dataesr/scientific-paragraphs-categorization huggingface dataset:
En changeant la valeur d'un paramètre d'entrée, notre outil de prédiction PYTHON offre la possibilité d'extrapoler précisément la consommation mémoire de GYSELA sur de plus grands maillages ou sur des configurations de super-calculateurs qui n'existent pas encore, p. ex. pour faire une projection à l'Exascale. Lorsqu'on fait de la prédiction, on peut se demander naturellement quel est le degré de précision atteint. Dans notre approche, pour chaque allocation la trace mémoire renseigne l'expression du nombre d'éléments du tableau alloué mais aussi sa taille effective en octets lors de l'exécution. Grâce à ces deux informations, le script de post-traitement détecte les différences potentielles entre la taille d'un tableau calculée à partir de son expression et sa taille effective enregistrée lors de l'exécution.
Parmi ces 8 approches, on peut regrouper les 4 premières comme étant des approches purement FORTRAN, par opposition aux 4 dernières qui consistent à appeler des fonctions C à partir d'un code FORTRAN. Ceci est possible de façon portable à partir de FORTRAN 2003 grâce au module ISO C BINDING. Ce module natif à FORTRAN 2003 a été rétro-porté dans la plupart des compilateurs FORTRAN 95. On peut aussi noter que la fonction calloc() assure que la zone mémoire retournée est mise à 0, dans ce cas l'initialisation à zéro de la part de l'utilisateur n'est pas nécessaire. Il n'y a pas d'équivalent FORTRAN pour cette fonction. Le tableau 3 nous permet de comparer ces 8 approches du point de vue temps d'exécution. Les simulations ont été réalisées avec 1 processus MPI et 16 threads OPENMP. Ces temps ont été obtenus sur un noeud de la machine Poincare (IDRIS, Orsay) équipée de Sandy Bridge E5-2670 (2.
Pour récupérer les temps d'allocation, des compteurs ont été placés juste autour de l'appel à la fonction d'allocation et de l'initialisation si elle est faite. N r = 128, N θ = 256, N ϕ = 64, N v = 64, N µ = 1 (2) A la vue de ce tableau, on peut séparer les simulations en deux catégories, celles où l'initialisation à zéro est faite côté application et les autres. Dans le cas où l'initialisation se fait dans le code utilisateur, le temps de l'opération d'allocation et initialisation occupe une portion non négligeable du temps d'exécution total (jusque 16 %), néanmoins ces temps sont moindres dans le cas allocate + init (3). On peut constater aussi qu'une initialisation en pur FORTRAN est aussi efficace qu'une initialisation avec la fonction memset() du C, le temps pris dépend directement de la bande passante mémoire.
Cela nous permet de repérer rapidement des bugs difficiles à détecter autrement. Ces initialisations ne peuvent pas être effectuées avec un simple à calloc(). Afin de limiter l'impact des initialisations à zéro, nous avons utilisé la fonction calloc() pour tous les tableaux nécessitant une initialisation. Pour se faire, nous avons étendu le module dédié à la gestion des allocations mémoires. Il permet dorénavant l'utilisation des fonctions d'allocation C grâce à l'ISO C BINDING. Dans la version de production de GYSELA, nous n'observons pas de gains significatifs de performance après la mise en place de l'appel à calloc(). Comme il est dit plus haut, durant la mise en place des allocations dynamiques, nous avons évité autant que possible l'initialisation à zéro des structures, ce qui limite l'impact des surcoûts dus aux initialisations.
Conclusion L'extensibilité mémoire du code GYSELA a été améliorée grâce à une gestion dynamique des allocations/désallocations. Une réduction de 50. 8% du pic mémoire a été réalisée sur certaines simulations à 32k coeurs. Cet article décrit un module de modélisation et de trace mémoire et quelques outils de post-traitement qui permettent de réduire le pic mémoire. Avec ce jeu d'outils, la modélisation du comportement de l'empreinte mémoire au cours du temps de GYSELA est accessible. L'outil de prédiction permet d'extrapoler de façon exacte la consommation mémoire pour différents jeux de paramètres d'entrées en mode horsligne ; cet aspect est important aussi bien pour les utilisateurs finaux qui ont besoin de plus grandes résolutions ou de fonctionnalités gourmandes en mémoire, que pour les développeurs qui ajustent leurs algorithmes pour une machine spécifique, p.
Introduction The tropical Atlantic Ocean (23 o S -23 o N) is a highly dynamic region. It is generally characterized by high carbon dioxide fugacity (f CO 2 ) in surface sea-water. Consequently, this region is considered as a source of CO 2 for the atmosphere  [1]. In order to quantify the variation of f CO 2 in this area, we will determine the variation of the CO 2 absorption capacity of the ocean  [1]. Data used in such studies, are usually collected during oceanographic cruises which are expensive in terms of time and money. Hence, here we also use another data source like satellite data, to cover much wider area than oceanographic cruises. In order to compute f CO 2 , we need several parameters, such as SSS (Sea Surface Salinity)and SST (Sea Surface Temperature). Unlike other satellite parameters such as SST which are available for several years, the SSS data provided from SM OS (Soil Moisture and Ocean Salinity) mission are available only since 2010.
b illustrates the relation between SSS and Latitude. In both techniques the function can be written as follows : SSS = f (Latitude, SST ). (1) In this paper, we compare two mathematical techniques to estimate SSS from SST measured by satellite : Multiple Non-linear Regression and Multi-Layer Perceptron. Two experiments are implemented. In both experiments, we start by establishing the models from a dataset which contain these three parameters, namely Latitude, SST and SSS. Thus, in the first experiment, models are tested with another dataset that is outside of the original observation area, this is an "Extrapolation". In the second experiment, the dataset used in the test is also different from the original dataset, but it is inside the observation area, this is an "Interpolation". We used MATLAB tm software for the implementations of these programs.
The regression equation is: y i = β 0 + β 1 * Lat + β 2 * SST + β 3 * Lat 2 + β 4 * Lat * SST + β 5 * SST 2 (5) The Curve Fitting Toolbox in MATLAB tm provides the most widely used techniques for fitting curves and surfaces to data, using linear and non-linear regression, interpolation, and smoothing. To find the surface that best fits the data, we use the most popular method to estimate the regression coefficients in  (5) , namely the Least Squares Method.
It is composed of two or more layers. Each one contains a set of neurons (e. g layers in Figure  5 ). The connections between the layers are associated with weights. There are many types of Neural Network (NN). They differ in structure, and the learning algorithm. In this work we proposed to apply the MLP (Multi-Layer Perceptron) that is a supervised method  [7]. It requires a desired output in order to learn using a back-propagation algorithm, for more details see  [7]. Model that maps the input to the output is then created. The goal is to produce an output when the desired output is unknown. Figure  5  shows the structure of MLP. The standard MLP network that is used for function fitting in the Neural Network Toolbox of MATLAB tm , is a two-layer feedforward network, with a sigmoid transfer function (Equation (  8 )) in the hidden layer and a linear transfer function (Equation (  9 )) in the output layer  [10].
Before learning starts, the default dataset division is made as follow: SSS = f (Lat, SST ) = β 0 + β 1 * Lat + β 2 * SST + β 3 * Lat 2 + β 4 * Lat * SST + β 5 * SST 2 • 70% for training, • 15% to validate that the network is generalizing, • 15% used as a completely independent test of network generalization. net j = w ij * I i (7) f (net j ) = 1 1 -e -netj (8) O j = f (net j ) (9) I is the input vector which contains Latitude and SST. O is the output vector that represents SSS. w ij is the weight from input i to neuron j. In the algorithm, we propose, initially the hidden layer contained one neuron, at each new iteration a new NN is created, on which one hidden neuron is added. Finally there are as many networks as iterations. In order to find the NN that gives a best performance, we compute MSE (Equation (  12 )) for each one.
To validate the two models, we used daily averaged satellite data (from MODISAqua mission) at the dates of the in situ data used to build the models. Figure  8  shows an example of one day of satellite SST as well as the measured in situ data of SST. To extract data we need, we sought satellite points corresponding to in situ points and containing SST information. Then, we use points that include both in situ SST and satellite SST (Figure  8  in red). Several parameters, such as cloud cover (Figure  8  in white) prevent satellite observations, hence decrease of data point number that we can use from 18300 (in situ data) to 9139 (with satellite data) in the first experiment, and from 15668 to 8247 in the second one (Figure  8  in blue represents points with only in situ SST data). We used both polynomial functions and neural networks to estimate SSS from these satellite data.
Estimated SSS is then compared with in situ SSS used in building the models. After this first step of validation, the models are tested with data points which are situated in a different geographical area. In the first experiment, we use data from the Santa Maria cruise. In the second experiment, we use data from the PIRATA cruise. Because of the use of a dataset which is in another geographical area, errors have increased compared to the validation step. RMSE of satellite SST (Table  4 ), show that in datasets validation, there are some satellite points with a large error in SST , which leads to large errors with the MLP method.
Estimation of SSS using MNR Results of the first step of validation are reported in Table  5. I. 1. a for the first experiment, and Table  5. II. 3. a for the second one. Comparing these results with those of Table  3 , we note that in the two experiments, errors have improved. This is because the validation dataset is in the same geographical area than the original data, and the number of data points is smaller than that of construction model. Table  5. I. 2. a contains results of the extrapolation. In Figure  9. a, we see that the MNR agree relatively well on estimating salinity. Table  5. II. 4. a shows that interpolation results are better than those of extrapolation. However, Figure  10. a shows a large margin between the theoretical and the regression lines. If we compare this fit with the MLP one (Figure  10.
A neural network is more complicated than a polynomial function. Thus for obtaining a robust neural network, many parameters can be changed, especially the structure, for example change the number of hidden layers, the number of hidden neurones, connections between neurones. The At present, additional datasets are required to better assess the model. We propose to perform a model for each month, or for each season of the year (Fall: September-October-November, Winter: December-January-February, Spring: March-April-May, Summer: June-July-August) depending upon the availability of in situ data. A threshold could be used to exclude the wrong satellite SST. Overall, this study opens the route of mapping SSS at the same temporal and spatial resolution as SST from satellites.
These datasets cover a large part of the Western Tropical Atlantic Ocean, with 18803 data points. The Underway f CO 2 , SST , and SSS were recorded on the research vessels. In this work, we use only SST , • Santa Maria (in 2003/2004/2005/2006/2007), • COLIBRI (in 2006/2007/2009), • PIRATA BRXI (in 2009), • Monte Olivia (in 2009), which have been performed as part of the EC-funded project CARBOOCEAN IP program (http://dataportal. carboocean. org/ ).
La mise en place de ce type de recherches, avec le concours des acteurs locaux, permettra : La constitution d'une base de données interministérielle croisant des données socioéconomiques, sanitaires et environnementales. Le développement de plateformes intégrées, à l'instar des initiatives de l'Ineris  (Caudeville et al. , 2011)   L'évaluation de l'équité des actions mises en oeuvre en matière de santé Il est nécessaire, pour prendre en compte les ISS dans les interventions territoriales visant à améliorer la qualité de l'air, d'évaluer, avant leur mise en oeuvre, dans quelle mesure l'intervention pourrait accroitre la vulnérabilité ou dégrader d'avantage des conditions de vie des populations socio économiquement défavorisées. Au cours de la dernière décennie sur le plan international et plus récemment en France, l'Evaluation d'Impact sur la Santé (EIS) s'est positionnée en tant que l'outil d'aide à la décision permettant la mise en place de « politiques en matière de santé publique ».
Supplementary Calculations/Data Processing The proportion of adsorbed zinc for each experiment is calculated with the formula: ZnðIIÞ sorbed ¼ ZnðIIÞ ½ i À ZnðIIÞ ½ f Â 100 ½ZnðIIÞ i ð1Þ where Zn(II) sorbed is expressed in percent of the initial Zn(II) concentration, and [Zn(II)] i and [Zn(II)] f are the initial and final Zn(II) concentration in the solution in mol/L, respectively. The calcite mass dissolved is calculated with the formula: M diss ¼ ½CaðIIÞ f Â V i where M diss is the dissolved calcite mass in g, [Ca(II)] f is the Ca(II) concentration measured at the end of the experiment in mol/L, V i is the initial volume used in the experiment in L and M CaCO 3 is the molar weight of calcium carbonate in g/mol. This calculation implies that the equilibrium of calcite dissolution is achieved within the first 25 hours of the experiments (before the first sampling).
Database Selection The question of the database is very important concerning simulations, especially when half of the experiments are performed in high salinity solutions (100 g/L of NaCl, i. e. 1. 71 M). Indeed, the extended Debye-Hückel model used in PHREEQC for the calculation of the activity coefficient is considered valid for solutions with ionic strengths below 1 M  (Parkhurst and Appelo, 1999) , which is about half of the current salinity. Simulations were performed with both a Debye-Hückel and a Pitzer model (minteq. v4. dat and pitzer. dat databases respectively), and a maximum of 20% of the Ca(II) concentration underestimation was observed for simulations run with the classic model at this salinity (see Appendix). More precisely, this underestimation was comprised between 16% and 20% for basic initial conditions and around 10% for acid initial conditions.
In the end, the lack of values for the elements studied in this paper forbade using Pitzer's database. The database minteq. v4. dat with the classic Debye-Hückel model for the calculation of the activity coefficient is thus used for all the subsequent simulations, despite its inadequacy for high salinity conditions. This is a recurrent issue for people working in high salinity systems, as stated in other studies  (De Lucia et al. , 2012).
Thermodynamic Precipitation of Secondary Phases The selection of the database is also justified by the amount of data available concerning equilibrium of other mineral phases. Indeed, several chemical elements (Zn(II), Mn(II) and Sr(II)) at various concentrations are considered in this study and could lead to precipitation of other mineral phases. For example, in the phase diagram of the system {Zn -H 2 O -CO 2 } the point corresponding to the conditions of this study belongs to the hydrozincite (Zn 5 (OH) 6 (CO 3 ) 2 ) predominance area  (Preis and Gamsjäger, 2001). The possible precipitation of this phase is often discussed in the literature, especially for zinc concentrations above 10 À5 M  (Zachara et al. , 1991) , thus its dissolution equilibrium value was taken from the database 'thermo.
Surface Complexation Model (SCM) The experimental results are used as an input dataset for surface complexation model simulations. The simulation of sorption is designed as a two-site SCM, according to the large literature available  (Lakshtanov and Stipp, 2007; Martin-Garin et al. , 2003; Pokrovsky et al. , 2000; Stipp, 1999; Villegas-Jiménez et al. , 2009b). The equilibrium equations accounting for surface complexation in the context of the study are presented in Table  2  with the related log K values at 25°C and I = 0. The equilibrium constants concerning the three elements of interest in this study are initially taken to be equal to values from the literature:  Glasner and Weiss (1980) ,  Tsusue and Holland (1966)  and  Zachara et al. (1991). Two types of SCM simulation are run: in the first, only Zn(II) ions interact with the surface.
Simulation of Calcite Dissolution The experimental values are then compared to the simulated concentrations obtained with PHREEQC (Tab. 5). The final pH values obtained by simulation present a good match with the experimental measures, with a maximum difference of 5%. Concerning the concentration of Ca(II), the experiments in aqueous solution match well with the simulated data (only 1 value with over 20% difference between result and simulation); but as expected, due to the non adequacy of the database in high salinity solutions, dissolution is overestimated in simulations (up to 45% in terms of concentration).
Equilibrium of the Dissolution/Precipitation Processes The values obtained for equilibrium Ca(II) concentration in the simulations and the experiments (comparison in Tab. 5) are in good accordance for experiments in aqueous solution (only 1 value showed over 20% of difference between the result and simulation, experiment 14); but in high salinity solutions, the difference between experiment and simulation increases (between 12% and 45%). To explain those higher differences for high salinity, the main argument is the impact of the database used and especially of the classic Debye-Hückel model. Indeed, as presented in Section 1. 3. 1. 1, a difference of up to 20% is introduced if we consider the Pitzer database as the reference (see Appendix). This variability can also be due to the interaction of calcite with solute species: Martin-Garin et al.
, 1991)  and corresponds effectively to the predominance area of hydrozincite in the phase diagram of the system {Zn -H 2 O -CO 2 } corresponding to the present conditions  (Preis and Gamsjäger, 2001). Even though no hydrozincite presence was evidenced by all the techniques used in this study, thin deposits (~10 nm) could certainly have been distinguished with an atomic force microscope, as showed by Freij  (Freij et al. , 2005). The solubility of phases related to Mn(II) and Sr(II) mineral species are numerous in the database used, and as for hydrozincite, their SI at the end of the simulations is sometimes slightly positive but again, it was never related to the observation of any newly formed phase. These discussions concerning dissolution, precipitation and equilibrium in the system ensure the validity of the major contribution of sorption in the interpretation of the results.
CONCLUSIONS The sorption experiments presented in this paper evidence the impact of different factors such as initial pH, initial Zn(II) concentration and salinity of the liquid phase on the dissolution/precipitation and sorption phenomena. A combination of multiple experimental techniques allowed us to obtain data which we then used to run simulations with the geochemical code PHREEQC. The results obtained both experimentally and by simulations intend to prove that the behavior of TE in such systems remains to be more deeply investigated. Indeed some behaviors could not be modeled with the classical methods, including for example the continuous release of Mn(II) and Sr(II) observed in experiments with an initial pH of about 3. For all experiments, the equilibrium of the main kinetic phenomenon (fast Zn(II) uptake) was reached within 200 hours maximum, with either the end of sorption or a lower rate phenomenon occurring then.
The simulations carried out with PHREEQC demonstrate that sorption of a relatively concentrated element (Zn(II) here) is well constrained by a simple log K value with the two site SCM scheme  (Davis and Kent, 1990 ) used along with the equilibrium constants from the literature  (Pokrovsky et al. , 2000; Villegas-Jiménez et al. , 2009b). However, these simulations remain dependent on the salinity and on the specific petrophysical and morphological properties of the natural carbonate  (Sverjensky, 2003). The impossibility of carrying out several simulations showed that mathematical representation used is not relevant when one wants to consider the evolution of the TE released from the bulk solid in a natural carbonate system following dissolution, i. e. with an initial pH acid. In addition, the validity of the database used in high salinity conditions remains disputable, but no other possibility is currently available when one is interested in TE behavior.
ACKNOWLEDGMENTS B. Auffray is grateful to  Frédéric Neyret-Martinez  and  Nathalie Texier  for their presence and advice during the ICP-AES analyses campaign, to  Anne-Sophie Gay  and  Yannick Blouet  for their help concerning the microprobe analyses and to  Véronique Bardin  for their availability during the liquid chromatography analyses. The authors also thank the three anonymous reviewers who helped to substantially enlighten this article. This paper is part of the PhD Thesis of B. Auffray, funded by  IFP Energies nouvelles.
, 2010; Villegas-Jiménez  et al. , 2009b)to ensure that sorption is dominant regarding dissolution. In addition, the quantity of TE released in solution is calculated for each experiment. The released amount of Zn(II) is estimated to a concentration of 10 À8 M, which is neglectible by comparison with the initial Zn(II) present in solution. Reactions Surface complexation equilibrium reactions log K°i nt (25°C, I = 0) 01 CaOH °-H + = > > CaO À À12 Data from 02 CaOH °+ H + = > CaOH 2 + 11. 5 literature and thermodynamic 03 CaOH °+ CO 3 CaHCO 3 °+ H 2 O 2À + 2H + = > 23. 5 databases 04 CaOH °+ CO 3 CaCO 3 À + H 2 O 2À + H + = > 17. 1 05 CO 3 H °= > CO 3 À + H + À5. 1 06 CO 3 H °+ Ca 2+ = > CO 3 Ca + + H + À1. 7 07 CO 3 Ca + + Zn 2+ = > CO 3 Zn + + Ca 2+ Fit from experimental data 08 CO 3 Ca + + Sr 2+ = > CO 3 Sr + + Ca 2+ 09 CO 3 Ca + + Mn 2+ = > CO 3 Mn + + Ca 2+.
Statistical analysis Data analyses were performed by SPSS 20. 0 (IBM SPSS, Chicago, IL, USA). We conducted a descriptive analysis of our NMNI Seng et al. Infections with streptococcal involvement population (chi-square test), then analysed the relapse prognostic factors after medicosurgical treatment. Univariate analysis was performed first to identify prognostic variables strongly associated (p <0. 2) with risk of relapse (proportional hazard assumptions verified on the representation of Schönefeld residues); then multivariate analysis was performed to assess the predictions specifically for Streptococcus species after adjusting for significant variables in the univariate analysis and/or risk factors such as those reported in the literature including diabetes mellitus, location in the foot and polymicrobial infection  [3, 4, 6, [31] [32] [33].
Participants The current study uses data from children and their families enrolled in the Millennium Cohort Study (MCS). The MCS is one of the largest population-based developmental cohort studies, with an original field response of 18, 553 families residing in the UK (England, Wales, Scotland, and Northern Ireland), with infants that were born between 2000 and 2002. The stratification procedures and sample characteristics for the entire cohort have been documented extensively elsewhere  (Dex & Joshi, 2005). As is common in longitudinal studies, attrition across waves of data collection resulted in a reduced sample size. The sample was restricted to children who had complete data on expressive language and prosocial behaviour at ages three and five (waves 2 and 3) only, which resulted in a sample of N = 14, 004.