49,304 research outputs found
On Representations of Conformal Field Theories and the Construction of Orbifolds
We consider representations of meromorphic bosonic chiral conformal field
theories, and demonstrate that such a representation is completely specified by
a state within the theory. The necessary and sufficient conditions upon this
state are derived, and, because of their form, we show that we may extend the
representation to a representation of a suitable larger conformal field theory.
In particular, we apply this procedure to the lattice (FKS) conformal field
theories, and deduce that Dong's proof of the uniqueness of the twisted
representation for the reflection-twisted projection of the Leech lattice
conformal field theory generalises to an arbitrary even (self-dual) lattice. As
a consequence, we see that the reflection-twisted lattice theories of Dolan et
al are truly self-dual, extending the analogies with the theories of lattices
and codes which were being pursued. Some comments are also made on the general
concept of the definition of an orbifold of a conformal field theory in
relation to this point of view.Comment: 11 pages, LaTeX. Updated references and added preprint n
Framed vertex operator algebras, codes and the moonshine module
For a simple vertex operator algebra whose Virasoro element is a sum of
commutative Virasoro elements of central charge 1/2, two codes are introduced
and studied. It is proved that such vertex operator algebras are rational. For
lattice vertex operator algebras and related ones, decompositions into direct
sums of irreducible modules for the product of the Virasoro algebras of central
charge 1/2 are explicitly described. As an application, the decomposition of
the moonshine vertex operator algebra is obtained for a distinguished system of
48 Virasoro algebras.Comment: Latex, 54 page
Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer
Methods for extracting marker genes that trigger the growth
of cancerous cells from a high level of complexity microarrays are of much interest from the computing community. Through the identified genes, the pathology of cancerous cells can be revealed and early precaution
can be taken to prevent further proliferation of cancerous cells. In this paper, we propose an innovative hybridised gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia disease. Our approach confirms that high classification
accuracy does not ensure the optimal set of genes have been identified and our model delivers a more promising set of genes even with a lower classification accurac
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