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 $F_2(\xi=x \ {\rm or}\ W^2,Q^2)$ 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 $0.1$ GeV$^2\leq Q^2\leq 10$ GeV$^2$. 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 $k_T$ factorization. From our calculations, we find that the decay branching ratios for color allowed tree-dominated decays $B\to a_{2}^{0}\pi^{+}$ and $B\to a_{2}^{-}\pi^{+}$ modes are of order $10^{-6}$ and $10^{-5}$, 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 $10^{-5}$ to $10^{-8}$, 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 $f_{2}$ and $f_{2}'$, the decay rates are enhanced significantly for some decays involving $f_{2}^{\prime}$ 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.

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