dataesr/scientific-paragraphs-categorization huggingface dataset:
After adding the non-local regularizer the motion boundaries are more accurate and the outlier is removed. Table  9  provides a quantitative comparison of different estimations of the optical flow obtained with different energy functionals (we average the results for 10 subsets of 10% of frames randomly selected from the final training set of the MPI-Sintel database). In order to provide for a more complete analysis of the results, the authors of MPI Sintel also proposed to evaluate different versions of the average endpoint error (EPE). When we compute the EPE for all pixels, we denote it EP E all as introduced in Equation  1 ; if we only compute the EPE for non-occluded pixels, we have EP E mat , where mat stands for matched pixels, as those which are not occluded in the current frame. Contrarily, when we only evaluate the error on occluded or unmatched areas, this is expressed as EP E umat for briefness.
EpicFlow 6  , a very competing sparseto-dense technique, obtains the worst results, specially analytically, showing its lack of robustness with a very limited number of seeds. Visually it is more or less capable of discerning the direction of the moving objects despite totally missing their boundaries. The multi-scale approach with T V -L1 energy fails to correctly estimate the flow direction and magnitude despite being able to identify the moving areas. We also include the result obtained with LDOF 7  , which obtains comparable results to the latter method. In this case it has been obtained with their own seeds (using their original binary code). Recent optical flow datasets as MPI-Sintel  [6]  contain different and challenging effects, such as illumination changes, large displacements, blur, etc.
In MPI-Sintel these effects have been artificially created to provide naturalistic video sequences. However, the evaluation on these datasets does not take into account the robustness to the shot noise that appears in any real sequence, being noise one of the main limitations to any imaging system. Thus, we evaluate the robustness of several approaches to noise. To this goal, we corrupt the clean images from  [6]  with additive white Gaussian noise for different standard deviations σ. Sparse-to-dense techniques are very dependent of the initial seeds that are used to obtain a dense optical flow. It is important to note that FALDOI works even with an extremely sparse set of initial seeds. This fact allows us to choose the best matching method according to the image peculiarities, without caring that much about the density of the correspondences.
These tests were analyzed with the entire MPI-Sintel Dataset (final and clean pass) and average and median results were plotted. Moreover, the optimal values were different for each matcher: SIFT benefited of smaller (more restrictive) thresholds, minimum error with ǫ = 0. 45; while DeepMatching yielded better results with bigger values, minimum error with ǫ = 12. 55. As a consequence, in the online demo, the default values are the ones which yield the minimum error for the approximated range specified above. In the online demo, four parameters can be tuned: 1) the matcher algorithm used (SIFT or Deep-Matching); 2) the patch radius (patch size = 2 * patch radius + 1), assuming square patches. The allowed values are those tested throughout this article, that is: 3, 4 or 5 (equivalent to 7 × 7, 9 × 9 and 11 × 11 patch sizes, respectively).
This study examines the consistency and representativeness differences of daily integrated water vapour (IWV) data from ERA-Interim reanalysis and GPS observations at 120 global sites over a 16-year period (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). Various comparison statistics are analysed as a function of geographic, topographic, and climatic features. A small ( ± 1 kg m -2 ) bias is found in the reanalysis across latitudes (moist in northern and southern midlatitudes and dry in the tropics). The standard deviation of daily IWV differences is generally below 2 kg m -2 but peaks in the northern and southern storm-track regions. In general, the larger IWV differences are explained by increased representativeness errors, when GPS observations capture some small-scale variability that is not resolved by the reanalysis.
A representativeness error statistic is proposed which measures the spatiotemporal variability in the vicinity of the GPS sites, based on reanalysis data at the four surrounding grid points. It allows to predict the standard deviation of daily IWV differences with a correlation of 0. 73. In general, representativeness differences can be reduced by temporal averaging and spatial interpolation from the four surrounding grid points. A small number of outlying cases (15 sites) which do not follow the general tendencies are further examined. It is found that their special topographic and climatic features strongly enhance the representativeness errors (e. g. steep topography, coastlines, and strong seasonal cycle in monsoon regions). Discarding these sites significantly improves the global ERA-Interim and GPS comparison results.
Similarly, the GPS measurement and/or processing errors can change over space and time (e. g. during disturbed/severe meteorological events the mapping function errors would be larger;  Boehm et al. , 2007). The primary goal of this study is thus to analyse the consistency global of daily IWV data from the ERA-Interim reanalysis and GPS station observations, and explain the contribution of representativeness errors/differences. To this purpose, we use simple statistics (mean differences and standard deviations, such as those found in most past studies) to quantify the differences between both datasets. We investigate the dependence of these statistics upon latitude, altitude, and time, as well as mean atmospheric moisture content and its spatial and temporal variability. A representativeness error statistic is introduced which quantifies the spatial variability in the ERA-Interim data at the surrounding grid points and explains to a good degree the observed differences between the reanalysis and the observations.
All the statistics are computed over a period of 16 years because we want to characterize the systematic ERA-Interim minus GPS differences and not their changes over time (e. g. due to inhomogeneity and/or changes in the quality in either of the datasets). The changes over time are small in magnitude  (Parracho et al. , 2018)  and have negligible impact on the average statistics computed here. After establishing the contribution of representativeness errors, we address the following specific questions: 1. By which means is it possible to mitigate the representativeness errors? 2. Does horizontal interpolation of model values degrade or increase the representativeness error? 3. Can we separate outlying results (e. g. sites with extreme biases and dispersion) due to enhanced representativeness errors those due to enhanced GPS instrument errors? To tackle this question, the seasonal variation of the comparison statistics and of the atmospheric environment (mean IWV and variability) is also analysed.
g. by establishing a parametric model of observation error depending on the spatiotemporal variability of IWV around the GNSS site computed from the model fields. The paper is organized as follows. Section 2 describes how the IWV data from the two datasets are prepared. Special effort is made to use a procedure that maximizes the consistency between the datasets. Section 3 presents the results of IWV difference statistics and analyses their dependence upon a variety of parameters. General tendencies are derived that describe the consistency between the reanalysis and GPS globally. Section 4 introduces a range check which detects 15 outlying sites for which the IWV differences are especially large. The geographic, topographic, and seasonal characteristics of these sites are analysed and site-specific representativeness errors are highlighted.
, 2018). No temporal interpolation is applied here so that only the 1-hourly ZTD estimates matching the times of the reanalysis (00:00, 06:00 UTC, etc. ) are converted. Finally, the daily IWV values are computed from five 6-hourly values between 00:00 UTC of the current day and 00:00 UTC of the next day, with weights 1/8, 1/4, 1/4, 1/4, and 1/8, respectively. Monthly averages are computed directly from the 6-hourly values within the given month to the condition that at least 60 values are available (similar to  Parracho et al. , 2018). As already mentioned above, inhomogeneities in the GPS IWV time series due to equipment changes are not corrected here. This does not impact the conclusions since we analyse only overall statistics (means and standard deviations) computed over 16 years but not linear trends, and the data have been screened beforehand.
ERA-Interim reanalysis ERA-Interim is a modern reanalysis produced by ECMWF using the Integrated Forecasting System (IFS) forecast model and the 4D-Var assimilation system in 12-hourly analysis cycles  (Dee et al. , 2011). The number of observations has increased from 10 6 per day in 1989 to 10 7 per day in 2010. The majority of data, and most of the increase over time, are from satellites. Ground-based GPS data were not assimilated, which make the GPS ZTD and IWV an independent validation dataset. We use ERA-Interim analysis pressure-level data (geopotential, air temperature, and specific humidity) extracted from the Meteorological Archival and Retrieval System (MARS) on a regular latitude-longitude grid with a horizontal resolution of 0. 75 • × 0. 75 •. For each and every GPS site, 6-hourly ERA-Interim fields are extracted for the four grid points surrounding the GPS station.
The IWV contents are computed by integrating the reanalysis specific humidity between the GPS station altitude and the top of atmosphere (1 hPa). For GPS station altitudes located between two pressure levels, the ERA-Interim data at the station are interpolated from the adjacent levels. For stations located below the lowest pressure level (1000 hPa), the reanalysis data are extrapolated. Interpolation and extrapolation are done linearly for specific humidity and temperature, and logarithmically for geopotential, as a function of pressure. To ensure the best spatial matching between GPS and ERA-Interim data, the IWV estimates from the four grid points surrounding the GPS station, IWV 1 to IWV 4 , are combined by bilinear interpolation, resulting in a value denoted by IWV interp. Daily and monthly IWV values are computed afterwards in the same manner as for the GPS IWV data (see above).
Comparison method Daily and monthly time-matched IWV values from GPS and ERA-Interim are compared for each and every station, and overall statistics are computed using the full time series (16 years). The overall statistics reveal the systematic or persistent biases and discrepancies between the two datasets. The goal is to identify the main causes of differences among the representativeness differences, errors in the GPS data, and deficiencies in the reanalysis (e. g. in data-sparse regions). The identification of representativeness differences is made by inspection of a number of statistics and their dependence upon characteristics of the GPS station's environment: moist or dry climate (measured by the mean IWV), strength of temporal variability (measured by the standard deviation of IWV and of its first derivative), and spatiotemporal variability of IWV in the vicinity of the station.
2 kg m -2 , while the value for the monthly series is 0. 51 kg m -2. The reduction of standard deviation due to averaging is 2. 35, which is smaller than the value of √ 30 = 5. 48 that one would expect with independent normally distributed data (when averaging over a mean month of 30 d). This inconsistency can be due to the serial correlation in the IWV differences revealing a dependence of the IWV differences upon the meteorological situation. This point might be further investigated by, e. g. separating the IWV differences in different weather regimes. Another means of reducing the discrepancy due to representativeness differences is to use a reanalysis with higher spatial resolution and improved physics representing the smaller-scale atmospheric processes. We compared, for instance, daily GPS IWV data to the Applications of Research to Operations at Mesoscale (AROME) West-Mediterranean operational analysis of Météo-France (this model has a horizontal resolution of 2.
2 3 4 5 6 (i. e. function of latitude, altitude, mean vs. SD scatter plots, etc. ). This methodology can be safely applied to other datasets. The reason why we chose non-symmetric limits with respect to zero for the mean differences is because the distribution is not centred on zero. The result is a detection of 15 outlying sites, some of which exceed the limits in more than one test: three sites have excessive absolute bias (CFAG, KIT3, and SANT); nine sites have excessive relative bias (CFAG, COSO, DAV1, KIT3, MAW1, MCM4, POL2, SANT, and SYOG); eight sites have excessive standard deviation of differences (AREQ, BLYT, CFAG, DHLG, IISC, KIT3, LONG, and SANT); and nine sites have excessive relative standard deviation of differences (AREQ, CFAG, KIT3, MAW1, MCM4, MKEA, POL2, SANT, and SYOG).
The problematic topographic features include steep orography such as that found for sites in the Andes Cordillera (AREQ, CFAG, and SANT), on the island of Hawaii (MKEA), and close to the Himalaya chain (KIT3 and POL2), as well as coastal sites in Antarctica (MCM4, SYOG, MAW1, and DAV1). The climatic features include large seasonal changes in the total IWV, such as those associated with the Indian monsoon (IISC, KIT3, POL2) or the North American monsoon (DHLG, BLYT, LONG, and COSO), and/or in the IWV synoptic variability (observed at most sites during either the transition seasons, winter or summer, depending on the geographic location). When these 15 stations are eliminated from the dataset, the comparison statistics become µ = 0. 36 ± 0. 49 kg m -2 , µ r = 2. 7 ±3. 5 %, σ = 1. 22 ± 0. 38 kg m -2 , and σ r = 8.
Inspection of the elevation of the four surrounding grid points with respect to the elevation of the GPS station and with respect to each other provides some indication of possible representativeness errors. Some correlation between IWV biases and altitudes at the individual grid points was found in extreme cases (Fig. 7 ). A simple a priori check based on the comparison of grid point altitudes to station altitudes would eliminate some of the problematic cases. We compared the statistics with and without selection of sites where the elevation of the grid points differs by more than 500 m from the GPS station. When the selection is applied to the nearest grid point only, 15 stations are eliminated, including four of the outlying sites discussed in Sect. 4. This test is not very efficient. When applied to all four surrounding grid points, 34 stations are eliminated, including 11 of the outlying sites (only CFAG, MCM4, BLYT, and IISC remain then in the dataset).
This outlier check is efficient and is thus recommended. However, none of the checks that we tested were able to detect all the 15 outlying sites. Hence, it is also advised to carefully analyse the comparison statistics in order to understand the possible causes of discrepancies and eliminate outlying stations a posteriori on a subjective basis as we have done in this study. This was possible here because the number of stations was small. In more extended networks, an automatic selection method based on, e. g. a clustering algorithm, would be necessary. Aside from the large representativeness errors found at a small number of sites, one should recognize that ERA-Interim and GPS IWV data are generally in good agreement globally, except perhaps in Antarctica where the comparison failed at four sites out of five.
Acknowledgements. This work is a contribution to the  European COST Action   ES1206 GNSS4SWEC  (GNSS for Severe Weather and Climate monitoring; http://www. cost. eu/COST_Actions/essem/ES1206, last access:  22 July 2019 ) aiming at the development of the global GPS network for atmospheric research and climate change monitoring. This study benefited from the  IPSL mesocentre ESPRI facility , which is supported by  CNRS ,  UPMC ,  Labex  L-IPSL, CNES, and  Ecole Polytechnique , which is funded by the  ANR  (grant no. ANR-10-LABX-0018 ) and by the  European FP7   IS-ENES2  project (grant no. 312979 ). Financial support. This research has been supported by the  Centre National de la Recherche Scientifique  (grant no. LEFE/INSU projet VEGA ). Review statement. This paper was edited by  Roeland Van Malderen  and reviewed by two anonymous referees.
Data availability. GPS IWV data have the following DOI: global GPS IWV data at 120 stations of the permanent IGS network; https://doi. org/10. 14768/06337394-73a9-407c-9997-0e380dac5591 (Bock, 2016). ERA-Interim data can be downloaded at https://www. ecmwf. int/en/forecasts/datasets/archive-datasets/ reanalysis-datasets/era-interim (last access: October 2018;  Dee et al. , 2011). The results presented in the paper are also provided in the Supplement.
Study duration A qualitative study to inform the intervention was carried out in May and June 2018. As shown in the SPIRIT figure (Fig. 1 ), recruitment occurred in February and March 2019. Baseline data were collected in April 2019, including primary and secondary outcome variables, for all participating patients. The intensive training sessions of the selected peer educators took place in April 2019 and the peer groups were established in July 2019. The first follow-up data will be collected in April 2020, including primary and secondary outcome variables. This will be followed by an analysis of the preliminary results. The final assessment will be conducted in January 2021, including primary and secondary outcome variables. This will be followed by an analysis of the results and the establishment of peer support groups in the control group if the intervention proves to be effective.
Out of the participants recruited, we determined potential peer educators based on three criteria: (1) their willingness to take on this role, (2) a recommendation from the health facility staff, and (3) how well they were already controlling their diabetes based on the HbA1c level from the baseline data. Following data collection, the puskesmas were randomized. Because recruitment took place before randomization, the researchers, participants, and facility managers did not know which facilities would be allocated to the treatment and control groups. We used covariate-constrained randomization to ensure the treatment and control arms were balanced in terms of baseline covariates while maintaining the randomness of the allocation  [20, 21]. The covariates took into account the composition of the group with regards to size of the group, and the age, sex, education, participation in other health programs,  3  and diabetes control (mean HbA1c values) of the members, as well as the location of the puskesmas (rural or urban).
Randomization was carried out using the statistics program Stata. Peer education groups were formed at each puskesmas in the treatment group. Peer educators will receive continuous training in diabetes management skills and will be supported in the administrative processes (e. g. , room booking) required to run their peer education group in their local sub-district. To ensure that the effect of using peer education can be observed in comparison to normal patient education efforts, the control group will not receive any additional education apart from standard therapy. As detailed in Fig. 1 , 9 months after the first peer education session, a first follow-up will assess the implementation and the effects of peer education. The final assessment will take place 18 months after the first peer education session to assess the long-term effects of the intervention and the experience of participants.
Economic evaluation Costs related to the implementation of peer education will be collected from the the documented project expenditures. Data on resource use for diabetes treatment and care, including equipment, drugs, and doctor and hospital visits, will be collected retrospectively using cost information from the health insurance provider if possible, or via the expert opinion of local specialists. These data will be supplemented with information on participants' health-care seeking behavior and utilization, drug use, and out-of-pocket payments collected through the surveys. To estimate cost-effectiveness, the incremental costeffectiveness ratio will be calculated from the healthcare system perspective and the societal perspective taking into account out-of-pocket expenditures and costs related to participating in peer education.
Data monitoring Because this study does not involve any testing of medications or procedures that are outside the usual care provided to individuals with type 2 diabetes, monitoring patient safety will be limited to mandatory reporting of adverse events and unanticipated problems. Participants will be made aware of unexpected test findings identified during the trial. Adverse events include clinical reactions to blood draws related to the study, as well as any reported stigma or physical or mental harm as a result of participation in the study, including disclosure of diabetes status.
Sample collection Microplastics were sampled using a 4. 4-m-long manta net of 333 μm mesh size with a net opening of 16 cm x 60 cm by trawling on 120 sites by day and by night (Fig  1 ). The manta towed at the sea surface for ca. 60 min behind the boat at an average speed of 2. 5 knots. Consequently, the average filtered volume of each manta trawl was about 507 m 3. The physical-chemical data were collected using a thermosalinograph (TSG) and a hyperspectral spectrophotometer (AC-S). Ocean Color satellite images were supplied by ACRI-ST and the Mercator circulation model was used to determine the areas of interest for the sampling. Geographical coordinates are available at Pangea Data Publisher http://www. pangaea. de.
Wet sieving was carried out with 5 L of distilled water using a slow flow rate (around 0. 04 L. s -1 ) through the sieve set. Then, the microplastics collected in each size class were placed in glass Petri dishes and left for around 24 hours in an oven at 50˚C. The particles belonging to size classes coarser than 0. 315 mm were then transferred to 96-well microplates identifier (i. e. : TM0001A1 for "Tara Méditerranée", microplate n ˚0001 and well n˚A1). Each of these microplastics was thus isolated and was named with a unique identifier. Owing to this procedure, the size class of each particle was noted. Particles finer than 0. 315 mm were stored in chromatography tubes and not included in this study. Basic information such as the unique identifier, the size range, the microplastic color, and any further observations were noted to provide input for the project database.
Acknowledgments We thank the commitment of the following institutions, persons and sponsors:  CNRS ,  UPMC, LOV, Genoscope/CEA , the  Tara Expeditions Foundation  and its founders: agnès b. 1 ,  Etienne Bourgois ,  Romain Trouble  ´, the  Veolia Environment Foundation, Lorient Agglomeration, Serge Ferrari , the  Foundation Prince Albert II de Monaco, IDEC , the "Tara" schooner and teams. We thank  MERCATOR-CORIOLIS  and  ACRI-ST  for providing daily satellite data during the expedition. We are also grateful to the  French Ministry of Foreign Affairs  for supporting the expedition and to the countries that graciously granted sampling permission. This article is contribution number 2 of  Tara Mediterranean. Dr. M. S. N. Carpenter  postedited the English style and grammar.