8,199 research outputs found
A Holographic P-wave Superconductor Model
We study a holographic p-wave superconductor model in a four dimensional
Einstein-Maxwell-complex vector field theory with a negative cosmological
constant. The complex vector field is charged under the Maxwell field. We solve
the full coupled equations of motion of the system and find black hole
solutions with the vector hair. The vector hairy black hole solutions are dual
to a thermal state with the U(1) symmetry as well as the spatial rotational
symmetry breaking spontaneously. Depending on two parameters, the mass and
charge of the vector field, we find a rich phase structure: zeroth order, first
order and second order phase transitions can happen in this model. We also find
"retrograde condensation" in which the hairy black hole solution exists only
for the temperatures above a critical value with the free energy much larger
than the black hole without hair. We construct the phase diagram for this
system in terms of the temperature and charge of the vector field.Comment: v3: 26 pages, 15 figures, references added, extra arguments added, to
appear in JHE
Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy
In medical research, it is common to collect information of multiple
continuous biomarkers to improve the accuracy of diagnostic tests. Combining
the measurements of these biomarkers into one single score is a popular
practice to integrate the collected information, where the accuracy of the
resultant diagnostic test is usually improved. To measure the accuracy of a
diagnostic test, the Youden index has been widely used in literature. Various
parametric and nonparametric methods have been proposed to linearly combine
biomarkers so that the corresponding Youden index can be optimized. Yet there
seems to be little justification of enforcing such a linear combination. This
paper proposes a flexible approach that allows both linear and nonlinear
combinations of biomarkers. The proposed approach formulates the problem in a
large margin classification framework, where the combination function is
embedded in a flexible reproducing kernel Hilbert space. Advantages of the
proposed approach are demonstrated in a variety of simulated experiments as
well as a real application to a liver disorder study
A Constraint-directed Local Search Approach to Nurse Rostering Problems
In this paper, we investigate the hybridization of constraint programming and
local search techniques within a large neighbourhood search scheme for solving
highly constrained nurse rostering problems. As identified by the research, a
crucial part of the large neighbourhood search is the selection of the fragment
(neighbourhood, i.e. the set of variables), to be relaxed and re-optimized
iteratively. The success of the large neighbourhood search depends on the
adequacy of this identified neighbourhood with regard to the problematic part
of the solution assignment and the choice of the neighbourhood size. We
investigate three strategies to choose the fragment of different sizes within
the large neighbourhood search scheme. The first two strategies are tailored
concerning the problem properties. The third strategy is more general, using
the information of the cost from the soft constraint violations and their
propagation as the indicator to choose the variables added into the fragment.
The three strategies are analyzed and compared upon a benchmark nurse rostering
problem. Promising results demonstrate the possibility of future work in the
hybrid approach
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