Duality and phase diagram of one dimensional transport

The observation of duality by Mukherji and Mishra in one dimensional transport problems has been used to develop a general approach to classify and characterize the steady state phase diagrams. The phase diagrams are determined by the zeros of a set of coarse-grained functions without the need of detailed knowledge of microscopic dynamics. In the process, a new class of nonequilibrium multicritical points has been identified.Comment: 6 pages, 2 figures (4 eps files

Josephson (001) tilt grain boundary junctions of high temperature superconductors

We calculate the critical current $I_c$ across in-plane (001) tilt grain boundary junctions of high temperature superconductors. We solve for the electronic states corresponding to the electron-doped cuprates, two slightly different hole-doped cuprates, and an extremely underdoped hole-doped cuprate in each half-space, and weakly connect the two half-spaces by either specular or random quasiparticle tunneling. We treat symmetric, straight, and fully asymmetric junctions with s-, extended-s-, or d$_{x^2-y^2}$-wave order parameters. For symmetric junctions with random grain boundary tunneling, our results are generally in agreement with the Sigrist-Rice form for ideal junctions that has been used to interpret ``phase-sensitive'' experiments consisting of such in-plane grain boundary junctions. For specular grain boundary tunneling across symmetric juncitons, our results depend upon the Fermi surface topology, but are usually rather consistent with the random facet model of Tsuei {\it et al.} [Phys. Rev. Lett. {\bf 73}, 593 (1994)]. Our results for asymmetric junctions of electron-doped cuparates are in agreement with the Sigrist-Rice form. However, ou resutls for asymmetric junctions of hole-doped cuprates show that the details of the Fermi surface topology and of the tunneling processes are both very important, so that the ``phase-sensitive'' experiments based upon the in-plane Josephson junctions are less definitive than has generally been thought.Comment: 13 pages, 10 figures, resubmitted to PR

Quantum Field Effects on Cosmological Phase Transition in Anisotropic Spacetimes

The one-loop renormalized effective potentials for the massive $\phi^4$ theory on the spatially homogeneous models of Bianchi type I and Kantowski-Sachs type are evaluated. It is used to see how the quantum field affects the cosmological phase transition in the anisotropic spacetimes. For reasons of the mathematical technique it is assumed that the spacetimes are slowly varying or have specially metric forms. We obtain the analytic results and present detailed discussions about the quantum field corrections to the symmetry breaking or symmetry restoration in the model spacetimes.Comment: Latex 17 page

Semileptonic BB Meson Decays Into A Highly Excited Charmed Meson Doublet

We study the heavy quark effective theory prediction for semileptonic $B$ decays into an orbital excited $F$-wave charmed doublet, the ($2^{+}$, $3^{+}$) states ($D^{*'}_{2}$, $D_{3}$), at the leading order of heavy quark expansion. The corresponding universal form factor is estimated by using the QCD sum rule method. The decay rates we predict are $\Gamma_{B\to D^{*'}_{2}\ell\overline{\nu}}=1.85\times10^{-19} {GeV}$ and $\Gamma_{B\to D_{3}\ell\overline{\nu}}=1.78\times10^{-19} {GeV}$. The branching ratios are $\mathcal {B}(B\to D_{2}^{*'}\ell\overline{\nu})=4.6\times10^{-7}$ and $\mathcal {B}(B\to D_{3}\ell\overline{\nu})=4.4\times10^{-7}$, respectively.Comment: 6 pages,2 figure

Thermal effects on nuclear symmetry energy with a momentum-dependent effective interaction

The knowledge of the nuclear symmetry energy of hot neutron-rich matter is important for understanding the dynamical evolution of massive stars and the supernova explosion mechanisms. In particular, the electron capture rate on nuclei and/or free protons in presupernova explosions is especially sensitive to the symmetry energy at finite temperature. In view of the above, in the present work we calculate the symmetry energy as a function of the temperature for various values of the baryon density, by applying a momentum-dependent effective interaction. In addition to a previous work, the thermal effects are studied separately both in the kinetic part and the interaction part of the symmetry energy. We focus also on the calculations of the mean field potential, employed extensively in heavy ion reaction research, both for nuclear and pure neutron matter. The proton fraction and the electron chemical potential, which are crucial quantities for representing the thermal evolution of supernova and neutron stars, are calculated for various values of the temperature. Finally, we construct a temperature dependent equation of state of $\beta$-stable nuclear matter, the basic ingredient for the evaluation of the neutron star properties.Comment: 18 pages, 10 figures, 1 table, accepted for publication in Physical Review

Effects of Line-tying on Magnetohydrodynamic Instabilities and Current Sheet Formation

An overview of some recent progress on magnetohydrodynamic stability and current sheet formation in a line-tied system is given. Key results on the linear stability of the ideal internal kink mode and resistive tearing mode are summarized. For nonlinear problems, a counterexample to the recent demonstration of current sheet formation by Low \emph{et al}. [B. C. Low and \AA. M. Janse, Astrophys. J. \textbf{696}, 821 (2009)] is presented, and the governing equations for quasi-static evolution of a boundary driven, line-tied magnetic field are derived. Some open questions and possible strategies to resolve them are discussed.Comment: To appear in Phys. Plasma

Learned versus Hand-Designed Feature Representations for 3d Agglomeration

For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data. Yet for problems that involve analysis of 3d objects, such as mesh segmentation, shape retrieval, or neuron fragment agglomeration, there remains a strong reliance on hand-designed feature descriptors. In this paper, we evaluate a large set of hand-designed 3d feature descriptors alongside features learned from the raw data using both end-to-end and unsupervised learning techniques, in the context of agglomeration of 3d neuron fragments. By combining unsupervised learning techniques with a novel dynamic pooling scheme, we show how pure learning-based methods are for the first time competitive with hand-designed 3d shape descriptors. We investigate data augmentation strategies for dramatically increasing the size of the training set, and show how combining both learned and hand-designed features leads to the highest accuracy