dataesr/scientific-paragraphs-categorization huggingface dataset:
Training Dataset For this study, we use the GeoLifeCLEF 2020 dataset, a detailed description of which is provided in  Cole et al. (2020). This dataset covering France and the USA consists of 1,921,123 observations (8,23,483 in France and 1,097,640 in the USA) belonging to 31,435 different species, mainly plants and animals. Each observation is coupled to a tensor extracted from remotesensing data (at high or VHR) at the position of the occurrence, refer to Figure  1. The four remote-sensing data used are, RGB and Near-IR imagery (from the 2009-2011 cycle of the National Agriculture Imagery Program (NAIP) in the United States 1  , and the BD-ORTHO R 2. 0 and ORTHO-HR R 1. 0 from IGN 2  in France), land-cover (National Land Cover Database (NLCD)  (Homer et al. , 2015)  for the United States and CESBIO 3  for France), and elevation (Shuttle Radar Topography Mission   (SRTM) 4 for both France and USA).
Environmental and Trait Data In this study, we also use environmental and species trait data. These data are not used for training the CNN but are used to study the ability of the model learned on VHR data to extract information related to the ecology of species. We use 19 bio-climatic rasters [30 arcsec 2 /pixel (above 1 km 2 /pixel) from WorldClim  (Hijmans et al. , 2005) ] and 8 pedologic rasters [250 m 2 /pixel, from SoilGrids  (Hengl et al. , 2017) ]. The detailed list and resolutions are presented in Table  2. This environmental data is also used to train an environmental random forest in order to compare this more classical approach and its performance to our model (refer to Section 2. 1. 4). We also use data related to the ecology of the species, more precisely Ellenberg indicator values (EIVs, refer to Table  3 )  (Ellenberg, 1988)  from  Julve (1998).
Environmental Random Forest For performance comparison purposes, we also train an environmental random forest model. This more classical model is trained using environmental rasters of climate and soil data (refer to Section 2. 1. 3). We extract the environmental realization (the value taken by the environmental variables) at the point of each occurrence, which associates an environmental vector of size 27 with each occurrence. Similar to CNN, the random forest is learned on all the training occurrences over France and the USA, with the exception of some occurrences in Florida which are out of the coverage of the provided raster and are removed (there is no such problem on the test occurrences). We use the random forest classifier of scikit-learn  (Pedregosa et al. , 2011). The optimization of the parameters is done using a validation set of occurrences made of 0.
5% of the occurrences randomly extracted from the training set. The final parameterization consists of a forest of 100 trees with a maximum depth of 10, the other parameters are left at their default values. The predictive power of random forests generally increases with the number of trees and the depth. However, the memory size of the model increases exponentially with the depth. Due to the size of the dataset, a depth of 10 was a limit with the resources we had available and using the scikit-learn implementation.
Very High Spatial Resolution Analysis In addition to the visualization of learning on the whole territory, we demonstrate a visualization at a finer scale and VHR. To do so, we analyzed the activation of the model in a selected geographical area. The choice of the area was made according to two criteria. (1) The area had to contain several distinct environments within a small spatial extent (about 10 km). (2)  The area had to be sufficiently documented and known by the authors to draw relevant conclusions. The result is the choice of an area of 10 × 15 km on the French Mediterranean coast close to Montpellier city, including a massif, coast line, salt ponds, garrigues, pine forests, agricultural areas, vineyards, urban areas, and some significant human infrastructure such as a highway. The model activation was performed every 50 m after extracting the input data for these points following the same procedure as for the official data set described in  Cole et al.
Ecological Interpretation of the Learned Features Here, we use the species, ecological traits and environment data that were not used during model training (refer to Section 2. 1. 3). These data allow us to assess how well the model is able to capture information related to the environment (climate and soil factors) and species ecology (species traits). To do so, we fit a linear model on each axis of the t-SNE (the two variables z1 and z2 ) using either the environmental or ecological trait variables as explanatory variables (R language, lm function). The correlation then captured between the feature space of the model (reduced by t-SNE) and these data may be indicative of the ability of the CNN model to capture information directly related to species ecology through VHR imagery data. The variable bio_7 was removed from the linear model on the environmental variables because by definition it is equal to bio_5-bio_6 and is, therefore, directly correlated to them.
RESULTS AND DISCUSSION The evaluation of the CNN based on VHR remote-sensing data against the environmental random forest highlights the performance of the CNN which obtains a better score than the more classically used environmental model on all metrics (Table  4 ). The CNN obtains 23. 5 vs. 20. 4% on the top-30 accuracy (A 30 ). The performance gap is even greater when evaluating the mean top-30 accuracy per species (MSA 30 ) with a score of 13. 2% for the CNN against 6. 9% for the random forest. This suggests that the CNN is particularly better on less represented species in the dataset as these species gain more weight in the MSA 30 compared to the A 30. Figure  2  confirms this by showing the performance of the two models as a function of the number of occurrences in the training set.
The difference in the performance of the two models increases rapidly as the number of occurrences decreases. In particular, for species between 270 and 92 occurrences, the CNN is already twice as good as random forest. The model evaluation in MeanAUC shows a good overall capture of the species distribution over the global dataset with both models over 0. 9 (0. 915 for the CNN and 0. 905 for the random forest). However, the separate MeanAUC evaluation shows lower scores than the overall evaluation for both countries. This illustrates the impact of pseudo-absence on particularly large and diverse study areas. In particular, the scores in France are significantly lower than in the US or the global evaluation. Two factors may be at play, the task may be more difficult (more species with few occurrences) but also the smaller size of the country may accentuate the previous remark.
This important result shows that the model is able to capture spatially consistent information at multiple scales from VHR imagery data with both high spatial accuracy and large-scale consistency. This provides a breakthrough in the well-known problem of trading precision for generality when studying ecological processes  (Levins, 1966). Figures 6, 7 highlight model ecological significance at a fine spatial scale. Figure  6B  is a projection of the 3D t-SNE of the feature space in this area (refer to Section 2. 4), it highlights that the model is able to differentiate many fine-scale environments. For example, we can see the temporary ponds (in purplish pink in the center), the highway (purple line in the North), the coast (in light pink), etc. This figure shows good identification of contrasting habitats, in terms of surface and nature.
For this species, the predictions of the two models are particularly similar. This species is frequent in the region and abundant in the dataset which may allow both models to have a consistent prediction. The main difference still lies in the resolution of the prediction. The CNN prediction can vary quickly where MaxEnt is smoother. Unlike the previous species, there are no clearly visible factors to visually explain the rapid variations in predictions by the CNN. It could be due to differences in vegetation type at a fine scale that might be better captured by the CNN-SDM (such as differences in forest cover density). Capsella bursa-pastoris (L. ) Medik. Figure  7C  is a cosmopolitan herbaceous species observed along paths, in crops lands, and wastelands. Here again, the prediction dynamics of the models are visually quite consistent.
Second, apart from the fact that the model is limited in its prediction area, the strongest activations of the prediction are in urban areas, which does not correspond to the ecology of the species. The difficulty in predicting this species by the MaxEnt model seems to lie both in the spatial definition of the rasters introducing biases on the observed niche of the species in the training and also in the fact that this species was particularly rare in the dataset (only 24 occurrences). On the contrary, the data used by the CNN and its resolution allow covering completely the coastline and previous studies have already shown the ability of the CNN to predict rare species by joint learning on many species  (Deneu et al. , 2021). This last result is confirmed in Figure  2. Despite close performances between the CNN and the random forest on the most frequent species, the CNN score is largely superior on the species with few learning occurrences.
FUNDING This project has received funding from the  French National Research Agency  under the Investments for the Future Program, referred to as  ANR-16-CONV-0004  ( #DigitAg ) and from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 863463  ( Cos4Cloud  project). Models were computed on the Jean Zay super-computer hosted by IDRIS in the context of the  French National Grand Equipment (GENCI).
We report the detection of gravity modes in RR Lyrae stars. Thanks to PAIX, the first Antarctica polar photometer. Unprecedented and uninterrupted U BV RI time-series photometric ground-based data are collected during 150 days from the highest plateau of Antarctica. PAIX light curve analyses reveal an even richer power spectrum with mixed modes in RR Lyrae stars. A nonlinear nature of several dominant peaks, showing lower and higher frequencies, occur around the dominant fundamental radial pressure mode. These lower frequencies and harmonics linearly interact with the dominant fundamental radial pressure mode and its second and third overtone pressure modes as well. Halfinteger frequencies are also detected, likewise side peak structures demonstrating that HH puppis is a bona-fide Blazhko star.
Collecting simultaneously multicolor light curves of several targets within the same 12. 2 x 8. 2 arcmin field of view. All PAIX components are remotely controlled and setup through PAIX ACquisition Software -PACS-implemented on PAIX experiment in Antarctica, accessible from anywhere through a virtual private network (VPN) and a remote desktop. For more technical details on the PAIX multi-band photometer and data we refer to the invited SPIE paper  Chadid et al. (2016).
PAIX Observations The PAIX data used in this study are the calibrated light curves from the magnitude extraction that we measure by PAIX CCD during the observing run starting on 12 May 2011 and ending on 25 September 2011. The 17 500 CCD images of the target, HH Puppis, 11. 31 V-mag, with J2000 α right ascension 07 h 20 m 35 s 55 and δ declination -46 h 42 m 30 s 11, have been collected throughout the UBVRI color filters with an exposure time ∆t ≤ 60 s, and processed even when the sun is at 8 • below horizon. In this case, the accumulated observable time spans, without any interruption, over 136 days of 2011 polar wintertime. We use for our purpose the V-filter passband in the Bessel Johnson-Cousins system  (Bessell (2005) ) stands for an effective wavelength midpoint λ ef f = 540 nm and Full width at half maximum ∆λ = 100 nm.
PAIX data processing pipeline -PPP- The PAIX Pipeline Package -PPP-provides work flows for processing PAIX observed polar photometric data. Such software tool has been developed and maintained to provide measurements of both magnitude and extinction extractions and their precision towards Light Curves and Bouger's lines simultaneously  (Chadid & Vernin (2019) ). The PAIX Pipeline -PPP-is principally based on a set of four softwares, (1) raw UBVRI images are corrected from dark, bias, flat fields and then corrected for hot pixels. The measurements of the magnitude of each star is performed with use of Sextractor, (2) the coordinates of the target and the position of a given known reference star are determined with the target magnitude precision, (3) the previous processing is used to plot light curves and finally (4) the magnitude variation of the reference star as a function of the air-mass is analyzed to compute extinction coefficient, according to Bouguer's theory, and plots Bouguer's lines.
DETECTION OF FREQUENCIES AND RESULTS Figure  1  shows the PAIX HH Puppis light curve folded with the pulsation period 0. 390 d over 136 days. The PAIX data of HH Puppis provide 349 consecutive pulsation cycles and a homogeneous and uninterrupted coverage. A frequency analysis of the photometric data was performed using Period04  (Lenz & Breger (2005) ) and PDM  (Zalian et al. (2014) ). Both softwares perform a projection of the discrete photometric signal on a trigonometric basis and all led to the same results with a slight difference at higher orders. In this study, we present the results of the Period04 frequency analysis. The Fourier decomposition consists of fitting the magnitude measurements by means of the series m(t) = A 0 + i=1,N A i sin[2π(F i (t -T 0 ) + Φ i )], (1) where T 0 is the initial epoch value of the data set PAIXJD T 0 = 245569 and Φ the normalized phase.
ACKNOWLEDGMENTS The PAIX project has been supported by the United States Air Force Research Laboratory through the European Office of Aerospace Research and Development -US Air Forces F61775-02-C002 -I am grateful to Jean Vernin and George Jumper from Hanscom Air Force Base, for their friendly and continuous multilevel support. I acknowledge all the explorers and the scientific researchers who have contributed to make my polar expeditions and Antarctica projects less bumpy rides under extreme conditions of Antarctica.
Statistical analysis All the statistical analyses were performed using GraphPad Prism 5 (GraphPad Software, Inc. ). Descriptive statistics were presented as mean ± standard error (SEM). The unpaired nonparametric Mann-Whitney U test was used to evaluate the differences between TRAPS patients, control groups and asymptomatic patients. Spearman correlation was used to study correlation. P values inferior to 0. 05 were considered statistically significant.
The aim of this article and associate map is to highlight the social and economic diversity of the Ruhr area in Germany through the use of multivariate analysis and visualisation. To this end we combine two different datasets. Demographic parameters stemming from the 2011 German census and socioeconomic indicators obtained from the microdialog of the German post service. Due to the different spatial resolution of the two datasets, we aggregated the data at the neighbourhood (Stadtteil ) level. The multivariate analysis was carried out at this scale using Self-Organizing Maps (SOM), an artificial neuron network, which uses an unsupervised learning mechanism for projecting multidimensional data in a low (in our case two) dimensional space. First we used a visualization technique to better comprehend the relationship between our observations via reducing the dimensionality or complexity of our input data.
Datasets This article brings together two different datasets to characterize the socioeconomic profiles of sub-city areas and identify their spatial distribution. The demographic parameters from the 2011 German census  (Zensus, 2011)  are combined with socioeconomic indicators obtained from the microdialog of the German post service  (Microdialog, 2017). Thereby indices for purchasing power and net rent price are calculated from the latter, whilst the percentage of foreign and German population, residents under 18 and over 65-year-olds as well as household characteristics are all downloaded from the official census website  (Zensus, 2011). Small scale census data is provided for 100x100 meter grid whilst microdialog data is delivered at the building level. Due to the different resolution of the two datasets, we aggregated the data at the neighbourhood or Stadtteil scale (in total 732 for the entire region as shown in Fig.
In comparison with other dimensionality reduction methods, e. g Principal Component Analysis (PCA) or t-distributed Stochastic Neighbour Embedding (t-SNE), an important advantage of SOM is that it provides information at each node position of the 2D map topology and as a result allows for detection and visualisation of the most characteristic patterns in the dataset  (Unglert, Radić, & Jellinek, 2016). It has also been shown that SOM is somewhat less robust than PCA in estimating the optimal number of clusters  (Unglert et al. , 2016)  but performs better when dealing with nonlinear problems  (Aguado, Montoya, Borras, Seco, & Ferrer, 2008). In the context of this study, the above mentioned visual clustering feature is ideal for identifying and representing multilevelled segregation phenomena over the observed territory.
Software To generate the maps in this article, the authors used geospatial python programming, which included the following packages: numpy, pandas, geopandas, matplotlib and seaborn. Furthermore, in order to carry out the self-organising maps part of the analysis we used the Python package "Sompy" developed by  Moosavi, Packmann, and Vallés (2014)  using PCA initialization and a rectangular grid structure. In addition to characterizing urban socioeconomic profiles, the ten here-analysed features (out of a total of 43) were specifically selected after numerous trials, so that their SOM-model would have a topographic error very close to zero (0. 072) and a low quantization error (0. 7783) according to Sompy-guidelines. Finally, for the layouting and design of our main map file we used the Adobe InDesign software.
Finally, since methods for data collection and processing differ between the two datasets, we may have introduced some level of inaccuracy to our calculations that could have been avoided with more consistent datasets. Notwithstanding, our results demonstrate that structural inequalities stemming from the processes of industrialization and deindustrialization in the Ruhr metropolitan region (Ruhrgebiet) are still widely persistent and result in characteristic patterns along the three major rivers: the Lippe, Emscher and the Ruhr. For example, areas that are still facing a multiplicity of socioeconomic challenges today concentrate in the mid-reaches of the Emscher river. Moreover, the three types of societal segregation patterns and their distributions over the Ruhr area (ethnic background, age group, economic performance) became conspicuous on our maps.
Acknowledgments J. L. acknowledges funding from the Mercator foundation within the  NEMO  Project and would like to thank both the project's team and its strategic partners; the  Regional Planning Association Ruhr (RVR)  and the  Emschergenossenschaft (EGLV)  for providing extensive data as well as continuous help and feedback. J. L. is grateful for  H. -W. Wehling  and  J. A. Schmidt  for numerous fruitful discussions and for their remarks.
Background: Thyroid gland malignancies are rare in pediatric patients (. % of tumors); only. % are observed in patients aged < years, with a higher prevalence recorded in women and adolescents. Risk factors include genetic syndromes, MEN disorders, autoimmune diseases, and exposure to ionizing radiation. Radiotherapy is also associated with an increased risk of secondary thyroid cancer. This study describes the clinical features and surgical outcomes of primary and secondary thyroid tumors in pediatric patients. Methods: Institutional data were collected from eight international surgical oncology centers for pediatric patients with thyroid cancer between and. Statistical analyses were performed using the GraphPad Prism software. Results: Among total cases of thyroid cancer, only (. %) were secondary tumors.
Materials and methods The authors retrospectively collected data on pediatric patients (<18 years of age) treated for thyroid cancer at eight participating international collaborative surgical oncology centers between 2000 and 2020: • Bambino Gesù Children's Hospital in Rome-Italy The patients were categorized into two distinct cohort study groups (primary and secondary tumors). Demographics, clinical outcomes, surgical outcomes, and survival data were analyzed. Statistical analysis was performed using Fisher's exact test and the Gehan-Breslow-Wilcoxon test. Event-free survival (EFS) and overall survival (OS) were calculated using GraphPad Prism Version 8. The study involving human participants was reviewed and approved by Institutional Ethical Committee. Written informed consent from the patients or legal guardians was not required to participate in this study in accordance with national legislation and institutional requirements.
Use of cryopreserved fragment by orthotopic graft was permitted by a National PHRC called PERIDATOR Statistical analyzes were performed using Stata software (version 13; StataCorp, College Station, Texas, USA), with a two-sided type1error risk of 5%. The population was described in terms of number of individuals and associated percentages for categorical variables, and by mean § standard deviation or median [interquartile range] for quantitative variables, with respect to their statistical distribution (normality tested using the Shapiro-Wilk test). Qualitative criteria were compared according to period (before or after set up of the platform) using the Chi2 or Fisher's exact test.
Population We identified non-hospitalized adults aged 60 to 79 years affiliated to EsSalud and registered in the CAM available database at national level. The database represented 0. 5% of all older adults affiliated to EsSalud. We included those older adults who had already been vaccinated (with any of the available vaccines) or who had planned to be vaccinated against COVID-19 according to the Peruvian Ministry of Health vaccination schedule at a vaccination site in Peru. We excluded adults aged 80 years or older because more than one month had elapsed since the start of vaccination in this age group by the initiation of the recruitment. We also excluded those who had some impediment to adequate communication with the interviewer via the telephone call, were diagnosed with COVID-19 in the last three months, had symptoms related to COVID-19, or refused to participate during the interview.
Finally, we considered a rejection rate of 10% and a loss to follow-up rate of 10%; then, we planned to invite 1168 participants to the study. Further details are found in the extended data (Supplementary methods: Sample size calculation). 39  Sampling After excluding those who did not meet the selection criteria or did not have identification or contact data, we took 7 685 older adults from the registered CAMs into the database as a sample frame. From them, we chose a randomized and stratified sample for each of the 30 healthcare networks (n = 1 686). In addition, the sampling was carried out independently for two age subpopulations: 60 to 69 years (n = 846) and 70 to 79 (n = 840) years. For each subpopulation, we chose half of the calculated total sample size (allocation ratio 1:1). We decided to choose two age subpopulations because the recruitment period was during the start of vaccination of adults older than 60 to 69 years and one month after the initiation of vaccinations to adults older than 70 to 79 years (https://cdn.