Decoupling structure of the principal sigma model-Maxwell interactions

The principal sigma model and Abelian gauge fields coupling is studied. By expressing the first-order formulation of the gauge field equations an implicit on-shell scalar-gauge field decoupling structure is revealed. It is also shown that due to this decoupling structure the scalars of the theory belong to the pure sigma model and the gauge fields sector consists of a number of coupled Maxwell theories with currents partially induced by the scalars.Comment: 13 page

Non-linear Realisation of the N=2, D=6 Supergravity

We have applied the method of dualisation to construct the coset realisation of the bosonic sector of the N=2, D=6 supergravity which is coupled to a tensor multiplet. The bosonic field equations are regained through the Cartan-Maurer equation which the Cartan form satisfies. The first-order formulation of the theory is also obtained as a twisted self-duality condition within the non-linear coset construction.Comment: 11 page

Algebraic Integration of Sigma Model Field Equations

We prove that the dualization algebra of the symmetric space coset sigma model is a Lie algebra and we show that it generates an appropriate adjoint representation which enables the local integration of the field equations yielding the first-order ones.Comment: 27p

Symmetric Space Sigma-model Dynamics: Internal Metric Formalism

For the symmetric space sigma model in the internal metric formalism we explicitly construct the lagrangian in terms of the axions and the dilatons of the solvable Lie algebra gauge and then we exactly derive the axion-dilaton field equations.Comment: 10 page

Using hardware performance counters for fault localization

In this work, we leverage hardware performance counters-collected data as abstraction mechanisms for program executions and use these abstractions to identify likely causes of failures. Our approach can be summarized as follows: Hardware counters-based data is collected from both successful and failed executions, the data collected from the successful executions is used to create normal behavior models of programs, and deviations from these models observed in failed executions are scored and reported as likely causes of failures. The results of our experiments conducted on three open source projects suggest that the proposed approach can effectively prioritize the space of likely causes of failures, which can in turn improve the turn around time for defect fixes