Solving kk-means on High-dimensional Big Data

In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement. For the $k$-means problem, this has led to the development of several $(1+\varepsilon)$-approximations (under the assumption that $k$ is a constant), but also to the design of algorithms that are extremely fast in practice and compute solutions of high accuracy. However, when not only the length of the stream is high but also the dimensionality of the input points, then current methods reach their limits. We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high dimensional data in one pass and output a solution of high quality. While piecy is suited for high dimensional data with a medium number of points, piecy-mr is meant for high dimensional data that comes in a very long stream. We provide an extensive experimental study to evaluate piecy and piecy-mr that shows the strength of the new algorithms.Comment: 23 pages, 9 figures, published at the 14th International Symposium on Experimental Algorithms - SEA 201

Post-neonatal Mortality, Morbidity, and Developmental Outcome after Ultrasound-Dated Preterm Birth in Rural Malawi: A Community-Based Cohort Study

Using data collected as a follow-up to a randomized trial, Melissa Gladstone and colleagues show that during the first two years of life, infants born preterm in southern Malawi are disadvantaged in terms of mortality, growth, and development

Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach

Typhoid fever is a major cause of death worldwide with a major part of the disease burden in developing regions such as the Indian sub-continent. Bangladesh is part of this highly endemic region, yet little is known about the spatial and temporal distribution of the disease at a regional scale. This research used a Geographic Information System to explore, spatially and temporally, the prevalence of typhoid in Dhaka Metropolitan Area (DMA) of Bangladesh over the period 2005-9. This paper provides the first study of the spatio-temporal epidemiology of typhoid for this region. The aims of the study were: (i) to analyse the epidemiology of cases from 2005 to 2009; (ii) to identify spatial patterns of infection based on two spatial hypotheses; and (iii) to determine the hydro-climatological factors associated with typhoid prevalence. Case occurrences data were collected from 11 major hospitals in DMA, geocoded to census tract level, and used in a spatio-temporal analysis with a range of demographic, environmental and meteorological variables. Analyses revealed distinct seasonality as well as age and gender differences, with males and very young children being disproportionately infected. The male-female ratio of typhoid cases was found to be 1.36, and the median age of the cases was 14 years. Typhoid incidence was higher in male population than female (χ2 = 5.88, p0.05). A statistically significant inverse association was found between typhoid incidence and distance to major waterbodies. Spatial pattern analysis showed that there was a significant clustering of typhoid distribution in the study area. Moran\u27s I was highest (0.879; p<0.01) in 2008 and lowest (0.075; p<0.05) in 2009. Incidence rates were found to form three large, multi-centred, spatial clusters with no significant difference between urban and rural rates. Temporally, typhoid incidence was seen to increase with temperature, rainfall and river level at time lags ranging from three to five weeks. For example, for a 0.1 metre rise in river levels, the number of typhoid cases increased by 4.6% (95% CI: 2.4-2.8) above the threshold of 4.0 metres (95% CI: 2.4-4.3). On the other hand, with a 1°C rise in temperature, the number of typhoid cases could increase by 14.2% (95% CI: 4.4-25.0)

Type 1 diabetes: awareness, management and challenges: current scenario in India

Type 1 Diabetes Mellitus (T1DM) has a wide presence in children and has a high mortality rates. The disease, if left unmanaged, poses various challenges to the patient and healthcare providers, including development of diabetic complications and thus decreasing the life expectancy of the affected child. The challenges of T1DM include awareness of the disease that is very poor among the general public and also in parents of T1DM children along with the health care professionals. The challenge of lack of awareness of T1DM can be met by increasing public awareness programs, conducting workshops for diabetes educators regarding T1DM in children, newsletters, CMEs, online courses, and by structured teaching modules for diabetes educators. Diagnosis of T1DM was a challenge a few decades ago but the situation has improved today with diagnostic tests and facilities, made available even in villages. Investigation facilities and infrastructure, however, are very poor at the primary care level, especially in rural areas. Insulin availability, acceptability, and affordability are also major problems, compounded by the various types of insulin that are available in the market with a varied price range. But effective use of insulin remains a matter of utmost importance.e courses, and by structured teaching modules for diabetes educators. Diagnosis of T1DM was a challenge a few decades ago but the situation has improved today with diagnostic tests and facilities, made available even in villages. Investigation facilities and infrastructure, however, are very poor at the primary care level, especially in rural areas. Insulin availability, acceptability, and affordability are also major problems, compounded by the various types of insulin that are available in the market with a varied price range. But effective use of insulin remains a matter of utmost importance