Matthew Scinto, conductor, September 19, 2015

This is the concert program of the Matthew Scinto, conductor performance on Saturday, September 19, 2015 at 2:00 p.m., at the Concert Hall, 855 Commonwealth Avenue. Works performed were I. Allegro Aperto from Violin Concerto No. 5 in A major, K. 219 by Wolfgang Amadeus Mozart and Symphony no. 5 in C minor, Op. 67 by Ludwig van Beethoven. Digitization for Boston University Concert Programs was supported by the Boston University Humanities Library Endowed Fund

Awk Awk

A listing of words that only use the letter A, W, and K

Inappropriate electrolyte repletion for patients undergoing endoscopic procedures

At Thomas Jefferson University Hospital (TJUH), there has been a perceived necessity among housestaff and fellows to routinely check and replete serum potassium and magnesium for inpatients prior to endoscopic procedures In addition, there was an unwritten policy that these electrolytes needed to be aggressively repleted, with a goal potassium above 4.0 and magnesium above 2.0 Contributing factors include absence of clear policy, fear of adverse outcomes during procedures, and fear of delay of procedures leading to increased hospital stay This practice has led to unwarranted lab draws, costs of lab tests and electrolyte riders, and possible delayed procedures Goals Clarify policies regarding electrolyte repletion Determine frequency of inappropriate electrolyte checking and repletion Determine monetary cost of this action Decrease frequency of inappropriate electrolyte lab check and repletionhttps://jdc.jefferson.edu/patientsafetyposters/1023/thumbnail.jp

Who\u27s Trending: #NBA vs #NHL

Many professional athletes across a wide variety of sport have obtained celebrity status and as a result, have become human brands much greater than themselves (Carlson and Donavan, 2013). Previous research analyzed specific cases, individual strategies, and consumer behaviors. However, this study has furthered new knowledge by obtaining a comprehensive description of the differing approaches set in place by the National Basketball Association and the National Hockey League with regard to branding of individual athletes on Twitter. The purpose of this study was to determine in which ways do different sport leagues contrast in how they brand athletes on Twitter. A cross-sectional design was employed to adequately collect data needed with a population that consisted of Twitter accounts of MLB and NBA teams. Six teams from both of leagues were sampled and obtained through stratified random sampling based on number of followers to accurately represent the greater population. Secondary quantitative data was collected and analyzed via an independent t-test. Overall, NBA accounts maintained a significantly higher degree of focus on individual athletes than the NHL on Twitter. Because of this, NBA teams maintain a greater connection between fans and players, communicate more information about players’ personal lives, and have taken advantage of their ability to become the new gatekeeper

Does More Money Make You Fat? The Effects of Quasi-Experimental Income Transfers on Adolescent and Young Adult Obesity

This paper examines how exogenous income transfers during adolescence affect contemporaneous body mass index (BMI) measures and young adult obesity rates using evidence from the Great Smoky Mountains Study of Youth. The effects of extra income differ depending on the households’ initial socio-economic status, tracing out an inverted U-shaped relationship between initial income and BMI. Youths who resided in families that had high pre-treatment annual incomes experience no change in young adult obesity rates as a result of the income transfers, while the BMI of poorer children increases. Part of this effect is due to differential increases in height, as well as weight. An exogenous annual transfer of $4,000 per adult family member results in an almost 4 cm gain in height-for-age. Adolescents coming from worse-off households experience an increase in weight only, without the corresponding change in height. The cumulative effects of the increase in household income persist for several years into young adulthood.obesity, health, cash transfer, adolescents, indigenous peoples

Bandit Parameter Estimation

Contextual bandit is useful algorithm for the recommendation task in many applications such as NETFLEX, Amazon Echo, etc. Many algorithms are researched and showed a good result in terms of high total reward or low regret. However, when user wants to receive a recommendation in the new task, these algorithms do not use information that learned from before task. We suggest new topic, Bandit Parameter Estimation, to solve that inefficient problem. In the same setting with Contextual bandit, we consider as user’s latent profile. And then we propose some algorithms to estimate as fast as possible. We conducted to experiment to verify algorithms that we proposed in two case by using a synthetic dataset. As a result of experiment, we found that our algorithm estimates parameters faster than other algorithms in Contextual bandit. ⓒ 2017 DGISTopenⅠ. Introduction 1-- 1.1 Overview 1-- 1.2 Background 2-- 1.2.1 Multi-Armed bandit 2-- 1.2.2 K-armed (Linear) Contextual bandit 3-- 1.3 Related work 4-- 1.3.1 algorithm 4-- 1.3.2 UCB 5-- 1.3.3 LinUCB 6-- Ⅱ. Materials 8-- 2.1 Problem setting for Bandit Parameter Estimation 8-- 2.2 The uncertainty ellipsoid of _(*) 9-- 2.2.1 (. ) 10-- 2.2.2 ((Σ_(t))) 11-- 2.2.3 ((Σ_(t)^(-1))) 11-- 2.2.4 Max(Det(Σ_(t))) 12Ⅲ. Method 13-- 3.1 Generating synthetic data 13-- 3.2 The experiment process 13-- Ⅳ. Experimental result 14-- 4.1 The experiment case 1 : Various k, fixed d 14-- 4.2 The experiment case 2 : Various d, fixed k 15-- Ⅴ. Discussion 17-- 5.1 Conclusion 17-- 5.2 Future work 17-- Reference 18-- Summary (Korean) 19최근 많은 애플리케이션에서 사용자 맞춤형 추천을 제공하고 있다. 이때 주로 사용되는 알고리즘은 Contextual Bandit이라는 형태로 이미 많은 연구가 진행되어 좋은 결과를 보여주고 있다. 하지만 이 알고리즘들은 특정 유저에 대해서 하나의 Task에서는 빠르게 사용자에게 맞는 추천을 제공하고 있으나 만약 새로운 Task에 대해 추천을 제공해야 할 때, 이전 Task에서 학습한 정보를 이용하지 못하고 Task 별로 독립적으로 다시 학습해야 하므로 효율적이지 않다. 이러한 점에서 동기를 얻어 Contextual Bandit과 같은 환경에서 최근 사용자의 프로필을 학습하기 위한 Bandit Parameter Estimation이라는 형태의 새로운 문제를 제시하였다. 빠른 학습을 위하여 The uncertainty ellipsoid을 수축하기 위한 몇 가지 알고리즘을 제시하였고 실험을 위해 만든 데이터 셋에서 제시한 알고리즘이 기존의 Contextual bandit 알고리즘보다 빠르게 Parameter Estimation을 수행하는 것을 확인했다. 또한 향후 연구 주제로 본 논문을 통해 확인된 알고리즘을 실제 데이터에 적용하여 알고리즘을 검증하는 것 그리고 학습된 사용자의 프로필을 추가적으로 이용하여 Contextual Bandit에 사용되는 알고리즘을 사용했을 때 프로필을 사용하지 않았을 때 보다 더 빠르게 좋은 추천을 제공하는지 확인하는 연구가 필요하다는 것을 제시하였다.MasterdCollectio