45,032 research outputs found
Numerical analysis of the master equation
Applied to the master equation, the usual numerical integration methods, such
as Runge-Kutta, become inefficient when the rates associated with various
transitions differ by several orders of magnitude. We introduce an integration
scheme that remains stable with much larger time increments than can be used in
standard methods. When only the stationary distribution is required, a direct
iteration method is even more rapid; this method may be extended to construct
the quasi-stationary distribution of a process with an absorbing state.
Applications to birth-and-death processes reveal gains in efficiency of two or
more orders of magnitude.Comment: 7 pages 3 figure
The SU(2) X U(1) Electroweak Model based on the Nonlinearly Realized Gauge Group
The electroweak model is formulated on the nonlinearly realized gauge group
SU(2) X U(1). This implies that in perturbation theory no Higgs field is
present. The paper provides the effective action at the tree level, the Slavnov
Taylor identity (necessary for the proof of unitarity), the local functional
equation (used for the control of the amplitudes involving the Goldstone
bosons) and the subtraction procedure (nonstandard, since the theory is not
power-counting renormalizable). Particular attention is devoted to the number
of independent parameters relevant for the vector mesons; in fact there is the
possibility of introducing two mass parameters. With this choice the relation
between the ratio of the intermediate vector meson masses and the Weinberg
angle depends on an extra free parameter. We briefly outline a method for
dealing with \gamma_5 in dimensional regularization. The model is formulated in
the Landau gauge for sake of simplicity and conciseness: the QED Ward identity
has a simple and intriguing form.Comment: 19 pages, final version published by Int. J. Mod. Phys. A, some typos
corrected in eqs.(1) and (41). The errors have a pure editing origin.
Therefore they do not affect the content of the pape
TASEP hydrodynamics using microscopic characteristics
The convergence of the totally asymmetric simple exclusion process to the
solution of the Burgers equation is a classical result. In his seminal 1981
paper, Herman Rost proved the convergence of the density fields and local
equilibrium when the limiting solution of the equation is a rarefaction fan. An
important tool of his proof is the subadditive ergodic theorem. We prove his
results by showing how second class particles transport the rarefaction-fan
solution, as characteristics do for the Burgers equation, avoiding
subadditivity. In the way we show laws of large numbers for tagged particles,
fluxes and second class particles, and simplify existing proofs in the shock
cases. The presentation is self contained.Comment: 20 pages, 13 figures. This version is accepted for publication in
Probability Surveys, February 20 201
Path-integral over non-linearly realized groups and Hierarchy solutions
The technical problem of deriving the full Green functions of the elementary
pion fields of the nonlinear sigma model in terms of ancestor amplitudes
involving only the flat connection and the nonlinear sigma model constraint is
a very complex task. In this paper we solve this problem by integrating, order
by order in the perturbative loop expansion, the local functional equation
derived from the invariance of the SU(2) Haar measure under local left
multiplication. This yields the perturbative definition of the path-integral
over the non-linearly realized SU(2) group.Comment: 26 page
Learning Visual Attributes
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as âredâ, âstripedâ, or âspottedâ. The model sees attributes as patterns of image segments, repeatedly sharing some characteristic properties. These can be any combination of appearance, shape, or the layout of segments within the pattern. Moreover, attributes with general appearance are taken into account, such as the pattern of alternation of any two colors which is characteristic for stripes. To enable learning from unsegmented training images, the model is learnt discriminatively, by optimizing a likelihood ratio. As demonstrated in the experimental evaluation, our model can learn in a weakly supervised setting and encompasses a broad range of attributes. We show that attributes can be learnt starting from a text query to Google image search, and can then be used to recognize the attribute and determine its spatial extent in novel real-world images.
Anisotropic KPZ growth in 2+1 dimensions: fluctuations and covariance structure
In [arXiv:0804.3035] we studied an interacting particle system which can be
also interpreted as a stochastic growth model. This model belongs to the
anisotropic KPZ class in 2+1 dimensions. In this paper we present the results
that are relevant from the perspective of stochastic growth models, in
particular: (a) the surface fluctuations are asymptotically Gaussian on a
sqrt(ln(t)) scale and (b) the correlation structure of the surface is
asymptotically given by the massless field.Comment: 13 pages, 4 figure
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