dataesr/scientific-paragraphs-categorization huggingface dataset:
This is in line with  Barnhart et al. (2016)  who discuss this question by looking at 26 d after the mainshock and using a similar dataset to ours (i. e. , 9 of the 15 GNSS stations plus InSAR data). They find some overlap between co-seismic and post-seismic slip but also conclude that this result is not robust enough to be interpreted. Finally, we also identify in our models a region where significant slip occurs that is located southeast of Salamanca (station SMLC). This region is further away from the co- seismic rupture area and is disconnected from the main regions of post-seismic slip. After some tests (see Supplement S5), we conclude that this patch is an unreliable feature and is therefore disregarded in the discussion section. When this region is not taken into account, the final geodetic moment decreases to 8.
This illustrates that this patch is stable, regardless of the dataset used. As a next step, we analyze the temporal evolution between the amount of aseismic afterslip and the number of aftershocks in the northern patch (Fig. 8 , and Supplement S7 for   2017 ) and shown with black dots. Detailed caption can be found in Fig. 3. the same comparison using the catalog of  Frank et al. , 2017). First, we find that the cumulative afterslip exhibits a logarithmic trend. Similarly to other studies carried out in Ecuador and Japan  (Tsang et al. , 2019; Milliner et al. , 2020, respectively) , we do not observe the acceleration phase of afterslip predicted by  Perfettini and Ampuero (2008). Indeed, before reaching the steady state, under a rate-and-state friction law, slip is expected to accelerate transiently before it starts to expand quasi-statically.
Code and data availability. The 30 s kinematic position time series used in this study can be found here: https://github. com/ cedrictwardz/2015_IllapelEarthquake_PositionTimeSeries. git (Twardzik, 2021a). A Python code used for building and applying the sidereal filter to the raw position time series can be found here: https://github. com/cedrictwardz/SiderealFilter. git (Twardzik, 2021b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/se-12-2523-2021-supplement. Author contributions. CT processed the GNSS data, performed the post-seismic slip inversion, and wrote the paper with inputs from all co-authors. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. The continuous GNSS stations are part of the Chilean GNSS network maintained by the  Centro Sismológico Nacional of the University of Chile  (Baez et al. , 2018). We thank  Christophe Vigny  for facilitating the access to the GNSS data. We thank the editorial team and  Bernd Schurr ,  Mathilde Radiguet , and one anonymous referee as well as the community reviewer Dietrich Lange. All the comments and suggestions have greatly improved the quality of the work presented here. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR 14-CE03-0002-01JCJC E-POST ), the  European Research Council ,  H2020   European Research Council  (grant no. PRESEISMIC (805256) ), and the  Centre National d'Etudes Spatiales. Review statement. This paper was edited by  CharLotte Krawczyk  and reviewed by  Bernd Schurr ,  Mathilde Radiguet , and one anonymous referee.
Dataset and analysis periods We use the operational Copernicus S5P TROPOMI NO 2 level 2 product, for which data have been available since 28 June 2018. These observations are tropospheric columns (from the surface to the top of the troposphere) with a pixel resolution of 5. 5 km by 3. 5 km since 6 August 2019 and 7 km by 3. 5 km before. The instrument can have an up-to-daily revisit at 13:30 mean local solar time (LST) assuming clearsky conditions. The quality flag (qa) provided with the retrieval is used to select only good-quality data (qa > 0. 75), which removes cloud-covered scenes, errors, and problematic retrievals  (Eskes and Eichmann, 2019). The TROPOMI data are then binned on a regular 0. 1 • × 0. 1 • grid to perform statistical analyses and to facilitate the processing of time series for the locations of interest, i.
e. large European cities in this study (see Sect. 2. 2), as well as the comparison with other datasets such as the 0. 1 • × 0. 1 • CAMS regional air quality models  (Marécal et al. , 2015)  and the 9 km resolution weather forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). In this study we consider February, March, and April 2020 and 2019 to assess the changes in NO 2 columns due to COVID-19 restrictions over Europe. Although the lockdown conditions and dates vary between countries, we consider the 15 March 2020 to be a representative starting date for the European-wide lockdown given that most European countries implemented their nation-wide social distancing measures along the 2-week period from 9 March 2020 (Italy) to 23 March 2020 (United Kingdom, UK). Two periods of the year are considered in this study: the pre-lockdown period from 1 February to 15 March and the lockdown period from 16 March to 31 April.
Non-weather-normalized changes in TROPOMI NO 2 tropospheric columns Changes in NO 2 tropospheric columns associated with the lockdown measures can be estimated by comparing NO 2 levels observed during the lockdown period in 2020 with a given baseline. In this section, we compare the results obtained with two different baselines: (1) the NO 2 levels observed during the pre-lockdown period in 2020 (hereafter referred to as the "before-during" approach), (2) the NO 2 levels observed during the same period of the year in 2019 (hereafter referred to as the "year-to-year" approach). We focus our study on the largest European urban areas for which the city population exceeds 0. 5 million inhabitants (according to the population database provided by https://simplemaps. com/data/ world-cities, last access: January 2021), resulting in a total of 100 locations.
This choice is motivated by the small size of the available training dataset (about 10 000 data points; see Table  1 ). After the hyperparameter tuning and evaluation of the model, the BAU observation simulations have been generated using 100 % of the January-May 2019 dataset to use the maximum number of data points possible. In 2019, the GBM predictions follow the variations seen in the observations but do, however, also show differences, being either above or below the observations. In 2020, similar behaviour is observed until the lockdown date, when the GBM predictions show consistently higher values than the observations but still follow the same variations as the obser-vations. This shows that the GBM predictions based on BAU predictors perform realistically and account for the variability in the BAU scenario.
At each station (independently), we estimated the BAU NO 2 mixing ratios that would have been observed during the lockdown period under an unchanged emission forcing. This was done using GBM models fed with meteorological inputs (2 m temperature, minimum and maximum 2 m temperature, surface wind speed, normalized 10 m zonal and meridian wind speed components, surface pressure, total cloud cover, surface net solar radiation, surface solar radiation downwards, downward UV radiation at the surface, and PBL height) taken from the 31 km horizontal resolution ERA5 reanalysis dataset  (Hersbach et al. , 2020)  in addition to other time features (date index, Julian date, weekday, hour of the day). The ERA5 reanalysis dataset is a consistent model version over time but at coarser resolution in comparison to the ECMWF high-resolution operational forecasts used in the TROPOMI estimates (31 km versus 9 km).
All GBM models were trained and tuned with data for the past 3 years (2017-2019) and tested with data from 2020 before the lockdown. Petetin et al. (2020)  showed that such duration for training the GBM models is generally sufficient for capturing the influence of the weather variability on surface NO 2 mixing ratios. As discussed in more detail in  Petetin et al. (2020) , the date index feature here allows the limitation of the potential issues related to the presence of trends in the NO 2 time series (between a 2 % and 4 % decrease per year; EEA 2020a). If a substantial trend exists, the GBM models will put more importance on this feature, which in practice will force the model to make NO 2 mixing ratio predictions (in 2020) in the range of the values observed during the last part of the training dataset, ignoring the oldest training data.
Results Table  3  shows the overall performance of the GBM models in the training and test data sets. Statistical results are similar to the TROPOMI NO 2 GBM model. Biases are low, correlation is high, and there is a significant RMSE. As explained in Sect. 2. 3. 2, statistical scores in the training set and the test set suggest that there is no apparent sign of overfitting in the predictions showing reasonable performance. Note that the RMSE and PCC are deteriorated compared to the statistics obtained over Spain in  Petetin et al. (2020) , mainly due to the fact that we are working with hourly estimates here. This is demonstrated by similar results as those of  Petetin et al. (2020)  that are obtained over this set of European cities when predicting NO 2 at the daily scale (for the test dataset: nRMSE = 28 %, PCC = 0.
The emission inventory used for the BAU reference simulation is the same that is used in the daily regional air quality forecasts of CAMS for Europe, i. e. the CAMS-REG-AP dataset (v3. 1 for the reference year 2016;  Granier et al. , 2019). It is compiled by TNO (Netherlands Organisation for Applied Scientific Research) under the CAMS emission service, based on official emissions reported by the countries to the EU (National Emissions reduction Commitments (NEC) Directive) and United Nations Economic Commission for Europe (UNECE; Longrange Transboundary Air Pollution (LRTAP) Convention-European Monitoring and Evaluation Programme (EMEP);  Kuenen et al. , 2014). The spatial resolution of the emissions is 0. 1 • × 0. 05 • but re-gridded to 0. 1 • × 0. 1 • to match the models' grid. The alternative emission scenario, corresponding to the lockdown period, was derived by combining the original CAMS-REG-AP inventory with a set of country-and sector-resolved reduction factors  (Guevara et al.
The modelling domain covers Europe at 0. 1 • × 0. 1 • resolution. The meteorological and chemical boundary conditions are obtained from the Integrated Forecasting System (IFS) of the ECMWF, which is the same system that provides part of the dataset for the ML-based estimations (see Sects. 2 and 3). The baseline simulation was using the BAU anthropogenic emissions as described above, and the lockdown scenario was using the lockdown-adjusted inventory, modulated by country and activity sectors. From the two sets of 11 model simulations, the median at each grid point is calculated from an ensemble simulation (as is routinely done for the operational CAMS predictions;  Marécal et al. , 2015). Differences between the BAU ensemble and the lockdown scenario ensemble are then used to calculate NO 2 reduction estimates.
Figure7. Weather-normalized estimation of NO 2 changes (%; relative to the BAU predictions) using surface observations during the lockdown period using business-as-usual (BAU) simulated observations as the baseline for urban areas with at least 0. 5 million inhabitants. Panel (a) shows the estimates using full hourly datasets, and panel (b) shows the estimates using the S5P-sampled overpass time dataset. The diameter of the circles is proportional to the population in each urban area.
Performance of the machine learning simulations of NO 2 tropospheric columns over all European urban areas included in the dataset. The training set and testing set cover January-May 2019 and are randomly sampled (90 % and 10 %, respectively) over that period. Shown are the mean bias (MB), normalized mean bias (nMB), root mean square error (RMSE), normalized root mean square error (nRMSE), Pearson correlation coefficient (PCC), and the number of data points (N).
Acknowledgements. The research leading to these results has received funding from the  Copernicus Atmosphere Monitoring Service (CAMS) , which is implemented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission. We acknowledge support from the  Ministerio de Ciencia, Innovación y Universidades  (MICINN), as part of the  BROWNING  project  RTI2018-099894-B-I00  and  NUTRI-ENT  project  CGL2017-88911-R ; the  AXA Research Fund ; and the  620 European Research Council  (grant no. 773051 , FRAG-MENT). We also acknowledge  PRACE  and  RES  for awarding access to Marenostrum4 based in Spain at the  Barcelona Supercomputing Center  through the eFRAGMENT2 and  AECT-2020-1-0007  projects. This project has also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement  H2020-MSCA-COFUND-2016-754433.
Carlos Pérez García-Pando  also acknowledges the support received through the  Ramón y Cajal  programme (grant no. RYC-2015-18690 ) of the MICINN. Modelling and satellite data were produced by the  Copernicus Atmosphere Monitoring Service. We thank the three anonymous reviewers for their helpful comments that improved this paper. Financial support. This research has been supported by the  Ministerio de Ciencia, Innovación y Universidades  (MICINN), as part of the  BROWNING  project  RTI2018-099894-B-I00  and  NUTRI-ENT  project  CGL2017-88911-R ; the  AXA Research Fund ; the 620  European Research Council  (grant no. 773051 , FRAGMENT);  PRACE  and  RES  through the eFRAGMENT2 and  AECT-2020-1-0007  projects; the  European Union 's  Horizon 2020 research and innovation programme  ( Marie Sklodowska-Curie  grant agreement  H2020-MSCACOFUND-2016-754433 ); and the  Ramón y Cajal programme  (grant no.
Links to the Python libraries and functions: -Scikit-learn Python https://scikit-learn. org/stable/index. html (last access: January 2021) -Gradient boosting function https://scikit-learn. org/stable/modules/generated/ sklearn. ensemble. GradientBoostingRegressor. html (last access: January 2021) -Grid search hyperparameter tuning https://scikit-learn. org/stable/modules/generated/ sklearn. model_selection. GridSearchCV. html (last access: January 2021) -Random dataset splitting https://scikit-learn. org/stable/modules/generated/ sklearn. model_selection. train_test_split. html (last access: January 2021) Data availability. The satellite data from the TROPOMI instrument can be accessed at https://doi. org/10. 5270/S5P-s4ljg54 (Copernicus Sentinel-5P, 2018). The surface station air quality measurements can be accessed from the AirBase database at https://doi.
ACKNOWLEDGEMENTS This study was supported by grants from the  Swiss National Science Foundation  ( 31003A_173219  to MPT), the  Bernese Cancer League , " Stiftung fur klinischexperimentelle Tumorforschung ", and the  Werner and Hedy Berger-Janser Foundation for Cancer Research  (to MH). SLM is supported by  Breakthrough Cancer Research, Ireland. SLM, SM, MPT, and MH also acknowledge the support of the  TRANSAUTO-PHAGY COST Action  ( CA15138 ). The Muller laboratory was funded by the  French Centre National de la Recherche Scientifique ,  Région Grand-Est , and the  University of Strasbourg Institute for Advanced Study (USIAS)  and the  Interdisciplinary Thematic Institute 2021-2028 program  of the  University of Strasbourg ,  CNRS , and  Inserm  ( ANR-10-IDEX-0002  and  ANR-20-SFRI-0012 ) in the frame of the  Strasbourg drug discovery and development Institute (IMS).
Survey questionnaire distribution Data collection for this study is done using a selfgoverning survey method and the https:/docs. google. com ('Google Docs') online survey portal. Questionnaire surveys were distributed to the 200 executives and 200 non-executives from one Government Link Company (GLC) in order to explore the relationship between variables and help to confirm or reject the assumptions derived for this study. However, by the end of the survey period, only 257 responses were collected, which equals 64. 3% out of the total employees who participated. The survey period was approximately one month in April 2019. There are a few advantages to using an online survey. Less time is needed to conduct an online survey compared to traditional manual research methods. It is also not expensive, as it was unnecessary to spend on postage.
Besides, online surveys take less time to enter the information in the database as the responses are transferred automatically, and the results are available at any time. The margin of error is significantly reduced with online surveys. Currently, most people prefer to respond to online surveys using smartphones to complete the survey anytime and anywhere. It also provides convenience to respondents as it is mobile and adopts a green approach with less or no use of papers. The questionnaire in this study was designed to be concise and straightforward, in the format of pre-coded closed-ended questions. The questionnaire was also prepared in bilingual form (English and Malay). A great deal of attention focuses on the wording, structure, and layout of the questionnaire to avoid any misunderstanding among the respondents in answering all the questions.
MATERIAL AND METHODS Participants 25 full-term, normal-hearing infants (12 females, 13 males) coming from a French-speaking environment were tested between 12 and 14 weeks after birth (mean age= 12 weeks and 6 days). An additional 16 participants were excluded from analysis because of: excessive agitation during the experimental session (n=6), insufficient number of trials after artifact rejection (n=3, the artifact rejection procedure is described below), technical problems during data collection (n=3), aberrant global field power (GFP) in the average of all syllable-related potentials (i. e. peak GFP<4µV, n=4). The protocol was approved by the regional ethical committee for biomedical research (CPP Region Centre Ouest 1). Parents gave their written informed consent before starting the experiment.
1 kHz. Recordings were first cleared from background noise in Audacity 2. 1. 3 (https://www. audacityteam. org) and further edited with PRAAT software  (Boersma & Weenink, 2017). Acoustic transients (clicks) were manually removed and stimuli length was adjusted to fall within the range of 350-425ms. Tokens were normalized for peak amplitude and average (i. e. root-mean square) intensity, obtaining maximal audibility and loudness equalization. All stimuli were placed on the left channel and a click was positioned on the right channel at the exact time-point of syllable onset. The left channel was connected to the audio amplifier (mono input to the loudspeakers) while the right channel was connected to the EEG amplifiers through the DIN port to create a TTL signal. Brain voltage and clicks were recorded simultaneously with the same temporal resolution providing a precise mapping between EEG recording and stimulation.
Procedure Subjects were tested in a soundproof Faraday cage equipped with a computer screen and loudspeakers on the top. Infants were hold by a caregiver, their position was chosen to guarantee personal comfort and at the same time enable good-quality data acquisition. Syllables were broadcast through the loudspeakers at 70 decibels, in a latinsquare randomized order and with an inter-stimulus interval (ISI) randomly picked between 600 and 1000ms. To minimize body movements we presented engaging visual animations that were unsynchronized with the auditory stream. Sleep was highly encouraged at any time; on average our subjects slept for 65% of the experimental session. Pauses were made whenever needed. The experiment finished with the presentations of 3136 tokens (corresponding to approximately 63 minutes of listening time) or as soon as infants became restless.
EEG recording and data preprocessing The electroencephalogram (EEG) was continuously digitized at 500 Hz (Net Amps 300 EGI amplifier combined with NetStation 5. 3 software) from 256 channels. We used a prototype HydroCel net (EGI; Eugene, OR, USA) referenced to the vertex. The sensor layout of this prototype diverges from the classical geodesic 128-locations partitioning  (Tucker, 1993)  in that 20 of the standard temporal positions are covered by 2 tight grids of sensors (70 electrodes on each side, organized in hexagonal pods) with no sponge inserts (Figure  S2 ). Electrodes are made of carbon fibers embedded within a plastic (ABS) substrate and coated with silver-chloride.
Artifact detection and correction Data preprocessing was conducted through custom-made MATLAB scripts based on the EEGLAB toolbox 14. 0  (Delorme & Makeig, 2004). While following the main preprocessing steps normally used in developmental studies, we introduced some modifications inspired by efforts carried to improve adult data quality  (Jas et al. , 2017; Mognon et al. , 2011). Namely, we identified artifacts on the continuous recordings with the employment of adaptive rather than absolute/predefined thresholds. In this way, we could account for inter-individual variability and the heterogeneous influence that reference distance and vigilance state exert on the voltage. Moreover, we did not discard but corrected local and transient artifacts, exploiting the redundancy of information provided by our dense sensor-layout (Figure  S2 ) and high sampling rate.
In case consonant and vowel were represented separately, we expected to obtain comparable scores within and across conditions; oppositely, a degradation in performance across conditions would be indicative of interdependence between the two. For cross-condition decoding we modified the cross-validation scheme described above so that models fitted on each training set were directly applied at all trials belonging to the untrained condition (i. e. the test set "across"). In this way, we capitalized on the independence of train and test sets. Concerning the splitting of single-condition datasets (i. e. the dataset "within"), the number of test trials was calibrated to guarantee a minimum of 2 microaveraged trials/class at test and at the same time maximize the amount of trials available for training.
Auditory spectrogram estimation and Representation Similarity Analysis This preliminary investigation was aimed at delineating the auditory representational geometry elicited by our stimuli set  (Kriegeskorte, 2008; Kriegeskorte & Kievit, 2013). The time-frequency auditory representation of the speech sounds was estimated according to a model of the peripheral auditory system  (Chi et al. , 2005)  as implemented in the NSL Matlab Toolbox (http://nsl. isr. umd. edu/downloads. html). This model comprises: a first step in which sound frequencies are spatially separated along the basilar membrane; a second stage that simulates the transduction of basilar membrane displacements into auditory nerve spikes; and a third phase of processing within the cochlear nucleus. The output of the model is an auditory spectrum of the signal as it enters the inferior colliculi.
, 2018). The outcome of this first approach is a series of 35 auditory distance matrices (Figure  S1B ), describing all together how pairwise auditory (dis)similarity unfolds over time. It has been proposed that the acoustic correlates of the place of articulation, a feature of major interest in the current study, have an integrative and dynamic nature  (Nossair & Zahorian, 1991). The employment of brief time slices could have then potentially precluded us from capturing meaningful cues derivable from the spectral shape as a whole. To account for this eventuality, our second approach relied on the Dynamic Time Warping (DTW) algorithm  (Sakoe & Chiba, 1978; Park & Glass, 2008)  as implemented in the Python module dtaidistance  (Meert & Van Craenendonck, 2018). This technique enabled us to find the best alignment between each pair of spectrograms by stretching and compressing them locally, along the time axis.