dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary and discussion Floodplain inundation is a key component of surface water dynamics in the Amazon Basin. A macroscale inundation scheme for representing floodplain inundation was incorporated into the Model for Scale Adaptive River Transport (MOSART) and the extended model was applied to the entire Amazon Basin. Efforts were made to deal with a few challenges in continental-scale modeling of surface hydrology in this vast basin: 1. We refined the floodplain topography by alleviating the spatially varying vegetation-caused biases in the Hy-droSHEDS DEM data. To our knowledge, this was the first time that the spatial variability of vegetation-caused biases in the DEM data was explicitly considered in hydrologic modeling for the entire Amazon Basin. 2. We improved the representation of spatial variability in channel cross-sectional geometry by refining the basinwide channel geometry formulae for various subregions.
3. The Manning roughness coefficient varied with the channel depth to reflect the general rule that the relative importance of riverbed resistance in river flow declined with the increase of river size. 4. We accounted for the backwater effects in the river routing method to better represent river flow in gentle-slope reaches. The model results were evaluated against in situ streamflow data as well as remote sensing river-stage and inundation data. The simulated streamflow results were compared with the observed data from 13 major stream gauges (Fig. 3 ). The streamflow hydrographs were reproduced fairly well for the majority of the 13 gauges. The simulated river stages were compared to the altimetry data obtained by the Envisat satellite for the 11 subbasins containing or close to 11 of the 13 gauges (Fig.
Data availability This study used the following datasets, which can be either accessed from the internet or acquired from the corresponding institution or person. 1. The HydroSHEDS DEM datasets were developed by United States Geological Survey and are available online (http://hydrosheds. cr. usgs. gov/). 2. The dataset of Global 1 km Forest Canopy Height  (Simard et al. , 2011)  is available online (http://webmap. ornl. gov/wcsdown/dataset. jsp?ds_id=10023) from the Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. 3. LBA-ECO LC-07 Wetland Extent, Vegetation, and Inundation: Lowland Amazon Basin data are available at doi:10. 3334/ORNLDAAC/1284 (Hess et al. , 2015b). 4. The surface and subsurface runoff inputs of 1 • resolution were produced by Bertrand Decharme at CNRM/Météo-France  (Getirana et al.
, 2014)  and can be acquired by contacting Augusto Getirana (augusto. getirana@nasa. gov). 5. The streamflow data of the stream gauges can be acquired by contacting the Brazilian Water Agency ANA (Agencia Nacional de Aguas). 6. The river water levels are mainly based on altimetry data from the Envisat satellite and available from the HydroWeb database (http://ctoh. legos. obs-mip. fr/ products/hydroweb) maintained by CTOH (Center for Topographic studies of the Ocean and Hydrosphere) at LEGOS, France. 7. The dataset GIEMS (Global Inundation Extent from Multi-Satellites) was developed by Catherine Prigent (Observatoire de Paris), Filipe Aires (Estellus and Observatoire de Paris) and Fabrice Papa (IRD, LEGOS), and can be acquired by contacting Fabrice Papa (fabrice. papa@ird. fr).
Acknowledgements. We would like to thank the two anonymous reviewers and the editor for their constructive comments and suggestions. We also thank the institutions/researchers for making the datasets described in the data availability section available for this study. This research was supported by the  Office of Science of the U. S. Department of Energy  as part of the  Earth System Modeling program  through the  Accelerated Climate Modeling for Energy (ACME)  project. The  Pacific Northwest National Laboratory  is operated by  Battelle  for the U. S. Department of www. geosci-model-dev. net/10/1233/2017 /Geosci. Model Dev. , 10, 1233-1259, 2017 1258 X. Luo et al. : Modeling surface water dynamics in the  Amazon Basin Energy  under contract  DE-AC05-76RLO1830. Edited by: J. Neal Reviewed by: two anonymous referees.
, 2006; Gerber et al. , 2008; Gaspard et al. , 2014). EEG remains the most important clinical tool for functional assessment of the central nervous system, being widely used as an essential element in the diagnostic workup of patients with epilepsy, critically ill patients, as well as patients with altered mental status and cognitive changes. Misinterpretation of EEG can affect a huge number of patients worldwide. Thus, there is a need to find computerized tools to improve the quality of EEG assessment and reporting in clinical practice, and to improve education in EEG. The main goal of SCORE is to give electroencephalographers a computerized tool that can be used in clinical practice to assess and report EEGs. The clinically relevant features observed in the EEG recordings are selected from a software that implements the SCORE terminology.
This process automatically generates a report and feeds the selected features into a database. The standardized and computerized process can potentially (1) increase inter-rater agreement; (2) contribute to quality assurance by guiding the user through the clinically relevant aspects in a context-sensitive way; (3) build a large database for clinical research; (4) constitute a valuable tool for education. Similar approaches of standardizing feature extraction and reporting are under development for other medical specialties such as radiology, pathology and endoscopy  (Morgan et al. , 2014; Ellis and Srigley, 2016; Bretthauer et al. , 2016). In 2013, the first version of SCORE was published as a European consensus, endorsed both by the European Chapter of the IFCN and by the International League Against Epilepsy (ILAE) -Commission on European Affairs  (Beniczky et al.
Patient information and referral To identify the patient, the following data are compulsory: name, social security number or healthcare provider number, gender and date of birth. In case social security number is not used for a security reason, an alternative number (identity string) can be entered in the report. Optional entries are: handedness, address, mother's name. Each patient may have several recordings in the database. For each recording, information related to the referral and to the recording conditions can be inserted. The following data can be entered for the referral: name and address of the referring physician/referring unit, indication for EEG (Table  1 ), diagnosis at referral (using ICD-10 codes, and for rare diseases orphanet codes) (Orphanet, 1997), seizure frequency, time since the latest seizure, medications (using the ATC WHO list).
Recording conditions This section comprises administrative data (study identification number, date and time of the recording, duration of the recording, name of technologist, name of physician/supervising physician) as well as technical data related to the recording. The age of the patient is calculated automatically from the date of birth and the date of the recording. The sensor-group is selected from a list defined in the sitesettings for each EEG-lab. This is according to the new IFCN guideline on EEG electrode array  (Seeck et al. , 2017). The type of EEG recording is selected from the following list: standard EEG, sleep EEG, short-term video-EEG monitoring, long-term video-EEG monitoring, ambulatory recording, long term video-EEG monitoring (LTM), recording in the ICU, intraoperative monitoring.
The technologist can score the alertness, orientation, and cooperation of the patient, using a multiple-choice list (awake, oriented, good cooperation, poor cooperation, disoriented, drowsy, asleep, unresponsive, comatose). The date and time for the latest meal can be specified. In case the patient has skull defect or has had brain surgery, this can be entered into the database and the location specified. Additional information related to technical description can be entered in free text.
For single discharges, this is scored as incidence (how often it occurs/time-epoch); for trains or bursts this is scored as prevalence (the percentage of the recording covered by the train/burst). Besides the choices listed in Table  6 , additional data can be entered for periodic graphoelements (duration of the time-interval between the discharges), rhythmic patterns (duration and frequency) and arrhythmic patterns (duration). For each described graphoelement, the influence of the modulators can be scored. Only modulators present in the recording are shown as options. Eye-closure sensitivity is also scored here. For most modulators, the selection-choices are: unmodified, increased, decreased, stopped by, triggered by/only during the modulator. For sleep, two additional choices are available: continuous during non-REM sleep (and free text option for entering spike-wave index) and change of pattern during sleep (+ free text).
The neonatal template For patients younger than 3 months a specific neonatal template is used for scoring. Gestational age is entered; postmenstrual age, chronological age and corrected age are automatically calculated in accordance with the American Academy of Pediatrics policy statement on age terminology in the perinatal period  (Engle, 2004). The American Clinical Neurophysiology Society standardized EEG terminology and categorization for the description of continu-   ous EEG monitoring in neonates is implemented in SCORE  (Tsuchida et al. , 2013). A specific neonatal module replaces the description of the background activity: neonatal ongoing activity. Two new folders with neonatal features are added to SCORE: transient patterns and rhythmic activity. The content of the neonatal template differs depending on the postmenstrual age (<30 weeks vs.
Rhythmic activity is the third specific folder of the neonatal template. One can select rhythmic activity (delta, theta, alpha, beta, gamma) and brief rhythmic discharges. Location and timerelated-features are scored as described above. The other SCORE-folders, containing interictal findings, rhythmic and periodic patterns in critically ill patients, episodes, patterns of uncertain significance, EEG artifacts, polygraphic channels, and trend analysis are available for neonates too. However, the classification of the epileptic seizure is different: it contains a specific neonatal module, with the latest classification of neonatal seizures, as currently proposed by the ILAE taskforce: myoclonic, clonic, spasm, tonic, automatisms, hypomotor, autonomic, mixed, unknown, electrographic.
The SCORE EEG software A software implementing the SCORE terminology has been developed by a group of programmers at Holberg EEG AS, under the supervision of one of the authors (HA). First, the software implemented the European version  (Beniczky et al. , 2013a). Then the software was modified to the second, revised version, based on the structure and terminology provided by the IFCN taskforce. Throughout this process, an online demonstration-version was available for the members of the taskforce, for testing the implemented revisions. Between 2013 and 2016 about 10 man-years of development was performed to establish the current software, including the functionality to integrate with electronic health record systems (HL7) in the advanced version. The revised version of SCORE contains 537 main terms (''names"); 308 of them are unchanged compared to the previous version.
At present, 776 features are available for further characterizing the main terms; 314 of them are unchanged compared to the previous version. SCORE-EEG has been used in clinical practice in Haukeland University Hospital Bergen, Oslo University Hospital, Danish Epilepsy Centre Dianalund, Aarhus University Hospital, Stichting Epilepsie Instellingen Nederland (SEIN), Beth Israel Deaconess Medical Center, and University Health Network Toronto Western Hospital. 12,160 EEG recordings have been described and reported using SCORE EEG. After the training phase when clinicians became familiar with the software, the time spent on reporting EEGs was the same, and for some even shorter compared to reporting in free-text formats. In two of the institutions that implemented SCORE-EEG in clinical practice, the integration with the electronic healthcare records (EHR) of the hospitals has also been tested, and it worked well.
A direct file-based import of reports from a userdefined file share into the EHR system can be used if the EHR system is not compatible with HL-7 or where local policy is preventing this. Alternatively standard copy paste functionality can be used to copy the reports from SCORE EEG to the EHR system. SCORE-EEG is installed in the IT environment of the hospitals. Each user logs on using an individual user name and password. The communication between the SCORE EEG software and the servers is encrypted, and the activity of each user is logged and traceable. When opening a new recording in SCORE, an empty matrix is generated, where, for all features the item ''not scored" is selected as default. The active choice ''not possible to determine" is also available (to avoid redundancy, this choice is not added to the tables in this paper).
For all items free-text can be added. Figs. 1 and 2 show the graphical user interface for scoring, and Fig. 3  shows an example of a generated report. SCORE-EEG helps the user to avoid omitting clinically relevant features, by presenting for each condition a list of specific items that have potential clinical relevance, thus guiding the user through the process of systematic assessment and reporting of EEGs. For example, when a seizure is scored, the software presents all aspects that can be relevant for the assessed seizure, and the report cannot be generated before the relevant items are addressed by the user. Since the terms and features in SCORE are fixed items, the use of standardized terminology is enforced by the structure of the system. In case SCORE is integrated with the EEG reader, snapshots with typical examples of the scored features can be included into the report.
Recordings can be marked (''flagged") for studies or for teaching database. The scoring standard was translated from English into 14 languages, and implemented in SCORE-EEG: Azerbaijani, Chinese, Czech, Danish, Dutch, Georgian, German, Norwegian, Portuguese, Russian, Spanish, Swedish, Turkish and Ukrainian. Eight other languages are under translation. Regardless of the language used, the codes in the database are the same. One can score a report in one language and convert the report into another language. The free version of the SCORE-EEG software and guidance for users can be requested from the following home-page: http://holbergeeg. com. SCORE-EEG automatically generates report based on the items selected by the user during the scoring process, and it saves the scored features in a local database.
Conclusion and Future perspectives The SCORE system has been developed by an international panel of experts, under the auspices of the IFCN. The structured template, containing standardized terminology, assists the electroencephalographers in extracting clinically relevant features and reporting them using a software. This process leads to an automatically generated report, and in the same time, it feeds the features into a database. The template guides the user through the logical steps of characterizing EEG phenomena (name, morphology, location, time-related features, modulators). Specific modules are available for scoring the electro-clinical features of seizures, for neonatal recordings (including features specific for this age group), and for Critical Care EEG. The feasibility of SCORE was tested in clinical practice on 12,160 EEG recordings.
The present version still has several limitations. Inter-observer agreement using this system has not been systematically investigated yet. Although the general opinion of the centers that implemented SCORE was, that after gaining experience with using the software, the time spent on reporting EEGs was not longer (or even shorter) than writing or dictating free text reports, this aspect has not been systematically addressed so far. At present SCORE-EEG generates local databases in the centers where it is implemented. Development of multi-center, national and international databases will be a powerful tool to promote clinical EEG research. clearly marked for efficient review. Since each EEG can be scored multiple times by different readers, local laboratories can assess their own interrater reliability by having all neurophysiologists and/or trainees score a group of EEGs selected by EEG type or by a particular EEG finding.
Future studies on inter-rater agreement are necessary to estimate the reliability of the various terms in SCORE. After building up a large international database, one could consider removing the terms that are never used and those that fail to achieve a substantial interobserver agreement. SCORE was designed as a dynamic system, allowing for updates (renaming or removing existing items, inserting new items) without losing data from the earlier versions. Considering the increasing knowledge in this field, we consider that revisions at regular intervals (5 years) are necessary.
Acknowledgements We are grateful to  Suzette M Laroche ,  Andrew J. Cole ,  Douglas Rose  for suggestion on the scoring standard, and to  Gunel Memmedxanli ,  Wu Xintong ,  Jie Mu ,  Giorgi Japaridze ,  Kristian Bernhard Nilsen ,  Alla Guekht ,  Victoria Fernandez ,  Mustafa Tansel  and  Nataliia Skachkova  for the translating SCORE.
Proposed Method To address the three problems of the BUCC 2017 shared task, we propose a method which contains three main steps: 1. 'Translating' the monolingual ZH corpus into English. 2. Searching for candidate source-target parallel sentence pairs. 3. Classifying candidate source-target sentence pairs to find parallel sentences. Note that in our case, the source data is a monolingual English (henceforth EN) corpus and the target data is a monolingual Chinese (henceforth ZH) corpus.
Evaluation protocol We perform three evaluations: two independent evaluations on the training set for Step 2 (Searching for candidate source-target parallel sentence pairs) and Step 3 (Finding parallel sentences in candidate source-target sentence pairs) and one evaluation on the training and test sets for the whole system. The first two evaluations aim to find the best parameters and configurations of their own part. The last one is for investigating the effectiveness and performance of the whole system. For the evaluation of Step 2, we use all the English sentences of the training data gold standard as the question set. According to the success evaluation result, we select the parameter N that provides the required success of 85%. Then a Solr score threshold is calculated as the highest threshold that maintains the success on top N.
To find the best configuration (kernel, class weight, C, gamma parameters) of the SVM classifier, we perform a 5-fold crossvalidation on the training data. As the training data (as well as the test data) is highly imbalanced (the number of negative examples is around 120 times higher than the number of positive examples), the class weight parameter, according to the scikit-learn web page, which sets the parameter C of class i to class weight[i]*C for the SVM classifier, plays an important role. For the whole system evaluation, after obtaining the final predicted source-target parallel sentence pairs, we use precision, recall and F1-score as evaluation measures: P = T P T P + F P ; R = T P T P + F N ; F 1 = 2P R P + R where TP stands for the number of source-target sentence pairs that is present in the gold standard, a false positive FP is a pair of sentences that is not present in the gold standard and a false negative FN is a pair of sentences present in the gold standard but absent from systems results.
Results and discussion The success obtained for the training data is shown in Figure  1. We note that the success is close to 85% when we retrieve the top 3 target sentences (N = 3) for each source sentence of the gold standard. If we increase N by 1, 88,860 more negative examples (the number of monolingual EN sentences in the training corpus) are added to the SVM classifier's training data, but the success improvement is small. We therefore decided not to increase N and set it to 3. Then the maximum Solr score threshold that does not significantly change the success when N = 3 is found to be 15. 4. The results obtained on the training and test sets are presented in Table  1. They are consistent across datasets (test and train): we did not overfit the training set. Our best configuration of SVM classifier for the training data, namely, kernel=rbf, class weight=1:8, C=1.
Remerciements Cet article présente  des résultats du projet de recherche Inove. com (Identité, Norme, Vulnérabilité, pour une approche  écologique de la consommation durable des ménages) ,  financé par le ministère de l'Environnement , de l'énergie et de la mer, dans le cadre du second appel à projet MOVIDA  (2013MOVIDA ( -2016)).
Nous verrons qu'il accompagne le projet jusqu'à une restitution éventuelle au public. Mais revenons au choix de la technologie Web-SIG : parmi les solutions de cartographie web, trois voies sont possibles : les logiciels clefs en main comme Google Mymap ou U-Map de la communauté OpenStreetMap ; les SIG en ligne comme Géo (anciennement DynMap) ou ArcGis Online ou QGISCloud par exemple ; et enfin des serveurs/clients cartographiques. Les raisons du choix d'une architecture client/serveur cartographique spécifique comme Chronocarto sont les suivantes : -Les produits clefs en main sont très performants mais très basiques dans leurs fonctionnalités. Il est difficile, voire impossible, de les faire évoluer selon ses propres intérêts si ceux-ci ne coïncident pas avec ceux d'une grande communauté d'utilisateurs (ce qui n'est pas le cas en archéologie…).
Des problèmes sur la confidentialité des données peuvent exister aussi. -Les SIG en ligne, beaucoup plus puissants, sont payants pour la plupart et les licences souvent annuelles deviennent vite rédhibitoires pour un laboratoire et encore plus pour des membres associés. Il n'est donc pas possible de confier ses données et ses cartes à de telles solutions. Les changements de format des éditeurs posent aussi le problème de la pérennité des cartes créées avec ces logiciels 4. Même une solution libre comme QGISCloud n'est pas gratuite si l'on doit gérer plusieurs bases de données et que l'on souhaite des cartes non publiques…. Les solutions client/serveur sont beaucoup plus souples et se basent sur des technologies libres de droit, gratuites et surtout soutenues par une vaste communauté, gage à la fois de trouver des documentations, forums et donc réponses aux problèmes rencontrés, et en perpétuelle évolution puisque soutenues par un grand nombre de développeurs suivant des normes assurant l'interopérabilité des produits.
Historique de Chronocarto Chronocarto a donc été développé en 2006 comme une solution client/serveur riche utilisant plusieurs bibliothèques simultanément en fonction du type de rendu côté client (bibliothèques javascript OpenLayers ou Leaflet) et de la mise en forme des données côté serveur (Mapserver plus GeoServer). Cette solution a été développée pour des archéologues et nous avons toujours eu à l'esprit la facilité d'utilisation du produit, ne voulant pas le faire évoluer vers les potentialités d'un SIG de bureau malgré plusieurs demandes. Elle inclut toutes les fonctionnalités de base d'un SIG  (Djindjian, 2008). Chronocarto a donc été développé dans la suite de GCServer en rajoutant les notions de copyright tant sur la donnée au moment de son dépôt (possibilité d'ajouter un nom, logo, lien Internet) qu'au niveau de la carte elle-même.