52 research outputs found
Geroch group for Einstein spaces and holographic integrability
We review how Geroch's reduction method is extended from Ricci-flat to
Einstein spacetimes. The Ehlers-Geroch SL(2,R) group is still present in the
three-dimensional sigma-model that captures the dynamics, but only a subgroup
of it is solution-generating. Holography provides an alternative
three-dimensional perspective to integrability properties of Einstein's
equations in asymptotically anti-de Sitter spacetimes. These properties emerge
as conditions on the boundary data (metric and energy-momentum tensor) ensuring
that the hydrodynamic derivative expansion be resummed into an exact
four-dimensional Einstein geometry. The conditions at hand are invariant under
a set of transformations dubbed holographic U-duality group. The latter fills
the gap left by the Ehlers-Geroch group in Einstein spaces, and allows for
solution-generating maps mixing e.g. the mass and the nut charge.Comment: v1: 1+24 pages, Latex, imbrication with arXiv:1403.6511 in sections 2
and 3. arXiv admin note: text overlap with arXiv:1510.0645
Thermal Flow Measurements by a Flexible Sensor, Implemented on the External Surface of the Flow Channel
AbstractA thermal gas flow sensor was developed and evaluated. The presented implementation requires only low-cost manufacturing techniques and readily available components, while maintaining a high level of detection range and sensitivity. Heater and sensing elements were integrated on a flexible substrate and the device was formed by bending the substrate so that the active elements were placed on the external surface of the formed channel, therefore zero flow interference is achieved and a wide variety of fluids can be measured without compromising the sensor integrity. Evaluation was made using air flow rates in the range of 0-65SLPM utilizing electrical measurements and IR imaging techniques simultaneously
Probabilistic forecast reconciliation with applications to wind power and electric load
New methods are proposed for adjusting probabilistic forecasts to ensure
coherence with the aggregation constraints inherent in temporal hierarchies.
The different approaches nested within this framework include methods that
exploit information at all levels of the hierarchy as well as a novel method
based on cross-validation. The methods are evaluated using real data from two
wind farms in Crete, an application where it is imperative for optimal
decisions related to grid operations and bidding strategies to be based on
coherent probabilistic forecasts of wind power. Empirical evidence is also
presented showing that probabilistic forecast reconciliation improves the
accuracy of both point forecasts and probabilistic forecasts
Holographic perfect fluidity, Cotton energy-momentum duality and transport properties
We investigate background metrics for 2+1-dimensional holographic theories
where the equilibrium solution behaves as a perfect fluid, and admits thus a
thermodynamic description. We introduce stationary perfect-Cotton geometries,
where the Cotton--York tensor takes the form of the energy--momentum tensor of
a perfect fluid, i.e. they are of Petrov type D_t. Fluids in equilibrium in
such boundary geometries have non-trivial vorticity. The corresponding bulk can
be exactly reconstructed to obtain 3+1-dimensional stationary black-hole
solutions with no naked singularities for appropriate values of the black-hole
mass. It follows that an infinite number of transport coefficients vanish for
holographic fluids. Our results imply an intimate relationship between
black-hole uniqueness and holographic perfect equilibrium. They also point
towards a Cotton/energy--momentum tensor duality constraining the fluid
vorticity, as an intriguing boundary manifestation of the bulk mass/nut
duality.Comment: V3: 1+39 pages, JHEP versio
A Deep Learning Approach for Dynamic Balance Sheet Stress Testing
In the aftermath of the financial crisis, supervisory authorities have
considerably improved their approaches in performing financial stress testing.
However, they have received significant criticism by the market participants
due to the methodological assumptions and simplifications employed, which are
considered as not accurately reflecting real conditions. First and foremost,
current stress testing methodologies attempt to simulate the risks underlying a
financial institution's balance sheet by using several satellite models, making
their integration a really challenging task with significant estimation errors.
Secondly, they still suffer from not employing advanced statistical techniques,
like machine learning, which capture better the nonlinear nature of adverse
shocks. Finally, the static balance sheet assumption, that is often employed,
implies that the management of a bank passively monitors the realization of the
adverse scenario, but does nothing to mitigate its impact. To address the above
mentioned criticism, we introduce in this study a novel approach utilizing deep
learning approach for dynamic balance sheet stress testing. Experimental
results give strong evidence that deep learning applied in big
financial/supervisory datasets create a state of the art paradigm, which is
capable of simulating real world scenarios in a more efficient way.Comment: Preprint submitted to Journal of Forecastin
Arteriovenous Malformation of the Pancreas
Pancreatic arteriovenous malformation (PAVM) is a very rare and mostly congenital lesion, with less than 80 cases described in the English-published literature. It is defined as a tumorous vascular abnormality that is constructed between an anomalous bypass anastomosis of the arterial and venous networks within the pancreas. It represents about 5% of all arteriovenous malformations found in the gastrointestinal tract. Herein, we present a 64-year-old patient with symptomatic PAVM involving the body and tail of the organ, which was successfully treated by transcatheter arterial embolization. The disease spectrum and review of the literature are also presented
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