131,107 research outputs found
MeSH term explosion and author rank improve expert recommendations
Get PDFInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
Get PDFBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
An Effective Model for Nematic Liquid Crystal Composites with Ferromagnetic Inclusions
Get PDFMolecules of a nematic liquid crystal respond to an applied magnetic field by reorienting themselves in the direction of the field. Since the dielectric anisotropy of a nematic is small, it takes relatively large fields to elicit a significant liquid crystal response. The interaction may be enhanced in colloidal suspensions of ferromagnetic particles in a liquid crystalline matrix-ferronematics-as proposed by Brochard and de Gennes in 1970. The ability of these particles to align with the field and simultaneously cause reorientation of the nematic molecules greatly increases the magnetic response of the mixture. Essentially the particles provide an easy axis of magnetization that interacts with the liquid crystal via surface anchoring. We derive an expression for the effective energy of ferronematic in the dilute limit, that is, when the number of particles tends to infinity while their total volume fraction tends to zero. The total energy of the mixture is assumed to be the sum of the bulk elastic liquid crystal contribution, the anchoring energy of the liquid crystal on the surfaces of the particles, and the magnetic energy of interaction between the particles and the applied magnetic field. The homogenized limiting ferronematic energy is obtained rigorously using a variational approach. It generalizes formal expressions previously reported in the physical literature
Log-concavity property of the error probability with application to local bounds for wireless communications
No full textclear understanding of the behavior of error probability (EP) as a function of signal-to-noise ratio (SNR) and other system parameters is fundamental for assessing the design of digital wireless communication systems. We propose an analytical framework based on the log-concavity property of the EP which we prove for a wide family of multidimensional modulation formats in the presence of Gaussian disturbances and fading. Based on this property, we construct a class of local bounds for the EP that improve known generic bounds in a given region of the SNR and are invertible, as well as easily tractable for further analysis. This concept is motivated by the fact that communication systems often operate with performance in a certain region of interest (ROI) and, thus, it may be advantageous to have tighter bounds within this region instead of generic bounds valid for all SNRs. We present a possible application of these local bounds, but their relevance is beyond the example made in this paper.FP7 Network of Excellence in Wireless Communications NEWCom++FP7 European project OPTIMIX (Grant Agreement 214625)IEEE Information Theory Societ
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Scholarly Communication and Publishing Lunch and Learn Talk #11: The ULS Open Access Author Fee Fund
No full textAt the May 2014 talk, you will learn about the ULS Open Access Author Fee Fund--what it is, why we do it, how it works, and how the program is going so far
The R&D Tax Incentives
No full textThis article sets out some background information and reflections of the author on the R&D tax incentive schemes included in the Common Corporate Tax Base (CCTB) Proposal. In particular the author analyzes the stimulus to private R&D through ad hoc tax incentives included in the CCTB Proposal and dives into the actual provisions included in the Proposal highlighting the most relevant issues connected with their design and interpretation. Moreover, the author explores the interaction between the CCTB Proposal and the granting by Member States of domestic R&D tax incentives
Russian Distributional Thesaurus (RDT): Word Embeddings
No full text<p>This resource is a part of the Russian Distributional Thesaurus (RDT): see http://russe.nlpub.ru/downloads and http://nlpub.ru/RDT. </p>
<p>This dataset contains a large scale word embeddings model for Russian trained using the SGNS model (Mikolov et al., 2013) on a 12.9 billion word collection of books in Russian. According to the results of our participation in the shared task on Russian semantic similarity (Panchenko et al., 2015), this approach scored in the top 5 among 105 submissions (Arefyev et al., 2015). Following our prior experiments (Arefyev et al., 2015) we have selected the following parameters for the model: minimal word frequency – 5, number of dimensions in a word vector – 500, three or five iterations of the learning algorithm over the input corpus, context window size of 1, 2, 3, 5, 7 and 10 words. Parameters of the model are listed below:</p>
<ul>
<li>Model: skip-gram</li>
<li>Corpus: a 150Gb sample of the lib.rus.ec book collection.</li>
<li>Context window size: 10 words</li>
<li>Number of dimensions: 500</li>
<li>Number of iterations: 3</li>
<li>Minimal word frequency: 5</li>
</ul>
<p>References:</p>
<ul>
<li>Panchenko A., Ustalov D., Arefyev N., Paperno D., Konstantinova N., Loukachevitch N. and Biemann C. (2016): Human and Machine Judgements about Russian Semantic Relatedness. In Proceedings of the 5th Conference on Analysis of Images, Social Networks, and Texts (AIST'2016). Communications in Computer and Information Science (CCIS). Springer-Verlag Berlin Heidelberg</li>
</ul>
<ul>
<li>Panchenko A., Loukachevitch N. V., Ustalov D., Paperno D., Meyer C. M., Konstantinova N. (2015): RUSSE: The First International Workshop on Russian Semantic Similarity. In Proceedings of the 21st International Conference on Computational Linguistics and Intellectual Technologies (Dialogue'2015). Moscow, Russia. RGGU</li>
</ul>
<ul>
<li>Arefyev N., Panchenko A., Lukanin A., Lesota O., Romanov P. (2015): Evaluating Three Corpus-Based Semantic Similarity Systems for Russian. In Proceedings of the 21st International Conference on Computational Linguistics and Intellectual Technologies (Dialogue'2015). Moscow, Russia. RGGU</li>
</ul>
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