Dyslexia can have different manifestations: this has motivated different theories on its nature, on its underlying brain bases and enduring controversies on how to best treat it. The relative weight of the different manifestations has never been evaluated using both behavioural and fMRI measures, a challenge taken here to assess the major systems called into play in dyslexia by different theories. We found that adult well-compensated dyslexics were systematically impaired only in reading and in visuo-phonological tasks, while deficits for other systems (e.g. motor/cerebellar, visual magnocellular/motion perception) were only very occasional. In line with these findings, fMRI showed a reliable hypoactivation only for the task of reading, in the left occipito-temporal cortex (l-OTC). The l-OTC, normally a crossroad between the reading system and other systems did not show the same level of intersection in dyslexics; yet, it was not totally silent because it responded, in segregated parts, during auditory phonological and visual motion perception tasks. This minimal behavioural and functional anatomical comorbidity demonstrates that a specific deficit of reading is the best description for developmental dyslexia, at least for adult well compensated cases, with clear implications for rehabilitation strategies. The reduced intersection of multiple systems in the l-OTC suggests that dyslexics suffer from a coarser connectivity, leading to disconnection between the multiple domains that normally interact during reading.

This paper presents some of the main strategies for dynamic and static verification of handwritten signatures and focuses the most promising directions of scientific research, starting from the analysis of the literature of the last decade

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Abstract. We report here a principled method for setting the regularization parameter in total variation filtering, that is based on the analysis of the distribution of the gray levels on the noisy image. We also report the results of an experimental investigation of the application of this framework to very low photon count digital radiography that shows the effectiveness of the method in denoising such images. Total variation regularization leads to a non-linear optimization problem that is solved here with a new generation adaptive first order method. Results suggest a further investigation of both the convergence criteria and/or the scheduling of the optimization parameters of this method.

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