Richard L. Bready Mount Hope Bay Sailing and Education Center \u27Puts RWU On the Map\u27

Multifunction facility to serve as a home for top ranked sailing team and a resource for STEM education

Education or Reputation? A Look At America's Top-Ranked Liberal Arts Colleges

This report examines the country's most prestigious liberal arts colleges. Despite endowments soaring as high as $1.8 billion, nearly all institutions increased tuition during the Great Recession to finance bloated administrative spending, with many college presidents enjoying salaries higher than Barack Obama's. This report peels back reputation to find out what students are really getting for their diploma's$240,000 price ta

Aiming Higher for Health System Performance: A Profile of Seven States That Perform Well on the Commonwealth Fund's 2009 State Scorecard

Identifies policies and practices linked to high performance in six top-ranked states and the most-improved state in 2007-09. Offers insights into improving coverage, prevention and treatment, avoidable hospital use and costs, equity, and healthy lives

How Do Median Graduate Economic Programs Differ from Top-ranked Programs?

This paper reports the results of a survey of median economics graduate programs and compares it with the results of a survey of top economics graduate programs done by Colander. Overall it finds that while there are some differences in the programs, there are large areas of similarity. Some of the particular finding are that there are more US respondents in median programs than in top programs, median students have more interest in econometrics, history of thought and economic literature than do students at top programs, although after the fifth year, their interest in any field drops significantly. It also finds that students at top schools are much more likely to be involved in writing scholarly papers, and that students at top schools give far less emphasis to excellence in mathematics as a path to the fast track than do students at median schools.

University of Sheffield TREC-8 Q & A System

The system entered by the University of Sheffield in the question answering track of TREC-8 is the result of coupling two existing technologies - information retrieval (IR) and information extraction (IE). In essence the approach is this: the IR system treats the question as a query and returns a set of top ranked documents or passages; the IE system uses NLP techniques to parse the question, analyse the top ranked documents or passages returned by the IR system, and instantiate a query variable in the semantic representation of the question against the semantic representation of the analysed documents or passages. Thus, while the IE system by no means attempts “full text understanding", this approach is a relatively deep approach which attempts to work with meaning representations. Since the information retrieval systems we used were not our own (AT&T and UMass) and were used more or less “off the shelf", this paper concentrates on describing the modifications made to our existing information extraction system to allow it to participate in the Q & A task

Click Carving: Segmenting Objects in Video with Point Clicks

We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of interest. Whereas conventional interactive pipelines take the user's initialization as a starting point, we show the value in the system taking the lead even in initialization. In particular, for a given video frame, the system precomputes a ranked list of thousands of possible segmentation hypotheses (also referred to as object region proposals) using image and motion cues. Then, the user looks at the top ranked proposals, and clicks on the object boundary to carve away erroneous ones. This process iterates (typically 2-3 times), and each time the system revises the top ranked proposal set, until the user is satisfied with a resulting segmentation mask. Finally, the mask is propagated across the video to produce a spatio-temporal object tube. On three challenging datasets, we provide extensive comparisons with both existing work and simpler alternative methods. In all, the proposed Click Carving approach strikes an excellent balance of accuracy and human effort. It outperforms all similarly fast methods, and is competitive or better than those requiring 2 to 12 times the effort.Comment: A preliminary version of the material in this document was filed as University of Texas technical report no. UT AI16-0

A Benchmarking of the Undergraduate Economics Major in Europe and the United States

We compare the undergraduate Economics majors and their underlying structure in the top-ranked Economics departments of Europe and the USA, identifying the fundamental courses included in an Economics major in top-ranked universities. We further distinguish between those courses that are required and those that are usually offered as electives, finding striking differences between Europe and the USA, especially regarding the nature of the main electives offered. The insights from this comparative study may be useful for the ongoing restructuring of undergraduate Economics majors in European countries caused by the Bologna Process.

Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06