6,787 research outputs found
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
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