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Multi-hop question answering (QA) requires an information retrieval (IR) system that can find multiple supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evidence to learn to 'hop' to other relevant evidence. In a setting, with more than 5 million Wikipedia paragraphs, our approach leads to significant boost in retrieval performance. The retrieved evidence also increased the performance of an existing QA model (without any training) on the HOTPOTQA benchmark by 10.59 F1. * Equal contribution. Correspondence to {agodbole, ra-jarshi}@cs.umass.edu 1 According to Yang et al. (2018), the easy (hard) subset primarily requires single (multi) hop reasoning. We only consider queries that have answers as spans in at least one paragraph.Question : What county is Ron Teachworth from? Ronald S. Teachworth is an American artist, writer and film director from Rochester Hills, Michigan.Rochester Hills is a city in northeast Oakland County of the U.S. state of Michigan, in the northern outskirts of Metropolitan Detroit area. As of the 2010 census, the city had a total population of 70,995.

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This paper investigates temporal and weather-related variation in taxi trips in New York City. A taxi trip data-set with 147 million records covering 10 months of activity is used. It is shown that there are substantial variations in ridership, taxi supply, trip distance, and pickup frequency for different time periods and weather conditions. These variations, in turn, cause variations in driver revenues which is one of the main measures of taxi supply-demand equilibrium. The findings are then used to discuss the anticipated impacts of two recently enacted taxi regulation changes: the first fare increase since 2006 and the E-Hail pilot program which allows taxi hailing with smart phone applications. The fare increase is estimated to cause varying levels of revenue increase for different time periods. E-Hail apps are not expected to offer considerable improvements at all times, but rather when both adequate taxi supply and demand occur simultaneously.

Due to unavailability of consistent income data at the sub-state or district level in developing countries, it is difficult to generate consistent and reliable economic inequality estimates at the disaggregated level. To address this issue, this paper employs the association between night time lights and economic activities for India at the sub-state or district-level, and calculates regional income inequality using Gini coefficients. Additionally, we estimate the relationship between night time lights and socio-economic development for regions in India. We employ a newly available data on regional socio-economic development (Social Progress Index), as well as an index that represents institutional quality or governance. Robust to the choice of socio-economic development indicators, our findings indicate that regional inequality measured by night time lights follow the Kuznets curve pattern. This implies that starting from low levels of socio-economic development or quality of institutions, inequality rises as regional socio-economic factors or quality of institutions improve, and with subsequent progress in socio-economic factors or quality of institutions, regional inequality declines.

This paper provides an evaluation of taxi dispatching procedures at New York City's John F. Kennedy International Airport (JFK). Curbside data collection and interviews with airport stakeholders were conducted to describe and quantify conditions for taxi drivers and passengers at JFK. A literature review was performed to identify operational similarities and differences between JFK and other high-volume airports with centralized taxi dispatching and to identify potential solutions for application at JFK. The outcomes of this study include (a) characterization of relationships between airportwide and terminal-level passenger demands and available taxi supply at JFK, (b) identification of sources of inefficiency in existing taxi dispatching procedures and taxi operations, and (c) identification of approaches for addressing supply–demand imbalances and next steps in evaluating those approaches.