dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, single-patch simulations were run for each of the 32 departments (plus the capital district of Bogotá) individually. Finally, multi-patch simulations were run for each department (except for Bogotá, which consists of a single municipality) with separate patches for each municipality (second administrative unit in Colombia). Within a patch, various aspects of the mosquito population and the extrinsic incubation period of the virus within the mosquito are affected by local climate variables. Parameter values used in all simulations are described in Table  1. Gridded daily temperature, precipitation, and relative humidity from 2013 to 2016 were initially modeled at a 5 km × 5 km resolution  [56]. The mean climate values at the country, department, and municipality scales were calculated by taking population-weighted averages of the daily values from the gridded data sets.
Epidemiological data and model fitting We obtained a time series of weekly suspected cases for each municipality in Colombia from the start of the epidemic through the end of the third week of 2016 from the national system of surveillance for public health of Colombia (SIVIGILA). A suspected case was defined as a person having an acute onset of fever (> 38 °C) and severe arthralgia or arthritis not explained by other medical conditions and being a resident or having visited epidemic or endemic areas within 2 weeks prior to the onset of clinical symptoms. In the 2014-2015 period, a laboratory-confirmed case was defined as a suspected case with positive reverse transcription polymerase chain reaction (RT-PCR), and in 2016 confirmed cases included RT-PCR or positive serology. These time series were used to estimate several model parameters separately at each spatial scale.
These movement rates were represented in the model as the fraction of individuals in patch i who travel on a given day to patch j. Movement events are assumed to last for 1 day, with a 100% probability that the individual will return to their home patch. Fitting of the transmission model was conducted by maximum likelihood using a gradient ascent iterative optimization algorithm called OptimTool that has been built into the EMOD-DTK software framework. The initial parameter values were drawn from the hypersphere of the specified parameter ranges, centered around an initial best guess for that parameter value with a mean search radius determined by the number of parameters and the standard deviation of the radius set at 1/10 of the mean. One hundred draws from this parameter space were conducted for each iteration of the model-fitting process.
Acknowledgments Not applicable. University of Notre Dame, Notre Dame, IN, USA. 2 Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, 75015 Paris, France. 3 CNRS UMR2000: Génomique évolutive, modélisation et santé (GEMS),  Institut Pasteur, Paris, France. 4  Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France. 5  Subdirección de Análisis de Riesgo y Respuesta Inmediata en Salud Pública ,  Instituto Nacional de Salud de Colombia, Bogotá, Colombia. 6Grupo de  Enfermedades Transmisibles ,  Instituto Nacional de Salud de Colombia, Bogotá, Colombia. 7Institute for Disease Modeling, Bellevue, WA, USA.
Weights computation for multi-atlas segmentation Multi-atlas methods are composed of three main steps: (1) registration of the learning dataset E on the input image I, (2) segmentation propagation; and (3) segmentation fusion. In the sequel, we focus on the fusion step (3). In particular, by assuming that segmentation fusion is performed based on Equation (  6 ) (with a normalization coefficient used for making the sum of all weights equal to 1, in each x), the discussion mainly deals with the way of computing the weights w i (x, y).
Optimization-based approach for weights computation Multi-atlas segmentation methods (  [28, 29, 30] ) implicitly rely on the isometry assumption, i. e. the latent representation (provided by the set of weights) of the current voxel x with respect to its neighbours in the learning dataset E is the same in the intensity space and in the segmentation space. Thanks to this assumption, the segmentation S of I at x is defined as a (normalized) linear combination of the segmentations, the weights being estimated from the intensity images (Eq. 4, 6). Bearing this assumption in mind, the computation of weights w i (x, y) can also be seen as an optimization problem expressed in the image space. Indeed, one can set the energy function: φ I (x) = P I (x) - n i=1 y∈N (x) w i (x, y)P I i (y) (13) that defines the distance between the patch P I (x) and the linear combination of the patches P I i (y) in I i for all the y of N (x).
Data The considered images are T2-weighted MRI data, made available  [36]  in the context of the Developing Human Connectome Project (dHCP) 1   [37]. Infants were recruited and imaged at the Evelina Neonatal Imaging Centre, London. Informed parental consent was obtained for imaging and data release, and the study was approved by the UK Health Research Authority. All infants were born and imaged at term age (37-44 weeks of age). Imaging was carried out on 3T Philips Achieva using a dedicated neonatal imaging system which included a neonatal 32 channel phased array head coil. The infants were imaged in natural sleep. T2w images were acquired in sagittal and axial slice stacks with in-plane resolution 0. 8 × 0. 8 mm 2 and 1. 6 mm slices overlapped by 0. 8 mm. Other parameters were: 12000/156ms TR/TE, SENSE factor 2.
11 (axial) and 2. 58 (sagittal). In this work, a set of 40 images with a voxel size of 0. 5 × 0. 5 × 0. 5 mm 3 is considered. Among these data, 30 are used for building the learning dataset E, while the other 10 are used as testing dataset. For each of these 40 images, the algorithmic pipeline dHCP 2 has been applied. The obtained segmentations, visually validated, are used as reference.
The IMAPA and NLM methods generate results defined as fuzzy maps. In order to compare these results with those of JLF and MANTIS (that generate binary maps), a thresholding of the fuzzy maps is set at value 0. 5. IMAPA (0) stands for the non-iterative IMAPA method, i. e. only the intensity patches are used for the weight computation (α = 0). A more qualitative assessment of the results obtained by the four methods is also available via illustrative samples of segmentations, visualized on 2D slices, in Figures  2 3 (axial slices), and as 3D visualizations of meshed obtained from binary volumes (sagittal view) in Figure  4  and using meshing tools from BrainVISA software 4   [40].
Discussion From a quantitative point of view (Table  1 ), the results obtained with the proposed IMAPA method are better than those obtained with the other three tested methods, for both Dice and PSNR measures. This improvement is significant compared to the JLF and MANTIS methods; it is lower compared to the NLM method (approximately 0. 01 gap for Dice and 0. 3 dB for PSNR). These results on the dHCP dataset tend to show that the proposed strategy of coupling patch-based and iterative optimization is indeed of interest, as the results are at the level of the state of the art. In addition to this algorithmic approach, the simultaneous use of a data-fidelity term and a regularization term from both the example images and their segmentations in same patches, also seems relevant. Indeed, compared to IMAPA, the NLM weight computation method, although being also patch-based, is neither iterative nor image/segmentation mixing.
Iterative Multi-Atlas Patch-Based approach Require: I: input image ; E = {E i = (I i , S i ), i = 1. n}: learning dataset, N: number of iterations, {α 0 = 0, α 1 ,. , α N -1 }: intensity vs segmentation trade-off ; S (0) : arbitrarily chosen initial segmentation Ensure: S: segmentation of I 1: for j = 1. N do 2:.
Acknowledgements The research leading to these results has been supported by the  ANR   MAIA  project, grant  ANR-15-CE23-0009  of the  French National Research Agency  (project  MAIA , "Multiphysics image-based analysis for premature brain development understanding", http://recherche. imt-atlantique. fr/maia), INSERM  and  Institut Mines Télécom Atlantique  (Chaire " Imagerie médicale en thérapie interventionnelle "),  Fondation pour la Recherche Médicale  ( FRM grant   DIC20161236453 ) and the  American Memorial Hospital Foundation.
Algorithm 1 1 Iterative Multi-Atlas Patch-Based approach Require: I: input image ; E = {E i = (I i , S i ), i = 1. n}: learning dataset, N: number of iterations, {α 0 = 0, α 1 ,. , α N -1 }: intensity vs segmentation trade-off ; S (0) : arbitrarily chosen initial segmentation Ensure: S: segmentation of I 1: for j = 1. N do 2:.
Parcel collisions are modeled by an isotropic solid stress function, depending on solid volume fraction. In this paper, the bubbling fluidized bed (BFB) upstream gasifier of the EU research infrastructure ZECOMIX (Zero Emissions of Carbon with Mixed technologies) has been simulated using a CPFD approach via Barracuda ® software. The effect of different fluidizing agent injection strategies on bed bubbling and mixing, for non-reacting cases, has been studied. The numerical results for a reacting case have been compared to the available experimental data, gathered during the coal gasification campaign. The model has proved to be very useful in the choice of the more efficient injection configuration that assures a more effective contact of the gas with the solid bed and a good bubbling fluidization regime, together with a satisfactory prediction of the outlet gas composition.
Liu et al. (2015)  studied a 3D full-loop fluidized bed biomass gasifier using the CPFD approach. The numerical results in terms of temperature profiles and output gas composition matched very well the experimental measurements. Karimipour et al. (2012)  investigated the capability of the CPFD method to simulate bubbling fluidized bed, verifying that the model was able to better capture bubbles properties when compared to the results obtained by means of Eulerian-Eulerian approach. The aim of the work is to study the upstream gasifier of the ZECOMIX plant using Barracuda ® software, which is based on the CPFD approach. The study is divided into two parts. In the first section, the fluidization of the non-reacting olivinecoal particles bed is analyzed for different gas mass flow rate and different gas injection configurations.
e. a cell must be large enough to contain a sufficient number of particles. Then several simulations have been carried out, with the aim to calculate a correct injection velocity for each mass flow rate used for the CPFD simulations. These series of simulations have been performed by Ansys-Fluent ® software, using a tetrahedral computational grid. As it is clearly reported by Fig. 3 , the flow exiting from the injector is deviated and guided by the cover elements toward the particles bed. The insertion angle is determined by the elements covering the injection gas, while the velocity magnitude has been calculated averaging the velocity distribution on the effective cross section surface. In the non-reacting fluidization simulations the initial bed is composed of 200 kg of olivine and 70 kg of coal, with size distribution reported in Fig.
CONCLUSIONS In this paper, the coal oxygen-blown gasifier of the ZECOMIX research infrastructure has been studied and simulated by means of the CPFD approach. The proposed method has resulted as a powerful and efficient approach for fluidized bed simulation where a huge number of particles has to be taken into account. In the non-reacting fluidization cases, the different injectors configuration have been evaluated with respect to bubbling and mixing effects. In the reacting cases, the evaluation of the syngas composition has resulted in a good agreement with the available experimental data collected in the ZECOMIX research infrastructure. The research infrastructure is composed of other types of fluid-bed reactors, such as the calciner and the carbonator, which can therefore enjoy the same computational advantages offered by the numerical approach used in this work, and which are currently being studied.
ACKNOWLEDGEMENTS This work has been funded and supported by the  Italian Ministry of Economic Development. The computing resources and the related technical support used for this work have been provided by  CRESCO/ENEAGRID High-Performance Computing infrastructure  and its staff (Ponti et al. 2014). CRESCO/ENEAGRID High-Performance Computing infrastructure  is funded by  ENEA , the  Italian National Agency for New Technologies, Energy and Sustainable Economic Development  and by  Italian  and European research programs, see http://www. cresco. enea. it/english for information.
Introduction The interest aroused by word embeddings in Natural Language Processing, especially for neural models, has led to propose methods for creating them from texts  (Mikolov et al. , 2013; Pennington et al. , 2014)  but also for specializing them according to a particular viewpoint. This viewpoint generally comes in the form of set of lexical relations. For instance,  Kiela et al. (2015)  specialize word embeddings towards semantic similarity or relatedness by relying either on synonyms or free lexical associations. Methods such as Retrofitting  (Faruqui et al. , 2015) , Counterfitting  (Mrkšić et al. , 2016)  or PARAGRAM  (Wieting et al. , 2015)  fall within the same framework. The specialization of word embeddings can also come from the way they are built. For instance,  Levy and Goldberg (2014)  bring word embeddings towards similarity rather than relatedness by using dependency-based distributional contexts rather than linear bag-of-word contexts.
Evaluation of Pseudofit Our first evaluation of Pseudofit at word level is a classical intrinsic evaluation consisting in measuring for a set of word pairs the Spearman's rank correlation between human judgments and the similarity of these words computed from their embeddings by the Cosine measure. This evaluation is performed for the nouns of three large enough reference datasets: SimLex-999  (Hill et al. , 2015) , method R prec. MAP P@1 P@2 P@5 INITIAL 13. 0 15. 2 18. 3 13. 1 7. 7 Pseudofit +2. 5 +3. 3 +3. 0 +2. 5 +1. 8 Retrofitting -0. 5 -0. 6 -0. 6 -0. 2 † -0. 3 Counter-fitting -0. 6 -0. 8 -0. 6 -0. 5 -0. 4 Table 2: Evaluation of Pseudofit for synonym extraction (differences / INITIAL, ×100) MEN  (Bruni et al. , 2014)  and  MTurk-771 (Halawi et al. , 2012). Table  1  clearly shows that Pseudofit significantly 2  improves the initial embeddings for the three datasets.
Sentence Similarity Our final evaluation, which is fully extrinsic, examines the impact of Pseudofit on the identification of semantic similarity between sentences. More precisely, we adopt the STS Benchmark dataset on semantic textual similarity  (Cer et al. , 2017). The overall principle of this task is similar to the word similarity task of our first evaluation but at the level of sentences: the similarity of a set of sentence pairs is computed by the system to evaluate and compared with a correlation measure, the Pearson correlation coefficient, against a gold standard produced by human annotators. This framework is interesting for the evaluation of Pseudofit because the computation of the similarity of a pair of sentences can be achieved by unsupervised approaches based on word embeddings in a very competitive way, as demonstrated by  (Hill et al.
, 2016). More precisely, the approach we adopt is a classical baseline that composes the embeddings of the plain words of each sentence to compare by elementwise addition and computes the Cosine measure between the two resulting vectors. For building the representation of a sentence, we compare the use of our initial embeddings with that of the embeddings produced by Pseudofit max+fus-max-pooling, the best variant of Pseudofit. For this experiment, pseudo-senses are distinguished not only for nouns but more generally for all nouns, verbs and adjectives with more than 21 occurrences in the corpus. Table  5  shows the result of this evaluation for the 1,379 sentence pairs of the test part of the STS Benchmark dataset. As for the two previous evaluations, the use of the embeddings modified by Pseudofit leads to a significant improvement of results 6  compared to the initial embeddings, which demonstrates that the improvement at word level can be transposed at a larger scale.
NONLOCAL DAMAGE FORMULATION In this paper, the RICRAG model developed by  Richard et al. (2010)  is one we used during the implementation of the present error indicator. Known for its efficiency and robustness, this model is already implanted in the finite element code Cast3m (2018) developed by the CEA in France and used in our simulations. This section consists in a brief presentation of its basic principles and essential formulation. The exhaustive presentation of this model is beyond the scope of this paper and can be found in  Richard et al. (2010). Formulated within the framework of the thermodynamics for irreversible processes a state potential was introduced such as it takes into account for elasticity, damage, sliding between cracked surfaces and hardenings as follows: 𝜌𝛹 = (1 -𝑑)〈𝜀 〉 -〈-𝜀 〉 + 2(1 -𝑑)𝜇𝜀 𝜀 + 2𝑑𝜇 𝜀 -𝜀 𝜀 -𝜀 + 𝛾𝛼 𝛼 + 𝜢(𝑧) (1) By coupling damage with elasticity, the first and second terms (into accolades) of the equation (1) above introduce a single scalar damage variable d in order to represent the stiffness reduction.
These two locals are described as function smoothing and discrete smoothing. For the first, it is assumed that a smoothed function is least squares fit to its corresponding unsmoothed function. With the second local discrete smoothing method, it is assumed that  , ( ,  ) is a least squares fit to selected values of  ( ,  ) at the Gaussian integration points s in which the stresses are approximated with high accuracy. Although it is an oldest technique, this method seems not only giving good results in terms of accuracy but also it especially offers a less expensive CPU time of calculations. This remark is demonstrated by the formulation below and confirmed by  Delmas (2011)  in the framework of tests carried out with the ASTER finite element code developed by EDF in France. These are the reasons that motivated our choice for the implementation of this discrete smoothing local technique with the present error indicator.
The applied loads F1= 10F/11 and F2= F/11 are illustrated in this figure 3 such as F increases incrementally from (0) zero to (138F) and F= 0. 7 KN. This convergence study will be essentially performed with the h-refinement method, which consists in uniform and successive subdivisions meshes as shown in figure  4. For this purpose the QUA4 four nodes quadrilateral element type will be used. Latin American Journal of  Solids  and Structures, 2018, 15(9), e104  9/25 Moreover it should be noted that the implementation of this error indicator is achieved using the Cast3m finite element code developed by the CEA in France. Indeed a plane stress mode analysis is performed under the assumptions of small deformations with quasi-static incremental loading. As mentioned in the introduction, the tests are carried out with the use of the RICRAG nonlocal damage model  (Richard and al, 2010)  and by adopting the following material parameters of table 1.
This card size is prescribed to the mesher used by the finite element code Cast3m. An optimized mesh made of 5958 four nodes elements and 5858 nodes (figure  24 ) is provided. The developed indicator provides also a global accuracy å = 4. 86% with a relative stress error åó = 3. 39% and a relative damage error of åd 3. 48%. The elements contributions to global, stress and damage errors of this adapted mesh can respectively be seen on figures 25, 26 and 27. The second example is also illustrated with its boundary conditions and applied loads in figure  28. The same material properties as showed in table 1 are adopted. For this test, the characteristic length  is taken equal to 10mm. The initial mesh is made of 842 elements (figure  29 ). The smoothed (reference solution) and non smoothed (finite element solution) damage field distributions are also represented in figure  30.
pisi isolate Up-Co01 (2 mg spores pot -1 ) diluted in pure talc (1:10, v:v) using a spore settling tower. The plants were incubated for 24 h at 20 • C in complete darkness and at 100% relative humidity. Then they were transferred to a growth chamber at 20 • C with a photoperiod of 14 h of light and 10 h of darkness and 148 µmol m -2 s -1 of irradiance at plant canopy. DS and IT values were recorded 10 days after the inoculation as above mentioned. DS values measured in both field and controlled conditions were normalized by the arcsine transformation [y = arcsine ( √ DS)]. Pearson's linear correlations between rust resistance parameters were performed using Statistix (version 8. 0; Analytical Software, Tallahassee, USA).
The sequencing (single read) was run for 77 cycles. Sequences generated from each lane were processed using proprietary DArT analytical pipelines. In the primary pipeline, the FASTQ files were first processed to filter poorquality sequences, applying more stringent selection criteria to the barcode region compared to the rest of the sequence. Thus, the assignments of the sequences to specific samples carried in the "barcode split" step were more consistent. Approximately 2,500,000 (±7%) sequences per barcode/sample were used in marker calling. Finally, identical sequences were collapsed into "fastqcall files. " These files were used in the secondary pipeline for DArT P/L's proprietary SNP and SilicoDArT (Presence/Absence Markers in genomic representations) (present = 1 vs. absent = 0) calling algorithms (DArTsoft14).
The data were collected automatically by the detection of the different fluorescences and analyzed using GeneMapper v4. 0 software (Applied Biosystems). In addition 10 STS markers reported by  Gilpin et al. (1997)  were surveyed for polymorphism using the protocol described by  Barilli et al. (2010). When no polymorphism was detected, PCR products from both parents were digested with a range of restriction endonucleases which recognized 4-and 5-base sequences (BioLabs inc , Ipswich, MA, USA). A 0. 2 µl aliquot of restriction enzyme, 12 µl of sterile water, and 2. 5 µl of the buffer required for each enzyme were added to 10 µl of the PCR reaction, and the digestion was incubated overnight at 37 • C. View of PCR products and gel images analyses were performed as described by  Barilli et al. (2010).
Linkage Mapping and QTL Mapping The scores of all polymorphic DArTseq markers, SSR, STS, and SNP markers were converted into genotype codes ("A, " "B") according to the scores of the parents. Linkage groups were obtained using the software JoinMap version 4. 1  (Van Ooijen, 2006). The maximum likelihood mapping algorithm, which was optimized for constructing dense genetic maps using this software  (Jansen et al. , 2001) , was first used for grouping all of the polymorphic markers. Then, the method of regression mapping  (Haley and Knott, 1992)  was used for map construction with approximately 1,000 markers with appropriate genetic distance and the marker position and the order of markers for three rounds to merge the tightly adjacent markers into bins. The markers in adjacent loci with genetic distance below 0.