17,430 research outputs found
Possible singlet and triplet superconductivity on honeycomb lattice
We study the possible superconducting pairing symmetry mediated by spin and
charge fluctuations on the honeycomb lattice using the extended Hubbard model
and the random-phase-approximation method. From to doping levels,
a spin-singlet -wave is shown to be the leading
superconducting pairing symmetry when only the on-site Coulomb interaction
is considered, with the gap function being a mixture of the nearest-neighbor
and next-nearest-neighbor pairings. When the offset of the energy level between
the two sublattices exceeds a critical value, the most favorable pairing is a
spin-triplet -wave which is mainly composed of the next-nearest-neighbor
pairing. We show that the next-nearest-neighbor Coulomb interaction is also
in favor of the spin-triplet -wave pairing.Comment: 6 pages, 4 figure
Godel Metrics with Chronology Protection in Horndeski Gravities
G\"odel universe, one of the most interesting exact solutions predicted by
General Relativity, describes a homogeneous rotating universe containing naked
closed time-like curves (CTCs). It was shown that such CTCs are the consequence
of the null energy condition in General Relativity. In this paper, we show that
the G\"odel-type metrics with chronology protection can emerge in
Einstein-Horndeski gravity. We construct such exact solutions also in
Einstein-Horndeski-Maxwell and Einstein-Horndeski-Proca theories.Comment: Latex, 11 pages, references adde
Topological and Algebraic Properties of Chernoff Information between Gaussian Graphs
In this paper, we want to find out the determining factors of Chernoff
information in distinguishing a set of Gaussian graphs. We find that Chernoff
information of two Gaussian graphs can be determined by the generalized
eigenvalues of their covariance matrices. We find that the unit generalized
eigenvalue doesn't affect Chernoff information and its corresponding dimension
doesn't provide information for classification purpose. In addition, we can
provide a partial ordering using Chernoff information between a series of
Gaussian trees connected by independent grafting operations. With the
relationship between generalized eigenvalues and Chernoff information, we can
do optimal linear dimension reduction with least loss of information for
classification.Comment: Submitted to Allerton2018, and this version contains proofs of the
propositions in the pape
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