dataesr/scientific-paragraphs-categorization huggingface dataset:
Les indicateurs sont construits à partir de trois bases de données nationales disponibles sur l'ensemble du territoire : le recensement général de la population (RGP), la base Sirene, et la BDTopo de l'IGN. Les grandes catégories d'indicateurs, le rayon de voisinage dans lequel ils sont calculés et les bases de données qui permettent leur calcul sont résumés dans la figure1, et décrits avec plus de précision dans la seconde partie.
11 Les indicateurs sont construits à partir de trois bases de données nationales disponibles sur l'ensemble du territoire : le recensement général de la population (RGP), la base Sirene, et la BDTopo de l'IGN. Les grandes catégories d'indicateurs, le rayon de voisinage dans lequel ils sont calculés et les bases de données qui permettent leur calcul sont résumés dans la figure1, et décrits avec plus de précision dans la seconde partie.
The search for sustainable financing The lack of sustainable financing was one of the weaknesses of 1GMIS, with their resources stemming primarily from projects funded by donors. By targeting market players and using cell phones as the primary media of dissemination, the ability of MIS to generate resources by invoicing information received by users emerged as a new opportunity. Some 2GMIS, particularly those supported by private companies, state that they intend to sell information to cover their costs (but, as we shall see later, they are as yet a long way from achieving this). At the same time, many public MIS have managed to secure funding by including their costs in the State budget.
Financial sustainability: a transversal issue for all MIS Financial sustainability is key for all MIS. MIS supported by professional organizations are highly dependent on external project-based funding. MIS costs may sometimes be covered by the other activities of the host organization (but often these other activities themselves depend in part on projectbased funding). The situation is not very different for private MIS, despite the fact that they sell most of their services: the contribution made by users is marginal and they are still primarily funded by donors (see for example KACE in this issue) or NGOs (as is the case for Esoko-Ghana). The costs of collecting, processing and disseminating comprehensive and diverse information are high, while the most vulnerable players have very little capacity to pay for it.
This method, however, has many limitations insofar that it reduces MIS information (weekly prices on many markets) to a single annual price. Its application to the Agricultural Market Watch (OMA) in Mali (as proposed in the article) is rather theoretical given that at present this MIS does not provide any price forecasts. Finally,  Ferris (2014)  reports the main results of a qualitative survey on the use of information provided by the Ugandan public MIS. This survey was conducted among a sample of more than 450 stakeholders in the agricultural sector. It shows that farmers use MIS data to monitor market changes over time, and in consideration of this decide on what crops to grow and when and where to sell their products.
The visibility function Because of the large astronomical distances involved, observing stellar structures at arcsecond and sub-arcsecond resolution in the 1 acronym of SPectro-Interferometric Data Analysis Software Tool optical range needs imaging facilities like telescopes with large diameters (today up to 8-10 m). To reach milliarcsecond resolution, optical long-baseline interferometers are needed (up to many hundreds of metres in baseline length). Interferometers do not provide direct images. They measure the mutual degree of coherence (called the visibility function, and denoted V ij ) of the incident light collected by individual telescopes, at positions denoted i and j. According to the van Cittert-Zernike theorem, the complex visibility V ij , measured by an interferometer for the observation wavelength λ, is linked to the spatial extension of the object, via the Fourier Transform (denoted Ôλ ) of the monochromatic object brightness distribution O λ , as given by V ij (λ) = Ôλ f ij Ôλ (0) = +∞ -∞ +∞ -∞ O λ (x, y) e -2iπ (ux+vy) dx dy +∞ -∞ +∞ -∞ O λ (x, y) dx dy , ( 1 ) where  and (u, v)  are the Cartesian coordinates of f ij.
The usual indicator: the global closure phase (GCP) Our first approach to process our interferometric data provided by AMBER has been similar to the one described in  Ragland et al. (2006)  and  Tatebe et al. (2006). To measure the degree of departure from centrosymmetry of the brightness distributions of their targets, respectively, observed with the IOTA and the ISI facilities, these authors use the closure phase cl , estimated as the phase of the triple product measured through a standard H-band filter (λ c = 1. 65 μm, λ = 0. 3 μm), or a narrowband one (λ c = 1. 64 μm, λ = 0. 1 μm), and defined as ⎞ ⎟ ⎟ ⎠ , (4) where sgn stands for the sign function. The visibilities are measured over a finite spectral band, thus leading to a unique value of GCP. When this value significantly departs from zero degree (taking into account the uncertainty), the degree of asymmetry of the brightness distribution is measured (over the considered aperture triplet), otherwise centrosymmetry is the resulting conclusion.
The use of the spectral information Our data (visibilities) are obtained point-wise over the observed spectral band, with a large number of individual spectral channels (more than 500 with AMBER MR-K). This situation invites to explore the spectral behaviour of GCP, resulting in as many GCP values as the number of spectral channels. This enables to track possible morphological variations especially when spectral lines are present in the observed spectral band (as it is frequently the case with cool stars). To get a single indicator for asymmetry, a first possible approach is to integrate over the spectral band the monochromatic closure phases computed from monochromatic triple products. But this approach might result in a completely false indication of asymmetry even with centrosymmetric brightness distributions (e.
e. , farther up in the atmosphere with respect to the continuum-forming region  (Chiavassa et al. 2011a). The predicted granulation pattern causes SBAs, shown in Fig. 6 , that are responsible for deviations from centrosymmetry (already measured in detail with closure phases and visibilities in the works by  Chiavassa et al. ). These departures can be mimicked by a simple geometrical model (one-spot) yielding (nearly) the same CSP value. The search for such a geometrical model is done in the following way. (i) We compute intensity maps at each wavelength of the AMBER MR-K band from the RHD simulation (denoted ST35GM03N07), using the appropriate radiative transfer code OPTIM3D  (Chiavassa et al. 2009 ). The stellar parameters selected for the RHD simulation are: T eff = 3487 K, log g = -0. 335, R = 830 R , and M = 12 M  (Chiavassa et al.
2011b). For each wavelength, we fit the actual instrumental resolution (R = 1500) by using the appropriate top-hat filter on the set of wavelengths given by the RHD simulation. The simulation is scaled to an apparent angular diameter of 10 mas and the intensity maps are rotated by an angle of 71 • , so that the direction of the shift of the large spot is aligned on the direction of the linear configuration. (ii) We compute the 2D Fourier Transform of each monochromatic intensity distribution, as well as the triple product and the final CSP value as defined in equation (  5 ). The calculations are made using the method explained in  Chiavassa et al. (2009)  and for the two configurations A0-D0-H0 (linear) and A0-D2-H0 (triangular). In order to compare the values given by the SPIDAST code using a one-spot model and the ones given by the RHD simulation, we perform the following steps.
AC K N OW L E D G E M E N T S We are grateful to the reviewer,  Dr. S. Ragland , and to the Scientific and Assistant Editors for their constructive comments. PC thanks  Emeritus Professor F. Rouviere  from  Nice University  for helpful clarifications on partial differentiation of definite integrals with unconstant limits. CP is a PostDoc fellow supported by the  Belgian Federal Science Policy Office  via the  PRODEX Programme of ESA. CS and GS thank  Belspo  and  ESA Prodex  for the financial support.
ACKNOWLEDGMENTS The authors thank  Bruno Daronat  for his technical support. The authors also thank  Marie-Benedict Koko  and  Maria Ouboukhlik  for their experimental contributions. Mejdell T. , Knuutila H. , Hoff K. A. , Hallvard V. A. , Svendsen F. , Vassbotn T. , Juliussen O. , Tobiesen A. , Einbu A. (2011) Novel full height pilot plant for solvent development and model validation, Energy Procedia 4,  1753-1760. Mimura T. ,  Shimojo S. , Suda T. , Iijima M. , Mitsuoka S. (1995) Research and development on energy saving technology for flue gas carbon dioxide recovery and steam dioxide recovery in power plant, Energy Conversion Management 36, 6-9. Notz R. , Mangalapally H. P. , Hasse H. (2012) Post combustion CO 2 capture by reactive absorption: Pilot plant description and results of systematic studies with MEA, International Journal of Greenhouse Gas Control 6,  84-112.
The inverse problem, we are interested in, is to recover the source term from the Cauchy data pair obtained over the boundary of the domain. The statement and the modeling issues are presented in Section 1. Then, in Section 2, we propose a direct and non-iterative algebraic method used for the identification of the number, the intensity and the location of these sources. Finally, we present in Section 3, the numerical results that show the stability of the identification method with respect to the different parameters interfering in the reconstruction process.
Estimation de la BRDF Un premier concept qu'il semble intéressant d'explorer est une correction par estimation du BRDF (Bidirectional Reflectance Distribution Function, cf. figure  21 ). En effet, la correction alors effectuée serait basée sur la physique des objets et serait donc plus propre, faisant fi de l'éclairage lors de la prise de vue. Cependant, une telle correction nécessite une estimation d'un paramètre complexe pour chaque point terrain, et donc des données en entrée très volumineuses qui ne sont pas produites par un logiciel comme MicMac dans le cadre de la chaîne de traitement "normale". Une approche de la correction du BRDF est évoquée dans  (Coubard et al. , 2011) , mais est encore à ses débuts.
Remerciements Ces travaux ont été financés par le projet Culture 3D et supervisés par  Marc Pierrot-Deseilligny  (IGN) dans le cadre d'un stage de fin d' études de l'ENSG (Champssur-Marne). Je remercie les membres  du Laboratoire de Géomatique Appliquée (IGN/LGA) , du projet  Culture3D et du Département d'Imagerie Aérienne et Spatiale (IGN / ENSG / DIAS)  pour leur aide, conseils et soutien durant la conduite de ces recherches. Je remercie également les relecteurs anonymes de la RFPT pour leurs conseils et remarques avisés.
RESULTS Using the AUA4 transferable force field for amines, i. e. the original parameters without any modifications, we have studied the behavior of one primary amine (n-ButylAmine), one secondary amine (di-n-Butyl-Amine), one tertiary amine (tri-n-ButylAmine), and one multifunctional amine (1,4-ButaneDiAmine). With regards to the thermodynamic equilibrium properties, vapor-liquid phase equilibrium diagrams, vaporization enthalpies and vapor pressures were studied. Nearly all the experimental information was taken from the DIPPR  [39]  and  NIST [40]  databases. It should be noted that some of these values are based on predictions, this is the case of both vaporization enthalpies and vapor pressures for tri-n-ButylAmine based on the Clapeyron equation and Riedel's method with numerical uncertainties of 5% for both of them.
In other words, the excess enthalpies are important because they reflect the difference of molecular interactions between the pure components and the unlike components. If this property is not well predicted then the Henry constants will probably also be incorrect or unreliable because the interactions in the solvent are poorly described. Figure  5  shows a comparison between our excess enthalpy predictions and the experimental information  [43] [44] [45]  taken from the DETHERM database  [46]  for the n-ButylAmine + water system. It is worth pointing out that differences between experimental values can be found. Although all of them show the same qualitative behavior, there are significant discrepancies. As can be seen from this figure, our force field is able to reproduce the correct behavior.
Tsai uses the same column and methods as Seibert. Gas velocity was varied from 0. 6 to 2. 3 m/s which corresponds to a F-Factor of 0. 7 to 2. 6 Pa 0. 5. To complete hydrodynamics data of structured packings, dry and wet pressure drop measurements for the structured packings Mellapak 125. X, 250. X, 250. Y and 500. Y presented by  Spiegel and Meier (1992)  are considered. Experiments were carried out with air/water at ambient conditions in a 1 m internal diameter column. Liquid load, Q L , was varied from 0 to 200 m 3 /m 2 /h, superficial gas velocity, V SG , from 0 to 7. 5 m/s which leads to a F-Factor, F S , varying from 0 to 8. 3 Pa 0. 5. No information is given about the bed height and about the liquid distributor. Spiegel and Meier (1994)  present the capacity, pressure drop and holdup for the structured packings Mellapak 250.
Note that pressure drop values increase and that the flooding limit decreases when the geometric area increases. Present data show that I-Ring#50 has a flooding limit 10% lower than those of Kehrer's data; this slight difference can be explained by the column diameter influence and/or the experimental uncertainty. Figure  5  represents the capacity in a Wallis diagram  (Wallis, 1969)  for packing I-Ring#50 and IMTP50. The gas capacity factor, C G , has been determined from the flooding limit measurement. In this Wallis diagram, the square root of this gas capacity factor is represented as a function of the square root of the liquid capacity factor, C L , respectively given by Equations (  2 ) and (  3 ): C G ¼ V SG q G q L À q G 0:5 ð2Þ C L ¼ V SL q L q L À q G 0:5 ð3Þ where V SL is the superficial liquid velocity and the liquid density.
In this paper, we will base our presentation on another picture taken from the movie Titanic, which will be even more difficult to treat because of a higher similarity between frames. In the past decade, image retrieval and visual search at large scales have experienced an impressive progress, as demonstrated by Google Image search. These algorithms are typically constructed to be invariant to geometric transformations, contrast changes, noise, and changes of brightness. Nonetheless, searching for images in a movie sequence remains a very difficult task because: • a sequence is a family of images (frames) and even a small sequence is composed of a lot of images (for example 250 frames for a video of 10 sec). As a result, the search for the matching frame has to be performed in very large databases.
Our procedure consists of several steps: • The SIFT algorithm is applied to the query image I and to each image of the N images of the sequence. We thus obtain, for each image, a set of key points (of variable size) identified by their spatial position and their associated SIFT vector of dimension 128. • We localize the most relevant n(k) points of interest between I and every image k, k = 1,. , N. This is done thanks to an algorithm already implemented in Matlab (Vlfeat:  [5] ), which finds matches between the points of interest of two images using the distance between the associated vectors. The number n(k) changes with k because all the images do not necessarily have the same number of points of interest. • For each match i of two points of interest, a score S(k, i), for i = 1,. , n(k) is computed.
APPLICATIONS TO SPECTRAL IMAGES To test the proposed algorithm, and more specifically the usefulness of the spatial information in the segmentation/classification process, we used it on experimental data measured in the context of a study of coating processes in lutherie  [2]. The sample is observed using Fourier Transform Infrared microscopy (FTIR), producing a full infrared spectrum for each pixel of the image, aiming at the chemical characterization of the sample, and in particular of the coating layer(s).
The spectral image was obtained using a Bruker Hyperion 3000 microscope fitted with a 20Â Schwarzschild objective with a numerical aperture of 0:6, and using a focal plane array sensor made of 64 Â 64 pixels with projected pixel size of 2 lm. Acquisition was carried out through a 3 Â 3 tiling and the result cropped to a 128 Â 128 pixel region encompassing most of the section (Fig. 1 ). Spectra were measured from 4 000 cm À1 to 800 cm À1 with a resolution of 8 cm À1 (1 577 samples, one every 2 cm À1 ). To test the robustness of the proposed algorithm 4 images were collected, from a low to high signal-to-noise ratio: 1, 8 and 64 scan to serve as input for the algorithm and 256 scan to serve as ground truth. For these images the data acquisition required an measurement time of, respectively, 2, 5, 30 mn and 2 h.
The range of wavenumbers corresponding to atmospheric variation of carbon dioxide was removed from the spectra, hence removing all samples for which the wavenumber is in the range from 2 318 cm À1 to 2 418 cm À1 , hence practically reducing the dimension of the spectra from 1 577 to 1 528. Random projections were used to reduce the problem dimensions from 1 528 to 24 (Fig. 2 ). Each projection was generated using a centered uniform random number generator, then projected onto the sub-space orthogonal to the previously generated projections, and finally scaled to obtained a unitary L2 norm, hence producing an orthonormal basis of the random 24-dimensional subspace. Using a non-adaptative method for dimension reduction enabled us to use the exact same basis for all 4 datasets without privileging any particular dataset (e.
g. the one that would have been used to optimize the basis) nor having a projection basis dependent on data General view of the imaged sample. The background image is the visible color image of the section, surrounded by the MirrIR microscopy blade. The central region corresponds to the cropped image used in the spectral image analysis; the image corresponds to the total energy of the infrared absorbance spectra of each pixel in false colors, from blue for low values to red for hight values. Note that the tiling is different between the two modes of acquisition, visible light vs infrared. On the right, spectra obtained for two pixels (hide glue on the top, wood on the bottom) corresponding to 1, 8, 64 and 256 scan in red, violet, blue and black, respectively. from a different dataset which would have been the case if jointly adapting for all datasets at once.
[21]. In the cGMM model, the model complexity constant and the segmentation cardinal constant were set simultaneously using the same approach but estimating the asymptotic bilinear form. As expected, the critical slope for the number of components of the mixture increased at higher signalto-noise ratio: 4. 10, 8. 60, and 12. 39, respectively, for the 1-, 8-and 64-scans datasets for the GMM modelization, without spatial co-variables. The corresponding critical slope for the cGMM models are, respectively,  Projection on the 24-dimensional random orthonormal basis from the «fastest» image (top rows) to the reference image (bottom rows). To enhance the contrast, false colors from blue for low values to red for high values were used. No clear morphological differences are seen between the two extreme images, 1 vs 256 scans, but the effect of the reduced signal-to-noise ratio is clearly visible as a strong contrast degradation on the 1-scan projections.
6. 89, 21. 46 and 33. 87. The number of classes is not strictly identical among the various datasets, but they are consistent: between 14 and 16 for the GMM models and between 12 and 13 for the cGMM models. The cGMM model selection also involves a critical slope for the segmentation penalty which is consistent for the three datasets, ranging from 0. 037 to 0. 040; 0. 0393, 0. 0371 and 0. 0402, respectively, for the 1-, 8-and 64-scans dataset. This is expected, since the segmentation penalty is related to the image morphology's complexity rather than to the signal-to-noise ratio of the measurements. The result of the procedure is the estimation for each pixel of the posterior probability for it to correspond to each of the Gaussian components of the mixture. This can be visualized by plotting the "maps" for each component, displaying for each pixel the corresponding posterior probability, as in Figure  3.
Finally, weighted averaging can be performed for each pixel: averaging the Gaussian means using the posterior as weights, producing a ''denoised'' spectrum for each pixel. The noise reduction effect of the averaging is clear (comparing Fig. 1  and 5 ) in all three exposures, producing spectra of the same overall quality as the best measured spectra. Nevertheless, the produced results have to be taken qualitatively since the objective of the procedure is not to minimize quadratic error, hence the target is not to enhance the signal-to-noise ratio as such but rather to try to discriminate spectra as well as possible. Comparing the obtained spectra with those measured using 256 scan/2 h acquisition, one sees that they are not missing a single of the small peak features which are all clearly discernible compared with the raw pixel spectra for the 1 scan/2 mn and 8 scan/5 mn datasets.
The recomputed spectra from the three datasets (1-, 8-and 64-scan) are nearly identical among each other. When compared to the raw spectra obtained by a 256-scan/ 2 h acquisition, the exact same peaks are present in all spectra with the same positions in terms of local maxima; there are only slight variations in terms of the relative amplitudes of the various peaks. This latter difference only concerns the quantitative analysis of the chemical materials which would anyhow be better obtained using a regression method on raw data once the base constituents are identified. This identification is performed through the qualitative analysis we mentioned earlier, based on the presence and position of peaks and not relative amplitudes. 4 000 3 500 3 000 2 500 2 000 1 500 1 000 Wavenumber (cm -1 ) 4 000 3 500 3 000 2 500 2 000 1 500 1 000 Wavenumber (cm -1 ) Figure 5 Spectra re-computed from the model parameters for the same two pixels as in Figure  1.
Des comparaisons sont effectue´es entre les sche´mas explicite et implicite du polyme`re. Elles montrent que le sche´ma implicite est efficace, robuste et reproduit bien la physqiue. Les de´veloppements et les simulations ont e´te´re´alise´s avec le logiciel  PumaFlow [PumaFlow (2013)  Reference manual, release V600, Beicip Franlab].
Following the pioneering work of  Michaels and Miekka (1961)  and  Brand and Dautzenberg (1997) , we explored the viscosity behavior of PEC as a function of the polycation/ polyanion molar ratio. Under the experimental conditions that were probed, PAH was always in excess. The viscosity data were analyzed in conjunction with nanoparticle size, electrophoretic mobility and turbidity measurements. Particles were obtained by mixing the two oppositely charged polyelectrolyte solutions under vigorous stirring. We measured both the overall viscosity of the PEC suspensions and that of the supernatant phase containing PAH in excess (collected after centrifugation). From the viscosity data, we were able to characterize the binding stoichiometry, i. e. the polyanion/ polycation molecular ratio within the soft particles and to derive generic rules concerning the complexation process.