37,393 research outputs found
Mapping atmospheric pollutants emissions in European countries
In this paper we present a methodology which enables the graphical representation, in a bi-dimensional Euclidean space, of atmospheric pollutants emissions in European countries. This approach relies on the use of Multidimensional Unfolding (MDU), an exploratory multivariate data analysis technique. This technique illustrates both the relationships between the emitted gases and the gases and their geographical origins. The main contribution of this work concerns the evaluation of MDU solutions. We use simulated data to define thresholds for the model fitting measures, allowing the MDU output quality evaluation. The quality assessment of the model adjustment is thus carried out as a step before interpretation of the gas types and geographical origins results. The MDU maps analysis generates useful insights, with an immediate substantive result and enables the formulation of hypotheses for further analysis and modeling
Mapping atmospheric pollutant emissions in European countries
WOS:000300110900009 (Nº de Acesso Web of Science)In this paper we present a methodology which enables the graphical representation, in a bi-dimensional Euclidean space, of atmospheric pollutants emissions in European countries. This approach relies on the use of Multidimensional Unfolding (MDU), an exploratory multivariate data analysis technique. This technique illustrates both the relationships between the emitted gases and the gases and their geographical origins. The main contribution of this work concerns the evaluation of MDU solutions. We use simulated data to define thresholds for the model fitting measures, allowing the MDU output quality evaluation. The quality assessment of the model adjustment is thus carried out as a step before interpretation of the gas types and geographical origins results. The MDU maps analysis generates useful insights, with an immediate substantive result and enables the formulation of hypotheses for further analysis and modeling
Quantifying the power of multiple event interpretations
A number of methods have been proposed recently which exploit multiple
highly-correlated interpretations of events, or of jets within an event. For
example, Qjets reclusters a jet multiple times and telescoping jets uses
multiple cone sizes. Previous work has employed these methods in
pseudo-experimental analyses and found that, with a simplified statistical
treatment, they give sizable improvements over traditional methods. In this
paper, the improvement gain from multiple event interpretations is explored
with methods much closer to those used in real experiments. To this end, we
derive a generalized extended maximum likelihood procedure. We study the
significance improvement in Higgs to bb with both this method and the
simplified method from previous analysis. With either method, we find that
using multiple jet radii can provide substantial benefit over a single radius.
Another concern we address is that multiple event interpretations might be
exploiting similar information to that already present in the standard
kinematic variables. By examining correlations between kinematic variables
commonly used in LHC analyses and invariant masses obtained with multiple jet
reconstructions, we find that using multiple radii is still helpful even on top
of standard kinematic variables when combined with boosted decision trees.
These results suggest that including multiple event interpretations in a
realistic search for Higgs to bb would give additional sensitivity over
traditional approaches.Comment: 13 pages, 2 figure
Tensor Numerical Methods in Quantum Chemistry: from Hartree-Fock Energy to Excited States
We resume the recent successes of the grid-based tensor numerical methods and
discuss their prospects in real-space electronic structure calculations. These
methods, based on the low-rank representation of the multidimensional functions
and integral operators, led to entirely grid-based tensor-structured 3D
Hartree-Fock eigenvalue solver. It benefits from tensor calculation of the core
Hamiltonian and two-electron integrals (TEI) in complexity using
the rank-structured approximation of basis functions, electron densities and
convolution integral operators all represented on 3D
Cartesian grids. The algorithm for calculating TEI tensor in a form of the
Cholesky decomposition is based on multiple factorizations using algebraic 1D
``density fitting`` scheme. The basis functions are not restricted to separable
Gaussians, since the analytical integration is substituted by high-precision
tensor-structured numerical quadratures. The tensor approaches to
post-Hartree-Fock calculations for the MP2 energy correction and for the
Bethe-Salpeter excited states, based on using low-rank factorizations and the
reduced basis method, were recently introduced. Another direction is related to
the recent attempts to develop a tensor-based Hartree-Fock numerical scheme for
finite lattice-structured systems, where one of the numerical challenges is the
summation of electrostatic potentials of a large number of nuclei. The 3D
grid-based tensor method for calculation of a potential sum on a lattice manifests the linear in computational work, ,
instead of the usual scaling by the Ewald-type approaches
Disaggregating Input-Output Tables by the Multidimensional RAS Method
An unknown input-output table can be estimated by the RAS method when only
its row and column sums are known and some initial structure is assumed. The
RAS approach can also be utilized for disaggregation of an annual national
table to more detailed tables such as regional, quarterly and domestic/imported
tables. However, the regular RAS method does not ensure that the sums of
disaggregated tables are equal to the total table. For this problem, we propose
to use the multidimensional RAS method which besides input and output totals
also ensures regional, quarterly and domestic/imported totals. Our analysis of
the Czech industry shows that the multidimensional RAS method increases the
accuracy of table estimation as well as accuracy of input-output applications
such as the Leontief inverse, the regional Isard's model and the quarterly
value added
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