dataesr/scientific-paragraphs-categorization huggingface dataset:
Child Behavior Assessment With the goal of achieving a supervised autonomous system, the current behavior of the child must be appraised, which occurs in two phases (Figure  5 ). During the first phase, data is collected from the sensory system and mapped onto the child's identified behaviors. This mapping is based on training and validation sets of child-robot interactions that were previously annotated by knowledgeable therapists. From this process, the classifiers provide the probability that each behavior, among all of them, is currently observed. These probabilities are used during the second phase, where, based on the interaction history, the system attempts to infer the child's level of engagement, motivation, and performance of the task at hand. This second phase relies heavily on the semantic interaction knowledge of the therapists to provide insights into expected patterns.
Other applications have been developed as statistical software packages, but have a small number of indices: e. g. ,  Reardon and Firebaugh (2002)  The main advantage of these tools is that they are flexible, and allow control over many input parameters. They benefit also from the advantage of integration in statistical software which provides the possibility of further analysis without exporting results, integration into other software, automatization, etc. Their main disadvantage is that they require basic knowledge of programming in statistical software. The article is structured in two main sections. In Section 2, we provide a brief summary of segregation measurement, focusing on aspects that are relevant to the present work, i. e. , definition and computation. Section 3 uses some practical examples to show how to use OasisR.
A distinction is made between one-group indices (segregation of a group compared to the rest of the population), between group indices (which measure the segregation between pairs of groups), multi-group indices (which analyze the distribution of several population groups simultaneously) and social diversity indices (which can be understood as zonal multi-group or local indices). We compare the results of OasisR with those from two other software implementations. The geo-segregation analyzer (GSA version 1. 1) developed by  Apparicio et al. (2014) , is the most complete automatic tool available so far. The application needs an input shape file (data and maps), and produces 43 indices. The second tool is the R package seg, version 0. 5-1, developed by  Hong et al. (2014). It computes 11 indices, most available only for a population composed of two social groups.
It measures the departure from evenness as the population weighted average deviation of each spatial unit from the area's entropy (or social diversity). In the case of a population with more than two groups, the local and area entropy need to be calculated for each group as the "minority" and the rest of the population as the "majority". The results obtained using OasisR and GSA are identical apart from the entropy index. According to its definition, the entropy index for a two group population should have the same value for both groups. If we take the example of complete segregation from the theoretical distributions, the index should be equal to 1 (as in OasisR), while GSA provides H 1 = 0. 75 and H 2 = 0. 25. Using empirical data with several social groups produces identical results. The Gorard index is computed only in OasisR, and in the case of the Atkinson index, the user is limited in GSA to three standard values of the inequality aversion parameter δ (0.
In Wong's original paper both indices have errors in their mathematical definition. The first error is the division by 2 of the spatial interaction term and the second is the row standardization of the spatial   2014 ) developed scripts to present the R package seg and similar to OasisR, he defines the spatial matrix using a global standardization. Wong and Chong (1998)  presented improved versions of the indices formulae, where the proportions in the spatial interaction effect are clearly defined, but the definition of the spatial interaction matrix seems incorrect since  Wong and Chong (1998)  use double standardization of the spatial matrix (row standardization followed by overall standardization). Furthermore, there are ambiguities concerning the definition of each spatial unit's perimeter, necessary for the computation of D k 1 k 2 (s).
To obtain the same results as in  Wong (1993)  and  Hong et al. (2014) , we need to use only the "internal" perimeter of each spatial unit, defined as the sum of the boundaries shared with other spatial units, and ignore the area's external borders. To overcome this issue, in OasisR the user can choose the perimeter definition. For the theoretical two-group distribution, using the corrected mathematical formula (see Appendix A) and the "internal" definition of the perimeter, OasisR provides the same results as the seg package and Wong's original paper. The results of GSA are incorrect because the software uses the original wrong mathematical definitions. We also generalized Wong's indices to a case with more than two groups, and its one-group form. Apparicio et al. (2008)  define these indices mathematically but these definitions have similar problems to Morrill's index generalization, and carry the errors from the original definition.
This means also that the mean proximity between persons regardless of group should have a between group form P oo k 1 k 2 , as used to compute SP k 1 k 2. The spatial proximity index is computed using only the gravity form, but can also be used with linear distance in the opposite interpretation. If we compute the gravity form of the spatial proximity index using the wrong null diagonal, we obtain the same result as with the seg package (which computes only the multi-group form) and GSA (which provides only the between group version). The index can also be computed as the one-group version SP k , which compares the proximity among the members of a group P xx k and the average proximity of the population P oo. Clustering indices  Massey and Denton (1988)  propose two clustering measures. The absolute clustering index ACL k expresses the average number of members of groups in nearby spatial units as a proportion of the total population in those proximate spatial units.
Values are rounded to four digits. As support, we use a very simple 10×10 grid theoretical example which is used in many studies  (Morrill 1991; Wong 1993; Lee et al. 2015; Hong et al. 2014) ; the data are provided by the package. From the various available distributions (Hong 2014), we chose two situations: one with complete segregation of the minority, and one with a particular social groups mix. The population in the dark gray cells is formed only of minority members, in the white cells it is formed only of majority members, and in the light gray ones the population contains a mix of the two groups. In each cell, we consider that the total population number is 100 individuals. For more complex examples, see Appendix B. To compute an aspatial segregation index, the script is basic since we need to use the function name and a distribution table: R> A <-segdata@data[, 1:2] R> DIDuncan(A) [,1] [,2] [1,] 0 1 [2,] 1 0 In the case of spatial indices, there are three ways to introduce spatial data in OasisR.
The first solution is to provide a spatial R object, using the spatobj argument. It is possible also to import a shapefile, by using two arguments: (1) folder, to provide the path where the shapefile is located on the drive, and (2) shape, the name of the shapefile (without an extension). The shape import uses the readOGR function in the rgdal package  (Bivand, Keitt, and Rowlingson 2019). Finally, spatial information can be provided directly as vectors/matrices of contiguity, common boundaries, areas, distances, etc. This spatial information can be computed within OasisR because the package includes geographical functions based on the spdep  (Bivand, Pebesma, and Gómez-Rubio 2013; Bivand 2019 ) and rgeos (Bivand and Rundel 2019) packages, with appropriate output for the segregation functions.
R> foldername <-system. file("extdata", package = "OasisR") R> shapename <-"segdata" R> areavector <-area(segdata) R> Delta(A, spatobj = segdata) [1] 0. 75 0. 25 R> Delta(A, folder = foldername, shape = shapename) OGR data source with driver: ESRI Shapefile Source: "C:/R/win-library/3. 3/OasisR/extdata", layer: "segdata" with 100 features It has 19 fields [1] 0. 75 0. 25 R> Delta(A, a = areavector) [1] 0. 75 0. 25 Certain supplementary arguments emerge for specific indices. For example, in the Atkinson function, inequality aversion can be set via the delta argument. The argument variant specifies which variant of Wong's indices is chosen: variant = "w" or variant = "s". Section 2. 3 showed that certain proximity measures can be computed as multi-group, between group or one-group indices. The user can choose the index type via the argument itype.
R> B <-segdata@data[, 7:8] R> xPy(B) Spatial segregation functions based on distance have particular arguments. Spatial interactions can be defined via the fdist argument: "l" for the linear and "e" for the exponential inverse function of distance. Other distance functions can be used by introducing a user distance matrix in the R functions, and by setting a linear function. Metric conversions are based on the conv_unit function in the birk package  (Birk 2016) , with distin and distout the respective arguments for the input and output measures. Argument diagval defines the distance within a spatial unit: "0" for the null diagonal and "a" for White's formula (0. 6 square root of the area). Other versions can be used by introducing a user distance matrix in the function. Examples of how to use these functions are provided in Appendix B.
Conclusions OasisR is a package implemented in the R software which allows computation of many segregation indices. It was designed to respond to a range of applications in the easiest way possible. This package has the advantage that it is implemented in R which allows total control of the input arguments, most of which have default values that correspond to the standard use of indices. This feature enables less experienced users to conduct segregation analysis with ease. Moreover, there is the possibility to develop further analysis within R, to automate the scripts.
A fundamental question is how to synergistically combine the two approaches while maintaining the quality standards for accuracy. The objectives were : 1. To quantify the agreement between expert and crowd data and determine the conflation outcome (exclusion, partial conflation, or replacement) accordingly. 2. To evaluate the impact of different conflation strategies on accuracy measures. We selected cropland validation as study case due to its high uncertainty. Overall, the experts and the crowd converged but the level of agreement varied according to the strata and the cropland complexity. Results suggest that crowd samples can be integrated in validation datasets but total conflation is not recommended. Crowdsourcing appears particularly cost-effective in areas that are easy to interpret and allows for the identification of difficult or problematic samples, i.
Data collection Expert and crowdsourced interpretations were collected at more than 4,000 sample locations following a systematic stratified sampling approach with an adjusted number of replicates by latitude. The size of each sample unit was 300mx300m. Experts validated blocks of polygons and the crowd blocks of 25 sub-pixels. Consistency between experts and crowd Cropland proportions were derived for all sample units and a majority rule was applied to derive cropland/non-cropland. The consistency between the experts and the crowd was assessed using accuracy measures. We investigated how the agreement between the two approaches varied as a function of the crowd standard deviation. The crowd standard deviation is a potential indicator of the reliability of the crowd.
The taphonomic analysis of bird remains entailed recording the following taphonomic parameters: completeness, cracking, flaking, surface dissolution, color, presence/absence of manganese deposits, and presence/absence of marks, including root marks, trampling marks, and any marks indicative of probable carnivore and rodent activity, as well as possible anthropogenic modifications. A subset of twenty bones was selected for further analysis at the PACEA laboratory in Bordeaux, and permission was obtained to export this material. The selected bones were examined with a motorised Leica Z6APOA microscope equipped with a DFC420 digital camera and Leica Application Suite (LAS) software, including the Multifocus module and Leica Map DCM 3D software. The Multifocus module permits the acquisition of extended depth of field images by relying on adapted algorithms that combine digital images collected at different heights into a single, sharp, composite image.
The obtained microscopic images were digitized and edited in the Adobe® Photoshop® CS5. 1 Extended software. The Leica Map DCM 3D allowed production of 3D reconstructions of areas of interest. High-resolution surface topography was acquired with a Sensofar S neox confocal microscope driven by SensoScan 6 software (Sensofar, Barcelona). Two different objectives were used for 3D reconstruction and imaging. A 20× objective allowed for 0. 69 μm and 0. 31 μm lateral and vertical resolution respectively. In addition, a long-working distance 50× objective allowed for 0. 28 μm and 0. 17 μm in lateral and vertical resolution.
Acknowledgements The So'a Basin research project was funded by  Australian Research Council   Discovery Grants   DP0343334 ,  DP0770234 , and  DP1093342 , awarded to  Mike Morwood , and the  Geological Survey Centre of Indonesia. GDvdB also received funding from an  ARC   Future Fellowship  ( FT100100384 ). We thank the successive directors of the  Geological Survey Centre ,  Y. Kusumahbrata ,  A. Wibowo  and  A. Pribadi , the  Heads of the Geological Agency (R. Sukyiar and Surono) , and the successive directors of the  Geology Museum in Bandung (S. Baskoro and O. Abdurahman)  for facilitating and supporting this research. For permission to undertake this research, we thank the  Indonesian State Ministry of Research and Technology (RISTEK). Further research permissions were issued by the provincial government of  East Nusa Tenggara at Kupang  and the local administration of the Ngada District in central Flores.
We thank  Elizabeth G. Veatch  for discussions on taphonomy and  Kate McGrath  for discussions on 3D imaging. Scott Whittaker  (NMNH) took the Environmental Scanning Electron Microscope images. The laboratory work was supported by  Griffith University  and the  Research Council of Norway through its Centers of Excellence funding scheme (Centre for Early Sapience Behaviour , project number  262618 ), and by the  Labex LaScArBx-ANR  no  ANR-10-LABX-52.
Ethical Standards This study was carried out in accordance with the recommendations of an appropriate ethics review board (CEERB Paris Nord, IRB 00006477) with written informed consent from all subjects. All subjects (adolescents and their parents) gave written informed consent for the research and for the publication of the datasets (social and demographic characteristics of the study population and direct quotes from the participants) in accordance with the Declaration of Helsinki. The protocol was approved by the ethics review board CEERB Paris Nord. All interviews have been anonymized to get the datasets non identifiable.
Analysis of Results The analysis applied the method of Interpretative Phenomenological Analysis (IPA), which makes it possible to study the meaning subjects construct from their experiences  (49, 53, 69, 70). Meticulous analysis of the interviews as they were completed enabled us to identify a set of superordinate themes, each linked to several themes describing all of the experiences narrated. The themes were not chosen solely on the basis of their prevalence in the data. Other factors, including the richness of the passages showing the themes and the ways they illuminated other aspects of the narrative were also considered. Two researchers (SG and JL) analyzed the interviews to optimize the validity of the results. The methodological criteria were retrospectively verified according to the COREQ (COnsolidated criteria for Reporting Qualitative research) checklist (Data Sheet 1).
SUN-060 PNEUMOCOCCAL VACCINATION COVERAGE AT THE INITIATION OF CHRONIC DIALYSIS, AND ITS ASSOCIATION WITH MORTALITY DURING THE FIRST YEAR Pierre, E 1  , Pladys, A 2  , Tattevin, P 3  , Bayat-Makoei, S 2 , Vigneau, C* 1 Introduction: Limited data are available on the impact of pneumococcal immunization in hemodialysis patients. The purpose of this study was to estimated vaccination coverage among incident patients who initiated chronic dialysis in France in 2015 and its association with mortality according to patient characteristics and vaccination schedule. Methods: Data were extracted from two national data-bases, merged using a deterministic linkage method: Renal Epidemiology and Information Network (REIN) registry, which includes all dialysed and transplanted patients in France, and the French National Health Insurance Information System (SNIIRAM) which includes individual data on health expenditure reimbursement, including vaccines.
Data on health status at dialysis initiation, dialysis modalities, and all pneumococcal vaccine prescribed from 2 years before to 1 year after dialysis starting were collected. Univariate and multivariate Cox proportional hazard models were used to assess one-year all-cause mortality. Results: Among the 8,294 incident patients included, 1,849 (22. 3%) patients received at least one pneumococcal vaccine: 542 (6. 5%) during the two years before dialysis start, and 1,307 (15. 8%) after dialysis start, in 2015 or 2016. Among them, 35. 1% received 23 valent-pneumococcal polysaccharide vaccine (PPSV23) alone, 50. 7% 13 valentpneumococcal conjugated vaccine (PCV13), followed by PPSV23, and 14. 1% only PCV13. Vaccinated patients were younger, more likely to have a glomerulonephritis and less likely to start a dialysis in emergency start.
, 2018). It was found that slope, water bodies and coastal attractions (including beaches) were important to the public, with agricultural areas being less valued. Richards & Friess (2015)  used photo content to classify areas by cultural use in urban mangrove sites in Singapore, finding recreation photos being more prominent around built sites and photographs of organisms in terrestrial and mangrove habitats. A later study in Singapore used automated image recognition to study the content and group photographs from social media and found the method was accurate and saved extensive periods of manual classification  (Richards and Tunçer, 2018). To date, none of these studies using geo-tagged social media datasets have considered explicit user groups in recreational preferences, despite research showing that social media data can be used to identify users to contribute to conservation science (Di Minin,  Tenkanen and Toivonen, 2015).
Photo retrieval and annotation We retrieved images from the photo-sharing social media website Flickr using Python scripts and Flickr's Application Programming Interface (API). The images were downloaded with associated metadata, including longitude and latitude, date and time the photograph was taken, and the user ID of the photographer. A total of 20,051 images uploaded by 1292 users between 2007 and 2016 were downloaded. We used Google Cloud Vision, a machine learning algorithm for image analysis with the ability to detect labels, text, faces, landmarks, logos and image properties (Google Cloud Vision, 2017) that has been trained with extensive training sets. The Google Cloud Vision API was used for automatic annotation of descriptive terms for the image's content with confidence scores, examples including 'flamingo', 'performing arts' and 'coast'.
Typology of beneficiaries Latent semantic analysis (LSA) was performed using the R language and coding environment (Wild, 2015; R Core  Team, 2017). LSA is a technique used in this context to approximate meaning similarities between words or texts that are correlated with human cognitive phenomena such as semantic similarity  (Landauer, Foltz and Laham, 1998). In LSA, latent semantic space is where 'documents' (in this study users are the 'documents') and 'terms' (herein image annotations) are represented as vectors, before applying local and global weighting and then calculating a singular value decomposition (SVD) applied to a text matrix. Data were filtered to keep only those annotations with a Google Cloud Vision confidence score of ≥0. 6. The LSA package was used to create a document-term matrix M (in this study this is a user by image annotations frequency table, an m × n matrix where m (users) =1292 and n (image annotations) =549) removing 'stop words' (commonly used words such as 'the'), with a minimum of 5 instances for each term to be included.
This method allows the identification of a point in the curve where the signal transitions to noise  (Kutz et al. , 2016). The R NbClust package  (Charrad et al. , 2014)  was used with the 6 dimensions to calculate indicators for between 2 and 15 clusters to ascertain the optimal number of clusters to the reduced D matrix. Users were partitioned into this best number of clusters result (in this case six partitions) using the Ward-D algorithm (intra-cluster variation minimisation) (see Table  S1 ). Word clouds of all terms for all images in a group were generated to aid identification of the type of visitors group.
Spatio-temporal patterns of beneficiary groups We applied self-organising maps (SOM) to the mapped PUD data to identify the spatio-temporal patterns of use for all groups of beneficiaries. SOM is an unsupervised neural network, a competitive learning algorithm, uniquely suited for finding patterns in complex, high-dimensional datasets. It allows both (1) visualising complex data sets by reducing their dimensionality and (2) performing cluster analysis by grouping observations (grid cells in a map) into exclusive sets based on their similarity. Although caution is required when standardising input data and comparing outcomes of multiple model runs, the advantage of SOMs is that they depend less on expert rules or supervised threshold selection and are not restricted by the number of input features (variables and sample size).
The quality of the data is vital for the quality of the outputs, with a larger PUD database giving more robust results. As the SOM method is sensitive to outliers, we standardised the PUDs to zero mean and unit variance within each beneficiaries group. This Z-score standardisation also helps to interpret the results in terms of how much and in which direction the characteristic variable in each cluster deviates from the overall average. Optimum cluster size was determined using a Davies-Bouldin (DB) Index and mean distance to cluster centroids (see Supplementary information, Fig. S5 ) calculated for a variety of cluster sizes ranging from 3 to 20 clusters. Identifying a natural break in both measures, we chose five clusters as they provided an optimal trade-off between the number of clusters and their quality of data representation.
Ungrouped data also showed the same significant trend (Fig. 2 ). All PUDs were mapped for ungrouped data (Fig. 3 ) and individual groups (Figs. 4 5 6 7 8 9 ) to provide an indication of visitation and recreation. The ungrouped data showed a distribution of pixels across the landscape with aggregations around the coast near the western village of Saintes-Maries-de-la-Mer and past Phare de la Gacholle and Beauduc to Salin-de-Giraud, Port Saint-Louis du Rhône and Piemason in the east of the Camargue. Photos were distributed across the remaining landscape, including visual clustering around the edge of the Vaccarès lagoon. The highest visitation was at Parc Ornithologique de Pont de Gau and Saintes-Maries-de-la-Mer throughout spring, summer and autumn, with the latter being the sole hotspot in winter.