dataesr/scientific-paragraphs-categorization huggingface dataset:
Medline training and test sets We provided training data based on Medline data for eight of the language pairs that we addressed, namely, fr/en, en/fr, de/en, en/de, pt/en, en/pt, es/en, and en/es. We released test sets for all 10 language pairs, which are the official test sets used for the shared task. The creation of the Medline training and test sets was as follows. Document retrieval. For the training data, we downloaded the Medline database 2  that included the citations available until the end of 2018. For the test sets, we downloaded the Medline update files available for 2019 until the end of February. XML parsing. We parsed the Medline files using a Python XML library. 3  Based on the metadata available, we selected the citations that contained abstracts both in English and in at least one of the foreign languages addressed in the shared task, namely, Chinese (zh), French (fr), German (de), Portuguese (pt), and Spanish (es).
Language detection. Even though the citations in Medline include the language of the abstracts, we found some mistakes in the data from last year in which some abstracts were tagged with the wrong language, e. g. Italian instead of German. Therefore, we automatically detected the language of the article using the Python langdetect library. 4  For instance, when building the training data, we detected a total of 156 abstracts that were identified with the wrong language. For the training data, this was the data that was released to the participants after removal of the abstracts in the wrong language. When building the test sets, we kept only 100 articles for each language pair, i. e. 50 articles for each direction es/en and en/es. Sentence splitting. For the test sets, we considered only the abstracts in the Medline citations and segmented them into sentences, which is a necessary step for automatic sentence alignment.
For all language pairs except for zh/en, we used the syntok Python library 5. For zh/en, we used Ling-Pipe's Medline-specific API 6  to segment the English abstracts. Splitting the Chinese ones by the language-specific period punctuation "。" (using our own script) was sufficient. Sentence alignment. For the test sets in all language pairs except for zh/en, we automatically aligned the sentences using the GMA tool. 7 We relied on the same configuration and stopword lists used for the test sets in 2018  (Neves et al. , 2018). For zh/en, we used the Champollion tool 8  , also relying on the same configurations and stopword lists used in 2018. Manual validation. We performed a manual validation of the totality of the aligned sentences in the test sets using the Quality Checking task in the Appraise tool.
, 2016)  and tested on the Brazilian thesis corpus  (Soares et al. , 2018b). For Chinese we used the United Nations Parallel Corpus  (Ziemski et al. , 2016). The data was preprocessed using standard tools from the Moses toolkit  (Koehn et al. , 2007) : tokenisation, cleaning of training data and truecasing. Subword segmentation  (Sennrich et al. , 2015)  was then trained jointly over both source and target languages and applied using FastBPE. 10 The number of merge operations for BPE was set to 85000. The models trained were shallow RNN encoderdecoders. 11 They were trained on a GTX 1080 Ti with 8 GPUs. Validation using cross-entropy and BLEU was performed every 10,000 updates, and models were trained until there was no improvement on either metric for 5 consecutive updates. Training of a single model took approximately 2 days.
Discussion. Compared to the traditional domain of news translation, biomedical MT poses additional challenges; biomedical texts contain a large amount of specialised, in-domain vocabulary, and in-domain training data is less readily available. Baselines 2 and 3: OpenNMT We also provide two additional baselines trained using OpenNMT-py  (Klein et al. , 2017)   12  , one with a small network size, and a second one with a higher number of hidden units. The data used Table  2 : Statistics (number of sentences and percentages) of the quality of the automatic alignment for the Medline test sets. For each language pair, the total of sentences corresponds to the 100 documents that constitute the two test sets (one for each language direction). for training was the Medline abstracts corpora. We trained these two baselines using the following parameters: • 2-layer LSTM for both the encoder and decoder (300 and 500 hidden units) • Vocabulary size: 32,000 • Training steps: 100,000 • Batch size: 64 • Optimization: SGD • Dropout: 0.
Manual Evaluation For the Medline test sets, we performed manual evaluation of the primary runs, as identified by the participants, for all teams and language pairs. We carried out pairwise comparisons of translations taken either from a sample of the translations from the selected primary runs or the reference translations. Specifically, sets of translation pairs, consisting of either two automatic translations for a given sentence (derived from submitted results), or one automatic translation and the reference translation for a sentence, were prepared for evaluation. Table  9  presents the primary runs that we considered from each team. We performed a total of 62 validations of pairwise datasets. We relied on human validators who were native speakers of the target languages and who were either members of the participating teams or colleagues from the research community.
We also preferred to use validators who were familiar enough with the source language so that the original text could be consulted in case of questions about the translations, and for most language pairs this was the case. We carried out the so-called 3-way ranking task in our installation of the Appraise tool (Federmann, 2010). 14. For each pairwise dataset, we checked a total of 100 randomly-chosen sentence pairs. The validation consisted of reading the two translation sentences (A and B) and choosing one of the options listed below: • A<B: the quality of translation B is higher than translation A; • A=B: both translations have similar quality; • A>B: the quality of translation A was higher than translation B; • Flag error: the translations do not seem to come from the same source sentence, probably due to errors in the corpus alignment.
Analysis of results and methods Across all language pairs, the best performing runs were those based on the Transformer architecture trained on as much data as possible from the general and biomedical domain (cf. the submissions by the ARC, Talp upc, and UCAM teams). Ensembled runs tended to perform well and gen-erally outperformed using the single best system (cf. OOM, Talp upc, and UCAM). Differences in the amount of training data available across languages appeared to have a direct impact on translation quality. The Scielo and Medline corpora are larger for es/en and en/es than for the other languages, which is reflected in the BLEU scores. For example, results for team UCAM were more than 10 points higher for es/en and en/es than for de/en and en/de, results which were mirrored for baseline 1.
Regarding zh/en and en/zh for which we do not yet provide any training data, results were inferior to the best-performing language pairs (es/en and en/es), but still surprisingly good. However the best-performing teams trained on additional in-house data (cf. ARC's submission), which was not available to the community. We compared results for this year's shared task in comparison to the previous year's  (Neves et al. , 2018). The addition of the Medline training data this year resulted in an improvement for en/de (from 24. 30 to almost 28. 00), but not for de/en. Similarly, we observed no real improvement for es/en and en/es, the highest BLEU scores for both remained in the range of 43-45 points. However, a considerably improvement occurred for en/fr, whose scores increased from almost 25 to almost 40 points, and for fr/en from almost 27 to around 35 points.
Quality of the test sets To evaluate the quality of the MEDLINE test sets, we performed an evaluation of the sentence alignment using Appraise to classify sentence pairs between "OK", "Target > Source", "Source > Target" and "No Alignment". During this process, we also noted any observation on the quality of the reference translation. Of note for this dataset, the reference translation is produced by the original authors of the papers who are scientists with likely no training in translation and whose writing competence in the languages involved is unknown. We can make the hypothesis that the authors have acquired English as a second language while they have native or near-native competence in the non English language. The quality of the alignment in the Medline test sets varied from as low as around 68% (for de/en and en/de) to as high as 84.
Acknowledgments We would like to thank all participants in the challenges, and especially those who supported us for the manual evaluation, including  Melana Uceda  (es/en). We also would like to thank the participants  Aihu Zhang  (team OOM),  Antoni Oliver Gonzalez  (team  MT-UOC-UPF ),  Fabien Cromières  (team KU),  Sadaf Abdul-Rauf  (team  NRPU ) and  Stefania Duma  (team  UHH-DS)  for providing summaries about their systems, which we included in the manuscript. MK and FS acknowledge support from the  encargo de gestión SEAD-BSC-CNS of Plan  for the  Advancement of Language Technology (Plan TL ) and the  Interreg Sudoe ICTUSnet  project.
Cell staining Cells were first blocked with purified rat anti-mouse C16/CD32 Fc block (BD Pharmingen, San Jose, CA) for 30 min at 4 °C. After a wash with FACS buffer, cell surface staining was performed at 4 °C for 30 min using the following markers: monoclonal rat antimouse CD45 PeCy7 (1/500, eBioscience, San Diego, CA), rat anti-mouse Ly6C BV421 (1/800, BD Horizon, Mississauga, ON, Canada), rat anti-mouse Ly6G FITC (1/1000, BD Pharmingen), and rat anti-mouse CD11b AF700 (1/400, BD Pharmingen). Cell viability was assessed using the Live/Dead Fixable Aqua Dead Cell Stain Kit (Invitrogen, Carlsbad, CA). Stained cells were acquired on a LSR Fortessa cytometer (BD Immunocytometry Systems, San Jose, CA) and data collection was obtained using the BD FACS Diva software (BD Bioscience, Mississauga, Ontario, Canada).
Substitution of the primary antibody served as negative control. A Zeiss Observer Z1 fluorescent microscope with the AxioVision rel4. 8 program (Zeiss, Oberkochen, Germany) was used to read the slides in a blinded fashion. For the astrocytes (GFAP) and microglia (Iba-1) quantification, three pictures (20x) around each ventricle were taken, and the positive pixels were measured using the imageJ software (v 1. 6. 0). For apoptotic neuron quantification (NeuN and cleaved caspase-3), six random pictures (× 20) were taken throughout the whole section. As suggested in the literature  [18] , two blinded investigators manually determined co-localized signals and an average was obtained per mice.
Statistical analysis The results are expressed as mean +/-standard error of the mean (SEM) and were analyzed by Student's t test (with Bonferroni correction when appropriate). P < 0. 05 was deemed to be significant for all tests. For human cytokine analyses, within-group analysis with repeated measure ANOVA was used to confirm significant variation in levels of blood-derived inflammatory factors across time in individual patients. Mean levels of inflammatory mediators stratified by dichotomized outcomes (good [mRS 0-3] or poor [mRS 4-6]) were plotted against time with their 95% confidence interval (CI) using the groupwiseMean function from the rcompanion package in R. Ordinal logistic regressions were used to model the association between each serum-derived inflammatory mediator at admission and clinical outcomes.
Neuropathophysiology of MCI and AD Since improvement in algorithms in electroencephalography (EEG) offers a promising approach to developing a database for assessing MCI and AD subjects, several researchers have underscored links between EEG signal and AD/MCI. With age, fast rhythms have been found to decrease  [5, 6]. Most commonly in the healthy aging process, EEG changes are not consistently observable but might involve a slight decrease in frequency and amplitude in the alpha band  (8) (9) (10) (11) (12) , an increase in delta (1-4 Hz) and theta (4-8 Hz) activities unlike young healthy subjects  [7, 8]. In AD, theta and delta rhythms are abnormally more dominant in temporal regions of the brain than in other cerebral regions and posterior alpha rhythms significantly decrease  [9] [10] [11] [12] [13].
Sample size The sample size calculation was estimated on the basis of our feasibility study evaluating the effects of NF training on cognitive performances in MCI subjects (registered at ClinicalTrials. gov under the identifier: NCT03526692, on 16 May 2018). In an analysis of covariance study (ANCOVA), sample sizes of 20 are obtained for each of the three groups whose expected averages of TMT B are 140 in the control group and 105 s in two intervention groups with a standard deviation of 45. The supposed R-squared of the model is 0. 20. The total sample of 60 subjects has a power of 80% to detect differences between means using an F test with a significance level of 0. 05.
From our previous pilot study described above and carried out by our multidisciplinary team (data unpublished), we noticed that the nature of the task in NF training was very important to keep motivation and concentration in our sample. To avoid sleepiness or annoyance, we chose to keep the video of historical reportage as a NF task that had been unanimously appreciated. Thus, during the 30 sessions, videos of historical reportage will be watched for 40 min, divided in eight rounds of 5 min with 20-s break between. Session will last 45 min (Fig. 2 ). The breaks prevent drowsy episodes and permit the recommencing of sustained attention. The rest of the video will be shown at the next training. The subject receives visual and auditory feedback. To modulate brain activity in the SMR frequency band or in the beta1 band, the video will indicate a bad contrast and a particular sound each time the thresholds are not reached.
EEG data acquisition Electroencephalography will be recorded by a technician in EEG and EEG data will be analyzed by a neurophysiologist "blinded" to the participants' training conditions. Neurofeedback training sessions will be performed by a doctoral student trained in EEG and EEG NF application. For each participant, EEG will be recorded and an EEG power spectrum will be calculated in pre-(T0) and post-NF training/psycho-pedagogical care at T1 and T2. We will use an electrocap of 19 scalp locations (Electro-cap, International Inc. Elmiko Medical, Warsaw, Poland) according to the international 10-20 EEG placement system, in FP1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2 with reference at the left mastoid and the ground at the right mastoid. The recording will use the sampling frequency of 500 Hz and band-pass filter (0.
Data management Technical specifications of the trial database, such as variable names, age, disease, frequency of visits and sessions will be predefined and documented in the database manual and in the electronic Case Report Form. All information on the Case Report Forms will be confidential and traceable to source documents in the patient's file; the original documents will be kept by the main investigators of the study at the Broca University Hospital in Paris. At the end of the study, personal information will be deleted. The final trial dataset and disclosure of contractual agreements will be kept at the Broca University Hospital in Paris. Interim analyses on the acquired data will be conducted and presented in meetings and conferences. Results of the final analyses will be presented at scientific conferences and published in scientific journals.
Statistical analysis will be applied to all outcome measures in a comparative analysis of data collected at baseline (T0), after the last NF training session at the 4-month (T1) and 3-month follow-ups (T2). A T test for paired data will be used for intra-group comparison. As few participants as possible should leave the program but patients who drop out should be followed up in order to record data as required by the intention-to-treat principle. Missing data will be imputed using multiple imputations by chained equations. No interim analysis and no subgroup analysis were planned. All tests will be two-sided at a 0. 2 significance level. All statistical analyses will be carried out using R statistical software version 3. 3. 2 (R Foundation for Statistical Computing, Vienna, Austria).
EEG analysis EEG data will be processed with MATLAB R2011b, MathWorks Inc. , Natick, MA, USA. We will calculate the power spectral density of EEG signals to extract the relative power (RP) and absolute power (AP) for each frequency band. By definition, the RP is a value that compares the AP within a frequency band with the total AP. So, relative theta, relative alpha, relative SMR and relative beta will be calculated in the central, frontal and temporal areas during the rest ECB and rest EOB conditions in pre-NF and post-NF training. The ANCOVA model will provide the differences in RP between these two times. When the difference is positive, it means that the RP in post-NF training for that channel is larger than the RP in pre-NF training. The variables will be used to assess differences between the SMR NF group and the control group.
Quality assurance To ensure that a large number of participants finish the study and participate in as many sessions as possible with motivation, the PhD candidate specialized in electrophysiology, neuropsychology and gerontology will meet each participant to explain specific program information, show the equipment (EEG, and EEG NF), and give a demonstration of EEG NF training before assessment at baseline. All participants will receive times and contact details of study investigators if they have questions or encounter difficulties during the intervention program. An independent technician specialized in EEG will record EEG in each patient. A neuropsychologist with full knowledge of all measurement scales and administration tests will ensure assessments and data collection. Neurofeedback training sessions will be ensured by a PhD candidate specialized in electrophysiology, neuropsychology and gerontology.
This paper describes the main characteristics of CNRM-CM6-1, the fully coupled atmosphere-ocean general circulation model of sixth generation jointly developed by Centre National de Recherches Météorologiques (CNRM) and Cerfacs for the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6). The paper provides a description of each component of CNRM-CM6-1, including the coupling method and the new online output software. We emphasize where model's components have been updated with respect to the former model version, CNRM-CM5. 1. In particular, we highlight major improvements in the representation of atmospheric and land processes. A particular attention has also been devoted to mass and energy conservation in the simulated climate system to limit long-term drifts. The climate simulated by CNRM-CM6-1 is then evaluated using CMIP6 historical and Diagnostic, Evaluation and Characterization of Klima (DECK) experiments in comparison with CMIP5 CNRM-CM5.
6  (Madec et al. , 2017) , GELATO version 6, and a new TRIP version redeveloped at CNRM and called CTRIP  (Decharme et al. , 2019). The coupling software was also updated to OASIS3-MCT  (Craig et al. , 2017) , which now allows for parallel exchanges of fields. A novelty of the ©2019. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. system is the inclusion of an output manager, the XIOS server  (Meurdesoif, 2018) , which allows efficient online output processing. As a major update of CNRM-CM5. 1, CNRM-CM6-1 is expected to better represent the climate system, both in terms of mean state and variability.
The snow is represented by a 12 layer detailed internal process snow model including a simple ice sheet runoff to avoid unrealistic snow accumulation over continental glaciers. A two-way coupling between ISBA and CTRIP is set up to account for, first, a dynamic river flooding scheme in which floodplains interact with the soil and the atmosphere through free-water infiltration and evaporation and, second, a two-dimensional diffusive groundwater scheme to represent unconfined aquifers and upward capillarity fluxes into the superficial soil. The land surface in one grid cell is tiled into 12 patches in order to account for the variety of soil and vegetation behaviors within a grid point. They aggregate the 500 land cover units at 1-km resolution present in the ECOCLIMAP-II database  (Faroux et al.
, 2013). Mean seasonal cycles of both the snow-free albedo and the leaf area index are prescribed from Moderate Resolution Imaging Spectroradiometer products at 1-km spatial resolution. The soil textural properties (clay, sand, and soil organic carbon content) are given by the Harmonized World Soil Database (http://webarchive. iiasa. ac. at/Research/LUC/External-World-soil-10. 1029/2019MS001683 Journal of Advances in Modeling Earth Systems database/HTML/) at a 1-km resolution. Finally, the mean topography is derived from the 1-km Global Multi-resolution Terrain Elevation Data 2010 (https://topotools. cr. usgs. gov/gmted_viewer/). More details can be found in  Decharme et al. (2019). Lakes, including both the Caspian and the Aral seas, are represented using the bulk FLake model, which computes the temporal evolution of the vertical lake temperature profile from the surface mixing layer to the bottom.
Modeled lakes can be covered with ice and snow. A skin temperature of a 1-mm thickness was introduced to simulate a surface temperature representative of the energy budget at the lake surface. The spatial distribution of lakes at the global scale is given by the ECOCLIMAP-II database, while the lake depth was specified from the 1-km Global Lake Depth database  (Kourzeneva, 2010). More details can be found in Le  Moigne et al. (2016). Note that FLake does not simulate the water mass evolution. The associated imbalance between precipitation, runoff, and evaporation over lake is thus artificially spread uniformly over the ocean. Over the ocean, SURFEX resolves the exchange of momentum, energy, and water across the air-sea interface. The radiative properties of the seawater are handled by the ocean surface albedo scheme proposed by  Séférian et al.
Sea Ice Component GELATO Sea ice within CNRM-CM6-1 is represented by Gelato 6. Most upgrades from version 5 of the code  (Chevallier et al. , 2013; Voldoire et al. , 2013)  aimed at improving the overall consistency of the code (in 10. 1029/2019MS001683 Journal of Advances in Modeling Earth Systems particular, salt, water, and energy conservation) and increasing its computational efficiency. In contrast with Gelato 5, Gelato 6 is a fully parallel code. It is embedded within NEMO, has the same horizontal grid, and inherits the global domain decomposition of the ocean code. Within the standard configuration of CNRM-CM6-1, Gelato has a time step of 3,600 s. It is used with five ice thickness categories, based on the World Meteorological Organization classification (less than 0. 30, 0. 3-0. 7, 0.