dataesr/scientific-paragraphs-categorization huggingface dataset:
Real Quantitative Data Set As a complement to the simulated data, we considered a real and publicly available data set, which has been collected during a study designed to compare human primary tumor-derived xenograph proteomes of the two major histological nonsmall cell lung cancer subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC), using Super-SILAC and label-free quantification. 17 The raw files were analyzed by MaxQuant (version 1. 3. 0. 5). Peaks were searched against the UniProt human database (released July 2012; http://www. uniprot. org) using the Andromeda search engine included in MaxQuant. The data set within this package contains proteins intensity for six ADC and six SCC samples. The complete MaxQuant output file is available on the repository of the ProteomeXchange Consortium,  18  with the data set identifier PXD000438.
Accuracy Measurements In most of the experiments, the imputation step was followed by the aggregation of peptide abundances into protein abundances. (We estimated each protein abundance with the median abundance over the protein specific peptides. ) However, in few specific experiments (see Section 4), the aggregation was conducted first (i. e. , on peptide abundances that still contain missing values) and followed by imputation at the protein level. In both cases, we evaluated the performances of the imputation algorithms in the same way: We considered the differences between the protein abundances in the original complete quantitative data set and in its counterpart containing missing values that have been imputed (either at protein or peptide level). Such differences are classically summarized by the root-mean-square error (RMSE), yet many other variants exist.
CONCLUSIONS Let us first summarize the conclusions of this work into four points. (1) Imputation should be performed at the peptide level because aggregating peptides into proteins beforehand amounts to performing a first implicit and, in most of the cases, suboptimal imputation. (2) In the absence of knowledge about the nature(s) of missing values in a particular quantitative proteomics data set, it makes sense to rely on a MCAR/MAR imputation method. This is supported by numerous experiments, including ours as well as those from ref 13 but also by theoretical arguments: By definition, missing values that should be imputed by small intensities can also show up in a MCAR context (so that they can also be imputed to some extent by MCAR-devoted imputation methods), while, on the contrary, a method devoted to left-censored missing value will systematically perform poorly on other types of missing values.
This work was supported by the following funding: ANR-2010-GENOM-BTV-002-01 (Chloro-Types), ANR-10-INBS-08 (ProFI project, "Infrastructures Nationales en Biologie et Sante", "Investissements d'Avenir"), EU FP7 program (Prime-XS project, Contract no. 262067), the Prospectom project (Mastodons 2012 CNRS challenge), and the BBSRC Strategic Longer and Larger grant (Award BB/L002817/1).
However, high pollution episodes with enhanced aerosol concentrations and decreasing visibility still occur in coastal regions, due to increases in relative humidity increasing aerosol sizes  (Flowers et al. , 2010). The monsoon flux also induces transport of high ozone concentrations inland from coastal city emissions  (He et al. , 2008). In order to assess model performance over east Asia for air quality as well as climate, we use a variety of different data sets covering the urban, regional and continental scales. Ozone, aerosol and precursor data at surface sites in urban and rural locations are used, together with CAREBEIJING aircraft data collected in the lower troposphere south of Beijing, to evaluate model performance in terms of local and regional pollution from major emission regions.
2 as well as the meteorological situation during summer 2008. Evaluation of simulated ozone and its precursors on local, regional and continental scales are presented in Sect. 3. This includes comparison with the Infrared Atmospheric Sounding Interferometer (IASI) CO and ozone data, the Global Ozone Monitoring Experiment (GOME) NO 2 data and surface/aircraft data collected in the vicinity of Beijing and surface trace gas data collected at downwind sites in South Korea and Japan. Comparison of modelled and observed trace gas correlations are used to draw conclusions about whether model discrepancies are due to emissions, chemical processing (VOC or NO x limited ozone production) and/or transport. Comparisons between observed and modelled aerosol optical properties, as well as available surface/aircraft data on aerosol chemical composition, are discussed in Sect.
Ground-based data Surface data collected at urban, rural and remote sites in China, Japan and South Korea were used in this study from SNU/EANET (Seoul National University/Acid Deposition Monitoring Network in east Asia) and Peking Uni-versity (PKU) stations. Site locations are shown in Fig. 1  and station coordinates are given in Table  2. For example, during the CAREBEIJING 2008 campaign, SLP concentrations were measured at the air quality observatory of PKU in Beijing, which can be considered a typical urban environment. Instrumentation deployed at the observatory measured ozone, NO x (defined as the sum of NO and NO 2 ) and CO  (Chou et al. , 2011) , as well as particulate matter (PM: PM 2. 5 and PM 10 ), organic carbon (OC) and BC  (Huang et al. , 2010). The Gosan observatory  (Kim et al.
The strong underestimation by ECLIPSE models of aerosol loadings identified over northern India is in agreement with the work of  Gadhavi et al. (2015) , who showed that BC concentrations are strongly underestimated in southern India even when aerosol removal processes in one model were completely switched off. In our study, the fact that observed AODs in northern India are larger than those simulated by most ECLIPSE models suggests that the emissions of BC and precursors of other aerosols are underestimated for India in the ECLIPSE emission data set. This could be related to the rapid recent growth of emissions in India  (Klimont et al. , 2013) , which may be underestimated in the inventories. For example, higher emissions from kerosene lamps were identified  (Lam et al. , 2012)  and included in the next-generation ECLIPSEv5 data set, increasing the BC estimate for India by about 25 %  (Klimont et al.
Observed BC concentrations are quite low (2 ± 1 µg m -3 ) in August-September 2008, but the model mean overpredicts concentrations by a factor of 3. Only one model (OsloCTM2) agrees well with the observations at this site. Three models simulate surface sulphate concentrations reasonably well over Beijing (WRF-Chem, HadGEM, OsloCTM2) with an average value of 10 ± 2 µg m -3. The other models strongly overestimate surface sulphate concentrations by a factor 3, driven by the general overestimation in SO 2 concentrations over Beijing. Good agreement with sulphate data in HadGEM and OsloCTM2 may be fortuitous since the evaluation of trace gases suggested excessive oxidising chemistry in the OsloCTM2 model and lacking oxidising chemistry in the HadGEM model. Measured OC concentrations are of the order of 14 ± 6µg m -3 over Beijing.
Vertically resolved aerosol lidar data collected downwind over Japan and satellite data were used to assess model behaviour on regional and continental scales in the lower troposphere as well as over the total atmospheric column. The assessment of model performance over different scales is important for radiative forcing and air quality estimates. Models show systematic positive biases in ozone, especially at rural surface locations and compared to satellite data downwind of major Chinese emission regions. The general underestimation of CO over and downwind of emissions is linked to this, most likely due excessive destruction by OH, suggesting that CO lifetimes are too short. The causes of ozone discrepancies varies between models but is linked to model ability to simulate VOC and NO x regimes in polluted and less polluted environments.
Satellite-derived AOD measurements are reproduced quite well by the models over China although surface BC and sulphate are overestimated in urban China in summer 2008. The effect of short-term mitigation measures taken during the Olympic Games in summer 2008 is too weak to explain differences between the models and observations. Our results rather point to an overestimation of emissions close to the surface in urban areas, particularly for SO 2. A potential reason for this is the fact that the spatial distribution of power plant emissions has been changing dramatically in the last decade in China  (Liu et al. , 2015) , a change that had not been taken into account in the emission inventory used in this study. ECLIPSE models strongly underestimate aerosol loadings over northern India, suggesting that emissions of BC and other aerosol precursors are underestimated in the ECLIP-SEv4a data set.
Satellites IASI data (CO and ozone profiles, averaging kernel and a priori profiles) were collected on-line at http://www. pole-ether. fr. Data access is provided upon request. NO 2 total column measured by GOME-2 were downloaded from a public repository available at http://www. temis. nl/ airpollution/no2col/data/gome2_v2. Aerosol optical depth data were taken from the NASA data platform, accessible at https://ladsweb. nascom. nasa. gov/data/, data sets used were the MYD08_D3 and MOD08_D3 products. Finally, the CALIPSO attenuated backscatter profiles presented in this paper were produced following the method described in  Ancellet et al. (2014). Access to this data can be granted upon request to gerard. ancellet@latmos. ipsl. fr.
Acknowledgements. The authors would like to thank the  European Union  for funding the  FP7 ECLIPSE project  (grant agreement no. 282688 ). Jerome Fast (PNNL)  is acknowledged for providing advice about the WRF-Chem simulations. This work was granted access to the  HPC resources of IDRIS  under the allocation 2014-017141 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). The MODIS data used in this study were acquired as part of the  NASA's Earth-Sun System Division  and archived/distributed by the  MODIS Adaptive Processing System (MODAPS). We also acknowledge the free use of tropospheric NO 2 column data from the GOME-2 sensor from www. temis. nl. IASI is a joint mission of  EUMETSAT  and the  Centre National d' Etudes Spatiales (CNES, France). We are grateful to  Juliette Hadji-Lazaro  (LATMOS),  Pierre Coheur  and  Daniel Hurtmans  ( ULB ) for providing the IASI O3-FORLI data.
Training and test sets We use source treebanks from the Universal Dependencies (UD) project, version 1. 2  (Nivre et al. , 2015). 2 They are harmonized in terms of POS tag inventory (17 tags) and dependency annotation scheme. In our experiments, we use the canonical data splits, and disregard lemmas, morphological features and alternative POS from all treebanks. Out of the 33 languages currently in UD1. 2, we drop languages for which the treebank does not distribute word forms (Japanese), and languages for which we have no parallel unlabeled data (Latin, Ancient Greek, Old Church Slavonic, Irish, Gothic). Languages with more than 60k tokens (in the training data) are considered source languages, the remaining 6 smaller treebanks (Estonian, Greek, Hungarian, Latin, Romanian, Tamil) are strictly considered targets.
This results in 22 treebanks for training source taggers and parsers. We use two additional test sets: Quechua and Serbian. The first one does not entirely adhere to UD, but we provide a POS tagset mapping and a few modifications and include it as a test language to deepen the robustness assessment for our approach across language families. The Serbian test set fully conforms to UD, as a fork of the closely related Croatian UD dataset. 3 This results in a total of 30 target languages.
Multi-parallel corpora We use two sources of massively parallel text. The first is the Edinburgh Bible Corpus (EBC) collected by  Christodouloupoulos and Steedman (2014)  containing 100 languages. EBC has either 30k or 10k sentences for each language, depending on whether they are made up of full Bibles or just translations of the New Testament, respectively. We also crawled and scraped the Watchtower Online Library website to collect what we will refer to as the Watchtower Corpus (WTC). 4 The data is from 2002-2016 and the final corpus contains 135 languages with sentences in the range of 26k-145k. While some EBC Bibles are written in dated language, we do not make any modifications to the corpus if the language is also present in WTC. However, as Basque is not represented in WTC, we replace the Basque Bible from 1571 with a contemporary version from 2004, to enable the use of Basque in the parsing experiments.
7 in WTC. The UD treebank tokenization differs from the tokenization used for the multi-parallel corpora. The UD dependency annotation is based on syntactic words, and the tokenization guidelines recommend, for example, splitting clitics from verbs, and undoing contractions (Spanish "del" becomes "de el"). These tokens made up of several syntactic words are 6 https://github. com/bplank/ multilingualtokenizer called multiword tokens in the UD convention, and are included in the treebanks but are not integrated in the dependency trees, i. e. , only their forming subtokens are assigned a syntactic head. 7 In order to harmonize the tokenization, we eliminate subtokens from the dependency trees, and incorporate the original multiword tokens-which are more likely to be naive raw tokens-in the trees instead.
10 In addition, we use the IBM1 aligner efmaral 11  by Östling (2015). The intuition behind using IBM1 is that IBM2 introduces a bias toward more closely related languages, and we confirm this intuition through our experiments. We modify both aligners so that they output the alignment probability for each aligned token pair. Tagging and parsing The source-sides of the two multi-parallel corpora, EBC and WTC, are POStagged by taggers trained on the respective source languages, using TnT  (Brants, 2000). We parse the corpora using TurboParser  (Martins et al. , 2013). The parser is used in simple arc-factored mode with pruning. 12 We alter it to output per-sentence arc weight matrices. 13.
SELF-TRAIN: For this result we parse the targetlanguage EBC and WTC data, train parsers on the output predictions, and evaluate the resulting parsers on the evaluation data. Note this result is available only for the source languages. Also, note that while we refer to this as self-training, we do not concatenate the EBC/WTC training data with the source treebank data. This upper bound tells us something about the usefulness of the parallel corpus texts. FULL: Direct in-language supervision, only available for the source languages. We train parsers on the source treebanks, and use them to parse the source test sets. Evaluation All our datasets-projected, training, and test sets-contain only the following CoNLL-X features: ID, FORM, CPOSTAG, and HEAD. 16 or simplicity, we do not predict dependency labels (DEPREL), and we only report unlabeled attachment scores (UAS).
19 Our approach is languageindependent and would benefit from extension to datasets beyond EBC and WTC. 6 Related work POS tagging While projection annotation of POS labels goes back to Yarowsky's seminal work, Das and  Petrov (2011)  recently renewed interest in this problem. Das and Petrov (2011)  go beyond our approach to POS annotation by combining annotation projection and unsupervised learning techniques, but they restrict themselves to Indo-European languages and a coarser tagset. Li et al. (2012)  introduce an approach that leverages potentially noisy, but sizeable POS tag dictionaries in the form of Wiktionaries for 9 resource-rich languages. Garrette et al. (2013)  also consider the problem of learning POS taggers for truly low-resource languages, but suggest crowdsourcing such POS tag dictionaries.
Annotation projection has been explored in the context of cross-lingual dependency parsing since  Hwa et al. (2005). Notable approaches include the soft projection of reliable dependencies by  Li et al. (2014) , and the work of  Ma and Xia (2014) , who make use of the source-side distributions through a training objective function. Tiedemann and Agić (2016) provide a more detailed overview of model transfer and annotation projection, while introducing a competitive machine translation-based approach to synthesizing dependency treebanks. In their work, we note the IBM4 word alignments favor more closely related languages, and that building machine translation systems requires parallel data in quantities that far surpass EBC and WTC combined. The best results reported to date were presented by  Rasooli and Collins (2015).
Over the last 25 years, flash floods in the South of France have killed almost 250 people. The protection of prone populations is a priority for the French government. It is also a goal of the 2007 European flood directive. However, no accurate database exists gathering the fatalities due to floods in France. Fatalities are supposed to be rare and hazardous, mainly due to individual behaviour. A Ph. D. work has initiated the building of a database gathering a detailed analysis of the circumstances of death and the profiles of the deceased DJH JHQGHU« 7KH VWXG\ DUHD FRYHUV the French Mediterranean departments prone to flash floods over the period 1988-2015. This presentation details the main features of the sample, 244 fatalities collected through newspapers completed with field surveys near police services and municipalities.
Introduction Governments and institutions view the safety of human life as a priority in case of disaster. In the European Union, the 2007 flood directive reaffirmed that the protection of human health in general is a major goal of flood prevention. Meanwhile, losses of life are often considered as hazardous and rare. Fatalities may occur when people put themselves in danger. But those assumptions do not always rely on any real observations. In Europe, there is little knowledge about who dies during flash flood, under which circumstances, where« To answer such basic questions, a database has been set up putting together nearly 250 fatalities that occurred in southern France between 1988 and 2015. This paper introduces existing studies concerning flood-related fatalities and exposes the method employed to collect information on flash flood-related deaths.
Available data on flood fatalities The starting point of this research was the compilation of the information contained in existing databases: the international CRED EM-DAT database, the basis of the Dartmouth Flood Observatory and the Prim. net website managed by the French Ministry of Ecology in charge of flood prevention. These databases are well-known and frequently used by researchers to get a global overview of mortality (see  [9]  in  [10] ). The CRED EM-DAT is the best known database  [11]. It gives a list of events providing, when available, a number of fatalities. But the events with fewer than 10 deaths are not always included. Furthermore, the location of the fatalities is not accurate and the circumstances of death are not given. The Prim. net Database, managed by the French Ministry of Ecology, can be downloaded for free but, like the previous database, it only provides general observations.
The census of flood fatalities is not exhaustive. Nevertheless, two other specific studies are especially dedicated to flood-related fatalities in France. The first study surveys fatalities in France from 1971 to 1995  [12]. The second is a paper based on the inventory of floodrelated deaths in four departments of southern France from 1316 to 1999  [13]. To complete the list of potentially fatal floods, research and reports on flash floods in southern France  [14]  have been collected. Since the end of the 1990s, all huge events are submitted to a specific back analysis report providing general information on deaths. But in those reports, as in the French Ministry of Ecology database, information is general and never gives details about the circumstances or the exact location of deaths. To add to the list of potentially fatal floods, the website Once the events listed, the next step was to verify, to locate in a GIS and to collect accurate information around each fatality.
Study field The database was first compiled at the University of Montpellier based on the work of Antoine et al. [13]  quoted here above. Then, the Ph D. of L. Boissier  [15]  completed the database core by reinvestigating existing dataset, defining the rules of data collection and providing a first analysis. The database is now being consolidated within the framework of Clotilde Saint-Martin ¶V Ph D. The following guidelines steered the compiling of the database.
Selected fatalities The originality of the approach is to collect information for each fatality. Thus, each case corresponds to a line in the database. The lines can then be aggregated by event (date), spatial unit or year. It is a real challenge to gather information about every individual victim  [16]. Another original aspect of this database is that all fatalities are accurately georeferenced in a GIS database, whereas other studies  [17]  work on a larger sample of flash flood events but without accurate location of dead people. In this case, tKH VWDWLVWLFDO XQLW LV ³D IODVK IORRG with a fatality(ies)´ The database concerning flood victims in the French Mediterranean region is called ³9LFW-,Q GDWDEDVH´ The key criterion to select a flood-related fatality is the causal relation between flooding and the death.
Sources and verification of information Given the lack of databases in the public services, the first useful source was the press. Newspapers provide information on the death and sometimes publish a list of victims. Of course, that information has to be verified on the field. Municipalities were the second source of information to confirm the figures collected in newspapers. Some municipalities had collected information especially on huge events e. g. the municipality of Vaison-La-Romaine hit by severe floods in September 1992. For recent events, firemen services in charge of rescue are a good source of information when they are willing to collaborate. The police services are not an interesting VRXUFH RI LQIRUPDWLRQ HLWKHU WKH\ GRQ ¶W FROOHFW GDWD RU they FDQ ¶W publish it. Finally, for each death, field recognition was undertaken where the death was likely to have occurred.
Watershed size upstream fatalities places Using the Cartographic database ³BD CARTHAGE´ edited by IGN, the size of the watershed upstream of each georeferenced death was calculated. This value is known with accuracy for 203 cases (Fig. 11 ). The sizes of watersheds stretch from 0. 2 km 2 to 95 500 km² (the latter is the Rhône river basin where people died after dyke break in 2003 near the mouths). Half of the deaths occurred in less than 150 km² wide basins, many of those watersheds are non-permanent rivers. Peak discharge up to 15 m 3 /s/km 2 was observed in such small Mediterranean basins  [34] ,  [35]. It explains why the quick onset of water level is an aggravating circumstance during flash floods. It also points out the problem of real time forecasting, which is a key issue in small basins.