811 research outputs found
Probabilistic representation for solutions of an irregular porous media type equation: the degenerate case
We consider a possibly degenerate porous media type equation over all of
with , with monotone discontinuous coefficients with linear
growth and prove a probabilistic representation of its solution in terms of an
associated microscopic diffusion. This equation is motivated by some singular
behaviour arising in complex self-organized critical systems. The main idea
consists in approximating the equation by equations with monotone
non-degenerate coefficients and deriving some new analytical properties of the
solution
Model selection for a semi - Markov continuous time regression observed in the discrete time moments
La crise économique et financière impacte-t-elle la mission d'audit légal et de certification des comptes ?
L'article vise à montrer comment le commissaire aux comptes (CAC) doit adapter sa mission à un contexte de crise économique et financière. En effet, le CAC doit mettre en œuvre des diligences spécifiques au contexte économique, puisque certains risques sont ainsi amplifiés, comme ceux tenant à la continuité d'exploitation et à la mauvaise estimation du résultat comptable. En outre, le rapport général du commissaire peut lui aussi être nuancé en raison de l'incertitude pesant sur certains éléments, ce qui peut induire des asymétries d'information. L'analyse théorique de la situation est complétée par une étude de cas portant sur une société située dans un secteur touché par la crise financière actuelle, à savoir la fabrication de matériels électriques de pointe.crise financière ; commissaires aux comptes ; mission générale ; France ; étude de cas ; financial crisis ; auditors ; France ; case study
Revisiting loss-specific training of filter-based MRFs for image restoration
It is now well known that Markov random fields (MRFs) are particularly
effective for modeling image priors in low-level vision. Recent years have seen
the emergence of two main approaches for learning the parameters in MRFs: (1)
probabilistic learning using sampling-based algorithms and (2) loss-specific
training based on MAP estimate. After investigating existing training
approaches, it turns out that the performance of the loss-specific training has
been significantly underestimated in existing work. In this paper, we revisit
this approach and use techniques from bi-level optimization to solve it. We
show that we can get a substantial gain in the final performance by solving the
lower-level problem in the bi-level framework with high accuracy using our
newly proposed algorithm. As a result, our trained model is on par with highly
specialized image denoising algorithms and clearly outperforms
probabilistically trained MRF models. Our findings suggest that for the
loss-specific training scheme, solving the lower-level problem with higher
accuracy is beneficial. Our trained model comes along with the additional
advantage, that inference is extremely efficient. Our GPU-based implementation
takes less than 1s to produce state-of-the-art performance.Comment: 10 pages, 2 figures, appear at 35th German Conference, GCPR 2013,
Saarbr\"ucken, Germany, September 3-6, 2013. Proceeding
A numerical framework for modelling tire mechanics accounting for composite materials, large strains and frictional contact
Presentation delivered by Alejandro Cornejo from CIMNE during the 17th International Conference on Computational Plasticity, Fundamentals and Applications (COMPLAS) taking place from 5 – 7 of September in Barcelona, Spain.The Fatigue4Light project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 10100684
Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France
The land monitoring service of the European Copernicus programme has
developed a set of satellite-based biogeophysical products, including
surface soil moisture (SSM) and leaf area index (LAI). This study
investigates the impact of joint assimilation of remotely sensed SSM
derived from Advanced Scatterometer (ASCAT) backscatter data and the
Copernicus Global Land GEOV1 satellite-based LAI product
into the the vegetation growth version of the Interactions
between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model
within the the externalised surface model (SURFEX) modelling
platform of Météo-France. The ASCAT data were bias corrected with
respect to the model climatology by using a seasonal-based CDF
(Cumulative Distribution Function) matching technique. A multivariate
multi-scale land data assimilation system (LDAS) based on the extended
Kalman Filter (EKF) is used for monitoring the soil moisture,
terrestrial vegetation, surface carbon and energy fluxes across the
domain of France at a spatial resolution of 8 km. Each model grid
box is divided into a number of land covers, each having its own set of
prognostic variables. The filter algorithm is designed to provide
a distinct analysis for each land cover while using one observation
per grid box. The updated values are aggregated by computing
a weighted average.
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In this study, it is demonstrated that the assimilation scheme works
effectively within the ISBA-A-gs model over a four-year period
(2008–2011). The EKF is able to extract useful information from the
data signal at the grid scale and distribute the root-zone soil
moisture and LAI increments throughout the mosaic structure of the
model. The impact of the assimilation on the vegetation phenology and
on the water and carbon fluxes varies from one season to another. The
spring drought of 2011 is an interesting case study of the
potential of the assimilation to improve drought
monitoring. A comparison between simulated and in situ soil moisture
gathered at the twelve SMOSMANIA (Soil Moisture Observing
System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly
correlations for eight stations
Fatigue behaviour of Glass-Fiber-Reinforced Polymers Numerical versus experimental characterisation
Presentation delivered by Bàrbara Alcayde from CIMNE during the 17th International Conference on Computational Plasticity, Fundamentals and Applications (COMPLAS) taking place from 5 – 7 of September in Barcelona, Spain.The Fatigue4Light project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 10100684
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