dataesr/scientific-paragraphs-categorization huggingface dataset:
The ICR5 participants discussed the merits, limits, and challenges of reanalyses for monitoring climate variability and change, for supporting policy makers in the development of adaptation and mitigation measures, and for providing information complementary to that from other climate sources. ICR5's presentations, oral and posters, 2  were organized around five topics: 1) status and plans for reanalysis production (production, inclusive of all Earth system thematic areas-atmosphere, land, ocean, and cryosphere), 2) observations for reanalysis (preparation, organization in large archives, data rescue, and reanalysis feedback), 3) methods for reanalysis (data assimilation, Earth system coupling, uncertainty estimation, and challenges specific to regional reanalyses), 4) evaluation of reanalyses (comparisons with observations, other types of analysis and models, intercomparisons, and diagnostics), and 5) applications of reanalyses (generation of time series of essential climate variables for climate monitoring, climate services, and industry applications).
WHAT ARE REANALYSES? • Reanalyses are digital datasets that represent the evolution of the Earth's climate system. They cover the diversity and complexity of the Earth's climate system: multicomponents (e. g. , atmosphere, ocean, land, ice), multivariables (e. g. , temperature, humidity, wind, ozone), and multidimensions (e. g. , subdaily to seasonal scales, regional to global scales, ocean subsurface depths to the surface where we live to the upper atmosphere), with a growing role for the human factor (anthropogenic), without which one cannot understand or reproduce some recent climate evolutions (such as ozone depletion and the increase in greenhouse gases). • Reanalyses bring together the full history of observations and our growing computing power, and blend all this using the latest science (including advances in mathematics, physics, and information technology).
Registered user counts at several institutions exceed tens of thousands. uncertainty quantification, use in reanalysis, and feedback analysis. 2) Funding of essential datasets needs to be increased and sustained. Funding for their maintenance and improvements has been cut to below a sustainable level. 3) In situ and satellite observing systems that have been proven to serve as critical references (also referred to as anchors) for reanalysis need to be preserved and extended in time. These include measurements of sea surface temperature, sea ice concentration and thickness, atmospheric surface pressure, and soil moisture, as well as measurements by radiosondes, ocean profiling floats and the moored buoy array, GPS radio occultation, and scatterometers from satellites. 4) Data rescue of historical observations and reprocessing of observations (e.
g. , satellite) to improve their quality and utility needs to be continued. 5) The sea ice and meteorological observations for the Southern Ocean, recently discovered in various archives for the late nineteenth and early twentieth centuries, should be digitized to improve our understanding and representation of this key region. Methods for reanalysis. Reanalysis methods have been improving considerably in the past five years. Ensembles are increasingly being used, either exclusively or in hybrid systems. Other techniques, such as particle filters/smoothers, are being tested. Coupled approaches (e. g. , ocean, land, cryosphere, and atmosphere) have been shown to be promising and yield benefits. Historical reanalyses going back to the nineteenth century and beyond have been created. Improvements in the methods, to include more robust uncertainty quantification associated with observations, models, and data assimilation, are needed.
In this area of work, the following recommendations are made: 1) Reanalysis is not a research priority for most national and international programs. However, reanalyses are among the most used datasets. As a result of this lack of priority, most of the data assimilation development adopted for reanalysis currently takes place where resources are available, at numerical weather prediction (NWP) centers. They predominantly focus on their usual (i. e. , near-real time) requirements, rather than the use of historical observations, to reconstruct a climate record. These centers and the community at large need to incorporate clearly articulated requirements for climate reanalysis in the development pathways for data assimilation. Systems do not work "off the shelf" to reanalyze observations 10, 20, or 100 years earlier.
2) There is a gap in research funding for designing data assimilation methods specifically targeting climate reanalysis. Those developments are required and need to be adequately resourced and prioritized. 3) The EU Copernicus project has brought substantial resources to improving data access, understanding end-user requirements, enhancing observational databases, carrying out global and European regional reanalyses, and providing operational services based on reanalysis. However, there is no similar system or mechanism for supporting the underlying research needed to improve and sustain these services and some of the downstream regional reanalysis. This also appears to be the case across non-EU countries. Evaluation of reanalyses. The evaluation of new, stateof-the-art reanalyses has shown improvement in the representation of several different processes, among them precipitation and soil moisture.
Applications of reanalyses. There are a growing number of applications, in a variety of sectors (e. g. , wind energy and hydrology). There is a common request to have easier data access, and more clear guidance (e. g. , on the estimation of reanalyses' uncertainty). Desire for higher resolution and accuracy for surface variables is a driving demand for regional reanalysis and various downscaling techniques. With respect to applications of reanalyses, the following recommendations are made: 1) The priority given to reanalysis activity needs to be raised to the level of its impact. Reanalyses are crucial for climate monitoring, to initialize weather reforecasts used to generate operational weather products [see, e. g. , the ECMWF and the National Centers for Environmental Prediction (NCEP) experiences] and climate predictions, and to verify their accuracy across long time records.
They represent a fundamental reference to which those models can be compared in order to evaluate the merits and skill of different prediction systems across a wide range of complementary metrics. 2) Continuous production is recommended to achieve higher resolution, faster access, longer time series, better quality, and couplings with the components of the Earth system (atmosphere, ocean, land, ice, atmospheric chemistry, and the biosphere and hydrosphere). Due consideration of the anthropogenic influence may need more attention in future reanalyses (e. g. , irrigation, land-use change, and urbanization). 3) Better and more "actionable" uncertainty characterization is required: users request simple measures of the reanalyses' uncertainty. 4) The input needed by applications is still dependent on the uncertainty and accuracy of observations and data assimilation techniques.
There are still many observations missing in data-sparse regions, or from historic periods, and a need for less constrained parameters, such as SST, sea ice, deep ocean information, and soil moisture. 5) Intercomparison and evaluation activities need to continue for global and regional reanalyses, in order to benchmark each dataset. 6) The user community is asking for reanalyses of higher resolution, faster access, longer time series, and better quality. A special interest was noted for coastal and urban applications. 3) Methods for reanalysis-Although the conference reported on new ideas (smoothers), there is a general gap in research funding to design data assimilation and coupling methods across the Earth system specifically designed for reanalysis. 4) Evaluation of reanalyses-Diagnostic and evaluation activities that look at the mean climate, variability, and extremes of globally coupled atmosphere-ocean-land Earth system reanalyses should be promoted, taking stock of successful examples of ocean (and atmosphere) reanalysis intercomparison projects.
Increased interaction between research groups working on regional and global reanalyses would be highly beneficial. The World Climate Research Programme (WCRP) Task Team for the Intercomparison of Reanalyses (TIRA; https://reanalyses. org/wcrp-task-team -intercomparison-reanalyses-tira) is charged with developing plans for promoting and coordinating such activities with the community and platforms, such as Reanalyses. org and the NCAR Climate Data Guide. 5) Applications of reanalyses-Synergies between reanalyses' producers and users must be increased. There is a need for better and more applicable uncertainty characterization. The proper quantification and communication of the quality of reanalyses must be promoted and would broaden their usage in operational services and policy making.
to enhancing the scope and range of reanalyses, by allowing datasets to be extended back in time with high-quality observations. Integrated datasets for marine and land surface, such as the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), are essential. Looking ahead to the continuation of climate time series, there is an urgent need to strengthen and enhance the Global Climate Observing System (GCOS) and the Global Ocean Observing System (GOOS), which includes support for implementing critical in situ and satellite measurements.
to enhancing the scope and range of reanalyses, by allowing datasets to be extended back in time with high-quality observations. Integrated datasets for marine and land surface, such as the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), are essential. Looking ahead to the continuation of climate time series, there is an urgent need to strengthen and enhance the Global Climate Observing System (GCOS) and the Global Ocean Observing System (GOOS), which includes support for implementing critical in situ and satellite measurements. 7) Enhanced communication between producers and users is required. Platforms for exchanging information-like the website Reanalyses. org or the National Center for Atmospheric Research's (NCAR) Climate Data Guide-should continue to promote such discussion.
Acknowledgments: We are grateful to  Marie-Christine Enos  for her administrative and technical support. This research was funded by the  French National Research Agency (ANR)  and by the  Regional Council of Basse-Normandie  through the  ANR TecSan PROMOCART program  to P. G. (917RB020 and 917RB072 respectively), by the  French Ministry of Research and Technology  to PG, by three  ERDF (European Regional Development Funds)  grants to P. G. and M. D. (HIPPOCART 1 no. 2897/33535 ,  917RB148 ;  HIPPOCART 917CB174 ,  EQUISTEM 917CB210 ), by a  Regional Council of Basse-Normandie program  to PG and  MD (HIPPOCART N • 2013-AGRI-236/13P07492 ,  917CB166 ), by  Fonds Eperon  to PG and MD (EQUISTEM,  N80-2014 ,  917CB194 ), by  ERDF  and  Regional Council of Basse-Normandie  grant to P. G. and M. D.
Several indicators of landscape composition and structure in Plaine Mothaise were calculated. Land cover features of the study site were determined using aerial photographs (Google Earth) and field inspections. Linear landscape characteristics such as riparian, hedge, continuous  and intermittent vegetation were also distinguished. The following final land use categories were then established: cropland, grassland, forest, connection, roads and urban. Landscapes were mapped using Arcmap 9. 3 (ESRI 2004) as a main geographical information system and the database was created. Table  2  shows the correlation between the degree of response (as measured by the diversity of isopods) and the landscape descriptors. Results show that the riparian vegetation and other connecting elements in the landscape provide higher diversity of terrestrial isopods.
Acknowledgements The funding is partly grant-aided by the following 2015-2020 programs: the  State-Region Planning Contracts (CPER)  and the  European Regional Development Fund (FEDER). Thanks are due to  Julian Reynolds  ( Ireland ) for advice and improving the English. We are also thankful for the help of  Mark Hassall  ( University East Anglia, Norwich, UK ) for increasing the quality of the English and suggesting the conceptual diagram in order to introduce a clearer view of the content of the manuscript. We would like to thank  Katalin Szlávecz  ( Johns Hopkins University, Baltimore, MD, USA ) for her helpful comments. Thanks to  Dr. Aline Quadros  for assisting with the revised version of figures and tables.
Group 3 pulmonary hypertension (PH) is a common complication of advanced chronic lung disease. Our hypothesis was that group 3 PH is associated with a more severe baseline presentation and a more severe prognosis compared to group 1 pulmonary arterial hypertension (PAH), chronic thromboembolic PH (group 4), and group 5 PH. We retrospectively analyzed consecutive incident PH patients in a single center between January 2006 and November 2014. Data were acquired from a prospective database. Clinical, functional, and hemodynamic characteristics, as well as survival, were compared between the four groups of precapillary PH. A total of 363 patients were analyzed; 164 patients (45. 2%) belonged to group 1 PAH, 109 (30%) to group 3 PH, 65 (17. 9%) to group 4 PH, and 25 (6. 9%) to group 5 PH. Group 3 patients were predominantly male and were more frequently in New York Heart Association (NYHA) class III/IV.
Study population This monocentric study was conducted in the pneumology department of the Louis Pradel Hospital, Hospices Civils de Lyon (Lyon), France. This department is designated as a PH expert center as well as a national reference center for rare pulmonary diseases. We performed a retrospective analysis of data acquired in a prospective database. Incident patients were included if a diagnosis of precapillary PH was established using right heart catheterization (RHC) with a mean PAP (mPAP) 25 mmHg and a pulmonary capillary wedge pressure (PCWP) 15 mmHg. Pulmonary vascular resistance (PVR) > 3 Wood Units (WU) was required for the diagnosis of PAH. Patients with post-capillary PH (PCWP > 15 mmHg) were excluded. Inhaled nitric oxide (NO) was used for vasoreactivity testing. Pulmonary vascular pressures were measured at the end of expiration at RHC.
The database was anonymous and complied with the restrictive requirements of the CNIL (Comission nationale de l'informatique et des liberte´s, the French organization that regulates the collection, storage, and use of personal data) and the CCTIRS (Comite´consultatif sur le traitement de l'information en matie`re de recherche dans le domaine de la sante´, the French advisory committee on health research data processing). Written consent was obtained from all patients. The study was approved by the CEPRO (Comited 'e´valuation des protocoles de recherche observationnels, the Institutional Review Board of the French learned society for respiratory medicine, Socie´te´de Pneumologie de Langue Francaise).
Statistical analysis Statistical analysis was performed with SPSS Statistics (International Business Machines, New York, NY, USA). Quantitative variables were expressed as median (minimummaximum value) and were compared using Mann-Whitney and Kruskal-Wallis tests. Qualitative variables were compared using the chi-squared test and Fisher's exact test when necessary. Survival status was estimated using the Kaplan-Meier method. The Log-rank test was used to compare survival distribution. Cox regression model was used to analyze factors associated with mortality. The proportional hazards assumption was verified by creating a Cox model with timeby-covariate interactions for each variable (introducing products between the variables and a linear function of time) and testing for their significance.
8%) had mild obstructive disease with FEV1 > 60% and no significant radiological abnormalities; hence, they were subsequently not classified as group 3 patients. Complete hemodynamic measures were missing for eight patients (0. 02%). However, survival data were obtained for all patients and no other data concerning the baseline characteristics were missing. Another limitation of our study is its monocentric design, which may hinder the extrapolation of results. However, our study shows similar one-year survival rates for PAH patients in comparison to other registries (85% vs. 79-84%). In conclusion, the four groups of precapillary PH have different clinical, functional, and hemodynamic presentations. Group 3 PH is characterized by the worst prognosis, partially due to the higher proportion of elderly people.
Statistical analyses All statistical analyses were conducted with SPSS® Statistics 21 program. The possible association between the size of broodstocks and the number of oocytes expelled was studied using the Pearson correlation factor. The best survival rate was determined studying the effect of the tank volume (fixed factor, three levels) and the larvae density (fixed factor, three levels) and calculated by a two-way factorial ANOVA. The effect of photoperiod (fixed factor, two levels), food dose (fixed factor, three levels) and rearing day on larvae survival, was investigated using a three-way factorial ANOVA followed by a post-hoc analysis (DHS-Tukey) to establish significant groupings. All data were tested for ANOVA assumptions of normality (Levene's test) and homogeneity of variances (Cochran's test).
In experiments on the CoNLL-2005 and CoNLL-2012 datasets we show that our linguistically-informed models out-perform the syntax-free state-of-the-art. On CoNLL-2005 with predicted predicates and standard word embeddings, our single model out-performs the previous state-of-the-art model on the WSJ test set by 2. 5 F1 points absolute. On the challenging out-of-domain Brown test set, our model improves substantially over the previous state-of-the-art by more than 3. 5 F1, a nearly 10% reduction in error. On CoNLL-2012, our model gains more than 2. 5 F1 absolute over the previous state-of-the-art. Our models also show improvements when using contextually-encoded word representations  (Peters et al. , 2018) , obtaining nearly 1. 0 F1 higher than the state-of-the-art on CoNLL-2005 news and more than 2.
(2015). More recent neural models are syntax-free. Zhou and Xu (2015) ,  Marcheggiani et al. (2017)  and  He et al. (2017)  all use variants of deep LSTMs with constrained decoding, while  Tan et al. (2018)  apply self-attention to obtain state-ofthe-art SRL with gold predicates. Like this work,  He et al. (2017)  present end-to-end experiments, predicting predicates using an LSTM, and He et al. (2018) jointly predict SRL spans and predicates in a model based on that of  Lee et al. (2017) , obtaining state-of-the-art predicted predicate SRL. Concurrent to this work,  Peters et al. (2018)  and  He et al. (2018)  report significant gains on PropBank SRL by training a wide LSTM language model and using a task-specific transformation of its hidden representations (ELMo) as a deep, and computationally expensive, alternative to typical word embeddings.
We find that LISA obtains further accuracy increases when provided with ELMo word representations, especially on out-of-domain data. Some work has incorporated syntax into neural models for SRL. Roth and Lapata (2016)  incorporate syntax by embedding dependency paths, and similarly  Marcheggiani and Titov (2017)  encode syntax using a graph CNN over a predicted syntax tree, out-performing models without syntax on CoNLL-2009. These works are limited to incorporating partial dependency paths between tokens whereas our technique incorporates the entire parse. Additionally,  Marcheggiani and Titov (2017)  report that their model does not out-perform syntax-free models on out-of-domain data, a setting in which our technique excels. MTL  (Caruana, 1993)  is popular in NLP, and others have proposed MTL models which incorporate subsets of the tasks we do  (Collobert et al.
5 (de  Marneffe and Manning, 2008). A detailed description of hyperparameter settings and data pre-processing can be found in Appendix A. We compare our LISA models to four strong baselines: For experiments using predicted predicates, we compare to  He et al. (2018)  and the ensemble model (PoE) from  He et al. (2017) , as well as a version of our own self-attention model which does not incorporate syntactic information (SA). To compare to more prior work, we present additional results on CoNLL-2005 with models given gold predicates at test time. In these experiments we also compare to  Tan et al. (2018) , the previous state-of-the art SRL model using gold predicates and standard embeddings. We demonstrate that our models benefit from injecting state-of-the-art predicted parses at test time (+D&M) by fixing the attention to parses predicted by  Dozat and Manning (2017) , the winner of the 2017 CoNLL shared task  (Zeman et al.
2), but LISA with D&M parses still achieves the highest F1: nearly 1. 0 absolute F1 higher than the previous state-of-the art on WSJ, and more than 2. 0 F1 higher on Brown. In both settings LISA leverages domain-agnostic syntactic information rather than over-fitting to the newswire training data which leads to high performance even on out-of-domain text. To compare to more prior work we also evaluate our models in the artificial setting where gold predicates are provided at test time. For fair comparison we use GloVe embeddings, provide predicate indicator embeddings on the input and reencode the sequence relative to each gold predicate. Here LISA still excels: with D&M parses, LISA out-performs the previous state-of-the-art by more than 2 F1 on both WSJ and Brown. Table  3  reports precision, recall and F1 on the CoNLL-2012 test set.
We observe performance similar to that observed on ConLL-2005: Using GloVe embeddings our SA baseline already out-performs  He et al. (2018)  by nearly 1. 5 F1. With its own parses, LISA slightly under-performs our syntax-free model, but when provided with stronger D&M parses LISA outperforms the state-of-the-art by more than 2. 5 F1. Like CoNLL-2005, ELMo representations improve all models and close the F1 gap between models supplied with LISA and D&M parses. On this dataset ELMo also substantially narrows the Dev P R F1 GloVe  He et al. (2018)   2017 ) by 1. 5-2 F1 points even on the out-ofdomain Brown test set, suggesting that multi-task learning works well for SRL predicate detection.
Conclusion We present linguistically-informed self-attention: a multi-task neural network model that effectively incorporates rich linguistic information for semantic role labeling. LISA out-performs the state-ofthe-art on two benchmark SRL datasets, including out-of-domain. First, we compare the impact of Viterbi decoding with LISA, D&M, and gold syntax trees (Table  7 ), finding the same trends across both datasets. We find that Viterbi has nearly the same impact for LISA, D&M and gold parses: Gold parses provide little improvement over predicted parses in terms of BIO label consistency. We also assess SRL F1 as a function of sentence length and distance from span to predicate. In Figure  5  we see that providing LISA with gold parses is particularly helpful for sentences longer than 10 tokens.
Acknowledgments We are grateful to  Luheng He  for helpful discussions and code,  Timothy Dozat  for sharing his code, and to the  NLP reading groups at Google and UMass  and the anonymous reviewers for feedback on drafts of this work. This work was supported in part by an  IBM   PhD Fellowship Award  to E. S. , in part by the  Center for Intelligent Information Retrieval , and in part by the  National Science Foundation  under Grant Nos. DMR-1534431  and  IIS-1514053. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.