dataesr/scientific-paragraphs-categorization huggingface dataset:
Trapezoidal rule. Figure  8b  complements the above results with the performance of the Trapezoid rule on the YouTube dataset. Again, we obtain the best performance with a KNN-to-RPS ratio of 3/2. With a KNN view of 15 and an RPS view of 10, the mean estimation ratio settles at 1. 14. Moreover, slightly smaller or slightly larger KNN-to-RPS ratios impact this result only to a limited extent. In our tests, we observed that this results from the fact that when one view remains constant, performance consistently improves when increasing the size of the other. Polynomial interpolation. Next, we evaluate the effectiveness of Mignon using polynomial interpolation. To this end, we used the Gregory-Newton interpolation algorithm as implemented in SciPy. Figure  9  shows the results. Both datasets exhibit similar behaviors.
Influence of Sample & Collide We now assess the impact of errors on the network-size estimation. As previously stated, nodes do not need to recompute the size of the network for every new upload as we assume the network to be relatively stable. Nonetheless, it is possible to limit the cost of size estimation by means of protocols like Sample & Collide  [26]. Such a protocol yields an estimate with a 10% error at a very limited network cost. We estimate the impact of this error in Table  2  where we shows the absolute value of the error on the mean estimation ratio for both Mignon's approaches in the presence of a positive or negative error on the estimation size. The data shows that the error on the network size has almost no impact on YouTube, and a relatively low one on MovieLens.
A further six glassy samples (95-99 vol. % glass) could not be successfully prepared for FTIR analysis. For these 15 samples, H 2 O and CO 2 contents were calculated using the mass-balance method (see Supplementary Data Appendices 3 and 4 for details), and the 35 samples for which FTIR measurements were obtained were used as internal standards. The standard error for these calculations is large at 61Á4 wt % and 61000 ppm for calculated H 2 O and CO 2 values, respectively.
ACKNOWLEDGEMENTS Many thanks go to  Nicole Metrich ,  Michael Carroll ,  Peter Ulmer , and an anonymous reviewer for helpful and extensive reviews of the original paper, and to  Marie Edmonds  for a detailed review on the portion of this paper that contributed to K. I. 's PhD thesis. We thank  Bill McIntosh  for assistance in sample collection on Ross Island;  Marina Alletti ,  Juan Andu  ´jar, and  Remı Ćhampellier  for assistance with experiments;  Ida Di Carlo  for assistance on the SEM;  Nial Peters  for his FTIR spectra background fitting code;  Jacob Lowenstern  for use of the mapping FTIR system and for advice on analyzing experiments;  Iris Buisman ,  Raul Carampin , and  Kenneth Domanik  for assistance with electron microprobe work;  Matthijs van Soest  for use of the surface mapping microscope.
Acknowledgments This research was partially funded by the  National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU)  in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, in partnership with  Public Health England (PHE) , the  NIHR Imperial Biomedical Research Centre , and the  NIHR Imperial Patient Safety Translational Research Centre. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , the  Department of Health , or Public Health England. This work was supported by  Imperial College Healthcare Charity  (grant no. 7006/P36U ). The authors would like to thank  Professor Cliodna McNulty ,  Head of Primary Care Unit, Public Health England , for her support and contribution to the early phases of the research, including the design of the survey.
Introduction Large datasets are essential for sufficient statistical power to characterize subsets of disease. The usefulness of single cohorts can be enhanced by combining several studies to facilitate analyses of pooled individual patient data (IPD). However, to date such studies have only collected primary outcomes measured on comparable scales or, in the case of biomarkers, using the same assay platforms. Different assay platforms may vary in their sensitivity, precision, and concentration ranges. In such cases, valid methods of standardization of laboratory data are required in order to aggregate individual patient data. The Global Pregnancy Collaboration (CoLAB) was established in 2011 (http://pre-empt. cfri. ca/colaboratory/global-pregnancy-collaboration) to facilitate data and sample sharing between research groups studying preeclampsia and other pregnancy disorders.
This paper focuses on clinical and laboratory data for placenta protein, placental growth factor (PlGF) to predict and/or diagnose hypertensive disorders of pregnancy (HDP). These disorders are associated with severe reductions in circulating PlGF concentrations  [1, 4, 5]. In the cohorts included in this study, PlGF was quantified on one of four laboratory platforms, each with different analytic performance. We developed a method of standardizing PlGF data to allow pooling. Additionally, concentrations of PlGF are known to change with gestational age (GA) and to show the power of the pooled data, we developed a best reference curve (BRC) over six gestational age groups. The rate of accurate identifi-cation of women with HDP using the merged BRC was compared to unmerged (platform-specific) rates.
Study database In 2011-2012, we invited principal investigators with studies of circulating maternal angiogenic factors in pregnancy to participate in this study. We included any study in which maternal blood samples were collected at least once at any time during pregnancy (uncomplicated or otherwise) and had been analyzed for PlGF. Adequate clinical, demographic and pregnancy outcome information was necessary for inclusion. 22 cohorts were included in the present analyses (Supplementary material Table  1 , with references to detailed information about each study, including individual patient consent and formal study research ethical approval). The datasets varied in sample size, maternal demographics as well as study design, including both low and high risk pregnancies. Missing data were retrieved, where possible.
Individual datasets were integrated into one central database, which was cleaned and checked to ensure data integrity was maintained. Reported measures of PlGF below the limit of detection for each of the four platforms were recorded as the threshold value. These occurred in less than 1. 5% of the observations and were not removed because these observations are expected to include the most severe cases of placental dysfunction associated with HDP. The final database contained information on 16,462 pregnancies. Here we included only those women (n = 13,429) who had at least one PlGF measurement at or after 20 weeks' gestation (the time when preeclampsia presents clinically, by definition). Four different analytical platforms had been used by the included cohorts: Alere Triage PlGF, Roche Elecsys PlGF, R&D Systems PlGF and Beckman-Coulter PlGF.
Z-Score transformation parameter estimates The mean (l p[i]g  [i]  ) and standard deviation (r p[i]g  [i]  ) for the Z-Score transformation of the log-transformed PlGF measurements were estimated using 10,000 bootstrap iterations. The estimated mean and standard deviation of the non-case PlGF measurements in each platform-GA subgroup are presented in Table  2  alongside the associated bootstrap standard errors. These estimates were used to transform the original PlGF measurements (Eq. (  1 )). The transformed datasets were tested for normality using the Anderson-Darling test (data not shown). For 62. 5% of the platform-GA subgroups (15 of 24) the null hypothesis (that the distributions are Normal) was not rejected at the 5% significance level (pvalues ranging from 0. 06 to 0. 91). Kolmogorov-Smirnov tests (data not shown) confirmed that in each GA category, transformed non-case measurements on each of the four platforms followed the same distribution, thus validating our decision to merge these transformed data sets.
MoM transformation parameter estimates The median of the log-transformed PlGF measurements in each platform-GA group (m p[i]g  [i]  ) to be used in the Multiple of the Median transformation algorithm was estimated with 10,000 bootstrap iterations (Table  2 ). Kolmogorov-Smirnov tests (data not shown) confirmed the suitability of merging MoM transformed measurements from the four platforms within each GA category. These estimated parameters were used in the MoM transformation of the original PlGF measurements (Eq. (  2 )). The PlGF measurements on all four platforms are measured in pictograms per milliliter (pg/ml). The transformed PlGF measurements do not have equivalent physical units. The transformed measurements are therefore referred to as the PlGF Z-Scores (or PlGF MoMs).
Our method has been developed to study pregnancy-related screening or diagnosis data but is applicable to any medical condition where there is intrinsic variability in the tests that measure the same biomarker(s). The choice of BRC for any given study and any biomarker will in general depend on the distribution of the data being used. It is clear from our results in this PlGF merging study of pregnancy blood samples that the diagnostic information itself has not been degraded by merging the data from these heterogeneous platforms and cohorts. Of the possible biases in our study, some are intrinsic to constructing reference ranges whether from single or multiple datasets. They are considered no further than to say that the validity of our approach depends on our definition of non-cases and the requirement that no non-case contributed more than one value to the BRC.
By LOO-CV we also excluded the possibility of a dominant contribution from any single cohort. In relation to this specific study, since we did not systematically seek every known pregnancy cohort globally and also since relevant pregnancy cohorts with angiogenic factor analyses from low and middle income countries are lacking, there is a possibility of potential inclusion bias. We believe any such bias to be minimal but this will be addressed more specifically in our future clinical analyses of this database. The accuracy of medical diagnostic tests is usually considered  [7]  in relation to a clear, verifiable, single diagnosis using laboratory methods that are already standardized for the purpose of introduction into routine clinical practice. Examples include tests in prenatal screening for fetal chromosomal abnormalities  [8].
It is at this level that our method is likely to be most useful. We have constructed a ''PlGF converter" that enables any researcher or clinician to calculate a general PlGF percentile based on a PlGF concentration measured during pregnancy after week 20, on any of the four platforms included in our study (It also includes options for comparison against merged thresholds (both Z-Score and MoM) and platform-specific thresholds). A link to the PlGF converter is on the CoLAB home page (http://pre-empt. cfri. ca/colaboratory/global-pregnancy-collaboration). We hope that the awareness of our merging method will encourage researchers to plan their studies, in any biomarker field, to allow their data to be merged with other future datasets in order to gain more statistical power and research value for rare study outcomes.
Pregnancy disorders are not the only relevant conditions where this strategy could be useful: other complex syndromes such as the metabolic syndrome or polycystic ovarian syndrome present the same problems. Overall, we show here that heterogeneity of laboratory assays in biomarker studies need not be a barrier for inclusion in pooled analysis of individual patient datasets. The method shows how to transform and validate merging for any set of biomarkers obtained from different laboratory platforms in any field.
Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced.
Brazil Land-use land-cover surveys were performed in December 2014 by collecting data along roads that covered the main part of the site area. A land-cover class was assigned to most of the large fields clearly identifiable from the road, and according to JECAM's nomenclature. A total of 847 field measurements were collected, from which 326 were within the area selected for this study to have the highest diversity in terms of land cover. A total of 13 different land cover has been found in more than three records, at the highest precision level (crop species). When the soil was bare, the previous crop was identified from residuals left inside the field when possible; this crop was considered as the final class. This was done because some bare soil could be found either on cropland or non-cropland land cover.
China Field work was carried out in mid-May and late August 2014. The field surveys were based on RapidEye imagery acquired in June 2012. The crop types of the sampled fields and other land-cover types were recorded using handheld GPS, and the polygon of the corresponding land-cover types were drawn according to very high resolution imagery. Based on the field survey, six classes including winter wheat, cotton, vegetables, tree, fallow land, and nonarable land were identified. Non-arable land areas include residential area, road, and waterbody. The sample points from wheat and maize yield measurement survey were also used to enrich the field samples. Those points were expanded to polygon samples based on the segmentation of a high-resolution satellite image. One Chinese Satellite GaoFen-1 (GF-1) Wide Field Imager (WFI) data was acquired in 26 May 2014 over China site at peak growing stage.
Russia Field data collection was fulfilled during the year 2014, when field surveys data as well as farmers' remotely verified information was aggregated. Each local farm surveyed provided spatial information on field limits and the corresponding crop types within borders of their competence. Exact field boundaries were reconstructed from Landsat images for the year 2014. Crop type information was validated using MODIS seasonal time-series, when objects with suspicious phenology or one different from class majority were separated. These fields preserved cropland class, but crop type information was suspended. There were 495 fields collected within Stavropol krai borders, encompassing 12 main crop types covering more than 90% of krai's crop sown area. Since only cropland data was accumulated during these surveys, additional efforts were aimed at non-cropland data collection for the benchmarking experiment.
Methodology The processing steps from the feature extraction to the classification and its assessment are henceforth described. Section 3. 1 describes the five classification methods that are benchmarked; Section 3. 2 presents the different techniques used for validation (accuracy indicators, McNemar's tests and Pareto boundary analysis), while Section 3. 3 describes how the sensitivity of the map accuracy to a reduction of the training data set size was assessed. Table  2. High-resolution imagery used to generate the reference cropland map for each site and their coverage with respect to the MODIS imagery.
Cropping intensity derived from timeseries NDVI data is also considered to identify cropland and non-cropland. For pixels with two growing seasons, four temporal features were only extracted from the first growing season. The smoothing was achieved by applying a Savitzky-Golay filter  (Savitzky and Golay 1964; Tsai and Philpot 1998). Based on the extracted parameters and the training samples, a decision tree was generated using Classification and Regression Tree (CART) algorithm and applied to the whole study area to produce a land-cover map.
The Accumulated Minimum Multi-year Index (AMMI) values are derived from PVI time-series data as described below: AMMI ¼ k À P N 1 ðPVIÞ i min P N 1 ðPVIÞ i mean ( 5 ) where ðPVIÞ i min and ðPVIÞ i mean are the minimum and mean values within the summer time-window, respectively, and k is an arbitrary constant exceeding 1. Summer timewindow corresponds to 20 May to 1 September for Northern Hemisphere and half-yearly shifted for Southern Hemisphere. LSAM is based on multi-year time-series of MODIS data and was tailored to show best performance at large (continental) scales  (Bartalev, Plotnikov, and Loupian 2016). The arable lands mapping method utilizes the Locally Adaptive Global Mapping Algorithm (LAGMA) developed by the Space Research Institute of Russian Academy of Sciences (IKI), which is a supervised classification technique considering the spatial variability of intra-class spectral properties  (Bartalev et al.
Neural networks are fast to process new data but the training phase is resource-and time-consuming. Similarly, bootstrapping approaches such as TSAEC avoid random sampling anomalies and provides a proxy for the spatial variability of the classification accuracy. Yet, this comes at a higher cost in terms of processing time. A higher spatial resolution time-series would be required for application in a more fragmented landscape. Landsat-8, Satellite HJ-1 CCD, and Sentinel-2 data have a good potential, especially if combined. As those satellites acquire data in a larger number of spectral bands than the 250 m MODIS, algorithms relying on reflectance data (NNE, KBC 2 ) might see their accuracy increase consequently. Other methods relying on vegetation index solely would not benefit from wider spectral information.
Acknowledgements The authors thank the anonymous reviewers who helped to improve the quality of the manuscript. This research was conducted in the framework of the  SIGMA  project funded by the  European Commission  in the Seventh Programme for research, technological development, and demonstration under grant agreement no. 603719. The authors also thank the  JECAM network  for making possible this cross-site study opportunity. The arable lands mapping over the Russian Federation was supported by the  Russian Science Foundation  under grant number  14-17-00389.
Energy dispersive spectroscopy studies The compositions of the film and the substrate have been lo cally determined by EDS with X Max silicon drift X ray detector of JSM 7800F microscope. Data have been acquired at a low accel eration voltage of 2 kV. A super hybrid lens with gentle beam produced large probe current at low voltage enabling an express analysis of dielectric structures. Fig. 6  shows EDS spectra of a sample with partly deposited film, corresponding to the stage shown in Fig. 4(b ). If the electron beam is focused on the surface of the substrate, see Fig. 6 (a), the EDS spectrum shows lines arising from boron, oxygen, gallium and iron. In this case, the iron line is due to iron borate nucleation centers of nanometric size. Otherwise, if the electron beam is fo cused on the surface of the film, see Fig.
Acknowledgments This work was partially supported by the  Russian Foundation for Basic Research  and the  Ministry of Education, Science and Youth of the Republic of Crimea , in the framework of scientific project Grant no. 15 42 01008  "р_юг_а". The authors are grateful to  S. A. Smagulova  and her group from the  Academic Technologic Laboratory "Graphene Nanotechnolo gies" of the North Eastern Federal University (Yakutsk) , for the opportunity of using a JSM 7800F electronic microscope.
The exercises of the attentional and visuospatial modules were selected in the SBT software database. First, the selected exercises were proposed to children with normal cognitive functioning in order to appreciate the technical feasibility. Second, each exercise was adapted (levels one to nine) in order to be proposed to children with intellectual disability. The third step was the development of a facial emotion recognition task. Each Cognitus' facial expression was based on the Ekman pictures (Appendix 4 in Supplementary Material) with three levels of intensity  (44). «Cognitus & Moi» The «Cognitus & Moi» program involves a variety of exercises in a paper and/or pencil (n = 30) or a computerized format (n = 29) and a strategy coaching approach. Therapists make use of techniques known to benefit the rehabilitation of cognitive syndromes.
A C K N O W L E D G E M E N T S HA was supported by the Centre National d' Études Spatiales (CNES). TA acknowledges the ANR (Agence Nationale de la Recherche) (ANR-08-BLAN-0234-01) for financial support and the program PNP of INSU. We thank Nicolas Gillet, Richard Holme and two anonymous reviewers for constructive comments that significantly improved the paper.
Data Variables, Data Sources, Data Validation, and Definitions. Aggregate data were collected for this study and included number of presumptive TB cases, number of notified TB cases (all types), number of NPBCT cases (patients never treated or treated with anti-TB drugs for less than 1 month, whose TB diagnosis was bacteriologically confirmed on sputa by smear microscopy, culture, or WHOapproved rapid diagnostics such as Xpert MTB/RIF), new pulmonary clinically diagnosed TB (patients with pulmonary disease never treated or treated with anti-TB drugs for less than 1 month who did not fulfil the criteria for bacteriological confirmation but were diagnosed with active TB by a clinician who decided to give the patient a full course of TB treatment), new extrapulmonary TB (patients never treated or treated with anti-TB drugs for less than 1 month with a bacteriological or a clinical diagnosis of TB involving anatomical sites other than the lungs), retreatment TB (or previously treated patients who received 1 month or more of TB treatment in the past.
Acknowledgments The authors thank  Professor Martin GNINAFON , the former TB Programme manager, for his great work at the head of the institution during thirty years and all the health workers involved in TB care in the country. The authors also thank the  International Union against Tuberculosis  and  Lung Disease  ( The Union), Paris, France , for technical support. Serge Ade  is a  Union Operational Research Fellow. He is part time financially supported by the  International Union against Tuberculosis and Lung Disease  ( The Union ),.
CONCLUSION Patients with non-valvular AF and a single additional stroke risk factor may be denied OAC because of the misperception that their risk of stroke is not sufficiently high to justify the use of OAC (either VKAs or NOACs). Observational data from real-world AF cohorts show that the annual stroke rates in such patients are higher than in patients with no additional stroke risk factors and are around or well above the contemporary threshold for OAC treatment. Well-controlled VKA or NOACs therapy in these patients has been associated with a positive net clinical benefit owing to reduction in the risk of stroke, systemic embolism, or death in comparison to no therapy or aspirin, with no increase in the risk of bleeding relative to aspirin.