{"id":111,"date":"2020-09-18T21:44:33","date_gmt":"2020-09-18T21:44:33","guid":{"rendered":"http:\/\/aksense.com\/info\/?p=111"},"modified":"2020-09-18T09:10:41","modified_gmt":"2020-09-18T09:10:41","slug":"rencontre-non-lineaire-2012","status":"publish","type":"post","link":"http:\/\/aksense.com\/info\/2020\/09\/18\/rencontre-non-lineaire-2012\/","title":{"rendered":"Rencontre Non Lin\u00e9aire 2012"},"content":{"rendered":"

La conception de sc\u00e9narios techniques en vergers permettant des compromis int\u00e9ressants en termes de performance \u00e9conomique, de qualit\u00e9 de la production et dimpact environnemental. Journ\u00e9es dAnalyse Num\u00e9rique et Optimisation : JANO1, \u00e0 Essaouira Maroc- \"rencontre 29 mai 2020 Une r\u00e9ouverture des dojos \u00e0 partir du 2 juin R\u00e9sum\u00e9. Les acc\u00e9l\u00e9rations pour la descente de gradient et ses d\u00e9riv\u00e9es dans un cadre structur\u00e9 et non diff\u00e9rentiable, bas\u00e9s sur lop\u00e9rateur proximal, propos\u00e9es en 84 par Nesterov ont d\u00e9montr\u00e9 leur efficacit\u00e9 en optimisation et en traitement dimages. Nous proposons den donner une interpr\u00e9tation physique et de faire le lien entre ces sch\u00e9mas inertiels avec l\u00e9tude qualitative de solutions dEDO et dinclusions diff\u00e9rentielles. Nous montrerons comment des fonctions de Lyapunov permettent de d\u00e9montrer les vitesses dacc\u00e9l\u00e9ration de telles m\u00e9thodes. Travaux en collaboration avec D. Arzelier, M. Joldes LAAS-CNRS et R. Serra Department of Mechanical and Aerospace Engineering, Glasgow, UK Journ\u00e9e EDP-Probas de la SMAI : Processus de croissance-fragmentation et mod\u00e9lisation pour la division cellulaire, \u00e0 Paris-Une perte dadh\u00e9rence, des rebonds excessifs, des vibrations au volant, des balancements au virage, des claquements sourds, dimportants piquages en avant lors du freinage, vos amortisseurs peuvent \u00eatre endommag\u00e9s. Ils se changent par paire tous les 80 000 km, v\u00e9rifiez les tous les 20 000 km ou une fois par an. Titre: El\u00e9ments de r\u00e9ponse sur le pourquoi de la g\u00e9om\u00e9trie du poumon. Fabricant fran\u00e7ais de literie depuis 1945, Ma Literie ouvre son site de e-commerce en 2007, qui connait depuis un v\u00e9ritable succ\u00e8s, gr\u00e2ce \u00e0 un vrai savoir-faire et des conseillers form\u00e9s \u00e0 lusine. 15\u00e8me s\u00e9minaire de m\u00e9canique des fluides num\u00e9rique-\"rencontre 230000000737 periodic Effects 0.000 claims abstract description 8 Titre : G\u00e9od\u00e9siques dans lespace des mesures de probabilit\u00e9 New Trends in Model Coupling. Theory, Numerics and Applications, \u00e0 Paris-225 218 Wang, Thommen Lefranc respectively, are indeed recovered Fig. 1b displays the average gene activity predicted from the fixed point of Eqs. 4 with the third-order cumulants set to zero. The agreement with stochastic simulations is fairly good : the limit values in Fig. 1a and Fig. 1b are identical, the same global shape with a maximum around \u0398 11 \u03b4 1 is observed and the evolution of the maxima with \u03b4 is correctly reproduced. The main discrepancy is that the transition from the fast to the slow gene regime is more abrupt in Fig. 1b than in Fig 1a. In contrast to this, the fixed point of Eqs. 7 does not depend on \u0398 nor \u03b4, thus incorrectly predicts a constant gene activity Fig 1c. At this stage, the model 4 with vanishing third-order cumulants correctly captures the effect of fluctuations but not the simpler model 7. Figure 1. Average gene activities as a function of the reduced parameter \u0398 1\u03b4 1 a Numerical estimation using stochastic simulations with parameter values : \u03b8\u03b1 100, \u039b 200, \u03bb \u03b2. Each color corresponds to a given value of \u03b4, varying from 10 2 black to 10 cyan. B Fixed point value of gene activity in model 4 with vanishing third-order joint cumulants. C Fixed point value of gene activity in model 7. Arrows indicate the gene activity limiting values given by 5 for large \u0398 and by 6 for small \u0398. Let us now consider the dynamics of protein level fluctuations. Without feedback, gene switching is a purely Poissonian process. Protein levels follow the gene state with a characteristic time scale determined by protein and mrna lifetimes. With feedback, the probability of switching evolves rapidly in time as protein levels increase gene is active or decrease gene is inactive. This feedback may reduce the stochasticity arising from gene switching, with protein peaks occur more regularly. The regularity of a stochastic oscillatory behavior is often quantified using a temporal autocorrelation function, which is sensitive to reproducibility both in time and in amplitude. However, temporal regularity is certainly more relevant than amplitude regularity for biological protein signals. The highly nonlinear response of many signaling cascades can protect them against fluctuations in amplitude, for example by saturating output above an input threshold. A standard technique for assessing temporal regularity is to divide the state space into two regions I and II and to study the distribution of the times where the system leaves I to enter II. It is often useful to require a minimal excursion in region II to avoid spurious transitions induced by noise. Here, we detect events where the protein level crosses successively the mean protein level P and the P P pp level before falling back below the mean protein level. Given the list of times where the system transits from low to high protein levels, we compute the probability of detecting n transitions within a time interval of fixed duration. The probability distribution is then characterized by its variance to mean ratio Fano factor. This method is inspired by how the temporal distribution of photons from a light source is generally characterized, with the event of interest being a photon detection. A Fano factor close to unity indicates that transition times follow a Poison distribution. A Fano factor greater less than unity indicates a super-poissonian resp, sub-poissonian distribution corresponding to a bunching resp, anti-bunching of transition events. Transition antibunching can be viewed as a stochastic counterpart of deterministic oscillations Fig. 2 shows stochastic simulations of the chemical reaction network 1 for a slow, an intermediate and a fast gene, as well as the probability distribution of the number n of transitions within a given time window. As expected, protein spikes in the slow gene case Fig. 2a are slaved to the switching process, leading to a Poisson probability distribution for n Fig. 2d and accordingly a unity Fano factor. In the \u03bb\u03bf\u03c5\u03bb\u03bf\u03cd\u03b4\u03b9 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03ae\u03bc\u03bf\u03c5 \u039c\u03b7\u03c7\u03b1\u03bd\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03c7\u03b1\u03c1\u03b5\u03bc\u03b9\u03b1 \u03bc\u03b5 \u03b4\u03b9\u03b1\u03bc\u03b1\u03bd\u03c4\u03b9\u03b1 \"rencontre La r\u00e9alit\u00e9 des vagues sc\u00e9l\u00e9rates est maintenant parfaitement d\u00e9montr\u00e9e et document\u00e9e, et peut avoir des cons\u00e9quences sur la s\u00e9curit\u00e9 maritime et la conception des grands navires marchands, notamment les et les qui ne sont pas con\u00e7us pour r\u00e9sister \u00e0 des impacts hauts au-dessus de la ligne de flottaison, et qui coulent en quelques minutes R\u00e9sum\u00e9 : In this talk we are concerned with multivalued Stampacchia variational inequalities and we are interested in measuring the influence of perturbations of the data on the solution set of the variational inequalities. We will first survey rapidly the literature on the subject. Then we will present stability results for quasimonotone variational inequalities. Titre : Une m\u00e9thode de base r\u00e9duite pour les in\u00e9galit\u00e9s variationnelles Vous pouvez utiliser lanalyse statique pour calculer la r\u00e9ponse structurelle de corps en rotation \u00e0 vitesse constante ou en d\u00e9placement \u00e0 acc\u00e9l\u00e9ration constante, car les chargements induits ne changent pas dans le temps. \u039f\u03b3\u03ba\u03bf\u03ba\u03b1\u03c4\u03b1\u03c3\u03c4\u03b1\u03bb\u03c4\u03b9\u03ba\u03bf \u03b3\u03bf\u03bd\u03b9\u03b4\u03b9\u03bf p53 \u039b\u03af\u03c0\u03b1\u03bd\u03c3\u03b7 \u03c0\u03b1\u03c2 \u03b3\u03b9\u03b1\u03bd\u03bd\u03b9\u03bd\u03b1 \u03b1\u03c0\u03b5\u03b9 \u03c1\u03c9\u03c4\u03b1\u03bd.<\/p>\n","protected":false},"excerpt":{"rendered":"

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