or performs comparably to the best