Preliminary designs for X-ray source modifications for the Marshall Space Flight Center's X-ray calibration facility

The objective of this investigation is to develop preliminary designs for modifications to the X-ray source of the MSFC X-Ray Calibration Facility. Recommendations are made regarding: (1) the production of an unpolarized X-ray beam, (2) modification of the source to provide characteristic X-rays with energies up to 40 keV, and (3) addition of the capability to calibrate instruments in the extreme ultraviolet wavelength region

Fees and Surcharging in automatic teller machine networks: Non-bank ATM providers versus large banks

This paper develops a spacial model of ATM networks to explore the implications for banks and non-banks of interchange fees, foreign fees and surcharges applied to transactions by customers at other than an own-bank ATM. Surcharging raises the price (foreign fee plus surcharge) paid by customers above the joint profit-maximizing level achieved by setting the interchange fee at marginal cost and not surcharging. Similar size banks would agree not to surcharge, but such an agreement is typically not possible between a bank and a non-bank. A high cost of teller transactions modifies the tendency towards high ATM fees.

ANTIQUE: A Non-Factoid Question Answering Benchmark

Considering the widespread use of mobile and voice search, answer passage retrieval for non-factoid questions plays a critical role in modern information retrieval systems. Despite the importance of the task, the community still feels the significant lack of large-scale non-factoid question answering collections with real questions and comprehensive relevance judgments. In this paper, we develop and release a collection of 2,626 open-domain non-factoid questions from a diverse set of categories. The dataset, called ANTIQUE, contains 34,011 manual relevance annotations. The questions were asked by real users in a community question answering service, i.e., Yahoo! Answers. Relevance judgments for all the answers to each question were collected through crowdsourcing. To facilitate further research, we also include a brief analysis of the data as well as baseline results on both classical and recently developed neural IR models

Personal propulsion unit Patent

Lightweight propulsion unit for movement of personnel and equipment across lunar surfac

Target Type Identification for Entity-Bearing Queries

Identifying the target types of entity-bearing queries can help improve retrieval performance as well as the overall search experience. In this work, we address the problem of automatically detecting the target types of a query with respect to a type taxonomy. We propose a supervised learning approach with a rich variety of features. Using a purpose-built test collection, we show that our approach outperforms existing methods by a remarkable margin. This is an extended version of the article published with the same title in the Proceedings of SIGIR'17.Comment: Extended version of SIGIR'17 short paper, 5 page

Target Apps Selection: Towards a Unified Search Framework for Mobile Devices

With the recent growth of conversational systems and intelligent assistants such as Apple Siri and Google Assistant, mobile devices are becoming even more pervasive in our lives. As a consequence, users are getting engaged with the mobile apps and frequently search for an information need in their apps. However, users cannot search within their apps through their intelligent assistants. This requires a unified mobile search framework that identifies the target app(s) for the user's query, submits the query to the app(s), and presents the results to the user. In this paper, we take the first step forward towards developing unified mobile search. In more detail, we introduce and study the task of target apps selection, which has various potential real-world applications. To this aim, we analyze attributes of search queries as well as user behaviors, while searching with different mobile apps. The analyses are done based on thousands of queries that we collected through crowdsourcing. We finally study the performance of state-of-the-art retrieval models for this task and propose two simple yet effective neural models that significantly outperform the baselines. Our neural approaches are based on learning high-dimensional representations for mobile apps. Our analyses and experiments suggest specific future directions in this research area.Comment: To appear at SIGIR 201