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Weighting Scheme (weighting + scheme)
Selected AbstractsTowards a More Rational IMF Quota Structure: Suggestions for the Creation of a New International Financial ArchitectureDEVELOPMENT AND CHANGE, Issue 3 2000Raghbendra Jha The authors of this article argue that, in the absence of a well-founded quota formula, the very basis of the creation of the IMF as an institution at the centre of international financial arrangements was flawed; that there is no clear rationale for the determinants of quota structures and their weighting scheme; and that the quota allocation as an instrument seeks to target too many objectives. As a result, large and arbitrary cross-country variations exist in the relative impact of different determinants on the quota shares of different countries. The quota formulas therefore need to be reviewed and an alternative approach evolved, in which emphasis is placed on the size of the economy rather than its openness, along with efficiency parameters. The authors suggest some principles which might underpin redefined quota structures in support of a new financial architecture. They provide illustrative calculations using India as a case study, and trace the impact of the redefined quota structures against the backdrop of the impact of the Eleventh General Review on India's quota position. [source] Estimating temperature normals for USCRN stationsINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 14 2005Bomin Sun Abstract Temperature normals have been estimated for stations of the newly developed US Climate Reference Network (USCRN) by using USCRN temperatures and temperature anomalies interpolated from neighboring stations of the National Weather Service Cooperative Station Network (COOP). To seek the best normal estimation approach, several variations on estimation techniques were considered: the sensitivity of error of estimated normals to COOP data quality; the number of neighboring COOP station used; a spatial interpolation scheme; and the number of years of data used in normal estimation. The best estimation method we found is the one in which temperature anomalies are spatially interpolated from COOP stations within approximately 117 km of the target station using a weighting scheme involving the inverse of square difference in temperature (between the neighboring and target station). Using this approach, normals of USCRN stations were generated. Spatial and temporal characteristics of errors are presented, and the applicability of estimated normals in climate monitoring is discussed. Copyright © 2005 Royal Meteorological Society [source] Some Comments on Pareto Thinking, Test Validity, and Adverse Impact: When ,and' is optimal and ,or' is a trade-offINTERNATIONAL JOURNAL OF SELECTION AND ASSESSMENT, Issue 3 2008Denise Potosky De Corte, Lievens, and Sackett add to the literature on selection test validity and adverse impact (AI). Their Pareto-based weighting scheme essentially asks organizations if they are willing to give up some validity to hopefully achieve some reduction in AI. We considered their approach and conclusions in relation to the regression weighting method we used, and we offer five points that reflect our observations as well as our shared goals. We hope our comments, like their work in this field, will invigorate the pursuit of new ways of examining, and one day resolving, the persistent concern regarding the AI associated with valid selection tests. [source] Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal databaseJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 6 2010Xinhai Liu We propose a new hybrid clustering framework to incorporate text mining with bibliometrics in journal set analysis. The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of processing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. Three different algorithms are extended by the proposed weighting scheme and they are employed on a large journal set retrieved from the Web of Science (WoS) database. The clustering performance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experimental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other methods in clustering performance and efficiency. The proposed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields. [source] Linear least-squares method for unbiased estimation of T1 from SPGR signalsMAGNETIC RESONANCE IN MEDICINE, Issue 2 2008Lin-Ching Chang Abstract The longitudinal relaxation time, T1, can be estimated from two or more spoiled gradient recalled echo images (SPGR) acquired with different flip angles and/or repetition times (TRs). The function relating signal intensity to flip angle and TR is nonlinear; however, a linear form proposed 30 years ago is currently widely used. Here we show that this linear method provides T1 estimates that have similar precision but lower accuracy than those obtained with a nonlinear method. We also show that T1 estimated by the linear method is biased due to improper accounting for noise in the fitting. This bias can be significant for clinical SPGR images; for example, T1 estimated in brain tissue (800 ms < T1 < 1600 ms) can be overestimated by 10% to 20%. We propose a weighting scheme that correctly accounts for the noise contribution in the fitting procedure. Monte Carlo simulations of SPGR experiments are used to evaluate the accuracy of the estimated T1 from the widely-used linear, the proposed weighted-uncertainty linear, and the nonlinear methods. We show that the linear method with weighted uncertainties reduces the bias of the linear method, providing T1 estimates comparable in precision and accuracy to those of the nonlinear method while reducing computation time significantly. Magn Reson Med 60:496,501, 2008. © 2008 Wiley-Liss, Inc. [source] Detecting bispectral acoustic oscillations from inflation using a new flexible estimatorMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 4 2010Martin Bucher ABSTRACT We present a new flexible estimator for comparing theoretical templates for the predicted bispectrum of the cosmic microwave background (CMB) anisotropy to observations. This estimator, based on binning in harmonic space, generalizes the ,optimal' estimator of Komatsu, Spergel and Wandelt by allowing an adjustable weighting scheme for masking possible foreground and other contaminants and detecting particular noteworthy features in the bispectrum. The utility of this estimator is illustrated by demonstrating how acoustic oscillations in the bispectrum and other details of the bispectral shape could be detected in the future Planck data provided that fNL is sufficiently large. The character and statistical weight of the acoustic oscillations and the decay tail are described in detail. [source] Cosmic flows on 100 h,1 Mpc scales: standardized minimum variance bulk flow, shear and octupole momentsMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 4 2010Hume A. Feldman ABSTRACT The low-order moments, such as the bulk flow and shear, of the large-scale peculiar velocity field are sensitive probes of the matter density fluctuations on very large scales. In practice, however, peculiar velocity surveys are usually sparse and noisy, which can lead to the aliasing of small-scale power into what is meant to be a probe of the largest scales. Previously, we developed an optimal ,minimum variance' (MV) weighting scheme, designed to overcome this problem by minimizing the difference between the measured bulk flow (BF) and that which would be measured by an ideal survey. Here we extend this MV analysis to include the shear and octupole moments, which are designed to have almost no correlations between them so that they are virtually orthogonal. We apply this MV analysis to a compilation of all major peculiar velocity surveys, consisting of 4536 measurements. Our estimate of the BF on scales of ,100 h,1 Mpc has a magnitude of |v| = 416 ± 78 km s ,1 towards Galactic l= 282°± 11° and b= 6°± 6°. This result is in disagreement with , cold dark matter with Wilkinson Microwave Anisotropy Probe 5 (WMAP5) cosmological parameters at a high confidence level, but is in good agreement with our previous MV result without an orthogonality constraint, showing that the shear and octupole moments did not contaminate the previous BF measurement. The shear and octupole moments are consistent with WMAP5 power spectrum, although the measurement noise is larger for these moments than for the BF. The relatively low shear moments suggest that the sources responsible for the BF are at large distances. [source] Bi-criteria optimal control of redundant robot manipulators using LVI-based primal-dual neural networkOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 3 2010Binghuang Cai Abstract In this paper, a bi-criteria weighting scheme is proposed for the optimal motion control of redundant robot manipulators. To diminish the discontinuity phenomenon of pure infinity-norm velocity minimization (INVM) scheme, the proposed bi-criteria redundancy-resolution scheme combines the minimum kinetic energy scheme and the INVM scheme via a weighting factor. Joint physical limits such as joint limits and joint-velocity limits could also be incorporated simultaneously into the scheme formulation. The optimal kinematic control scheme can be reformulated finally as a quadratic programming (QP) problem. As the real-time QP solver, a primal-dual neural network (PDNN) based on linear variational inequalities (LVI) is developed as well with a simple piecewise-linear structure and global exponential convergence to optimal solutions. Since the LVI-based PDNN is matrix-inversion free, it has higher computational efficiency in comparison with dual neural networks. Computer simulations performed based on the PUMA560 manipulator illustrate the validity and advantages of such a bi-criteria neural optimal motion-control scheme for redundant robots. Copyright © 2009 John Wiley & Sons, Ltd. [source] Identifying important areas for butterfly conservation in ItalyANIMAL CONSERVATION, Issue 1 2009M. Girardello Abstract The current study combines the use of niche modelling with a site prioritization method to identify important areas for butterfly conservation in Italy. A novel machine learning method (bagging predictors) was used to predict the distribution of 232 species of butterflies across the Italian Peninsula. The results of the models were used to identify high-value sites with a multispecies prioritization method called zonation. In order to identify important areas for species of conservation concern, we incorporated a species weighting scheme to zonation analyses. We also used the results of the zonation analyses to identify a series of management landscapes on the basis of the similarity in species composition among sites. The basic zonation showed that most important areas for butterfly conservation are located in the Alps, the Appennine, the Apulia region and in the island of Sardinia. The inclusion of a species weighting scheme in the zonation analyses revealed the importance of two new areas located in Southern Italy and emphasized the importance of the Alps for species of conservation concern. The landscape identification procedure selected a series of landscapes, which provide protection to a full range of species ranging from the Alps to Mediterranean areas. Our study shows that the areas selected in our analyses should be given high priority in future conservation plans and monitoring schemes. [source] Statistical prediction of global sea-surface temperature anomaliesINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 14 2003A. W. Colman Abstract Sea-surface temperature (SST) is one of the principal factors that influence seasonal climate variability, and most seasonal prediction schemes make use of information regarding SST anomalies. In particular, dynamical atmospheric prediction models require global gridded SST data prescribed through the target season. The simplest way of providing those data is to persist the SST anomalies observed at the start of the forecast at each grid point, with some damping, and this strategy has proved to be quite effective in practice. In this paper we present a statistical scheme that aims to improve that basic strategy by combining three individual methods together: simple persistence, canonical correlation analysis (CCA), and nearest-neighbour regression. Several weighting schemes were tested: the best of these is one that uses equal weight in all areas except the east tropical Pacific, where CCA is preferred. The overall performance of the combined scheme is better than the individual schemes. The results show improvements in tropical ocean regions for lead times beyond 1 or 2 months, but the skill of simple persistence is difficult to beat in the extratropics at all lead times. Aspects such as averaging periods and grid size were also investigated: results showed little sensitivity to these factors. The combined statistical SST prediction scheme can also be used to improve statistical regional rainfall forecasts that use SST anomaly patterns as predictors. Copyright © Crown Copyright 2003. Published by John Wiley & Sons, Ltd. [source] Weighting hyperspectral image data for improved multivariate curve resolution resultsJOURNAL OF CHEMOMETRICS, Issue 9 2008Howland D. T. Jones Abstract The combination of hyperspectral confocal fluorescence microscopy and multivariate curve resolution (MCR) provides an ideal system for improved quantitative imaging when multiple fluorophores are present. However, the presence of multiple noise sources limits the ability of MCR to accurately extract pure-component spectra when there is high spectral and/or spatial overlap between multiple fluorophores. Previously, MCR results were improved by weighting the spectral images for Poisson-distributed noise, but additional noise sources are often present. We have identified and quantified all the major noise sources in hyperspectral fluorescence images. Two primary noise sources were found: Poisson-distributed noise and detector-read noise. We present methods to quantify detector-read noise variance and to empirically determine the electron multiplying CCD (EMCCD) gain factor required to compute the Poisson noise variance. We have found that properly weighting spectral image data to account for both noise sources improved MCR accuracy. In this paper, we demonstrate three weighting schemes applied to a real hyperspectral corn leaf image and to simulated data based upon this same image. MCR applied to both real and simulated hyperspectral images weighted to compensate for the two major noise sources greatly improved the extracted pure emission spectra and their concentrations relative to MCR with either unweighted or Poisson-only weighted data. Thus, properly identifying and accounting for the major noise sources in hyperspectral images can serve to improve the MCR results. These methods are very general and can be applied to the multivariate analysis of spectral images whenever CCD or EMCCD detectors are used. Copyright © 2008 John Wiley & Sons, Ltd. [source] On the timing characteristics of the apparent diffusion coefficient contrast in fMRIMAGNETIC RESONANCE IN MEDICINE, Issue 2 2002Stacey L. Gangstead Abstract For the past 10 years, functional MRI (fMRI) has seen rapid progress in both clinical and basic science research. Most of the imaging techniques are based on the blood oxygenation level-dependent (BOLD) contrast which arises from the field perturbation of the paramagnetic deoxyhemoglobin due to the mismatch between the local oxygen demand and delivery. Because the changes of oxygenation level take place mostly in the veins, the dominant signal sources of the BOLD signal are intra- and extravascular proton pools of the veins. Perfusion imaging methods, developed parallel to the BOLD technique, seek to quantify the blood flow and perfusion. Recently, perfusion imaging using arterial spin tagging methods have been used to study brain function by investigating the changes of the blood flow and perfusion during brain activation, thereby generating an alternative contrast mechanism to the functional brain imaging. Since most of these methods require tagging pulse and wait time for blood to be delivered to the imaged slice, the temporal resolution may not be optimal. Dynamic intravoxel incoherent motion (IVIM) weighting schemes using apparent diffusion coefficient (ADC) contrast were suggested to image the relative changes of the in-plane blood flow during brain function. In this report, it was demonstrated that, in addition to the spatial discrepancies of the activated areas, the time course based on the ADC contrast consistently precedes that from the BOLD contrast with timing offset on the order of 1 sec. Since arterial networks would have different spatial locations and preceding temporal characters, the findings in this report are indicative that the ADC contrast is sensitive to the arterial blood flow changes. Magn Reson Med 48:385,388, 2002. © 2002 Wiley-Liss, Inc. [source] Molecular systematics of cowries (Gastropoda: Cypraeidae) and diversification patterns in the tropicsBIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 3 2003CHRISTOPHER P. MEYER This study produces a nearly comprehensive phylogeny for the marine gastropod group Cypraeidae (cowries) and uses this topology to examine diversification patterns in the tropics. The dataset is based on molecular sequence data from two mitochondrial genes and includes 210 evolutionary significant units (ESUs) from 170 recognized species (>80%). Systematics for the group is revised based on well-supported clades, and tree topology is generally consistent with previously proposed classification schemes. Three new genera are introduced (Cryptocypraea gen. nov, Palmulacypraea gen. nov, and Contradusta gen. nov) and two previous genera are resurrected (Perisserosa and Eclogavena). One new tribe is proposed (Bistolidini). Topologies produced by a range of transition:transversion (Ti:Tv) weighting schemes in parsimony are pooled and evaluated using maximum likelihood criteria. Extensive geographical coverage shows persistent, large-scale geographical structure in sister-groups. Genetic divergence between subspecies is often equivalent or even greater than that between recognized species. Using ESUs as a metric, diversity throughout the Indo-West Pacific (IWP) increases by 38%. Intra- and inter-regional diversification patterns show that the IWP is the centre for speciation in cowries. The other major tropical regions of the world are inhabited by a predominantly relictual fauna; from a cowrie's eye-view. Good dispersal ability begets larger ranges, increased extinction resistance and morphological stasis; whereas shorter larval duration results in smaller ranges, higher speciation rates, but also higher turnover. Larval duration and dispersal ability appear correlated with ocean productivity as taxa with longer-lived larvae are associated with oligotrophic conditions; whereas taxa with shorter larval durations are associated with eutrophic, continental conditions. This tendency is carried to the extreme in temperate or upwelling regions where a planktonic phase is completely lost and crawl-away larvae evolve multiple times. A strong phylogenetic trend supports these observations as lineages leading up to and including the derived Indo-West Pacific Erroneinae clade contain taxa predominantly restricted to continental habitats and have undergone the greatest evolutionary radiations in their respective regions. © 2003 The Linnean Society of London, Biological Journal of the Linnean Society, 2003, 79, 401, 459. [source] Combining Multiple Biomarker Models in Logistic RegressionBIOMETRICS, Issue 2 2008Zheng Yuan Summary In medical research, there is great interest in developing methods for combining biomarkers. We argue that selection of markers should also be considered in the process. Traditional model/variable selection procedures ignore the underlying uncertainty after model selection. In this work, we propose a novel model-combining algorithm for classification in biomarker studies. It works by considering weighted combinations of various logistic regression models; five different weighting schemes are considered in the article. The weights and algorithm are justified using decision theory and risk-bound results. Simulation studies are performed to assess the finite-sample properties of the proposed model-combining method. It is illustrated with an application to data from an immunohistochemical study in prostate cancer. [source] Escaping the matrix: a new algorithm for phylogenetic comparative studies of co-evolutionCLADISTICS, Issue 4 2004Maggie Wojcicki An algorithm for generating host cladograms from parasite-host cladograms derived from parasite phylogenies, Phylogenetic Analysis for Comparing Trees (PACT), is described. PACT satisfies Assumption 0, that all the information in each parasite-host cladogram must be used in a co-evolutionary analysis, and that the host relationships depicted in the final host cladogram must be logically consistent with the phylogenetic relationships depicted in every part of every parasite-host cladogram used to construct the host cladogram. It accounts for cases of speciation by host switching and expansion of host range, and reticulated host relationships, in addition to co-speciation, sympatric speciation, and extinction in all input parasite-host cladograms, and does so without a priori weighting schemes and without a posteriori manipulation of the data. [source] | ||||||||||