We measure the out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called "Lens Depth'' (LD) combined with Fermat Distance, which is able to capture precisely the "depth'' of a point with respect to a distribution in feature space, without any distributional assumption. Our method also has no trainable parameter. The method is applied directly in the feature space at test time and does not intervene in training process. As such, it does not impact the performance of the original model. The proposed method gives excellent qualitative results on toy datasets and can give competitive or better uncertainty estimation on standard deep learning datasets compared to strong baseline methods.

An innovative technique, based on a generalization of the conjugate gradient (CG) method in Banach spaces, is first proposed to enhance the spatial resolution of microwave radiometer measurements. This approach allows reducing the over-smoothing effects and the oscillations that are often present in Hilbert reconstructions and it is very effective in terms of processing time. Experiments undertaken on actual SSM/I data confirm the soundness of the proposed approach and show that CG is able to provide reconstructions similar to the conventional Landweber ones but with a significantly reduced processing time.

In this article we describe our implementations of the FGMRES algorithm for both real and complex, single and double precision arithmetics suitable for serial, shared-memory, and distributed-memory computers. For the sake of portability, simplicity, flexibility, and efficiency, the FGMRES solvers have been implemented in Fortran 77 using the reverse communication mechanism for the matrix-vector product, the preconditioning, and the dot-product computations. For distributed-memory computation, several orthogonalization procedures have been implemented to reduce the cost of the dot-product calculation, which is a well-known bottleneck of efficiency for Krylov methods. Furthermore, either implicit or explicit calculation of the residual at restart is possible depending on the actual cost of the matrix-vector product. Finally, the implemented stopping criterion is based on a normwise backward error.

Abstract In this paper we propose a distributed packed storage format that exploits the symmetry or the triangular structure of a dense matrix. This format stores only half of the matrix while maintaining most of the efficiency compared with a full storage for a wide range of operations. This work has been motivated by the fact that, in contrast to sequential linear algebra libraries (e.g. LAPACK), there is no routine or format that handles packed matrices in the currently available parallel distributed libraries. The proposed algorithms exclusively use the existing ScaLAPACK computational kernels, which proves the generality of the approach, provides easy portability of the code and provides efficient re‐use of existing software. The performance results obtained for the Cholesky factorization show that our packed format performs as good as or better than the ScaLAPACK full storage algorithm for a small number of processors. For a larger number of processors, the ScaLAPACK full storage routine performs slightly better until each processor runs out of memory. Copyright © 2006 John Wiley & Sons, Ltd.

In this article we describe our implementations of the GMRES algorithm for both real and complex, single and double precision arithmetics suitable for serial, shared memory and distributed memory computers. For the sake of portability, simplicity, flexibility and efficiency the GMRES solvers have been implemented in Fortran 77 using the reverse communication mechanism for the matrix-vector product, the preconditioning and the dot product computations. For distributed memory computation, several orthogonalization procedures have been implemented to reduce the cost of the dot product calculation, which is a well-known bottleneck of efficiency for the Krylov methods. Either implicit or explicit calculation of the residual at restart are possible depending on the actual cost of the matrix-vector product. Finally the implemented stopping criterion is based on a normwise backward error.

It is now well established that Synthetic Aperture Imaging Radiometers (SAIR) promise to be powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency (ESA) is currently developing the SMOS mission. This propagation of modelling errors within a reconstruction process that attempts to retrieve the brightness temperature of a scene under observation from interferometric measurements depends on the knowledge of the values of the parameters involved in the modelling of the instrument. This contribution describes an approach to characterize these modelling parameters, once the instrument has been launched into space, with an accuracy such that the propagation of errors through the reconstruction process is still under control.