Détection hors contexte des émotions à partir du contenu linguistique d'énoncés oraux : le système EmoLogus

The ANR Emotirob project aims at detecting emotions in an original application context: realizing an emotional companion robot for weakened children. This paper presents a system which aims at characterizing emotions by only considering linguistic content. It is based on the assumption that emotions can be compound: simple lexical words have an intrinsic emotional value, while verbal and adjectival predicates act as a function on the emotional values of their arguments. The paper describes the algorithm of compositional computation of the emotion and the lexical emotional norm used by this algorithm. A quantitative and qualitative analysis of the differences between system outputs and expert annotations is given, which shows satisfactory results, with a good detection of emotional valency in 90.0% of the test utterances

Dynamics of a spherical capsule in a planar hyperbolic flow: influence of bending resistance

International audienceWe consider an initially spherical capsule freely suspended in a planar hyperbolic flow and study the influence of the wall bending resistance on the capsule dynamics. The capsule wall is assumed to be made of a three-dimensional homogeneous elastic material. The fluid-structure interaction between the capsule and the external flow is modeled numerically by coupling a boundary integral method with a shell finite element method. It is found that, for given three-dimensional wall mechanical properties, the capsule deformability is drastically reduced as the bending resistance is increased. But, if one expresses the same results as a function of the two-dimensional mechanical properties of the mid-surface, which is how the capsule wall is modeled in the thin-shell model, the capsule deformed shape is identical to the one predicted for a capsule devoid of bending resistance. The bending rigidity is found to have a negligible influence on the shape and deformation: the capsule main deformation mode is thus solely a function of the elastic stretching of the mid-surface. The wall bending resistance still plays a role locally in the regions where buckling occurs. Its influence is studied in the low flow strength regime, for which wrinkling of the wall is observed to persist at steady state. We show that the wrinkle wavelength only depends on the bending number, which compares the relative importance of bending and shearing phenomena, and provide the correlation law. This result is interesting as it allows bending resistance to be estimated from experiments on capsules in a planar hyperbolic flow at low flow strength

Emologus - A Compositional Model of Emotion Detection based on the Propositionnal Content of Spoken Utterances

International audienceThe ANR EmotiRob project aims at detecting emotions in an original application context: realizing an emotional companion robot for weakened children. This paper presents a system which aims at characterizing emotions by only considering the linguistic content of utterances. It is based on the assumption of compositionality: simple lexical words have an intrinsic emotional value, while verbal and adjectival predicates act as a function on the emotional values of their arguments. The paper describes the semantic component of the system, the algorithm of compositional computation of the emotion value and the lexical emotional norm used by this algorithm. A quantitative and qualitative analysis of the dierences between system outputs and expert annotations is given, which shows satisfactory results, with the right detection of emotional valency in 90% of the test utterances

A weakly overlapping domain decomposition preconditioner for the finite element solution of elliptic partial differential equations

We present a new two-level additive Schwarz domain decomposition preconditioner which is appropriate for use in the parallel finite element solution of elliptic partial differential equations (PDEs). As with most parallel domain decomposition methods each processor may be assigned one or more subdomains, and the preconditioner is such that the processors are able to solve their own subproblem(s) concurrently. The novel feature of the technique proposed here is that it requires just a single layer of overlap in the elements which make up each subdomain at each level of refinement, and it is shown that this amount of overlap is sufficient to yield an optimal preconditioner. Some numerical experiments-posed in both two and three space dimensions-are included to confirm that the condition number when using the new preconditioner is indeed independent of the level of mesh refinement on the test problems considered

The Moment Guided Monte Carlo method for the Boltzmann equation

In this work we propose a generalization of the Moment Guided Monte Carlo method developed in [11]. This approach permits to reduce the variance of the particle methods through a matching with a set of suitable macroscopic moment equations. In order to guarantee that the moment equations provide the correct solutions, they are coupled to the kinetic equation through a non equilibrium term. Here, at the contrary to the previous work in which we considered the simplified BGK operator, we deal with the full Boltzmann operator. Moreover, we introduce an hybrid setting which permits to entirely remove the resolution of the kinetic equation in the limit of infinite number of collisions and to consider only the solution of the compressible Euler equation. This modification additionally reduce the statistical error with respect to our previous work and permits to perform simulations of non equilibrium gases using only a few number of particles. We show at the end of the paper several numerical tests which prove the efficiency and the low level of numerical noise of the method.Comment: arXiv admin note: text overlap with arXiv:0908.026

The combination of a blood test and Fibroscan improves the non-invasive diagnosis of liver fibrosis

Background and aims: Blood tests and liver stiffness evaluation (LSE) by ultrasonographic elastometry are accurate tools for diagnosing liver fibrosis. We evaluated whether their synchronous combination in new scores could improve the diagnostic accuracy and reduce liver biopsy requirement in algorithm. Methods: Three hundred and ninety patients with chronic liver disease of miscellaneous causes were included. Five blood fibrosis tests were evaluated: APRI, FIB-4, Hepascore, Fibrotest and FibroMeter. The reference was fibrosis Metavir staging. Results: Diagnosis of significant fibrosis (Metavir F≥2). The most accurate synchronous combination was FibroMeter+LSE, which provided a significantly higher area under the receiver operating characteristic curve (0.892) than LSE alone (0.867, P=0.011) or Fibrometer (0.834, P<10−3). An algorithm using the FibroMeter+LSE combination and then a liver biopsy in indeterminate cases had 91.9% diagnostic accuracy and required significantly fewer biopsies (20.2%) than previously published Bordeaux algorithm (28.6%, P=0.02) or sequential algorithm for fibrosis evaluation (SAFE) (55.7%, P<10−3). The Angers algorithm performance was not significantly different between viral hepatitis and other causes. Diagnosis of cirrhosis. The most accurate synchronous combination was LSE+FibroMeter, which provided ≥90% predictive values for cirrhosis in 90.6% of patients vs 87.4% for LSE (P=0.02) and 57.9% for FibroMeter (P<10−3). An algorithm including the LSE+FibroMeter combination, and then a liver biopsy in indeterminate cases, had a significantly higher diagnostic accuracy than the SAFE algorithm (91.0 vs 79.8%, P<10−3), and required significantly fewer biopsies than the Bordeaux algorithm (9.3 vs 25.3%, P<10−3). Conclusion: The synchronous combination of a blood test plus LSE improves the accuracy of the non-invasive diagnosis of liver fibrosis and, consequently, markedly decreases the biopsy requirement in the diagnostic algorithm, notably to <10% in cirrhosis diagnosis

Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve

Is there an optimal strategy for real-time continuous glucose monitoring in pediatrics? A 12-month French multi-center, prospective, controlled randomized trial (Start-In!)

AIM: To compare the efficacy of three strategies for real-time continuous glucose monitoring (RT-CGM) over 12 months in children and adolescents with type 1 diabetes. METHODS: A French multicenter trial (NCT00949221) with a randomized, controlled, prospective, open, and parallel-group design was conducted. After 3 months of RT-CGM, patients were allocated to one of three groups: return to self-monitoring of blood glucose, continuous CGM (80% of the time), or discontinuous CGM (40% of the time). The primary outcome was hemoglobin A1c (HbA1c) levels from 3 to 12 months. The secondary outcomes were acute metabolic events, hypoglycemia, satisfaction with CGM and cost. RESULTS: We included 151 subjects, aged 2 to 17 years, with a mean HbA1c level of 8.5% (SD0.7; 69 mmol/mol). The longitudinal change in HbA1c levels was similar in all three groups, at 3, 6, 9 and 12 months. The medical secondary endpoints did not differ between groups. The rate of severe hypoglycemia was significantly lower than that for the pretreatment year for the entire study population. Subjects reported consistent use and good tolerance of the device, regardless of age or insulin treatment. The use of full-time RT-CGM for 3 months costs the national medical insurance system €2629 per patient. CONCLUSION: None of the three long-term RT-CGM strategies evaluated in pediatric type 1 diabetes was superior to the others in terms of HbA1c levels. CGM-use for 3 months decreased rates of severe hypoglycemia. Our results confirm the feasibility of long-term RT-CGM-use and the need to improve educational support for patients and caregivers

Global Mapping of DNA Conformational Flexibility on Saccharomyces cerevisiae

In this study we provide the first comprehensive map of DNA conformational flexibility in Saccharomyces cerevisiae complete genome. Flexibility plays a key role in DNA supercoiling and DNA/protein binding, regulating DNA transcription, replication or repair. Specific interest in flexibility analysis concerns its relationship with human genome instability. Enrichment in flexible sequences has been detected in unstable regions of human genome defined fragile sites, where genes map and carry frequent deletions and rearrangements in cancer. Flexible sequences have been suggested to be the determinants of fragile gene proneness to breakage; however, their actual role and properties remain elusive. Our in silico analysis carried out genome-wide via the StabFlex algorithm, shows the conserved presence of highly flexible regions in budding yeast genome as well as in genomes of other Saccharomyces sensu stricto species. Flexibile peaks in S. cerevisiae identify 175 ORFs mapping on their 3’UTR, a region affecting mRNA translation, localization and stability. (TA)n repeats of different extension shape the central structure of peaks and co-localize with polyadenylation efficiency element (EE) signals. ORFs with flexible peaks share common features. Transcripts are characterized by decreased half-life: this is considered peculiar of genes involved in regulatory systems with high turnover; consistently, their function affects biological processes such as cell cycle regulation or stress response. Our findings support the functional importance of flexibility peaks, suggesting that the flexible sequence may be derived by an expansion of canonical TAYRTA polyadenylation efficiency element. The flexible (TA)n repeat amplification could be the outcome of an evolutionary neofunctionalization leading to a differential 3’-end processing and expression regulation in genes with peculiar function. Our study provides a new support to the functional role of flexibility in genomes and a strategy for its characterization inside human fragile sites