Induced Lorentz- and CPT-violating Chern-Simons term in QED: Fock-Schwinger proper time method

Using the Fock-Schwinger proper time method, we calculate the induced Chern-Simons term arising from the Lorentz- and CPT-violating sector of quantum electrodynamics with a $b_\mu \bar{\psi}\gamma^\mu \gamma_5 \psi$ term. Our result to all orders in $b$ coincides with a recent linear-in-$b$ calculation by Chaichian et al. [hep-th/0010129 v2]. The coincidence was pointed out by Chung [Phys. Lett. {\bf B461} (1999) 138] and P\'{e}rez-Victoria [Phys. Rev. Lett. {\bf 83} (1999) 2518] in the standard Feynman diagram calculation with the nonperturbative-in-$b$ propagator.Comment: 11 pages, no figur

Calculation of a Class of Three-Loop Vacuum Diagrams with Two Different Mass Values

We calculate analytically a class of three-loop vacuum diagrams with two different mass values, one of which is one-third as large as the other, using the method of Chetyrkin, Misiak, and M\"{u}nz in the dimensional regularization scheme. All pole terms in \epsilon=4-D (D being the space-time dimensions in a dimensional regularization scheme) plus finite terms containing the logarithm of mass are kept in our calculation of each diagram. It is shown that three-loop effective potential calculated using three-loop integrals obtained in this paper agrees, in the large-N limit, with the overlap part of leading-order (in the large-N limit) calculation of Coleman, Jackiw, and Politzer [Phys. Rev. D {\bf 10}, 2491 (1974)].Comment: RevTex, 15 pages, 4 postscript figures, minor corrections in K(c), Appendix B removed, typos corrected, acknowledgements change

Statistical physics of cerebral embolization leading to stroke

We discuss the physics of embolic stroke using a minimal model of emboli moving through the cerebral arteries. Our model of the blood flow network consists of a bifurcating tree, into which we introduce particles (emboli) that halt flow on reaching a node of similar size. Flow is weighted away from blocked arteries, inducing an effective interaction between emboli. We justify the form of the flow weighting using a steady flow (Poiseuille) analysis and a more complicated nonlinear analysis. We discuss free flowing and heavily congested limits and examine the transition from free flow to congestion using numerics. The correlation time is found to increase significantly at a critical value, and a finite size scaling is carried out. An order parameter for non-equilibrium critical behavior is identified as the overlap of blockages' flow shadows. Our work shows embolic stroke to be a feature of the cerebral blood flow network on the verge of a phase transition.Comment: 11 pages, 11 figures. Major rewrite including improved justification of the model and a finite size scalin

Effects of turbulence mixing, variable properties, and vaporization on spray droplet combustion

Combustion of liquid fuels in the form of spray droplets is simulated numerically. Various vaporization models are examined as to their performance in finite element calculations involving a turbulent flow field. The Eulerian coordinate for the gas and Lagrangian coordinate for the liquid spray droplets are coupled through source terms being updated in the equations of continuity, momentum, and energy. The k-epsilon and modified eddy breakup models are used for simulating turbulent spray combustion flow field. Numerical results for the droplet trajectories, droplet heating, recirculation characteristics, and effects of evaporation models are evaluated. It is also shown that the finite element method is advantageous in dealing with complex geometries, complex boundary conditions, adaptive unstructured grids

Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification

The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random Projection (LaRP) framework, where we model the linear kernels and nonlinearity separately for increased training efficiency. The proposed LaRP framework was assessed using the MNIST hand-written digits database and the COIL-100 object database, and showed notable improvement in object classification performance relative to other state-of-the-art random projection methods.Comment: 5 page