CHRYSAOR: an Agent-Based Intelligent Tutoring System in Virtual Environment

11 pagesInternational audienceThe various existing Intelligent Tutoring Systems (ITS) models do not capitalize on all the possibilities permitted by the use of virtual reality. In this paper, we first establish the important characteristics of ITS (genericity, modularity, individualization, scenario edition, adaptativity). Subsequently we present our studies using an agent metamodel (Behave) based on an environment metamodel (Veha), in order to make a generic ITS. We focus on describing our agent model and its knowledge of the pedagogical situation and incorporate a pedagogical scenario model in our ITS. The use of this ITS is illustrated by an application of a virtual biomedical analyzer which enables to learn the technical procedures of the device

Postural Control during the Stroop Test in Dyslexic and Non Dyslexic Teenagers

Postural control in quiet stance although simple still requires some cognitive resources; dual cognitive tasks influence further postural control. The present study examines whether or not dyslexic teenagers experience postural instability when performing a Stroop dual task for which their performances are known to be poor. Fifteen dyslexics and twelve non-dyslexics (14 to 17 years old) were recruited from the same school. They were asked to perform three tasks: (1) fixate a target, (2) perform an interference Stroop test (naming the colour or the word rather than reading the word), (3) performing flexibility Stroop task: the subject performed the interference task as in (2) except when the word was in a box, in which case he had to read the word. Postural performances were measured with a force platform. The results showed a main task effect on the variance of speed of body sway only: such variance was higher in the flexibility task than for the other two tasks. No group effect was found for any of the parameters of posture (surface, mediolateral and anteroposterior sway, variance of speed). Further wavelet analysis in the time-frequency domain revealed an increase in the spectral power of the medium frequency range believed to be related to cerebellum control; an accompanying increase in the cancellation time of the high frequency band related to reflexive loops occurred for non-dyslexics only. These effects occurred for the flexibility task and could be due to its high cognitive difficulty. Dyslexics displayed shorter cancellation time for the medium frequency band for all tasks, suggesting less efficient cerebellar control, perhaps of eye fixation and attention influencing body sway. We conclude that there is no evidence for a primary posture deficit in 15 year old teenagers who come from the general population and who were recruited in schools

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Retrieval of Mars surface physical properties from OMEGA hyperspectral images using Regularized Sliced Inverse Regression

14 pagesInternational audienceIn this paper, a method based on modeling and statistics is proposed to evaluate the physical properties of surface icy materials on Mars from hyperspectral images collected by the OMEGA instrument aboard the Mars Express spacecraft. The approach is based on the estimation of the functional relationship F between observed spectra and relevant physical parameters such as compound abundances and granularity. To this end, a database of synthetic spectra is generated by a radiative transfer model simulating the reflection of solar light by a granular mixture of H2O ice, CO2 ice, and dust. The database constitutes a training set used to estimate F. The high dimension of spectra is reduced by Gaussian regularized sliced inverse regression (GRSIR) to overcome the "curse of dimensionality" and, consequently, the sensitivity of the inversion to noise (ill-conditioned problems). Compared with other approaches, such as the k-NN, the partial least squares, and the support vector machines (SVM), GRSIR has the advantage of being very fast, interpretable, and accurate. For instance, on simulated test data, the same level of accuracy is obtained by GRSIR and SVM for the estimation of the proportion of dust with a normalized root-mean-square error of 13%, but GRSIR performs 100 times faster. On real data, parameter maps generated by GRSIR from a sequence of three OMEGA observations of the bright permanent polar cap (BPPC) are much smoother, detailed, and coherent than with other competing methods. They indicate that coarse-grained dry ice completely dominates (≈99.55-99.95 wt%) the material forming the top few centimeters of the BPPC with dust and water only present as traces (from 300 to 1000 ppm). The maps show clear regional variations of water and dust contamination as well as CO2 ice state of densification (mean free path around 5 cm on the average, with variations of ±50%) that must be related to meteorological and microphysical phenomena

Support vectors machines regression for estimation of Mars surface physical properties

Abstract. In this paper, the estimation of physical properties from hyperspectral data with support vectors machines is addressed. Several kernel functions are used, from classical to advanced ones. The results are compared with Gaussian Regularized Sliced Inversion Regression and Partial Least Squares, both in terms of accuracy and complexity. Experiments on simulated data show that SVM produce highly accurate results, for some kernels, but with an increased processing time. Inversion of real images shows that SVM are robust and generalize well. In addition, the analysis of the support vectors allows to detect saturation of the physical model used to simulate data.

Machine learning techniques for the inversion of planetary hyperspectral images

International audienceIn this paper, the physical analysis of planetary hyperspectral images is addressed. To deal with high dimensional spaces (image cubes present 256 bands), two methods are proposed. The first method is the support vectors machines regression (SVM-R) which applies the structural risk minimization to perform a non-linear regression. Several kernels are investigated in this work. The second method is the Gaussian regularized sliced inverse regression (GRSIR). It is a two step strategy; the data are map onto a lower dimensional vector space where the regression is performed. Experimental results on simulated data sets have showed that the SVM-R is the most accurate method. However, when dealing with real data sets, the GRSIR gives the most interpretable results

Postural stability measurements in upright stance (25.6 s. duration).

<p>For 15 and 12 control adolescents and for the 27 subjects together. Means and standard errors of surface of CoP, standard deviations of lateral (SDx) and of anteroposterior (SDy) body sway, variance of speed, PII, and PI and CI for each plane (respectively PIy, PIx, CIy and CIx) for each frequency bands (0.05–0.50 Hz, 0.50–1.50 Hz and 1.50–10.00 Hz) for each condition i.e. the quiet fixation task (FT), the Stroop interference test (SIT) and the Stroop flexibility test (SFT).</p

Illustration of posturography testing conditions.

<p>The subject viewed the Stroop test on the screen at 100 cm, at the eye level.</p