11,204 research outputs found
Pauli-Villars Regularization in nonperturbative Hamiltonian approach on the Light Front
The advantage of Pauli-Villars regularization in quantum field theory
quantized on the light front is explained. Simple examples of scalar
field theory and Yukawa-type model are used. We give also an
example of nonperturbative calculation in the theory with Pauli-Villars fields,
using for that a model of anharmonic oscillator modified by inclusion of ghost
variables playing the role similar to Pauli-Villars fields.Comment: LaTeX, 10 pages, 2 figures. Article will be published in AIP
Conference Proceedings, the final publication will be available at
http://scitation.aip.org/content/aip/proceeding/aipc
Different canonical formulations of Einstein's theory of gravity
We describe the four most famous versions of the classical canonical
formalism in the Einstein theory of gravity: the Arnovitt-Deser-Misner
formalism, the Faddeev-Popov formalism, the tetrad formalism in the usual form,
and the tetrad formalism in the form best suited for constructing the loop
theory of gravity, which is now being developed. We present the canonical
transformations relating these formalisms. The paper is written mainly for
pedagogical purposes.Comment: LaTeX, 18 pages, some misprints in formulas (131)-(134) are correcte
Canonical formulation of the embedded theory of gravity equivalent to Einstein's General Relativity
We study the approach in which independent variables describing gravity are
functions of the space-time embedding into a flat space of higher dimension. We
formulate a canonical formalism for such a theory in a form, which requires
imposing additional constraints, which are a part of Einstein's equations. As a
result, we obtain a theory with an eight-parameter gauge symmetry. This theory
becomes equivalent to Einstein's general relativity either after partial gauge
fixing or after rewriting the metric in the form that is invariant under the
additional gauge transformations. We write the action for such a theory.Comment: LaTeX, 17 page
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Learning biases may prevent lexicalization of pragmatic inferences:a case study combining iterated (Bayesian) learning and functional selection
Natural languages exhibit properties that are difficult to explainfrom a purely functional perspective. One of these properties isthe systematic lack of upper-bounds in the literal meaning ofscalar expressions. This investigation addresses the develop-ment and selection of such semantics from a space of possiblealternatives. To do so we put forward a model that integratesBayesian learning into the replicator-mutator dynamics com-monly used in evolutionary game theory. We argue this syn-thesis to provide a suitable and general model to analyze thedynamics involved in the use and transmission of language.Our results shed light on the semantics-pragmatics divide andshow how a learning bias in tandem with functional pressuremay prevent the lexicalization of pragmatic inferences
Learning biases may prevent lexicalization of pragmatic inferences:a case study combining iterated (Bayesian) learning and functional selection
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