1,712 research outputs found
MeSH term explosion and author rank improve expert recommendations
No full textInformation 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
A solver for clustered low-rank SDPs arising from multivariate polynomial (matrix) programs
No full textIn this thesis, we give a primal-dual interior point method specialized to clustered low-rank semidefinite programs. We introduce multivariate polynomial matrix programs, and we reduce these to clustered low-rank semidefinite programs. This extends the work of Simmons-Duffin [J. High Energ. Phys. 1506, no. 174 (2015)] from univariate to multivariate polynomial matrix programs, and to more general clustered low-rank semidefinite programs. Clustered low-rank semidefinite programs are programs with low-rank constraint matrices where the positive semidefinite variables are only used within clusters of constraints. The free variables can be used in any constraint, and can be used to connect clusters. The solver uses this structure to speed up the computations in two ways. First, the low rank structure is used to reduce matrix products to products of the form uT M v, where M is a matrix and u and v are vectors, as already suggested by Löfberg and Parrilo in [43rd IEEE CDC (2004)]. Second, an additional block-diagonal structure is introduced due to the clusters. This gives the possibility to do operations such as the Cholesky decomposition block-wise. We apply this to sphere packing and spherical cap packing. For sphere packing, the speed of the solver is compared to the often used arbitrary precision solver SDPA-GMP, showing the theoretical speedup in time complexity. We give a new three-point bound for the maximum density when packing spherical caps of sizes on the unit sphere. https://github.com/nanleij/Clustered-Low-Rank-SDP-solver Repository link Github repository with the Julia code of the solverApplied Mathematics | Optimizatio
Reduced-rank adaptive least bit-error-rate detection in hybrid direct-sequence time-hopping ultrawide bandwidth systems
No full textDesign of high-efficiency low-complexity detection schemes for ultrawide bandwidth (UWB) systems is highly challenging. This contribution proposes a reduced-rank adaptive multiuser detection (MUD) scheme operated in least bit-errorrate (LBER) principles for the hybrid direct-sequence timehopping UWB (DS-TH UWB) systems. The principal component analysis (PCA)-assisted rank-reduction technique is employed to obtain a detection subspace, where the reduced-rank adaptive LBER-MUD is carried out. The reduced-rank adaptive LBERMUD is free from channel estimation and does not require the knowledge about the number of resolvable multipaths as well as the knowledge about the multipaths’ strength. In this contribution, the BER performance of the hybrid DS-TH UWB systems using the proposed detection scheme is investigated, when assuming communications over UWB channels modeled by the Saleh-Valenzuela (S-V) channel model. Our studies and performance results show that, given a reasonable rank of the detection subspace, the reduced-rank adaptive LBER-MUD is capable of efficiently mitigating the multiuser interference (MUI) and inter-symbol interference (ISI), and achieving the diversity gain promised by the UWB systems
Parameters behind "nonparametric" statistics: Kendall's tau,Somers' D and median differences
No full textSo-called "nonparametric" statistical methods are often in fact based on population parameters, which can be estimated (with confidence limits) using the corresponding sample statistics. This article reviews the uses of three such parameters, namely Kendall's tau, Somers' D and the Hodges-Lehmann median difference. Confidence intervals for these are demonstrated using the somersd package. It is argued that confidence limits for these parameters, and their differences,are more informative than the traditional practice of reporting only p-values. These three parameters are also important in defining other tests and parameters, such as the Wilcoxon test, the area under the receiver operating characteristic (ROC) curve, Harrell's C, and the Theil median slope. Copyright 2002 by Stata Corporation.confidence intervals, Gehan test, Harrell's C , Hodges-Lehmann median difference, Kendall's tau, nonparametric methods, rank correlation, rank-sum test, ROC area, Somers' D, Theil median slope, Wilcoxon test
Higher rank motivic Donaldson-Thomas invariants of A3 via wall-crossing, and asymptotics
No full textWe compute, via motivic wall-crossing, the generating function of virtual motives of the Quot scheme of points on A(3), generalising to higher rank a result of Behrend-Bryan-Szendroi. We show that this motivic partition function converges to a Gaussian distribution, extending a result of Morrison
Aggregation and Other Biases in the Calculation of Consumer Elasticities for Models of Arbitrary Rank
No full textConsumer-related policy decisions often require analysis of aggregate responses or mean elasticities. However, in practice these mean elasticities are seldom used. Mean elasticities can be approximated using aggregate data, but that introduces aggregation bias for full and compensated price elasticities, though interestingly not for expenditure elasticities. The biases corresponding to incorrect approximations of mean elasticities depend on the type of data (micro or aggregate), the type and rank of the model, and generalized measures of income inequality. These biases are distinct from the biases (already noted in the literature) when using aggregate data to estimate micro elasticites at mean income.Aggregate price and expenditure elasticities, aggregation bias, consumer demand, generalized measures of income inequality, income distribution
Polynomial Invariants for Arbitrary Rank D Weakly-Colored Stranded Graphs
No full textPolynomials on stranded graphs are higher dimensional generalization of Tutte and Bollobás-Riordan polynomials [Math. Ann. 323 (2002), 81-96]. Here, we deepen the analysis of the polynomial invariant defined on rank 3 weakly-colored stranded graphs introduced in arXiv:1301.1987. We successfully find in dimension D≥3 a modified Euler characteristic with D−2 parameters. Using this modified invariant, we extend the rank 3 weakly-colored graph polynomial, and its main properties, on rank 4 and then on arbitrary rank D weakly-colored stranded graphs.Numerous discussions with Joseph Ben Geloun and Mahouton N. Hounkonnou have been hugely
beneficial for this work and gratefully acknowledged. The author acknowledges the support of
Max-Planck Institute for Gravitational Physics, Albert Einstein Institute, and the Association
pour la Promotion Scientifique de l’Afrique. The ICMPA is also in partnership with the Daniel
Iagolnitzer Foundation (DIF), France
Preservice Teacher Perceptions of the Role an Agriculture Teacher During Their Early Field Experience
No full textSchool-based agriculture teachers (SBAE) hold many roles inside and outside of the school. Identifying and understanding the many roles an agriculture teacher may have in their career is an important part of an early field experience (EFE). In this study, EFE students (n = 18) submitted written reflections focused on the role of an agriculture teacher. Open coding of the preservice teacher reflections revealed five themes including work-life balance, public relations, role of an FFA advisor, student success, and school responsibilities. Codes within these five themes indicate a student-centered approach among agriculture teachers. Additionally, EFE students described many school responsibilities outside of the agriculture program that may contribute to a work-life imbalance among agriculture teachers. It is recommended that teacher education programs have current practicing teachers be engaged as part of the preparation process for pre-service students to learn about agriculture teacher roles. Consideration needs to be taken when selecting EFE sites to ensure students are receiving a quality experience, which reinforces the teacher preparation program objectives.This article is published as Smalley, S. W., & Rank, B. D. (2019). Preservice Teacher Perceptions of the Role of an Agriculture Teacher during Their Early Field Experience. Journal of Agricultural Education, 60(2), 99-108. doi: 10.5032/jae.2019.02099. Posted with permission.</p
A Pragmatic Approach to Spearman's Rank Correlation Coefficient
No full textThis article, created by D. Griffiths, describes Spearman's coefficient of rank correlation and attempts to explain how its algebraic structure arises. The author states the goals of this lesson as, "A full understanding of correlation requires an appreciation of bivariate distributions, but increasingly rank correlation coefficients are being used as a measure of agreement with pupils for whom such appreciation is not possible. How can we justify the formula used?" The author first provides a description of the statistical method, then an example and finally a description of the mathematical formula used. This is a great introduction to this statistical method
Synthesis of Contemporary SAE Research 1994–2014
No full textIn the 1990s, a series of research syntheses were conducted regarding supervised agricultural experience. These syntheses included supervised agricultural experience (SAE) research from 1964 through 1993. With these past syntheses as the premise, contemporary SAE research was identified, synthesized, and coded into emerging themes. Inclusion criteria for this synthesis required articles to (a) be published in a peer-reviewed journal or national/regional American Association for Agricultural Education research conference proceedings, (b) include research specifically pertaining to SAE, (c) be available and accessible through the search procedures, and (d) be published between January 1994 and December 2014. An exhaustive search was conducted using library databases as well as digital journals and conference proceedings. Themes that emerged from this synthesis were (a) participation, (b) teacher education, (c) benefits, (d) professional development, (e) supervision, (f) scope/structure, (g) economic impact, (h) program quality, (i) learning theory, and (k) international settings. Similar to the previous syntheses, research conducted between 1994 and 2014 was primarily descriptive, conceptually broad, and often limited to relatively small populations such as single states. Additional multistate and national studies are recommended to describe the content and context of SAE instruction in teacher education and to refine quality indicators related to SAE practice.This article is published as Rank, B. D.* & Retallick, M. S. (2016). Synthesis of Contemporary SAE Research 1994-2014. Journal of Agricultural Education, 57(4), 132-146. doi: 10.5032/jae.2016.04132. Posted with permission.</p
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