11,398 research outputs found
Towards gravitationally assisted negative refraction of light by vacuum
Propagation of electromagnetic plane waves in some directions in
gravitationally affected vacuum over limited ranges of spacetime can be such
that the phase velocity vector casts a negative projection on the time-averaged
Poynting vector. This conclusion suggests, inter alia, gravitationally assisted
negative refraction by vacuum.Comment: 6 page
The full set of -invariant factorized -matrices
We use the method of the tensor product graph to construct rational (Yangian
invariant) solutions of the Yang-Baxter equation in fundamental representations
of and thence the full set of -invariant factorized -matrices.
Brief comments are made on their bootstrap structure and on Belavin's scalar
Yangian conserved charges.Comment: 10p
The Loss Rank Principle for Model Selection
We introduce a new principle for model selection in regression and
classification. Many regression models are controlled by some smoothness or
flexibility or complexity parameter c, e.g. the number of neighbors to be
averaged over in k nearest neighbor (kNN) regression or the polynomial degree
in regression with polynomials. Let f_D^c be the (best) regressor of complexity
c on data D. A more flexible regressor can fit more data D' well than a more
rigid one. If something (here small loss) is easy to achieve it's typically
worth less. We define the loss rank of f_D^c as the number of other
(fictitious) data D' that are fitted better by f_D'^c than D is fitted by
f_D^c. We suggest selecting the model complexity c that has minimal loss rank
(LoRP). Unlike most penalized maximum likelihood variants (AIC,BIC,MDL), LoRP
only depends on the regression function and loss function. It works without a
stochastic noise model, and is directly applicable to any non-parametric
regressor, like kNN. In this paper we formalize, discuss, and motivate LoRP,
study it for specific regression problems, in particular linear ones, and
compare it to other model selection schemes.Comment: 16 page
Depolarization volume and correlation length in the homogenization of anisotropic dielectric composites
In conventional approaches to the homogenization of random particulate
composites, both the distribution and size of the component phase particles are
often inadequately taken into account. Commonly, the spatial distributions are
characterized by volume fraction alone, while the electromagnetic response of
each component particle is represented as a vanishingly small depolarization
volume. The strong-permittivity-fluctuation theory (SPFT) provides an
alternative approach to homogenization wherein a comprehensive description of
distributional statistics of the component phases is accommodated. The
bilocally-approximated SPFT is presented here for the anisotropic homogenized
composite which arises from component phases comprising ellipsoidal particles.
The distribution of the component phases is characterized by a two-point
correlation function and its associated correlation length. Each component
phase particle is represented as an ellipsoidal depolarization region of
nonzero volume. The effects of depolarization volume and correlation length are
investigated through considering representative numerical examples. It is
demonstrated that both the spatial extent of the component phase particles and
their spatial distributions are important factors in estimating coherent
scattering losses of the macroscopic field.Comment: Typographical error in eqn. 16 in WRM version is corrected in arxiv
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