dataesr/scientific-paragraphs-categorization huggingface dataset:
Hybrid system based on SMT For the second approach, we combined Statistical Machine Translation (SMT) system with morphological output of MADAMIRA  (Pasha 2014 ) and some automatic rules to correct the text. We build three different SMT systems based on the Moses toolkit  (Koehn 2007)  with different input for training the phrase-based translation models. For the first system (Tech-1), we used the output of MADAMIRA morphological analyzer and the corrected texts to train a MADAMIRA/correct translation model. We used the text from the Aljtrain-2014 data and apply corrections to build a parallel MADAMIRA/correct text corpus of 20,428 sentences and we train a phrase based translation model. The Alj-dev-2014 data is used for Moses to tune the translation models. The second system (Tech-2) is the same as the previous one, but we added Alj-dev-2014 in the training data and used Alj-Test-2014 as development data for tuning the translation models.
The third system (Tech-3) uses the original erroneous text instead of the MADAMIRA output to build a parallel error/correct text corpus and we train a phrase based model. As for Tech-1, the Aljdev-2014 data is used for Moses to tune the translation models. For the word alignment, we used GIZA++  (Och 2003). For the language model, we used corpora of newspapers publicly available and collected by Techlimed. The sources are coming from the Open Source Arabic Corpora  (Saad 2010 ) (20M words), the Adjir corpus  (Abdelali 2005 ) (147M words) and other corpora we collected from various online newspapers for a total of 300M words. The language model was created with the IRSTLM toolkit  (Federico, 2008). Table  3  System component description For each system, we then applied the following rules: • Convert eastern Arabic digits (۰۱۲۳٤٥٦۷۸۹) into western Arabic digits (0 1 2 3 4 5 6 7 8 9).
Conclusion This paper has reported on the participation of Techlimed in the 2015 QALB Shared Task on Automatic Arabic Error Correction. We developed two approaches, one based on Hunspell and the other based on a hybrid SMT system. The best results were obtained with the hybrid SMT system which was able to deal with the punctuation mark corrections. We also tested a hybrid system by combining Hunspell and the SMT system but did not get better results than the SMT system. Our perspective is to include the Di-iNAR lexical database  (Abbès 2004 ) and also a large dialectal corpus to improve the results.
Given this setting each model derives regional carbon prices that are consistent with the emission targets. To assess the carbon leakage effect, the modeling teams applied carbon prices instead of the quantity targets 14 for the two scenarios with additional policy ambitions. These scenarios assume specific measures by the EU and China to achieve additional emission reductions: 1. EU-Road Map: The EU adopts the climate policy Road-Map until 2030, whereas other regions adopt the pledges. The EU-Road-Map requires stronger emission reductions than 13 In this section we use the scenarios computed within work-package 3 of the AMPERE project. Here, the GDP assumptions were harmonized to meet similar economic development levels. Hence, GHG intensity improvements lead to similar-though not equal-emission limitation trajectories.
Acknowledgments The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  [FP7/2007-2013] under grant agreement n°[ 265139]. Funding from the  German Federal Ministry of Education and Research (BMBF) in the Call "Economics of Climate Change " (funding code  01LA11020B ,  Green Paradox ) is gratefully acknowledged by  Nico Bauer. Funding from  Office of Science of the U. S. Department of Energy , as part of the  Integrated Assessment Research Program , is gratefully acknowledged by  Katherine Calvin. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission or the  U. S. Government.
g. the autoregressive model described above). More precisely, the probability distribution simulating the effect of B should also be conditioned on what happens in system A. For instance, it can be very important that the probability distribution for the state of the atmosphere (e. g. surface winds) be conditioned on the state of the ocean model (e. g. mesoscale eddies), to simulate the interaction between A and B. Similarly, the probability distribution for unresolved scales or unresolved diversity usually depends on what happens in system A. This need to correctly simulate conditional probability distributions ex-plains why the tuning of the parameters is not easy, and why an extensive database to learn the statistical behaviour of the coupling between A and B is often necessary. In practice, this learning information can be obtained either from observations of the two systems or from other models explicitly simulating the coupling between A and B.
Acknowledgements. This work has received funding from the  European Community 's  Seventh Framework Programme   FP7/2007-2013  under grant agreements  283367  ( MyOcean2 ) and 283580 (SANGOMA), with additional support from  CNES. The calculations were performed using HPC resources from  GENCI-IDRIS  (grant  2014-011279 ). Edited by:  S. Valcke.
The remaining 30% forms in the 8 Gyr, from redshift 1 to 0. We find that 50% of the present day stellar mass was in place 9. 75 Gyr ago, by redshift 1. 6. The simulation is in good agreement with the observed growth of stellar mass across the whole of cosmic time, falling within the error bars of the observational data sets. We find that 3. 5% of the baryons are in stars at redshift zero, which is close to the values of 3. 5% and 4% reported by  Li & White (2009)  and  Baldry et al. (2012) , respectively. However, it should be noted that observed stellar mass densities are determined by integrating the GSMF, thereby excluding stellar mass associated with intra-cluster light (ICL). To carry out a fairer comparison, we apply a 3D 30 pkpc aperture to the simulated galaxies to mimic a 2D Petrosian aperture, as applied to many observations (see Section 2.
, 5). (www. dirac. ac. uk). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, and STFC DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the National E-Infrastructure. We also gratefully acknowledge PRACE for awarding us access to the resource Curie based in France at Trés Grand Centre de Calcul. This work was sponsored by the Dutch National Computing Facilities Foundation (NCF) for the use of supercomputer facilities, with financial support from the Netherlands Organization for Scientific Research (NWO). The research was supported in part by the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC Grant agreements 278594-GasAroundGalaxies, GA 267291 Cosmiway, and 321334 dustygal, the Interuniversity Attraction Poles Programme initiated by the Belgian Science Policy OWNce ([AP P7/08 CHARM]), the National Science Foundation under Grant No.
ACKNOWLEDGEMENTS The authors are very grateful for the endless technical support provided by  Dr. Lydia Heck  during the preparation of these simulations and during post processing. MF thanks  Violeta Gonzalez-Perez  and  Peter Mitchell  for providing observational data sets. RAC is a  Royal Society University Research Fellow. This work used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility.
Introduction The chemical composition of the air close to Earth's surface, generally referred as "air quality" (AQ), directly affects human and animal health and also the vegetation. For instance, ozone has a known impact on the respiratory system (e. g. WHO, 2004)  and on the vegetation development (e. g. Fuhrer and Booker, 2003). Recently, the World Health Organization reported that in 2012 around 3. 7 million deaths were attributable to ambient air pollution (http://www. who. int/phe/health_topics/outdoorair/databases/en/). This is why air quality has become a major concern, starting in the 1970s, particularly in Europe (e. g. WHO, 2013). Since the Helsinki Protocol in 1985, many regions and countries, including the European Union countries, have progressively put in place tools to regulate and to control the emissions of the main air pollutants.
gse-promote. org/) projects and pursued in the MACC  (Monitoring Atmospheric Composition and Climate, FP7, 2009-2011) , MACC-II (Monitoring Atmospheric Composition and Climate: Interim  Implementation, FP7, 2011 -2014)  and  MACC-III (Monitoring Atmospheric Composition and Climate-III, H2020, 2014 -2015)  projects. One of the major achievements accomplished in GMES (Global Monitoring for Environment and Security), MACC and MACC-II for European AQ objectives is the development and the exploitation of a pre-operational analysis and forecasting system run on a daily basis. This system is based on the combined use of an ensemble of seven air quality models. The general objective of this system is not to provide air quality forecasts and analyses for precise local situations but at the pan-European scale.
The delivery time of the analysis has also been shifted earlier in June 2014. The NRT hourly observations of O 3 , NO 2 , SO 2 , CO, PM 10 and PM 2. 5 from the European AQ monitoring stations are used for model assimilation to produce the daily analyses and also for the forecast and analysis evaluation. From 2009 until recently, they were gathered country by country through bilateral agreements with the project. Since 2014, a new system has been put in place to gather these observations from the centralised AirBase database maintained by the European Environment Agency (EEA). The database collects the NRT data and validated data from the European countries bound under Decision 97/101/EC to engage in a reciprocal exchange of information (EoI) on ambient air quality. The delivery time of the observations to EEA takes place earlier and there is on average more data available than when gathering them bilaterally country by country, although there is a large variability from one day to another in the number of data available.
For the use in the production of the analyses, we chose after a dataflow monitoring of the EEA database a cut-off time at 07:00 UTC on Day0 for the data set covering Day0-1. At this time of the day, more than 90 % (on average) of all data are available. The 07:00 UTC cut-off time is therefore a compromise between having enough data available for the model assimilation and a reasonable production time for the ensemble analysis that was at 14:30 UTC at the end of MACC-II. This production time is still too late for the forecasts to be initialised from the analysis, meaning that the forecast and the analysis products are currently run in two separate chains for each model. For the product evaluation, the observations covering Day0-1 available in the EEA database at 23:00 UTC on Day0 are used since there is less constraint on the time of delivery of evaluation products.
Moreover, only the data representative of the horizontal resolution of the regional models (10-20 km) are selected. There is currently no uniform and reliable metadata on site representativeness available for all regions and countries of Europe. This is why we chose to follow the work that has been done by  Joly and Peuch (2012)  to build an objective classification of sites, based on past validated measurements available in the AirBase database  (EEA). Stations are classified between 1 and 10 depending on the characteristics of their series of measurements (diurnal cycle, "weekend effect" and high frequency variability with periods lower than 3 days). The orig-Table  1. Portfolio of the MACC-II regional data products. Each product is provided once daily. Core species correspond to O 3 , NO 2 , CO, SO 2 , PM 10 , PM 2.
Model name Forecast or analysis Species these data are used for the verification of the forecast products. For the verification of analyses, the developments done during MACC-II were only put into place after the end of the project. This verification is done in the following way: a list of stations not used for the assimilation is kept aside for each pollutant for verification. This list is the same every day and it has been determined so that the stations are well spread inside the domain. The ratio of observations that are kept aside for the verification of analyses is roughly 20 % of the total amount of observations that are downloaded at 23:00 UTC. The plots of forecasts and analyses from the seven models and the ensemble median, as well as of their scores against observations are available daily at http: //macc-raq.
copernicus-atmosphere. eu/. Numerical data are publicly available and can be accessed at http://www. gmes-atmosphere. eu/request_regional_data/. The full set of numerical data as listed in Table  1  is made available as soon as it is produced on the Météo-France FTP (file transfer protocol) server. A subset of these data can also be interactively accessed through the Deutsche Zentrum für Luft-und Raumfahrt (DLR) World Data Center. Major sources of uncertainties in the regional AQ forecasts and analyses are the quality of the emissions used, the meteorological forcings, the representation of the atmospheric physical and chemical processes, the initial and boundary conditions for the chemical species and the uncertainties in observations and assimilation methods impacting the analysis. The approach chosen in MACC-II is to use the best available emissions over Europe, high quality meteorological forecasts and chemical boundary conditions in all seven chemistry-transport models.
The 2009 inventory is currently used in the MACC-II daily production. An important upgrade of the MACC-II inventory compared to the earlier MACC inventory is the provision of a particulate matter split between elemental carbon, organic carbon, SO 4 , Na and other aerosols. More details on this inventory can be found in  Kuenen et al. (2014). For the biogenic sources, each model deals with its own emissions based on dynamical parameterisations and/or inventories that are detailed in the following individual model description subsections. Additionally, emissions from fires are taken into account using the GFASv1. 1 product  (Kaiser et al. , 2012)  available daily at 0. 1 • × 0. 1 • resolution. GFASv1. 1 is based on fire radiative power retrievals from data of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites.
, 2009; Benedetti et al. , 2009)  that are used to force the aerosols in the regional CTMs at the boundaries. At the end of MACC-II project (summer 2014), for the chemical species the IFS was two-way coupled to the off-line MOZART (Model for OZone And Related chemical Tracers) global CTM. This allowed for assimilation of satellite data for O 3 , NO 2 , and CO in the IFS itself, while the detailed chemical processes were handled in the MOZART model  (Flemming et al. , 2009; Stein et al. , 2012; Inness et al. , 2013). Since 18 September 2014, the MACC-II global assimilation and forecasting system has been upgraded to a fully integrated system for aerosols and chemical species. Instead of the coupling with the MOZART model, the chemistry is now treated on-line in the IFS using chemistry modules based on the TM5 model  (Huijnen et al.
7 to version 1. 10. Detailed update information can be found on the model's web page, http://www. lotos-euros. nl. Since the end of MACC-II, the latest update to v1. 10 implemented operationally consists of changes in the SO 2 to SO 4 conversion rate, use of AQMEII (air quality model evaluation international initiative) conventions for the fine/coarse dust assignment, update of resistances for e. g. ozone (leading to an overall ozone increase), and improvement of the treatment of fire emissions. The model extends up to 3. 5 km above sea level, with three dynamic layers and a fixed 25 m thick surface layer. The lowest dynamic layer is the mixing layer, followed by two reservoir layers. The height of the mixing layer is obtained from the ECMWF meteorological input data used to drive the model.
(1993) , using the actual meteorological data. For land use the CORINE/Smiatek database has been enhanced using the tree species map for Europe made by  Koeble and Seufert (2001). Total PM 10 in the LOTOS-EUROS model is composed of chemically unspecified PM in the fine and coarse modes, black carbon, dust, ammonium, sulfate, nitrate and sea salt (Na in the fine and coarse modes). The LOTOS-EUROS model is equipped with a data assimilation package with the ensemble Kalman filter technique  (Barbu et al. , 2008; Timmermans et al. , 2009; Curier et al. , 2012). Data assimilation for the MACC-II daily analyses is performed with surface ozone observations  (Curier et al. , 2012 ). An extension to other surface and satellite data is foreseen in the near future. The LOTOS-EUROS model has been designed as a model of intermediate complexity, to favour short computation times.
Aerosols in SILAM are represented via sectional approach with species-specific size spectra. The aerosol species include primary anthropogenic aerosols, divided into PM 2. 5 and PM 10 , secondary inorganic aerosols (sulfates, nitrates and ammonia), and sea salt aerosols. The forecasts utilise the BVOC (biogenic volatile organic compound) emission term based on the NatAir project results  (Poupkou et al. , 2010)  and own development for the sea salt emissions  (Sofiev et al. , 2011). The data assimilation system of SILAM consists of 3DVar and 4DVar modules  (Vira and Sofiev, 2012). The MACC-II near-real time analysis suite uses the 3DVar method and assimilates hourly surface observations of NO 2 , O 3 and SO 2. PM observations have been assimilated in re-analysis simulations  (Vira and Sofiev, 2015).
General description The evaluation of the performances of a forecast system is a necessary step for rating its quality and thus proving its usefulness. The MACC-II air quality forecasts are evaluated against the NRT AQ surface monitoring data detailed in Sect. 2. 1. Note that this set of data is fully independent of the forecast since the analyses assimilating the NRT AQ data are produced too late to be used to initialise the forecasts. The tools to assess the performances of the analyses are not yet in place but this is planned to be ready in 2015. Since the focus of the MACC-II regional system over Europe is on air quality, meaning air composition close to the surface, no column observations (ground based or from satellite) or upper air in situ measurements (i. e. on board aircraft) are used operationally to evaluate the system performances.
gmes-atmosphere. eu/ documents/maccii/deliverables/ens/). Additionally, on a 6-month basis, reports are especially dedicated to the scientific analysis of the forecasts of the seven models and of the ENSEMBLE in the Mediterranean area (http://www. gmes-atmosphere. eu/documents/ maccii/deliverables/ens/). The Mediterranean area is recognised as challenging for models, in particular under summer conditions with very active photochemistry and because of its large variety of emission sources. The performances of the NRT analysis are not presented in this paper since there is only an ENSEMBLE production of one species (ozone) and the daily verification procedure against an independent data set was not yet in place at the end of MACC-II project.
Availability statistics The MACC-II regional air quality forecasting and analysis system is currently under a pre-operational status that can be seen as the demonstrator of a future operational system. The proper function of the seven model chains and of the EN-SEMBLE chain is monitored on working hours only since, at this stage, there had been no funding yet for a 7-day/7-day, 24 h/24 h control. Nevertheless, in their pre-operational configuration the production chains are reliable with availability in time (see Table  2 ) of the seven individual forecasts and analysis generally above 85 % during MACC-II. During the past year, the production suffered from failures because of the many changes that were applied to the individual and central systems to fit with fully operational standards (data format, file transfer, databases, processing softwares, etc.
Statistical performances of the forecasts on a seasonal basis In addition to the production of daily skill scores, statistical indicators are calculated for ozone, NO 2 and PM 10 at the surface on a seasonal basis since September 2009 for each of the seven models and for the ENSEMBLE. These skill scores and the analysis of their seasonal and year-to-year evolutions are presented in 6-month reports, each including two seasons. The model's statistical indicators are calculated against measurements from the European AQ surface station network available in NRT and selected as detailed in Sect. 2. 1. So far, the data provision in NRT is not fully operational. Therefore, there is some variability with time of the number of data available and of their location. Also, the spatial coverage of the surface AQ network in Europe is very inhomogeneous with a high density of stations in France, Germany, UK, Belgium and the Netherlands.
To complement the statistical evaluation done over the whole European domain, a scientific evaluation of the seven models and of the ENSEMBLE is also done for the Mediterranean region because of its specificities (emissions, population, topography, meteorology, photochemistry). Another important point to note is that major efforts have been put during MACC-II towards the full operationalisation of the system in order to improve its robustness. The regional air quality production was extended during MACC-II and further developments are underway to improve the quality, the variety and the timeliness of its products based on users' feedbacks. In the very short term, the EN-SEMBLE analysis that is only provided for ozone will be extended to NO 2 from January 2015 and verification statistics with independent data will be produced.
Product evaluation is done on the basis of the available NRT measurements from the European AQ monitoring network. Stations used are selected to be as representative as possible of the model horizontal resolution, by retaining only classes 1-5 from the Joly and Peuch (2012) classification. There is ongoing work to improve the station selection, still based on Joly and Peuch's classification, by determining the best class ranges to be used individually for each of the six pollutants (O 3 , NO 2 , SO 2 , CO, PM 10 , PM 2. 5 ) based on pollutant lifetime. Continuous research is pursued to improve the seven individual models and their assimilation systems. In particular, there is an important effort for the use of new satellite data or combinations of satellite data with surface measurements in the assimilation systems.
Acknowledgements. This study was funded by the  European Commission  under the  EU Seventh Research Framework Programme  (grant agreement no. 283576 , MACC II). In situ air quality data were provided by the  European Environment Agency. Additional financial support at the national level was given by the  French Ministère de l'écologie, du développement durable et de l'énergie  through the  ADONISS  project. This work was granted access to the  HPC resources of CCRT  under the allocation 2013-6695 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). IASI has been developed and built under the responsibility of the  Centre National d'Etudes Spatiales (CNES, France). Developments of the SILAM system were supported by projects ASTREX and IS4FIRES of the  Academy of Finland. NO 2 column retrievals from  AURA/OMI  and  METOP/GOME-2 , and CO profiles from TERRA/MOPITT have been used for assimilation in some of the individual models.