54,387 research outputs found
Color dipole cross section and inelastic structure function
Instead of starting from a theoretically motivated form of the color dipole
cross section in the dipole picture of deep inelastic scattering, we start with
a parametrization of the deep inelastic structure function for electromagnetic
scattering with protons, and then extract the color dipole cross section. Using
the parametrizations of by Donnachie-Landshoff
and Block et al., we find the dipole cross section from an approximate form of
the presumed dipole cross section convoluted with the perturbative photon wave
function for virtual photon splitting into a color dipole with massless quarks.
The color dipole cross section determined this way reproduces the original
structure function within about 10\% for GeV GeV.
We discuss the large and small form of the dipole cross section and compare
with other parameterizations.Comment: 11 pages, 12 figure
Nonleptonic two-body charmless B decays involving a tensor meson in the Perturbative QCD Approach
Two-body charmless hadronic B decays involving a light tensor meson in the
final states are studied in the perturbative QCD approach based on
factorization. From our calculations, we find that the decay branching ratios
for color allowed tree-dominated decays and modes are of order and , respectively.
While other color suppressed tree-dominated decays have very small branching
ratios. In general, the branching ratios of most decays are in the range of
to , which are bigger by one or two orders of magnitude than
those predictions obtained in Isgur-Scora-Grinstein-Wise II model and in the
covariant light-front approach, but consistent with the recent experimental
measurements and the QCD factorization calculations. Since the decays with a
tensor meson emitted from vacuum are prohibited in naive factorization, the
contributions of nonfactorizable and annihilation diagrams are very important
to these decays, which are calculable in our perturbative QCD approach. We also
give predictions to the direct CP asymmetries, some of which are large enough
for the future experiments to measure. Because we considered the mixing between
and , the decay rates are enhanced significantly for some
decays involving meson, even with a small mixing angle.Comment: 26 pages, 2 figure
Path integral Monte Carlo study of the interacting quantum double-well model: Quantum phase transition and phase diagram
The discrete time path integral Monte Carlo (PIMC) with a one-particle
density matrix approximation is applied to study the quantum phase transition
in the coupled double-well chain. To improve the convergence properties, the
exact action for a single particle in a double well potential is used to
construct the many-particle action. The algorithm is applied to the interacting
quantum double-well chain for which the zero-temperature phase diagram is
determined. The quantum phase transition is studied via finite-size scaling and
the critical exponents are shown to be compatible with the classical
two-dimensional (2D) Ising universality class -- not only in the order-disorder
limit (deep potential wells) but also in the displacive regime (shallow
potential wells).Comment: 17 pages, 7 figures; Accepted for publication in Phys. Rev.
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Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach
Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence
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