Enhancing retinal images by nonlinear registration

Being able to image the human retina in high resolution opens a new era in many important fields, such as pharmacological research for retinal diseases, researches in human cognition, nervous system, metabolism and blood stream, to name a few. In this paper, we propose to share the knowledge acquired in the fields of optics and imaging in solar astrophysics in order to improve the retinal imaging at very high spatial resolution in the perspective to perform a medical diagnosis. The main purpose would be to assist health care practitioners by enhancing retinal images and detect abnormal features. We apply a nonlinear registration method using local correlation tracking to increase the field of view and follow structure evolutions using correlation techniques borrowed from solar astronomy technique expertise. Another purpose is to define the tracer of movements after analyzing local correlations to follow the proper motions of an image from one moment to another, such as changes in optical flows that would be of high interest in a medical diagnosis.Comment: 21 pages, 7 figures, submitted to Optics Communication

Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β\beta-Mixing Processes

Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned from independently and identically distributed (IID) data, and it is particularly so for margin classifiers: there have been recent contributions showing how practical these bounds can be either to perform model selection (Ambroladze et al., 2007) or even to directly guide the learning of linear classifiers (Germain et al., 2009). However, there are many practical situations where the training data show some dependencies and where the traditional IID assumption does not hold. Stating generalization bounds for such frameworks is therefore of the utmost interest, both from theoretical and practical standpoints. In this work, we propose the first - to the best of our knowledge - Pac-Bayes generalization bounds for classifiers trained on data exhibiting interdependencies. The approach undertaken to establish our results is based on the decomposition of a so-called dependency graph that encodes the dependencies within the data, in sets of independent data, thanks to graph fractional covers. Our bounds are very general, since being able to find an upper bound on the fractional chromatic number of the dependency graph is sufficient to get new Pac-Bayes bounds for specific settings. We show how our results can be used to derive bounds for ranking statistics (such as Auc) and classifiers trained on data distributed according to a stationary {\ss}-mixing process. In the way, we show how our approach seemlessly allows us to deal with U-processes. As a side note, we also provide a Pac-Bayes generalization bound for classifiers learned on data from stationary $\varphi$-mixing distributions.Comment: Long version of the AISTATS 09 paper: http://jmlr.csail.mit.edu/proceedings/papers/v5/ralaivola09a/ralaivola09a.pd

Incentives to learn calibration: a gender-dependent impact

Miscalibration can be defined as the fact that people think that their knowledge is more precise than it actually is. In a typical miscalibration experiment, subjects are asked to provide subjective confidence intervals. A very robust finding is that subjects provide too narrow intervals at the 90% level. As a result a lot less than 90% of correct answers fall inside the 90% intervals provided. As miscalibration is linked with bad results on an experimental financial market (Biais et al., 2005) and entrepreneurial success is positively correlated with good calibration (Regner et al., 2006), it appears interesting to look for a way to cure or at least reduce miscalibration. Previous attempts to remove the miscalibration bias relied on extremely long and tedious procedures. Here, we design an experimental setting that provides several different incentives, in particular strong monetary incentives i.e. that make miscalibration costly. Our main result is that a thirty-minute training session has an effect on men''s calibration but no effect on women''s.miscalibration, overconfidence, incentives, gender effect

Incentives to Learn Calibration : a Gender-Dependent Impact

Miscalibration can be defined as the fact that people think that their knowledge is more precise than it actually is. In a typical miscalibration experiment, subjects are asked to provide subjective confidence intervals. A very robust finding is that subjects provide too narrow intervals at the 90% level. As a result a lot less than 90% of correct answers fall inside the 90% intervals provided. As miscalibration is linked with bad results on a experimental financial market (Biais et al., 2005) and entrepreneurial success is positively correlated with good calibration (Regner et al., 2006), it appears interesting to look for a way to cure or at least reduce miscalibration. Previous attempts to remove the miscalibration bias relied on extremely long and tedious procedures. Here, we design an experimental setting that provides several different incentives, in particular strong monetary incentives ; i.e. that make miscalibration costly. Our main result is that a thirty-minute training session has an effect on men's calibration but no effect on women's.Miscalibration, overconfidence, incentives, gender effect.

Learning to Read Bilingually Modulates the Manifestations of Dyslexia in Adults

Published online: 28 Mar 2018According to the Grain Size Accommodation hypothesis (Lallier & Carreiras, 2017), learning to read in two languages differing in orthographic consistency leads to a cross-linguistic modulation of reading and spelling processes. Here, we test the prediction that bilingualism may influence the manifestations of dyslexia. We compared the deficits of English monolingual and early Welsh–English bilingual dyslexic adults on reading and spelling irregular English words and English-like pseudowords. As predicted, monolinguals were relatively more impaired in reading pseudowords than irregular words, whereas the opposite was true for bilinguals. Moreover, monolinguals showed stronger sublexical processing deficits than bilinguals and were poorer spellers overall. This study shows that early bilingual reading experience has long-lasting effects on the manifestations of dyslexia in adulthood. It demonstrates that learning to read in a consistent language like Welsh in addition to English gives bilingual dyslexic adults an advantage in English literacy tasks strongly relying on phonological processing.This research was funded by the Fyssen Foundation, the European Commission (FP7-PEOPLE-2010-IEF, Proposal N°274352, BIRD, to M.L) the European Research Council (ERC advanced grant, BILITERACY, to M.C., and ERC- 209704 to G.T.), the Spanish government (PSI2015-65338-P to M.L, and PSI2015-67353-R to M.C.), and the Economic and Social Research Council UK (RES-E024556-1 to G.T.). BCBL acknowledges funding from Ayuda Centro de Excelencia Severo Ochoa SEV-2015-0490

Novel skeletal effects of glucagon-like peptide-1 (GLP-1) receptor agonists

Type 2 diabetes mellitus (T2DM) leads to bone fragility and predisposes to increased risk of fracture, poor bone healing and other skeletal complications. In addition, some anti-diabetic therapies for T2DM can have notable detrimental skeletal effects. Thus, an appropriate therapeutic strategy for T2DM should not only be effective in re-establishing good glycaemic control but also in minimising skeletal complications. There is increasing evidence that glucagon-like peptide-1 receptor agonists (GLP-1RAs), now greatly prescribed for the treatment of T2DM, have beneficial skeletal effects although the underlying mechanisms are not completely understood. This review provides an overview of the direct and indirect effects of GLP-1RAs on bone physiology, focusing on bone quality and novel mechanisms of action on the vasculature and hormonal regulation. The overall experimental studies indicate significant positive skeletal effects of GLP-1RAs on bone quality and strength although their mechanisms of actions may differ according to various GLP-1RAs and clinical studies supporting their bone protective effects are still lacking. The possibility that GLP-1RAs could improve blood supply to bone, which is essential for skeletal health, is of major interest and suggests that GLP-1 anti-diabetic therapy could benefit the rising number of elderly T2DM patients with osteoporosis and high fracture risk

Disc instability models, evaporation and radius variations

We show that the outcome of disc instability models is strongly influenced by boundary conditions such as the position of the inner and outer disc edges. We discuss other sources of uncertainties, such as the tidal torque, and we conclude that disc illumination, disk size variations and a proper prescription for the tidal torque must be included in models if one wishes to extract meaningful physical information on e.g. viscosity from the comparison of predicted and observed lightcurves.Comment: 8 pages, 1 figure. To be published in the proceedings of the conference "Disk Instabilities in Close Binary Systems - 25 Years of the Disk Instability Model

Hierarchical modelling of species sensitivity distribution: development and application to the case of diatoms exposed to several herbicides

The Species Sensitivity Distribution (SSD) is a key tool to assess the ecotoxicological threat of contaminant to biodiversity. It predicts safe concentrations for a contaminant in a community. Widely used, this approach suffers from several drawbacks: i)summarizing the sensitivity of each species by a single value entails a loss of valuable information about the other parameters characterizing the concentration-effect curves; ii)it does not propagate the uncertainty on the critical effect concentration into the SSD; iii)the hazardous concentration estimated with SSD only indicates the threat to biodiversity, without any insight about a global response of the community related to the measured endpoint. We revisited the current SSD approach to account for all the sources of variability and uncertainty into the prediction and to assess a global response for the community. For this purpose, we built a global hierarchical model including the concentration-response model together with the distribution law for the SSD. Working within a Bayesian framework, we were able to compute an SSD taking into account all the uncertainty from the original raw data. From model simulations, it is also possible to extract a quantitative indicator of a global response of the community to the contaminant. We applied this methodology to study the toxicity of 6 herbicides to benthic diatoms from Lake Geneva, measured from biomass reduction