Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints

In behavioral neuroscience, the adaptability of humans facing different constraints has been addressed on one side at the brain level, where a variety of functional networks dynamically support the same performance, and on the other side at the behavioral level, where fractal properties in sensorimotor variables have been considered as a hallmark of adaptability. To bridge the gap between the two levels of observation, we have jointly investigated the changes of network connectivity in the sensorimotor cortex assessed by modularity analysis and the properties of motor variability assessed by multifractal analysis during a prolonged tapping task. Four groups of participants had to produce the same tapping performance while being deprived from 0, 1, 2, or 3 sensory feedbacks simultaneously (auditory and/or visual and/or tactile). Whereas tapping performance was not statistically different across groups, the number of brain networks involved and the degree of multifractality of the inter-tap interval series were significantly correlated, increasing as a function of feedback deprivation. Our findings provide first evidence that concomitant changes in brain modularity and multifractal properties characterize adaptations underlying unchanged performance. We discuss implications of our findings with respect to the degeneracy properties of complex systems, and the entanglement of adaptability and effective adaptation

Fast and Robust Image Matching using Contextual Information and Relaxation

International audienceThis tackles the difficult problem of images matching under projective transformation. Recently, several algorithms capable of handling large changes of viewpoint as well as large changes of scale have been proposed. They are based on the comparison of local, invariant descriptors which are robust to these transformations. However, since no image descriptor is robust enough to avoid mismatches, an additional step of outliers rejection is often needed. The accuracy of which strongly depends on the number of mismatches. In this paper, we show that the matching process can be made robust to ensure a very few number of mismatches based on a relaxation labeling technique. The main contribution of this work is in providing an efficient and fast implementation of a relaxation method which can deal with large sets of features. Furthermore, we show how the contextual information can be obtained and used in this robust and fast algorithm. Experiments with real data and comparison with other matching methods, clearly show the improvements in the matching results

Une caracterisation des produits d'arbres et des grilles

We show that a grap G is a product of trees (a grid graph) if and only if G is median and has no convex subgraph isomorphic to K2,3 − e (to K2,3 − e and K1,3)

Generator-Preserving contractions and a Min-Max result on the graphs of planar polyominoes

In this paper, we deal with the convex generators 1 of a graphG V G E G( ( ), ( )). A convex generator being a minimal set whose convexhull is V(G), we show that it is included in the "boundary" of G. Then weshow that the "boundary" of a polymino's graph, or more precisely theseaweed's "boundary", enjoys some nice properties which permit us to provethat for such a graph G, the minimal size of a convex generator is equal to themaximal number of hanging vertices of a tree T, obtained from G by asequence of generator-preserving contraction