dataesr/scientific-paragraphs-categorization huggingface dataset:
The connection between voting behavior and redistribution preferences relied on a score attached to political parties that indicated the degree of its support for redistribution, welfare spending and education spending. The score was taken from the Manifesto project, a research and data collection project used for political comparisons that uses content analysis of political platforms to measure positions of political parties on a range of economic, social and other issues, including redistribution and the welfare state, and how much weight is attributed to these issues by the parties (see  Burst et al. , 2020). To identify the causal effect of immigration, the study used a shift-share instrument based on hypothetical regional proportions of immigrants under the scenario that immigrants arriving after 2005 were settled according to the same pattern as in 2005.
If corrected for, the average effect becomes negligible. Second, there is significant heterogeneity in the effect, which leads the authors to conclude that the specific context ultimately drives the effects and therefore the findings of a single study are not externally valid. What do these results reveal about the effect of ethnic diversity on redistribution preferences? Since far-right parties are on average more opposed to redistribution and more in favor of limiting the welfare state relative to other parties, conclusions reached in this literature provide at least partial information about the effect of ethnic diversity. We estimated regressions using data from the Manifesto database on the stance of political parties in Europe on issues related to redistribution and the welfare state.
To improve the edge drawing, we do not let any edge cross the empty space of the user (the inner sphere). To do this we push the edges into the area delimited by our two concentric spheres (figure  5a ). We first segmented the edges into a number of points, we then applied the same principle as for the nodes to push them between the spheres. Thus, the user can move freely in his space without being encumbered by edges. Figures  6  shows a 3D visualization of the graph of the dataset "Karate Club" (34 nodes and 78 edges) with a spatialization of the nodes carried out, respectively, by Fruchterman and Reingold's algorithm  [1]  and by our egocentric metaphor. The result in figure  6b  shows a good expansion of the nodes with our method, compared to the result of Fruchterman and Reingold's algorithm (figure  6a ).
Acknowledgments: This work has been funded by a grant from  Brittany region, France , and this work was also supported by  French government  funding managed by the  National Research Agency  under the  Investments for the Future program   (PIA)  grant  ANR-21-ESRE-0030  ( CONTINUUM ). We also gratefully acknowledge the support of  NVIDIA Corporation  with the donation of the Titan Xp GPU used for this research.
Sampling and recruitment of participants In total, 44 persons were invited to participate in the study, 25 accepted and 19 declined. The reasons for declining were: invalid contact information, no reply despite reminders, not working with migrants, recent arrival in French Guiana, on professional or personal travel, unavailable. Participants were recruited either during spontaneous or organized encounters, or by sending an email using contact details available online or provided by health care and prevention networks. Thus, participants were selected using a non-probability and convenience sample. The sample size was determined by the data saturation that emerged as the interviews progressed. Recruitment of participants ceased after 25 inclusions, as sufficient data were collected and data saturation was reached.
Data collection The data were collected during 45-min semi-structured interviews, conducted face-to-face in a confidential setting. They were conducted by GBR having a background in political sciences and public health and by MAT having a background in anthropology and ethnobotany and extensive experience in qualitative studies. An interview guide was developed and piloted prior to the study. It was composed of two parts: the professionals' and actors' representations of access to health care for migrants in French Guiana, and the professionals' and actors' practices concerning the support of migrants in their patient journey.
We continued the analysis by selecting verbatims and assigning them a code in the data analysis software. We worked with the MAXQDA Plus 2022 R software (Release 22. 2. 0). Following an iterative approach, some verbatims were recoded to enable recurring themes to be grouped together and to ensure coherence within our code matrix. The analysis' structuring continued with the interpretation of the results around several themes. Interpretation was based on the knowledge available in the literature, to propose explanations and comparisons. Interviews were recorded with the participants' consent. They were conducted and fully transcribed in French. Verbatims used in the article were translated into English by the main author, respecting the participants' statements as faithfully as possible, and were checked by the co-authors.
The socio-demographic characteristics of the participants were recorded in a spreadsheet and described in median and percentage with the help of R R software. Different techniques were used to ensure rigor and trustworthiness of data analysis. Data saturation was reached when participants reported similar experiences, when no new themes emerged during the interviews, and when data collected could be analyzed to both group and single out experiences. Audio recordings, transcripts and interpretation of results were verified by the co-authors (public health and anthropologist researchers). Interpretation of results was associated with the literature to illustrate or challenge article's results. Regarding data triangulation, our field observations have also contributed to highlight or question our interpretation of results.
The yellow outcrops are those shown in Figs. 2  and 3. All maps in this paper are generated using the open-source QGIS software; coordinates are in WGS 84 -UTM Zone 20 N system. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) from providing temporal succession and magnitude of explosive eruptions, well-constrained tephrochronological dates, and therefore reconstructing the eruptive succession, and determining the dynamical parameters of the eruptions older than 10 ka. This study focuses on the pyroclastic deposits erupted between 25 and 10 ka thanks to new and thorough field studies. The aims of this paper are (i) to refine the eruptive history of Montagne Pelée volcano based on our extensive stratigraphic records measured at 241 locations, (ii) to provide a first set of major element glass data to characterize the deposits from the newly identified eruptions, and (iii) to determine the dynamical parameters of each eruption from the tephra dispersal characteristics.
Fieldwork We carried out a series of three field campaigns in 2017, 2019 and 2021 in Martinique. We identified PDC and fallout deposits at >47 locations that were added to our complete field database that includes 241 outcrops distributed all around the volcano (Fig. 1 ) except to the northwest where exposure is very limited due to dense tropical forest and difficult conditions of access. At each outcrop, we first cleared the outcrop in order to take pictures and carefully built a detailed stratigraphy based on our field observations of framework, fabric, grain-type and size characteristics of the deposits. We measured the thickness of each layer of the sequence to construct isopach maps, which provide constraints on the volume of the deposits. We also excavated a standard 25 × 20 cm area of each fallout layer and measured the major axes of the five largest lithic fragments found in order to build isopleth maps that can be used to estimate the maximum column height (see Section 3.
Radiocarbon dating We performed radiocarbon dating measurements on nine paleosol samples in order to determine or refine the age of several eruptions. Ages were determined using an accelerator mass spectrometry at the LMC14  (Dumoulin et al. , 2017; Moreau et al. , 2020) , and calibrated using the free software OxCal 4. 4  (Bronk Ramsey, 2009)  with the atmospheric IntCal20 calibration curve recommended for the Northern hemisphere  (Reimer et al. , 2020). The uncalibrated ages obtained for our stratigraphically constrained samples were combined with those (when existing) of  Traineau (1982)  and  Westercamp and Traineau (1983) , and validated using the R_combine function of OxCal and χ 2 test prior to calibration  (Ward and Wilson, 1978).
Eruptive parameters We retrieved the eruptive source parameters (ESPs) of the newly identified eruptions from the field data using physical models of volcanic plumes. Following the recommendations detailed in  Aubry et al. (2021)  for presenting observational data on volcanic eruptions to maximize their usability across different applications, we estimated the maximum column height, the total erupted mass (and volume), the mass eruption rate (MER), the duration of the eruption, and their uncertainties. The tropical island of Martinique is subject to intense weathering, and only proximal (and incompletely preserved) deposits are available, much being lost at sea. Volume calculations are thus bound to provide minimum estimates only. For most eruptions, we inferred the volume of tephra fallout using deposit thinning profiles built from the isopach maps, and approximated by exponential  (Pyle, 1989; Fierstein and Nathenson, 1992) , power-law  (Bonadonna and Houghton, 2005)  and Weibull  (Bonadonna and Costa, 2012)  fits computed using the AshCalc software  (Daggit et al.
Acknowledgements The authors thank one anonymous reviewer,  R. Torres-Orozco , and the editor,  S. Cronin , for their useful comments, which greatly improved the original version of this paper. We are grateful to the staff of the  Montagne Pelée volcanological and seismological observatory (OVSM-IPGP)  for their assistance. We thank the  LMC14  staff ( Laboratoire de Mesure du Carbone 14 ), ARTEMIS national facility, ( LSCE  ( CNRS-CEA-UVSQ)-IRD-IRSN-MC ) for the results obtained with the accelerator mass spectrometry. This work was partially funded by the  INSU-CNRS  Artemis 2017 and 2022 programs, the  RAVEX  Project ( ANR  contract  ANR-16-CE03-0002 ) and the V- Care  project  (ANR  contract  ANR-18-CE03-0010 ). We thank the  Centre National d'Etudes Spatiales  for its support. This study contributes to the  IdEx  project  « Université de Paris »  ( ANR-18-IDEX-0001 ).
Tropical cyclone (TC) detection is essential to mitigate natural disasters, as TCs can cause significant damage to life, infrastructure and economy. In this study, we applied the deep learning object detection model YOLOv3 to detect TCs in the North Atlantic Basin, using data from the Thermal InfraRed (TIR) Atmospheric Sounding Interferometer (IASI) onboard the Metop satellites. IASI measures the outgoing TIR radiation of the Earth-Atmosphere. For the first time, we provide a proof of concept of the possibility of constructing images required by YOLOv3 from a TIR remote sensor that is not an imager. We constructed a dataset by selecting 50 IASI radiance channels and using them to create images, which we labeled by constructing bounding boxes around TCs using the hurricane database HURDAT2.
Bourdin et al. [5]  intercompared four physical models on the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th generation (ERA5). Such methodologies have been frequently used in the past decades  [4]  and show great ability, and potential in assessing TC activity  [6, 7]. With the enhancement of computational resources, high-resolution climate datasets have been growing increasingly. Studies demonstrate that the use in climate models of datasets with higher horizontal resolution shows great promise in improving the prediction of TCs  [7, 8]. The downside of this type of method is that they often require setting up a number of well-defined rules or thresholds. However, complex weather phenomena such as TCs remain difficult to quantify and predict  [9]. A frequently used method is the CLImatology and PERsistence (CLIPER) model  [10] , used as a baseline in most statistical forecasting.
More than 4 million IASI spectra covering the entire Earth are collected per day (17 Terabytes per year), from which a significant part (about 70%) is affected by the presence of clouds. Most of these cloud-contaminated data are discarded in retrieval processes of chemical species like CO, O 3 or NH 3 for instance  [14, 19]. Here we will exploit all the IASI data in an attempt to develop the value of these usually discarded cloudy data. No other instruments and no wind data are used. We attempt to detect TC activity in the North Atlantic Basin (between 5°and 75°N of latitude and between -110°and 18°E of longitude) using a deep learning object detection model applied to a TC dataset we created from IASI data. To our knowledge, no previous study attempted to exploit the added value of infrared sensors, such as IASI and their "raw" measurements, in the detection of TCs.
The aim of the paper is to assess the performance of the YOLOv3 object detection model and the potential of the IASI data in the TC detection problem. The paper is organized as follows. In Section 2, we present the IASI data and the YOLOv3 model, as well as the dataset creation, from the processing of the IASI data to the labeling procedure. In Section 3, we describe the experiments performed on the model, assess its performance, and analyze its predictions. Finally, conclusions are presented in Section 4.
We selected the 25 most sensitive. Whitburn et al. [21]  provided a list of 45 wavenumbers sensitive to clouds. We chose the 24 wavenumbers whose position is distant from the 25 selected wavenumbers sensitive to temperature (Figure  2 ). The selected wavenumbers are listed in Appendix A. In short, we selected 25 "temperature" and 24 "cloud" sensitive channels containing good information content on each and low contamination from other molecules  [20, 21]. These are raw radiances, also called Level 1C data in TIR instrument jargon. To these 49 selected wavenumbers, we add one IASI product, the total water vapor column, for a total of 50 channels. The total water vapor column is not a raw measurement but a EUMETSAT product obtained by applying an algorithm to Level 1C data, called Level 2. Other attributes were considered, such as temperature at 200 hPa (about 12 km) or cloud top pressure, but could not be used due to many missing data.
We used the YOLOv3 model  [25] , which is the third version of the YOLO series, where each version was improved. It has been proven successful with different kinds of datasets. Shakya et al. [30]  suggested that YOLO could be used for the detection of cyclone location, and Pang et al. [31]  successfully trained a YOLOv3 model to detect TC on meteorological satellite images with Average Precision (AP) of 81. 39%. YOLOv3 uses Darknet-53  [25]  as a backbone to extract image features: it is a convolutional neural network with 53 convolutional layers and residual connections. For the detection task, 53 more layers are stacked onto it, giving us a 106-layer fully convolutional underlying architecture for YOLOv3, with about 61. 9 million parameters to set. YOLOv3 divides the input image into N × N grid cells.
Each grid cell can predict B boxes, where B is the number of anchor boxes, which are pre-defined boxes. Instead of predicting boxes coordinates directly, the model predicts offsets from these anchor boxes. This simplifies the problem and makes it easier for the network to learn. These anchor boxes are the average characteristics of the groups of boxes found by K-means clustering performed on the training dataset. YOLOv3 uses 9 anchor boxes. For each box, several values are predicted: • four offsets coordinates t x , t y , t w , t h (center coordinates x,y of the box, width w, and height h) • a confidence score reflecting how likely the box contains an object and how accurate is the box • C values correspond to the class probabilities. In our case, C=1, as we only have one class (TC). For the detection part, YOLOv3 makes predictions at three different scales (as illustrated in Figure  3 ), which are precisely given by downsampling the dimensions of the input image by factors of 32, 16, and 8, respectively.
To train the object detection model, images need to be labeled. A bounding box (bbox) surrounding each TC must be defined. HURDAT2 (HURricane DATabase 2nd generation)  [34]  from the National Oceanic and Atmospheric Administration (NOAA) was used to locate TC positions in the images (available at www. nhc. noaa. gov/data/#hurdat, accessed on 23 May 2022). It gathers re-analyzed tracks of tropical and subtropical cyclones in the Atlantic Basin since 1851 at 6-hour intervals. More precisely, the HURDAT2 database gives: • cyclone center location (latitude/longitude) at 0, 6, 12, and 18 h UTC • 34 knots (about 63 km/h) wind radii maximum extent in northeastern, southeastern, southwestern, and northwestern quadrant (in nautical miles), i. e. , for each of the directions, the distance until which the wind speed is more than 34 knots is given.
Bounding boxes center were directly given by the HURDAT2 database. We deduced the size of the bounding boxes based on the wind radii values as shown in Figure  5. As each wind radius gives the distance (from the cyclone center) until which the wind speed is less than 34 knots, we simply keep the maximum distance in the four directions north, south, east, and west. We then convert the distance from nautical miles to degrees of longitude (north/south direction) or latitude (east/west direction), then add/subtract it to the center coordinate to obtain the limits of the bounding box. These bboxes constitute image labels. Because this method requires the 34 knots wind radii maximum extent to label the images, only TCs with maximum sustained wind of at least 34 knots were taken into account in this study.
With IASI data available starting October 2007 and the HURDAT2 database ending in 2021 (since the access date), we considered the period 2007/10/01-2021/12/31 for our study. During this period, a total of 234 TCs were tracked and listed in the database for a total of 4935 TCs positions (cyclone lifespan generally last several days). Matching IASI and HURDAT2 data was not straightforward. As HURDAT2 provides TCs position at 6 h-interval (0, 6, 12, and 18 h UTC) while IASI passes over each location 2 times a day at 9:30 a. m. and 9:30 p. m. local time (with data provided in UTC), there can be a discrepancy between given TC position and observed IASI data, resulting in cyclone center that might not be precisely over the correct location or having two TC positions being matched to the same IASI overpass.
For instance, the position for a TC at 6 h and 12 h UTC could be matched to the same IASI overpass at 9:30 a. m. local time. In that case, we merged the two boxes from 6 h and 12 h UTC, resulting in a larger final box (as illustrated in Figure  A2 ). If two positions are matched to the same IASI overpass, then one of them necessarily happened before the selected IASI overpass, while the other happened after. This is explained and illustrated in Appendix C. As a result, the TC is ensured to be inside the merged box and might also encompass more than necessary. However, as said in  [35] , this is preferable to encompassing too little of the TC to prevent the model from missing any key features. The dataset creation procedure is summarized in Appendix D. Based on IASI data and matched hurricane data from HURDAT2, we found over the period 2007-2021 (Table  1 ), 1985 images of shape 140 × 256 × 50 and 2594 labels/boxes in total (several TCs can be in the same image).
The dataset was divided into train (70%), validation (15%), and test (15%) sets. The distribution of each split (train, validation, or test) was carefully chosen to avoid unwanted bias. Indeed, since the dataset is based on tracks of TCs, two successive images can be quite similar if the TC does not move much. If we divide the dataset randomly, these similar images might be distributed in train and test, which could bias the results, as there will be images in test that are too similar to images in train. The dataset was, therefore, sampled randomly by years while ensuring the correct ratio for each set (see Table  2  for years distribution in each set). Both training and validation sets were used to build the model, i. e. , to select the best hyperparameters. Hyperparameters are variables that determine the network architecture and how the network is trained, such as the number of training iterations, for instance.
Description of Experiments In this work, two experiments were conducted: • Experiment A: First experiment consisted in using only three carefully selected channels from the 50 previously selected (Figure  2 ) to keep the original architecture of the YOLOv3 model unchanged, the latter expecting input images of three channels. • Experiment B: Second experiment considered all 50 selected channels and reduced them to three using a specific NN architecture: an autoencoder. These experiments were run on an Nvidia Tesla V100 16 GB GPU from the Jean Zay supercomputer installed at the Institute for Development and Resources in Intensive Scientific Computing (IDRIS). The code used for the YOLOv3 model is an implementation from Ultralytics  [36]  that we adapted to fit our dataset format. As YOLOv3 expects square input matrices and since our images are rectangular (140 × 256), the images were padded with zeros to ensure a square input of 256 × 256.
Input images were also transformed by applying Z-score normalization (or standardization) to have each channel on the same scale. The mean and standard deviation were calculated from the training set only to keep the validation and test sets as unseen data, then each sample of the entire dataset was transformed by subtracting the mean and dividing by the standard deviation. This is a standard procedure to compare the images better and reduce the number of significant figures within the radiance values, shown in Figure  2. After normalization, images corresponded to values between -3 and 6 approximately, with most values between -2. 5 and 2. In the following figures, we show the normalized images. Therefore, their values do not correspond to the radiances and have no particular meaning, and as such, no unit is assigned to them.
Experiment A: Three Channels For experiment A, we selected the most sensitive "temperature" channel (wavenumber at 969. 75 cm -1 ) according to the sensitivity ranking from Safieddine et al. [20] , one "cloud" channel (wavenumber at 1006. 5 cm -1 ) randomly chosen as there was no ranking available, and lastly the water vapor column, for a total of three channels. First training achieved Average Precision at threshold 0. 1 (AP@. 1) of 55. 67% and AP@. 5 of 15. 25% and resulted in overfitting, i. e. , the YOLOv3 modeled the training data too well, to the extent that it could not perform accurately against unseen data. It memorized the training data without learning the underlying pattern, which defeats its purpose. One of the potential causes of overfitting is the lack of training data. Indeed, deep learning models with a large number of parameters usually require a large quantity of data to have good performance.