Early stopping for statistical inverse problems via truncated SVD estimation

We consider truncated SVD (or spectral cut-off, projection) estimators for a prototypical statistical inverse problem in dimension $D$. Since calculating the singular value decomposition (SVD) only for the largest singular values is much less costly than the full SVD, our aim is to select a data-driven truncation level $\widehat m\in\{1,\ldots,D\}$ only based on the knowledge of the first $\widehat m$ singular values and vectors. We analyse in detail whether sequential {\it early stopping} rules of this type can preserve statistical optimality. Information-constrained lower bounds and matching upper bounds for a residual based stopping rule are provided, which give a clear picture in which situation optimal sequential adaptation is feasible. Finally, a hybrid two-step approach is proposed which allows for classical oracle inequalities while considerably reducing numerical complexity.Comment: slightly modified version. arXiv admin note: text overlap with arXiv:1606.0770

An approximate Roe-type Riemann solver for a class of realizable second order closures

International audienceA realizable, objective second-moment turbulence closure, allowing for an entropy carac-terisation, is analyzed with respect to its convective subset. The distinct characteristic wave system of these equations in non-conservation form is exposed. An approximate solution to the associated one-dimensional Riemann problem is constructed making use of approximate jump conditions obtained by assuming a linear path across shock waves. A numerical integration method based on a new approximate Riemann solver (ux-di erence-splitting) is proposed for use in conjunction with either unstructured or structured grids. Test calculations of quasi one-dimensional ow cases demonstrate the feasibility of the current technique even where Euler-based approaches fail

An approximate Riemann solver for second-moment closures

International audienc

Isolation of the ALG6 locus of Saccharomyces cerevisiae required for glucosylation in the N-linked glycosylation pathway

N-Linked protein glycosylation in most eukaryotic cells initiates with the transfer of the oligosaccharide Glc3Man9GlcNAc2 from the lipid carrier dolichyl pyrophosphate to selected asparagine residues. In the yeast Saccharomyces cerevisiae, alg mutations which affect the assembly of the lipid-linked oligosaccharide at the membrane of the endoplasmic reticulum result in the accumulation of lipid-linked oligosaccharide intermediates and a hypoglycosylation of proteins. Exploiting the synthetic growth defect of alg mutations in combination with mutations affecting oligosaccharyl transferase activity (Stagljar et al., 1994), we have isolated the ALG6 locus. alg6 mutants accumulate lipid-linked Man9GlcNAc2, suggesting that this locus encodes an endoplasmic glucosyltransferase. Alg6p has sequence similarity to Alg8p, a protein required for glucosylation of Glc1Man9GlcNAc

White-Matter Connectivity between Face-Responsive Regions in the Human Brain

Face recognition is of major social importance and involves highly selective brain regions thought to be organized in a distributed functional network. However, the exact architecture of interconnections between these regions remains unknown. We used functional magnetic resonance imaging to identify face-responsive regions in 22 participants and then employed diffusion tensor imaging with probabilistic tractography to establish the white-matter pathways between these functionally defined regions. We identified strong white-matter connections between the occipital face area (OFA) and fusiform face area (FFA), with a significant right-hemisphere predominance. We found no evidence for direct anatomical connections between FFA and superior temporal sulcus (STS) or between OFA and STS, contrary to predictions based on current cognitive models. Instead, our findings point to segregated processing along a ventral extrastriate visual pathway to OFA-FFA and another more dorsal system connected to STS and frontoparietal areas. In addition, early occipital areas were found to have direct connections to the amygdala, which might underlie a rapid recruitment of limbic brain areas by visual inputs bypassing more elaborate extrastriate cortical processing. These results unveil the structural neural architecture of the human face recognition system and provide new insights on how distributed face-responsive areas may work togethe

White-matter connectivity between face-responsive regions in the human brain

Face recognition is of major social importance and involves highly selective brain regions thought to be organized in a distributed functional network. However, the exact architecture of interconnections between these regions remains unknown. We used functional magnetic resonance imaging to identify face-responsive regions in 22 participants and then employed diffusion tensor imaging with probabilistic tractography to establish the white-matter pathways between these functionally defined regions. We identified strong white-matter connections between the occipital face area (OFA) and fusiform face area (FFA), with a significant right-hemisphere predominance. We found no evidence for direct anatomical connections between FFA and superior temporal sulcus (STS) or between OFA and STS, contrary to predictions based on current cognitive models. Instead, our findings point to segregated processing along a ventral extrastriate visual pathway to OFA-FFA and another more dorsal system connected to STS and frontoparietal areas. In addition, early occipital areas were found to have direct connections to the amygdala, which might underlie a rapid recruitment of limbic brain areas by visual inputs bypassing more elaborate extrastriate cortical processing. These results unveil the structural neural architecture of the human face recognition system and provide new insights on how distributed face-responsive areas may work together

Nonlinear control of high purity distillation columns

Two simple models of distillation columns are studied to investigate their suitability for the practical use with exact I/O-linearization. An extension of exact I/O-linearization, the asymptotically exact I/O-linearization is applied to the control of a high purity distillation column, using one of these models to derive the static state feedback law. Simulation studies demonstrate the advantage of asymptotically exact I/O-linearization versus classical exact I/O-linearization techniques. Experimental results show the excellent performance of asymptotically exact I/O-linearization using a simple distillation model