Optimization of the Target Subsystem for the New g-2 Experiment

A precision measurement of the muon anomalous magnetic moment, $a_{\mu} = (g-2)/2$, was previously performed at BNL with a result of 2.2 - 2.7 standard deviations above the Standard Model (SM) theoretical calculations. The same experimental apparatus is being planned to run in the new Muon Campus at Fermilab, where the muon beam is expected to have less pion contamination and the extended dataset may provide a possible $7.5\sigma$ deviation from the SM, creating a sensitive and complementary bench mark for proposed SM extensions. We report here on a preliminary study of the target subsystem where the apparatus is optimized for pions that have favorable phase space to create polarized daughter muons around the magic momentum of 3.094 GeV/c, which is needed by the downstream g 2 muon ring.Comment: 4 pp. 3rd International Particle Accelerator Conference (IPAC 2012) 20-25 May 2012, New Orleans, Louisian

An analysis of query difficulty for information retrieval in the medical domain

We present a post-hoc analysis of a benchmarking activity for information retrieval (IR) in the medical domain to determine if performance for queries with different levels of complexity can be associated with different IR methods or techniques. Our analysis is based on data and runs for Task 3 of the CLEF 2013 eHealth lab, which provided patient queries and a large medical document collection for patient centred medical information retrieval technique development. We categorise the queries based on their complexity, which is defined as the number of medical concepts they contain. We then show how query complexity affects performance of runs submitted to the lab, and provide suggestions for improving retrieval quality for this complex retrieval task and similar IR evaluation tasks

A comparative study of online translation services for cross language Information retrieval

Technical advances and its increasing availability, mean that Machine Translation (MT) is now widely used for the translation of search queries in multilingual search tasks. A number of free-to-use high-quality online MT systems are now available and, although imperfect in their translation behaviour, are found to produce good performance in CrossLanguage Information Retrieval (CLIR) applications. Users of these MT systems in CLIR tasks generally assume that they all behave similarly in CLIR applications, and the choice of MT system is often made on the basis of convenience. We present a set of experiments which compare the impact of applying two of the best known online systems, Google and Bing translation, for query translation across multiple language pairs and for two very different CLIR tasks. Our experiments show that the MT systems perform differently on average for different tasks and language pairs, but more significantly for different individual queries. We examine the differing translation behaviour of these tools and seek to draw conclusions in terms of their suitability for use in different settings

Transformation

The Nobel process for science are often somewhat controversial for who they omit. A posthumous Nobel honor could help recognize some neglected heroes

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects