dataesr/scientific-paragraphs-categorization huggingface dataset:
Pre-trained and segmentation-free OCR results on eBDtheque (Albums 1-11) and DCM772 (Albums 12-32) datasets. The first five columns show the album ID, the number of pages for the testing and training sets, and the number of text lines that have been extracted and validated, respectively. The next four columns indicate the average Character Error Rate (CER) and Word Error Rate (WER) of both OCR systems. The last column shows the number of iterations of the trained LSTM model that gives the lowest CER. The last row indicates the total or average of the values mentioned above.
Acknowledgments: This work was supported by the  University of La Rochelle (France) , the town of  La Rochelle , the  PIA-iiBD  (" Programme d'Investissements d'Avenir ") and the  MEDIABD  project from the  French administrative region "Nouvelle Aquitaine". We are grateful to all authors and publishers of comic book images from eBDtheque and DCM772 datasets for allowing us to use and share their works.
Model implementation To implement the constitutive model proposed in this paper, a simple computer program was developed under Matlab environment. The performance of this model was investigated by comparing the numerical simulation with a series of triaxial tests results for earthen materials (Lim and Mix1), at different hydraulic conditions  (23, 75 , and 97% relative humidity) and two confining pressure conditions (100 and 600 kPa). The parameters used for the study are presented in Table  1.
To improve the model's performance, a suction-dependency plastic potential is considered to use in the future work. By making a comparison between Figs. 5  and 6 , it is worth noting that the model also captures the increased deviator stress and overall reduced dilation with an increase in confining pressure. As confining pressure increases, particle crushing begins to dominate plastic deformation, resulting in a reducing tendency for a peak in the shear resistance and for a volumetric expansion. Triaxial compression tests on Mix1 at three different hydraulic states (RH = 23%, RH = 75% and RH = 97%) and at 100 kPa confining pressure were conducted in the laboratory. A good agreement is obtained between numerical simulation and experimental data as illustrated in Fig. 7.
However, their efficience decrease when data are less regular, especially on the modelisation of derivatives. Wavelet basis are designed to settle this sensibility to irregular signals. Coefficients are slightly longer to compute, but this basis has really good mathematical property and progressively replace Fourier basis in several applications  [29] [30]. For non periodic data, the classical choice is spline basis, particularly the cubic splines in practice  [6]. B-splines are piecewise polynomial functions and require few coefficients to define a good approximation, which make B-splines especially adequate when observations are sparse on the time domain  [31]. They allow to approximate a wide range of shapes with a rather good smoothness  [30]. From a computational point of view, one can use the R package fda, on which one can find methods to fit observations into functional data, and way more tools for FDA.
An overview of the fda package can be found in  [30]. Once the data set is approximated by functions, one may perform analysis on them, and some classical statistical tools have been extended in the functional context. One of the first and most important adapted method was the functional principal component analysis (FPCA). Although slightly different, FPCA provides analogous information as the finite dimensional version  [27]. This method allows to describe data into a non correlated low dimensional space. That is why it provides an excellent explanatory tool to visualize main features of the data as well as a way to reduce the number of informative dimensions. This can be particularly useful when one wants to apply algorithms on the vector {α 1 ,. , α N } of coefficients of the basis expansion, with N rather large.
Testing several algorithms on simulated data sets To begin, a comparative study of several classical functional clustering algorithms has been performed on simulated data. Few information are provided on the algorithms, but we invite readers to refer to the corresponding papers for details. For this work, the R package funcy, which compiles seven state of the art algorithms, was used. It gives a common syntax and format for the input and output data. The list below enumerates the algorithms, regrouped according to their family, that can be used with funcy. (ii) distance-based: • distclust: An approximation of the L 2 distance between curves is defined, and a k-means heuristic is used on individuals using this distance. This method is well designed in the context of sparsely observed functions with irregular measurements.
This method is well design for high-dimension curves, when variations such as peaks appears in data, and thus wavelets performe better than splines. [29]. Unfortunately, the current version (1. 0. 0) of the funcy package has some troubles with the funHDDC algorithm, which is not directly usable at the moment. All the remaining algorithms were applied on three simulated data sets, with K = 4 groups. The resulting clustering were compared to real group distributions using the Rand Index (RI)  [40]. This measure, between 0 and 1, is computed by counting according pairs of individuals between two different partitions of a data set. The RI is provided as a result of the funcit function of the funcy package, and compares the ability of each procedure to retrieve the actual groups. Then, graphs of centers of each curve clusters were drawn to analyse consistency of our results according to the original data.
To this purpose the funHDDC algorithm was used as clustering procedure, as this is one of the rare implemented algorithm that works in a multivariate case and thus allow to consider both curves and their derivatives simultaneously. One can find more details in the result section about the reasons of this choice. Although implemented in the funcy package, we chose to work with the original funHDDC R package, because of current problems of implementation on it. Several features of the package were used, as Bayesian Information Criterion (BIC), Integrated Classification Likelihood (ICL) and slope heuristic, to deal with problems of model selection and choice of the number K of clusters. Since no particular assumptions were made on the covariance structure or the number of clusters form a sport expert point of view, the hyper parameters of the model have been optimised from data.
Definition of the simulated data sets We defined three simulated data sets to test the algorithms of the funcy package on different contexts. We used the function sampleFuncy of the funcy package that provides an easy way to simulate data sets suited to apply directly methods from funcy on them. Simulated data sets are sampled from four different processes of the form f (t) + ε , with f and detailed in Table  1  below. For each process 25 curves are simulated, thereby leading to 100 curves in each sample. The aim of the following clustering procedure is to gather themselves curves that correspond to the same underlying process. An additional goal would be to retrieve, at least approximately, the shapes of deterministic functions f that generated each data curves within a cluster. Intuitively, Sample 1 depicts an easy situation with low noise and well separated processes, whereas Sample 2 represents the same processes in a higher variance context.
Finally, Sample 3 corresponds to a high-noise and crossing processes context, which is designed to be trickier. Moreover, in the case of Sample 3, observations of the curves are irregular on t-axis and thus, for three out of six algorithms of the package that are not implemented in this case, we had to proceed to a previous fitting step. We used the function regFuncy of the funcy package to this purpose. Sample 3 t -→ t -1 t -→ -t 2 t -→ t 3 t -→ sin(2πt) ε ∼ N (0, 0. 5) ≤ 10 points at irregular instants.
Indeed, even if the EM algorithm is really efficient to solve the mixture of Gaussian problem, the multiplicity of models and the number of clusters to test might take non negligible time to run (few hours in our case). For our purpose, which is to help a swimming federation with the detection of young promising athletes, computational time was not an issue since the aim was more of a long term decision making. Nevertheless, many sport-related problems need today to be solved quickly or even in live, and our methodological choices would have been different under such constraints. About the results on the swimming data set, we observe consistent outcomes from both mathematical and sport point of views. If our work does not give any certainty about the progression phenomenon of young swimmers, it gives some enlightenments of its general pattern and provide a practical tool to gather similar profiles.
An effective algorithm for the quasi-harmonic calculation of thermo-elastic stiffness constants of materials is discussed and implemented into the CRYSTAL program for quantum-mechanical simulations of extended systems. Two different approaches of increasing complexity and accuracy are presented. The first one is a quasi-static approximation where the thermal dependence of elastic constants is assumed to be due only to the thermal expansion of the system. The second one is fully quasi-harmonic, takes into account thermal expansion, and explicitly computes Helmholtz free energy derivatives with respect to strain. The conversion of isothermal into adiabatic thermo-elastic constants is also addressed. The algorithm is formally presented and applied to the description of the thermo-elastic response of the forsterite mineral.
) and of the conditions on which materials function (anharmonic thermal effects, phonon-phonon coupling, interaction with electric and magnetic fields, etc. )  [1] [2] [3]. In particular, the effective inclusion of thermal effects (fully neglected by static DFT calculations) is much needed in order to widen the application domain and enhance the predictiveness of the computational approach. In this context, let us briefly review some of the progresses made in the last few years in the development of the CRYSTAL program for quantum-mechanical simulations of the condensed matter  [17, 18]. A massively-parallel version of the code has been developed, which was recently shown to efficiently scale up to 32,000 cores and which has been used to study several large systems (the largest one containing about 14,000 atoms/cell)  [19].
From the volume-temperature relation V(T), the volumetric thermal expansion coefficient α V (T) can be defined as: α V (T) = 1 V(T) ∂V(T) ∂T P , (13) where the derivative is taken at constant pressure. The isothermal bulk modulus of the system, K T (T), can also be obtained as an isothermal second derivative of Equation (  12 ) with respect to the volume: K T (T) = V(T) ∂ 2 F QHA (V; T) ∂V 2 T. ( 14 ) A fully-automated algorithm for computing the thermal expansion of the system (and derived properties) within the QHA has recently been implemented in the CRYSTAL program  [18, 22, 23] , which relies on computing and fitting harmonic vibration frequencies at different compressed and expanded volumes (four volumes, including the equilibrium one, being enough for most systems) after having performed volume-constrained structural relaxations.
The Implemented Algorithm In this section, we sketch the algorithm that we have devised to compute quasi-harmonic thermo-elastic constants of materials. In particular, we explicitly illustrate the sequence of calculations that are required by stressing what steps can be performed automatically. The algorithm that we propose is the following: 1. A full structural relaxation of the system is performed (both atomic positions and lattice parameters are optimized). The static equilibrium structure, with volume V 0 , is obtained. 2. A space group symmetry-preserving QHA calculation is performed, which provides the thermal expansion of the system. A fully-automated algorithm is implemented in the CRYSTAL program to perform this task  [22, 23] , where four different volumes are explored (compressed and expanded with respect to V 0 ).
2. 2. A fully-automated algorithm has been implemented in the CRYSTAL program for this task. The starting point is represented by the optimized structure obtained at the end of step 3 above (i. e. , the equilibrium structure at temperature T). The structure is deformed, in terms of the strain shape η, into four strained configurations (two with positive and two with negative strain amplitude δ). At each strained configuration, atomic positions are relaxed and phonon frequencies computed. The computed quasi-harmonic free energy as a function of strain amplitude is fitted to a second-order polynomial and the corresponding second-derivative determined.
Computational Parameters All the calculations reported in this study have been performed with algorithms implemented in a developmental version of the CRYSTAL17 program  [18]. An all-electron atom-centered Gaussian-type-function (GTF) basis set is used for all atoms; silicon, oxygen and magnesium atoms are described by (8s) (6311sp) (1d), (8s) (411sp) (1d) and (8s) (511sp) (1d) contractions of primitive GTFs, respectively. The exponents (in bohr -2 units) of the most diffuse sp shells are 0. 32 and 0. 13 (Si), 0. 59 and 0. 25 (O), and 0. 68 and 0. 22 (Mg); the exponents of the d shells are 0. 6 (Si), 0. 5 (O), and 0. 5 (Mg). The same basis set has already been successfully utilized in a couple of recent studies of vibrational and spectroscopic properties of forsterite  [25, 54]. Thresholds controlling the accuracy of Coulomb and exchange series are set to default values.
Results and Discussion We apply the quasi-harmonic methodology outlined in Section 2 to compute the thermo-elastic response of the orthorhombic forsterite mineral, α-Mg 2 SiO 4 , that is characterized by nine independent elastic constants: C 11 , C 22 , C 33 , C 44 , C 55 , C 66 , C 12 , C 13 , C 23. The first step of our algorithm, as sketched in Section 3, consists of a full structural relaxation of the system (i. e. , both atomic positions and lattice parameters are optimized by minimizing the static internal energy E of the system). The second step of the algorithm is a lattice symmetry-preserving quasi-harmonic calculation, where harmonic phonon frequencies are evaluated at four volumes (one compressed, one equilibrium and two expanded) according to the fully-automated scheme previously suggested and implemented in the CRYSTAL program by some of the authors  [22, 23].
Conclusions Two computational approaches for the first-principles calculation of thermo-elastic stiffness constants of crystalline materials have been formally illustrated and implemented into the public CRYSTAL program for quantum-mechanical simulations of the condensed matter. Both schemes are based on the quasi-harmonic description of the thermal expansion of the system. The first approach, referred to as the quasi-static approximation, assumes that the whole thermal dependence of the mechanical properties of a solid is due to its thermal expansion and therefore involves second energy derivatives with respect to the strain in terms of the purely static internal energy E. The second scheme is a full quasi-harmonic one where thermal expansion is taken into account and second energy derivatives with respect to strain are computed in terms of the Helmholtz free energy F.
French-speaking population. The system of quotation is based on the presence of the required written forms, their adequate use in the written word, as well as their admissibility in the student's erroneous use. On the scientific plan, this approach of analysis allows a finer understanding of the graphemic knowledges of the students' writing skills. Both for the scientific circle and the teaching environment, this approach of analysis also allows to follow up the evolution of the graphemic representations of the young learners through time and to propose school interventions accordingly. We present here the prototype of the software DIDALEX.
Sur le plan scientifique, cette démarche multidimensionnelle d'analyse permet une compréhension plus fine des connaissances graphémiques des élèves dans le domaine de l'écriture. Tant pour le milieu scientifique que celui de la pratique, cette démarche d'analyse permet également de suivre l'évolution des représentations graphémiques des jeunes apprenants à travers le temps et de proposer des interventions scolaires en conséquence. Nous présentons ici le prototype du logiciel DIDALEX.
Configurations techniques de la plateforme DIDALEX La plateforme DIDALEX est une application web qui permet la récolte d'informations portant sur des phrases écrites par des élèves du primaire. Les données sont présentées à l'utilisateur sous forme de graphiques et enregistrées sur le serveur dans une base de données MYSQL qui met en relation plusieurs couches de traitement (configuration des phrases, configuration des différentes propositions d'écriture et propositions de l'élève). Ainsi, le logiciel associe tous les segments saisis (production des élèves) à un résultat en se basant sur les tables de correspondance préconfigurées aux différentes couches de traitement. Les données récoltées permettent l'analyse et le suivi de la progression individuelle des scripteurs en apprentissage. Pour ce faire, chaque année, les participants devront écrire trois phrases proposées par le logiciel.
Manual Viability Analysis To avoid bias, manual analysis were blinded and performed by four different examiners which did not know the results obtained with the automatized method. Each experimenter merged the green channel (corresponding to the MSC cytosols) with the blue or red channel. Then, selection of nuclei present in or out the green signal was visually performed on each picture of the stacks using the "Multi-point" tool of ImageJ 1. 51. ImageJ is an open source software especially developed for picture analysis and widely used in the world  (33). The viabilities presented in this article are calculated by dividing the number of nuclei stained with Hoechst by the total number of nuclei (number of nuclei stained with PI + Hoechst).
Automated Viability Analysis Regarding our automated method developed, previously acquired pictures were analyzed by a code elaborated with the ImageJ interface. The description of the code can be separated into four main steps as presented in Figure  1. Once the software is started, a window is displayed to set up several variables. First, fields to fill in allow to entry the folder's pathway containing the picture files, name, and numeration. This window also permits the configuration of the channel appellation and different filters concerning the minimal and maximal thresholds accepted during the binary transformation, the factor of regions of interest (ROIs) dilatation containing the detected MSCs, and a filter to suppress signal under the determined surface. Two check boxes allow to enhance the blue channel contrast if the signal is too low and to choose or not to analyze the viability of MSCs.
Then, the code loads the green channel, corresponding to the MSCs cytosol staining. The picture is transformed into a binary signal; the detection of the green areas is performed and saved as ROI. All ROIs detected are saved in the folder named "…/Analyse/ROI_Save. " Then, blue channel, corresponding to the living nuclei is loaded and transformed into a binary picture. In each ROI previously saved, the x, y coordinates of MSCs' nuclei are detected and saved in a matrix. The same analysis is performed with the red channel. The next step corresponds to the suppression of the MSCs' nuclei in blue and the red channel. For that step, we used the "MagicWand" function allowing a selection of all the black dots around the different x, y coordinates and replacing them by white dots. To avoid artifacts due to the analysis of the same nucleus on several slides, the code is able to detect if nuclei are present on the next slide at the exact location of those previously detected and if so, to delete the signal.
ACKNoWLedgMeNts CL was supported by a 3-year Ph. D. fellowship by the  foundation "Agir Pour les Maladies Chroniques, " Grenoble, France. The authors thank  Yasmina Saoudi  and the  Photonic Imaging Center of Grenoble Institute of Neuroscience PIC-GIN  for the images acquisition and  Maxime Pierron  from  Microsyst. for Biol. & Health Department-CEA-LETI -for cells encapsulation. The authors also thank  Cindy Tellier  and  Pauline Durrenbach  for the animal care during this study.
Discussion The observability of a complex system refers to the property of being able to infer its whole state space by measuring the dynamics of a limited set of its variables. Determining the conditions that guarantee the full observability of a system involves testing a number of possibilities that increases exponentially with the dimension of that system and, for each case, it is required to compute the determinant of the observability matrix defining the singular observability manifold, that is, the subset of the state space that cannot be observed from the measurement  25. It was shown in one of our previous works 2 that for a five-dimensional rational system, the analytical computation of such a determinant may already exceed the capacity of softwares like Maple ® or Mathematica ®. Therefore, alternative approaches to investigate large complex systems are needed.
With the help of the third dimension, i. e. the wavelength information missing from NB surveys, we have direct spectroscopic confirmation and also the possibility to explore the presence of other emission lines (e. g. , [C iv] λλ 1548,1550 and [He ii] λ 1640 ). More importantly, the use of Integral Field Spectroscopy (IFS) provides the ability to build control samples with essentially the same instrumental and observational conditions, as well as data reduction and analysis techniques, with respect to the main dataset. One drawback will be the relatively small MUSE field of view (MUSE FoV 1 × 1 ) with respect to previous NB images by C12 (VLT-FORS FoV ∼ 7 × 7 ) in exploring the fluorescent volume around the QSO. Indeed, based on the C12 work, we expect to find only 1 or 2 DGs per MUSE field around each QSO.
Data Reduction and Post-Processing The reduction of all 65 hr MUSE data was performed using some of the standard recipes from the latest version of the ESO MUSE Data Reduction Software (DRS, pipeline version 1. 6, Weilbacher 2015), complemented with the CubExtractor package (CubEx hereafter, version 1. 6; Cantalupo, in prep. ) developed to optimally improve the flat-fielding correction and the sky subtraction steps for our specific science case. After retrieving the raw data for each night, we first created the master calibration files using the MUSE pipeline, i. e. the master-bias, the master-flat, the twilight and illumination correction, and wavelength calibration files. Using the DRS routine MUSE scibasic we then processed each individual science exposure, both standard stars and QSO fields, applying the master calibration correction with the recommended parameters.
SUMMARY AND CONCLUSIONS Making use of medium-deep (∼ 10 hr) MUSE IFU GTO observations around five bright QSO and one Type-II AGN, we have searched for fluorescently illuminated dark galaxies at z > 3. 2 among Lyα emitters in proximity of the quasars. Differently than previous surveys based on narrow-band imaging (e. g. , C12) and therefore restricted to a fixed volume, we have been able to build control samples at large distances from the quasars using the same data, the same data reduction and analysis techniques thanks to the new capability of the MUSE instrument. Within a volume of 90 physical Mpc 3 , including the control sample regions, we have identified ∼ 200 line emitters using the automatic source extraction software CubExtractor  (Cantalupo, in prep. ) complemented with visual analysis.