Providing Diversity in K-Nearest Neighbor Query Results

Given a point query Q in multi-dimensional space, K-Nearest Neighbor (KNN) queries return the K closest answers according to given distance metric in the database with respect to Q. In this scenario, it is possible that a majority of the answers may be very similar to some other, especially when the data has clusters. For a variety of applications, such homogeneous result sets may not add value to the user. In this paper, we consider the problem of providing diversity in the results of KNN queries, that is, to produce the closest result set such that each answer is sufficiently different from the rest. We first propose a user-tunable definition of diversity, and then present an algorithm, called MOTLEY, for producing a diverse result set as per this definition. Through a detailed experimental evaluation on real and synthetic data, we show that MOTLEY can produce diverse result sets by reading only a small fraction of the tuples in the database. Further, it imposes no additional overhead on the evaluation of traditional KNN queries, thereby providing a seamless interface between diversity and distance.Comment: 20 pages, 11 figure

Utilization pattern of oral hypoglycemic agents for diabetes mellitus type 2 patients attending out-patient department at tertiary care centre in Bhopal, Madhya Pradesh, India

Background: Diabetes mellitus type 2 is a chronic disease that may be due to insulin deficiency and insulin resistance or both. The resultant hyperglycemia leading to micro and macro vascular complications.The objective was to evaluate drug utilization pattern of oral hypoglycemic agents in type-2 diabetic attending OPD.Methods: A prospective study was carried out in 200 out patients for a period of 3 months in a tertiary care hospital. Patients treated with oral hypoglycemic agents were taken for the study.Results: In the present study 102(51%) were male. Majority (40%) of patient were in the age group 50-60 years. Metformin was the most commonly prescribed drug (38.3%), followed by sulfonylurea class of drugs (35.6%). Majority of the patients n=143(71.5%) were on combination therapy in comparision to monotherapy (28.5%). Fixed dose combinations more preferred more. Brand name was prefered (98.1%) on generic drugs. Comorbid condition was found in 117 patients (58.5%).  And hypertension (34%) was the the most common comorbid condition. The average number of antidiabetic drugs per prescription was 2.2.Conclusions: Metformin was the most commonly used drug .The prescribing trend also appears to be more towards combination therapy. It was seen that particularly two drug were used

Understanding of effects of potassium on cardiac tissue by medical students: a critical appraisal

Potassium (K+) is one of the most important ion present in the human body and involved in numerous physiological activities. It mainly affects heart and skeletal muscle but the effects are not confined to theses organs only. The article precisely focuses on the explaining the physiological as well as pathological aspects of potassium on cardiac tissue. This article tends to explain: The cause of difference in extra cellular and Intra cellular concentration of potassium when potassium channels are open in resting conditions, why are Purkinje fibers and ectopic tissue are more sensitive to effect of potassium, mechanisms responsible for increased action potential duration by hypokalemia and decreased action potential duration by hyperkalemia. Hypokalemia generates ectopic activities and hyperkalemia inhibits them, therapeutic effects of potassium administration without causing hyperkalemia. These issues will be discussed and try to be explained with the help of Ohm’s law , Nernst equation and Nernst potential to sort out the complicated actions of potassium on cardiac tissue in a simplified manner. The primary aim of article is to improve understanding of potassium physiology by medical graduates, secondarily, convey message regarding improvement in teaching methodology in Pharmacology for the benefit of new generations

Multi-Document Summarization with Centroid-Based Pretraining

In multi-document summarization (MDS), the input is a cluster of documents, and the output is the cluster summary. In this paper, we focus on pretraining objectives for MDS. Specifically, we introduce a simple pretraining objective of choosing the ROUGE-based centroid of each document cluster as a proxy for its summary. Our objective thus does not require human written summaries and can be used for pretraining on a dataset containing only clusters of documents. Through zero-shot and fully supervised experiments on multiple MDS datasets, we show that our model Centrum is better or comparable to a state-of-the-art model. We release our pretrained and finetuned models at https://github.com/ratishsp/centrum.Comment: 4 pages, work-in-progres