dataesr/scientific-paragraphs-categorization huggingface dataset:
It will support various programming models (including OpenCL, OpenMP, MPI, MaxJ/OpenSPL, StarPU) and enable the interoperability between them, to provide performance portability. Additionally, it will integrate several fault tolerance mechanisms, at different levels: User-exposed fault-tolerance mechanisms for accelerators will be accessible to application developers. The runtime system will provide both redundancy and rollback-recovery mechanisms. These mechanisms will be evaluated in the 4 EXA2PRO applications and trade-offs between fault-tolerance/performance/energy and source code quality will be investigated. EXA2PRO will contribute to the exploitation of emerging exascale systems by applications from various scientific areas. We expect large number applications to be deployed in (pre-)exascale systems and to utilize computational resources and heterogeneity more efficiently.
EXA2PRO aims at providing a set of tools to assist the deployment of applications from various scientific fields in exascale computing systems. For instance, the high-level software abstractions offered by EXA2PRO (skeletons and multi-variant components) that support multiple programming languages (C/C++ and FORTRAN), along with the tools for improving the maintainability of the source code, will enable applications that have been deployed in conventional HPC systems to exploit the computational power of the emerging exascale architectures. Additionally, the computational power offered by exascale systems may drive the evolution of applications and their enhancement with new features. Thus, EXA2PRO will significantly contribute to the productive development of new features in existing applications with reduced programmersâĂŹ effort, by minimizing the amount of time that developers allocate in issues that are unrelated to development of application algorithms.
The present work aims at contributing to the proper design of networks of measuring moorings with regard to the downwelling solar irradiance at surface (DSIS). Considering the yearly profiles of the monthly DSIS, this article studies whether the 17 moorings within the existing PIRATA network are well geographically distributed so that they capture the spatial variations of the yearly profiles in the tropical Atlantic Ocean. A clustering technique is performed onto the HelioClim-3v5 satellite-derived gridded dataset of DSIS spanning 12 years, thus yielding 17 zones of similar yearly profiles. It was found that the mean yearly profiles of several zones are similar to the measured ones at the three moorings having enough measurements to perform the comparison. Zones have clear connections with the climatology of the region and circulation regimes.
The question may be reformulated as: are the 17 moorings geographically distributed in such a way that they capture the various yearly profiles? To answer this question, 17 zones, or classes, are created by a clustering technique applied to a satellite-derived gridded dataset of DSIS spanning 12 years. Geographical points in a given zone exhibit similar variations of the monthly DSIS. Then, it will be checked that there is one or more PIRATA mooring in each zone. If yes, and assuming that each PIRATA mooring exhibits yearly cycles of the DSIS similar to those of the zone to which it belongs, it will be concluded that the configuration of the PIRATA network is appropriate to reflect the spatial variations of the monthly DSIS. The PIRATA and satellite-derived gridded datasets are presented in Sect.
The measurements can be downloaded as 2 min averages from the web site of the Global Tropical Moored Buoy Array (GTMBA) of the Pacific Marine Environmental Laboratory (PMEL) of the National Oceanic and Atmospheric Administration (NOAA) in the USA. Measurements undergo quality checking at NOAA and suspicious measurements are flagged. The number of measurements and their quality depend on the mooring and year. Many moorings exhibit missing data or suspicious measurements in the early years of the network and the situation tends to improve. The analysis of the data and their flags indicates that all moorings seem to work properly. As a consequence, it is assumed that each mooring exhibits the same overall quality as the others and has its place within the network. A subset of moorings has been selected in order to validate the results of the clustering technique and their measurements will be considered as reference for the comparison of yearly profiles.
These moorings offer high quality data spanning at least two years with a limited number of missing data. A first selection was made by using the quality flags of the NOAA. As the data must serve as reference and considering the different sources of uncertainty which the PIRATA measurements are subject to (incorrect sensor levelling, shading caused by close structures, accumulation of dust, dew, water-droplets, bird droppings, miscalibration of sensors, electronic failures, time shifts in data loggers, maintenance mishandling, buoy motions, etc. see e. g. Muneer and Fairooz, 2002; Foltz et al. , 2014) , an additional quality control was performed at MINES ParisTech. The quality control comprises several tests of the 2 min DSIS against extremely rare limits and physically possible limits  (Korany et al.
, 2016). Values falling outside the limits were excluded from the time-series. Eventually, a visual analysis was performed to remove suspicious values. Only measurements that passed all tests successfully were kept. The hourly mean of DSIS was computed by averaging the 30 measurements within this hour if all measurements were declared valid. Otherwise, the hourly mean was declared invalid. Following the guidelines of the World Radiation Data Center (http://wrdc. mgo. rssi. ru/, last access: 1 July 2018), daily means were then computed if all hourly values were valid, and the monthly means were computed if more than 25 days were valid within the month. The yearly mean is then computed as the mean of the 12 valid monthly means as recommended by the World Meteorological Organization (http://www.
The HelioClim-3 v5 dataset (HC3v5) remapped Meteorological re-analyses and images acquired by satellites observing the ocean surface provide means of getting a synoptic view of the DSIS. Boilley and Wald (2015)  and  Trolliet et al. (2018)  compared the DSIS output of ERA-Interim, ERA5, MERRA, and MERRA-2 re-analyses to PI-RATA measurements. They observed that the re-analyses often report cloud-free conditions while actual conditions are cloudy, yielding an underestimation of surface irradiance by these data sets, and reciprocally, cloudless conditions as cloudy, yielding an overestimation. The standard deviation of the errors was large as a whole and the biases varied considerably in time and space. Such changes in bias denote a spatial distortion of the DSIS field and will induce errors in the description of the variability.
The authors do not recommend the use of such re-analysis datasets but instead suggest exploiting satellite-derived datasets of DSIS. Three of these, the HelioClim-3 version 5 (HC3v5), Surface Radiation Dataset -Heliosat (SARAH-2) and Copernicus Atmosphere Monitoring Service (CAMS) Radiation Service v2 datasets, have been compared to the PIRATA measurements in  Trolliet et al. (2018). The authors concluded that the quality of the three satellite-derived datasets is fairly similar. They also concluded that each of these is appropriate for studying the variability of the downward solar irradiance at the surface of the tropical Atlantic Ocean. For the sake of the simplicity, the HC3v5 database has been selected for this study. Images of the Meteosat Second Generation satellites are routinely processed by MINES ParisTech and a companion company Transvalor to yield the HC3v5 dataset.
HC3v5 covers Europe, Africa, the Middle East, parts of South America and the Atlantic Ocean. It is available from 2004 up to the present with a 15 min time step. The spatial resolution depends on the pixel position and is approximately 3-4 km in the tropical Atlantic Ocean. The processing method is the Heliosat-2 method  (Rigollier et al. , 2004; Lefèvre et al. , 2007) , modified by  Qu et al. (2014). The method itself, as well as the HC3v5 dataset, have been the subject of several comparisons against measurements of DSIS made at land-based sites, see e. g. Bengulescu et al. (2017) ,  Eissa et al. (2015) ,  Marchand et al. (2017 Marchand et al. ( , 2018)) , or  Thomas et al. (2016a, b). The DSIS from HC3-v5 has been downloaded from the SoDa Service (http://www. soda-pro. com/, last access: 1 July 2018)  (Gschwind et al.
, 2006). This web service performs the integration over time and the DSIS was downloaded as time-series of daily means from 2005 to 2016. The data have been geographically projected onto a regular grid in latitude and longitude with a spacing of 0. 25 •. The monthly means of the DSIS were constructed for each calendar month and each cell over the 12 years using the rules of the World Radiation Data Center discussed above. The standard deviation of daily means within each calendar month was also computed.
Zoning by a k-means classifier The spatial and temporal variations of the DSIS on the one hand and of KT on the other hand, have been summarized using spatial zoning. A cluster analysis was applied to input state vectors made of the 12 monthly means and 12 monthly standard deviations of the HC3v5 dataset to create clusters or classes. Two cells belong to the same class if they exhibit the same yearly profile of both the monthly means and the standard deviations. The clustering aims at forming classes such that cells of a given class are as similar as possible, while they are as different as possible from cells of other classes. A hierarchical cluster tree was created by applying the algorithm of  Ward (1963) , where the similarity between two cells is given by the Euclidean distance computed on the two corresponding state vectors.
Discussion The selected methodology may apply to other oceanic basins provided archives of gridded DSIS data are available. The selected methodology may apply to other variables than the DSIS, such as sea surface temperature which is routinely monitored by satellites. There are a number of arbitrary choices, e. g. clustering technique or input state vectors that merit a deeper discussion beyond the scope of this work which is a preliminary step in a climatic description of the changes in space and time of the irradiance in the tropical Atlantic Ocean. Zoning may help in designing or evolving a network. The recent advances in the assessment of DSIS from satellite data  may help in revising and completing the first studies, done several years ago, in the context of the deployment of the PIRATA network.
Fi-nally, this zoning highlights the high quality of the PIRATA network regarding the synoptic scales of the DSIS. Data availability. PIRATA measurements performed every 2 min were downloaded from the web site (www. pmel. noaa. gov/tao/ drupal/disdel/, last access: 9 July 2018) of the National Oceanic and Atmospheric Administration (NOAA) of the USA. The authors acknowledge the help of the GTMBA Project Office of NOAA/PMEL in getting the data and the PIRATA team for servicing the network and freely providing the data. Time series of HelioClim-3v5 data were downloaded from the SoDa Service web site (http://www. soda-pro. com/, last access: 9 July 2018) managed by the company Transvalor. Data are available to anyone for free for years 2004-2006 as a GEOSS Data-CORE (GEOSS Data Collection of Open Resources for Everyone) and for a charge for the most recent years with the amount depending on requests and requester.
The time series used in this article corresponds to these data remapped. The dataset is available for free in CSV format by request to Mélodie Trolliet. A similar dataset with a DOI will be available soon, in the context of another study (article to be submitted in the forthcoming weeks). Author contributions. All authors contributed equally to this work. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "17th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2017". It is a result of the EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2017, Dublin, Ireland, 4-8 September 2017. Acknowledgements. The authors thank the anonymous reviewers and the Editor for the valuable and encouraging comments greatly help in improving the readability of this article.
They acknowledge the key role of the PIRATA team in servicing the network and freely providing the data. The authors state that the PIRATA network is a unique and valuable means of studying and monitoring the surface irradiance in the tropical Atlantic Ocean and hope it will receive support for operations to further enrich the datasets. The authors acknowledge the help of the GTMBA Project Office of NOAA/PMEL in acquiring the data. Time-series of HelioClim-3v5 data were downloaded from the SoDa Service web site (http://www. soda-pro. com/, last access: 9 July 2018) managed by Transvalor. The authors thank the ISCCP team for their web site (https://isccp. giss. nasa. gov/, last access: 9 July 2018). Edited by: Rasmus Benestad Reviewed by: two anonymous referees.
This paper presents a simple framework for characterizing morphological complexity and how it encodes syntactic information. In particular, we propose a new measure of morphosyntactic complexity in terms of governordependent preferential attachment that explains parsing performance. Through experiments on dependency parsing with data from Universal Dependencies (UD), we show that representations derived from morphological attributes deliver important parsing performance improvements over standard word form embeddings when trained on the same datasets. We also show that the new morphosyntactic complexity measure is predictive of the gains provided by using morphological attributes over plain forms on parsing scores, making it a tool to distinguish languages using morphology as a syntactic marker from others.
Results For clarity, we focus on comparing results using form embeddings and gold morphological representations. They are given in Table  2. Because the analysis carries to the labeled case, we stick to unlabeled scores (UAS) for the analysis. A more complete table is provided in the appendix as well as a complete labeled accuracy score (LAS) table. Morphological complexity measures are also reported. One-hot gold morphological attributes consistently outerperform form embeddings. This is expected since forms embedding were trained on much fewer data than usually considered necessary. However, improvements are not consistent across languages, ranging from 1. 14 point for English to 15. 20 points for Finnish. While those differences are not explained by morphological productivity alone (Figure  1a ), a measure of preferential attachment gives a good account of them (Figure  1b ).
D. Interest Model for Auditory Goals The interest model for auditory goals endows the learner the ability to select goals that maximize the expected competence progress in order to improve the quality of its sensorimotor model, resulting in better control over it. It is derived from the model proposed in  [9]. The competence value for a goal is defined by c = e -|s g -s|  (7)  where s g is the auditory goal and s is the actual auditory production after executing a motor command m ∼ P(M | s g ). To construct the interest model, the auditory goal space is augmented with two extra dimensions: 1) the competence c ∈ C and 2) time tag t ∈ T. The number of vocalizations N IM considered to build the interest model is fixed. A GMM, G IM , with K IM components will be computed from the 8-D data set with N IM samples of the augmented goal space.
382 We assume that an n-dimensional input command space 383 X ∈ R n is mapped to an m-dimensional output space Y ∈ R m , 384 through a transform function y = f (x) + ε, where y ∈ Y, 385 x ∈ X and ε is random noise. When a dataset of couples 386 (x, y) is available, the EM-algorithm is used to obtain a GMM 387 which is defined by the parameters {π j , μ j , j } K j=1 , where π j , 388 μ j , and j are, respectively, the prior probability, the distribu-389 tion centroid and the covariance matrix of the jth Gaussian, for 390 j = 1, 2,. , K, being K the number of Gaussian components.
Despite recent achievements in handwritten text recognition due to major advances in deep neural networks, historical handwritten documents analysis is still a challenging problem because of the requirement of large annotated training database. In this context, knowledge transfer of neural networks pre-trained on already available labeled data could allow us to process new collections of documents. In this study, we focus on localization of structures at the word-level, distinguishing words from numbers, in unlabeled handwritten documents. We based our approach on a transductive transfer learning paradigm using a deep convolutional neural network pre-trained on artificial labeled images randomly generated with strokes, word and number patches. We designed our model to predict a mask of the structures positions at the pixel-level, directly from the pixel values.
The model has been trained using 100,000 generated images. The classification performances of our model were assessed by using randomly generated images coming from a different set of images of words and digits. At the pixel level, the averaged accuracy of the proposed structures detection system reach 96. 1%. We evaluated the transfer capability of our model on two datasets of real handwritten documents unseen during the training. Results show that our model is able to distinguish most "digits" structures from "word" structures while avoiding other various structures present in the documents, showing the good transferability of the system to real documents.
INTRODUCTION This work takes part of the CIRESFI project 1. The CIRESFI project aims to improve our knowledge about the Italian theater during the 18th century in France. The researchers in human and social sciences intend to reveal the cultural adaptation for the Italian actors in France. Whereas the political situation was against them, they succeeded in establishing themselves as an official and reputed institution. So, this projects aims at providing efficient tools to access to different kinds of information which are included in a set of financial records of the Italian comedy dating from the XVIII century with more 28 000 pages  (Luca, 2011)   (Cethefi, 2016). Information retrieval within a large collection of handwritten historical documents is a very complicated task, specifically when no ground truth training dataset is available.
As a first functionality, we focus on the detec-1 Project ANR-14-CE31-0017 "Contrainte et Intégration : pour une Réévaluation des Spectacles Forains et italiens sous l'Ancien Régime" tion of the structure of these financial documents. In this paper, we are concentrating on localization of all the number areas, distinguished from word or strokes structures, which are one the main constituents of these financial records of the Italian Comedy  (Cethefi, 2016). Later works could extract meaningful area and columns based on these typed localizations. Neural network-based deep learning have recently achieved great advances in the pattern recognition area including classification, regression and clustering  (LeCun et al. , 2015)   (Schmidhuber, 2015). In text recognition, recent works have shown that deep neural network (DNN) models can tackle the automatic detection of specific objects in handwritten documents  (Moysset et al.
, 2016)   (Butt et al. , 2016). However, since DNN models based their learning on the data, most of the applications need large amount of labeled data to train the models. In text recognition of handwritten document, training data and test data are generally taken from the same corpus of documents, respecting the same text arrangement, same handwriting style and/or similar paper type. Unfortunately, ground-truth in handwritten documents are rarely labeled and manual annotations are very time expensive. When the labels of the target data are not available, a transductive transfer learning strategy  (Pan and Yang, 2010)  can be considered to learn the detection task by training a model with annotated data from another database. Of course, the training dataset should present enough structural variability and representativeness with respect to the target dataset to both learn the detection task and ensure its transferability.
Thus, the challenge here is to learn a generic detection task without learning the specific characteristics and patterns present in the training dataset. Once trained on this train dataset, the system should be able to retrieve in the target dataset the same kind of patterns. In this paper, we propose to bridge the gap between a non-labeled dataset and a labeled dataset by generating randomly artificial images containing known patterns in known locations but with variable layouts and backgrounds. The strategy is based on a random placement of labeled patches of word and number structures on a background image from the target database. Clearly, the main advantage of such strategy is that it allows to generate as many images as necessary, can be adjusted to the target task, and ensure structural variability  (Delalandre et al.
, 2010). Note that this generation strategy is quite close to  (Kieu et al. , 2013)  where authors create full pages of realistic documents. Furthermore, we show that a fully convolutional network can be trained to solve a digit/word detection task. The paper is structured as follows: Section 2 introduces the notations, definitions and objectives of this work; Section 3 presents the images databases used for the generation of artificial documents and the real handwritten documents for which we want to detect number/word structures; Section 4 presents the architecture of the neural network trained for our pixel-wise digit spotting problem; Section 5 presents how we evaluated the classification performance of our model; Section 6 presents the results obtained with our model classification considering the artificial generated images and a qualitative evaluation on real handwritten mails and historical documents that were not used for training.
Real images Three databases were considered in this study: the IReste Online Offline database (IROnOff)  (Viard-Gaudin et al. , 1999) , the handwritten mail of the RIMES database  (Augustin et al. , 2006)  and the unlabeled registers accounts of Italian Comedy (RECITAL)  (Cethefi, 2016). RECITAL images were scanned by the BNF (National French Library) 2 at 400dpi. We used the IROnOff database to construct X l and S l to learn the transformation T l. The RECITAL database corresponds to the set of images X u for which we want to process automatic structuresspotting. The IROnOff database contains a total of 61,291 images (300dpi) of isolated handwritten characters, digits, words, with their corresponding transcriptions. We randomly separated the IROnOff database in two groups; the training set D train corresponding to 67% of the total dataset (40,861 images) and the test set D test corresponding to 33% of the dataset (20,432 images).
To evaluate the model during the learning, we picked 20% of D train as the validation set D valid  (8,172 images). Besides, we used the set of patches from the RIMES database to quantitatively evaluate the retrieval capability of our classifier. The RIMES database is a set of 1,500 images of labeled paragraphs from handwritten mails  (Grosicki and El-Abed, 2011 ) with a total of 66,979 patches of words and numbers present in the documents. In RIMES, patches with numbers correspond to isolated numbers and identification strings using digits (as license plates). A total of 918 patches contains digits.