dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Thanks are due to the staff who completed the survey. The authors would also like to thank the  CHOICE Study Team :  Dr. Amarnath Bhide , Consultant Obstetrician,  St George's University Hospitals Trust ;  Miss Dikshyanta Rana ,  Research Assistant, University of Glasgow ;  Professor Julia Sanders ,  Professor  of  Clinical Nursing & Midwifery, Cardiff University; Miss Neelam Heera, Patient and Public Involvement Representative ;  Mrs. Jane Huddleston ,  Patient and Public Involvement Representative ;  Professor Fiona Denison , Reader/Honorary Consultant in Maternal and  Fetal Medicine, University of Edinburgh ;  Professor Neena Modi ,  Professor of Neonatal Medicine, Imperial College London ;  Professor John Norrie ,  Professor of Medical Statistics  and Trial Methodology / Director of  Edinburgh Clinical Trials Unit / Co-Head of Centre for Population Health Sciences, University of Edinburgh.
Acknowledgements We acknowledge fruitful discussions with  S. Shiraiwa  and  N. Bertelli  (PPPL), and  R. Bilato  and  M. Usoltceva  ( IPP Garching ). This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under grant agreement no. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. Editor Per Helander  thanks the referees for their advice in evaluating this article.
Acknowledgements This research has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  [grant agreement 804144]. Further funding was provided by the  French government  through the  UCA JEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01 , by the  ANR  under grant number [ ANR-18-CE29-0004-01 ] as well as by the  project CALIPSOplus  under the Grant Agreement 730872 from the  EU   Framework Programme for Research and Innovation HORIZON 2020.
All three uses can be badly affected by the speckle noise of SAR data, as well as trivial and seasonal changes in optical images. As such, the pre-processing and the choice of the change-detection algorithm in the former, and the damage-assessment method in the latter, are key to mitigating these issues. On the other hand, since pre-disaster images are not always available, a second approach takes the stand that the post-disaster images should be self-sufficient and that the damage can be assessed by segmenting and classifying the different elements. In practice, this is often proven to be only partially true, at least for areas with water: the pre-disaster images or a reference dataset are needed to differentiate flooded areas from rivers, lakes and the sea  [7]. Finally, a distinction can be made between regular machine-learning methods, and more recent methods that rely on deep neural networks and, in particular, convolutional neural networks (CNN)  [33, 34].
Their proposed architecture is very simple and relies on pre-segmentation using existing blue prints before feeding the data to the SVM algorithm. They also tested different data sampling techniques in order to assess the sampling influence on the final results. They concluded that balanced random sampling gave the best results, with a F1 score of 0. 85 for change detection. This study is interesting, but sadly it does not go beyond the detection of change or no-change areas, thus not providing detailed categories of damage. Nevertheless, it is the first study that showed the superiority of Machine Learning compared to manual thresholding methods. Another SVM model was proposed by  [12]  on the same dataset. It relies on different input features built from the gray-level co-occurrence matrix (GLCM) in order to obtain better textures.
They used the curves proposed by Koshimura et al. [53] , as well as Suppasri et al. in  [54] , to produce and refine an adequate fragility function; see Figure  9. In other words, the supervision and the training data are replaced by an intensity parameter (e. g. , the inundation depth) and probabilistic information computed from expert knowledge through the fragility function. [54] ; The figure is modified from  [14]  with the corresponding author's permission. They apply their method to TerraSAR-X images of the Sendai bay area taken on October 12th 2010 and March 13th 2011. They use input features of the form x i = {1, d, r} where d is the average difference in backscattering between the two images, and r is the correlation coefficient. They try to predict two classes of buildings-collapsed and not collapsed-and manage to do so with an accuracy of 87.
While this method has the advantage that the cluster generation is fully automated and relies on a fast algorithm (K-Means is linear in O(N) for a dataset of size N. ), it has the following major issues: the 30 resulting clusters apparently need to be manually mapped to the classes of interest, with no guarantee that some of them will not be a mix of several classes. For such a scenario, where clusters have to be merged, hierarchical clustering  [61]  should be considered a better option, despite its higher time complexity. Finally, another problem with this study is that it does not provide any quality indication of the results in terms of accuracy compared with the ground-truth. In  [23] , Park et al. use a similar change-detection technique based on the Expectation Maximization algorithm  [62]  coupled with Markov Random Fields  [63]  to reduce the speckle noise.
The authors tested their three architectures on a dataset made of images taken in August 2000 for the pre-disaster images and 11th March 2011 for the post-disaster images. All architectures achieved scores between 93% and 96% accuracy in the detection of washedaway buildings. Given the important time-gap between the images before and after the disaster (11 years), the extremely high scores for only two classes, and the lack of detailed information on the neural networks used (the detailed architecture is not shown inside the paper), it is difficult to comment on the results of this paper and the re-usability of the proposed architecture.
A large number of studies still heavily rely on manual data annotation, either relying directly on the source data, or to feed a supervised machine-learning algorithm. While it is true that some of these models could, in theory, be re-used for future geo-disasters, in practice, transfer learning remains a difficult problem in machine learning, and there is no guarantee that any model trained on annotated data from the Tohoku tsunami could be directly re-applied to another disaster. First, several remote-sensing programs have been stopped or evolved since 2011 (see Table  1 ), and any model-especially deep learning ones-trained on these data cannot be directly re-used on another data format with different resolutions or channels. Then, optical images (VNIR, PAN and RGB) can vary greatly from one country to another due to differences in the vegetation and, most importantly, in the local architecture of the buildings, thus making a model trained over one area difficult to reapply to a different country, even for images of the same format and resolution.
(2) Exclude areas containing non-natural structures (metallic support beams, metal ropes, buildings, corner reflectors). (3) Exclude areas affected by radar shadow, and exclude areas outside of the main beam of the secondary antennas -these areas were detected by applying a threshold to the magnitude of the single-pass interferometric coherence γ of the secondary receiver in the VV channel. In every acquisition in the summer dataset, pixels with γ < 0. 85 were masked out; in the winter dataset pixels with γ < 0. 80 were masked out. A sliding window of 5×3 (range×azimuth) pixels was used for coherence estimation. The winter threshold is lower due to lower overall coherence in comparison to summer. The masks defining the ROI for each season are shown in Fig. 1. They cover practically the same region of the hillside.
The acquired calibrated SLC datasets were spatially multilooked using a 5 × 3 window to obtain the intensity images and analyzed in the radar polar geometry (range × azimuth angle). The intensity value Î (β) (the hat symbol ˆindicates it is a measured quantity) was computed for every acquisition by averaging the measured intensities of all pixels within the ROI. We analyzed only acquisitions with VV and HH polarization as the cross-polarized signal was too close to the noise floor to provide reliable data.
005 to β = 0. 21 •. The incidence angle at the scene center is θ = 32 • (orbit 154, descending). Time-averaged backscatter images of the study area are provided in the Supplement Figs. S2  and S3. Interferometric and polarimetric properties of the dataset were analyzed by  Leinss and Bernhard (2021). The Jungfrau-Aletsch region is highly glaciated with multiple peaks reaching above 4000 m. Cold firn, several tens of meters deep, is likely present throughout the year: depending on exposition, the transition to temperate firn is at 3400-4000 m, and the upper 15 m of firn experiences seasonal temperature cycles and can freeze in winter  (Suter et al. , 2001; Suter and Hoelzle, 2002; Jun et al. , 2002). At the end of The region contains the Great Aletsch Glacier (46. 50 • N, 8. 03 • E), the largest glacier in the European Alps.
TanDEM-X -radiometric calibration and computation of backscatter ratios Resolving the peak shape of the CBOE with a maximum expected peak height of 3 dB requires a precise radiometric calibration of the bistatic dataset. To avoid any terrain-dependent or incidence-angledependent calibration, we analyzed the ratio between the backscatter intensity Îbist observed by the bistatic receiver and the intensity Îmono observed by the monostatic transmitter-receiver: Îr,0 = Îbist / Îmono. (3) The index r, 0 indicates taking the ratio relative to I (β = 0). Averaging ratios like Îr,0 would result in a biased estimate. To estimate unbiased spatially or temporally averaged ratios, we first applied the averages to Îbist and Îmono and then computed the ratio Îr,0. We use Î to refer to the radar brightness commonly denoted by β 0  (Raney et al.
r,0,HH showed terrain-independent trends of a few percent, presumably due to different antenna patterns (Figs. S4  and S5 ). To compensate for these patterns, we calibrated the intensity Î uncal. bist at each polarization with the ratio of the pixel-wise temporal mean • temp. of 17 scenes with β < 0. 033 •. This threshold for β was chosen to be small enough to avoid any significant differences in backscatter enhancement between the monostatic and bistatic receiver. The bistatic intensity after antenna calibration is Î ant. cal. bist = Î uncal. bist Îmono temp. β<0. 033 • Î uncal. bist temp. β<0. 033 •. ( 4 ) To obtain the calibrated intensity Îbist , we compensated in each acquisition pair for the remaining spatially constant offset between the monostatic and bistatic data. (5) For calibration of the Jungfrau-Aletsch dataset we used areas that showed a temporally stable and baselineindependent backscatter ratio Îr,0.
(  11 ) from the actual observed mean ROI intensity Î (β), we approximate I (∞) as the mean value of Î (β) from all acquisitions within the corresponding dataset where β > 1 • (i. e. , values well within the flat region of the intensity curve): Îr,∞ (β) ≈ Î (β) Î (β) β>1 •. ( 12 ) Because the model B C = B C (β, T , A ) depends on the transport mean free path T and absorption length A through Eqs. (  6 ) and (  7 ), we can use Eq. (  11 ) to fit different values of T and A to the observed intensity curve Îr,∞ (β) by nonlinear least-squares minimization. For the fitting procedure, we used the TRF (trust region reflective) optimization method implemented via the curve_fit function of the scipy. optimize library  (Virtanen et al. , 2020). The initial parameter values of the ( T , A ) pair were set to (1 m, 100 m), and both parameters were restricted to the non-negative real-number domain.
5 to eventually 1. 0 when the CBOE is negligible. A lower limit for the enhancement B C (β = 0) can be quickly estimated: because B C (β max ) > B C (∞) = 0, it follows from Eq. (  13 ) that B C > I -1 r,0 (β max ) -1 = Îmono Îbist (β max ) -1; (14) i. e. , the enhancement B C is at least as large as the relative difference between the monostatic and the bistatic backscatter, Îmono and Îbist , at the largest available bistatic angle β max. To fit the model, we used winter data from the accumulation area (Sect. 2. 2. 1). To determine the optimal value of the parameter pair ( T , A ) and the 95 % confidence intervals, we used the TRF method (Sect. 2. 3. 1) and set the starting parameter of ( T , A ) to (2 m, 20 m). However, a sampling of the RMSE [ Îr,0 (β) -I r,0 (β)] in the parameter space of T , A around the optimal value revealed that the global minimum is weakly constrained, and solutions across a large span of values of A provide an acceptably low RMSE value.
Ground-based observations -KAPRI Figure  7  shows the observed intensity ratio Îr,∞ (β) defined by Eq. (  12 ) at HH and VV polarization and the least-squares best fit of the model defined by Eqs. (  6 ), (7), and (11). For the winter dataset a clear intensity peak is detected, with a HWHM of approx. 0. 25 • and amplitude B C (0) ≈ 0. 5 (1. 8 dB), corresponding to T ≈ (0. 4 ± 0. 1) m for the HH and VV polarization. The derived absorption lengths A are much longer than the scattering lengths with A ≈ (11 ± 7) m for the HH polarization and A ≈ (19 ± 12) m for the VV polarization. For the summer dataset, the flat profile of the observed intensity curve indicates that very little or no backscatter enhancement is present -this is reflected in the model fit in the low value and large confidence interval (relative to the value) of the absorption length A ≈ (0.
4±0. 4) m. The estimates of the scattering length in the summer dataset ( T ≈ (0. 40±0. 27) m and T ≈ (0. 43±0. 39) m for the VV and HH polarization respectively) have comparable value to the winter estimates; however the much larger confidence intervals indicate that the value of T could not be determined more precisely for the summer dataset due to the absence of a clear enhancement peak. The uncertainty in the value estimates corresponds to the 95 % confidence interval. Figure  8  shows the RMSE of the model fit to the dataset with VV polarization, depending on values of the fit param-.
Ground-based observations -KAPRI Figure  7  shows a statistically significant enhancement peak for the winter acquisition and a lack of such a peak for the summer dataset, which was acquired using an identical target region of interest, identical acquisition procedure (except platform substitution to allow movement on snow/the road), and identical processing pipeline. The summer dataset thus serves as a useful control which ensures that the detected enhancement peak is not an erroneous artifact of the bistatic data processing pipeline, and it also indicates that the enhancement peak is indeed caused by the snow layer present on the hillside. In the summer scenario (i. e. , absence of a clear backscatter enhancement peak), the model, described in Sect. 2. 3 and visualized in Fig. 6 , predicts that the absorption length is shorter than or equal to the scattering length ( A ≤ T ).
Applications based on the CBOE In TanDEM-X data we have observed a backscatter enhancement of at least 1. 3 dB for firn-covered areas of the European Alps and in the Karakorum, while for firn-free areas no backscatter enhancement could be observed. This suggests that detection of deep firn with the X-band is possible when large enough bistatic angles, β > 0. 2 • , are available. For seasonal snow, we observed a clear CBOE peak (∼ 1. 8 dB of backscatter enhancement) at the Ku-band using KAPRI, whereas in the X-band we were not able to observe an enhancement. The higher sensitivity of high-frequency systems (Ku-or possibly Ka-band) for detecting the CBOE in seasonal snow results from the shorter scattering length since sufficiently high-order scattering events can occur within the snow layer of limited thickness.
and b show the selected dataset at two different scales of β. The solution with the minimal RMSE value is indicated by the dashed black line. This solution corresponds to T = 2. 13 ± 0. 36 m and A = 21. 77 ± 2. 72 m (95 % confidence interval, VV polarization), an enhancement of B C = 35 % (+1. 3 dB), and a HWHM of.
Scattering length T , peak height B C (0), and peak width (HWHM) for a set of chosen absorption lengths A determined from the TanDEM-X dataset (VV) of dry firn in the high-altitude accumulation area. The bold-formatted rows indicate the optimal parameter pair at VV and HH polarization. RMSE is the root mean square error between the measured and the modeled value of I r,0. A (m) T (m) B C (0) HWHM (deg) RMSE 1000 0.
Acknowledgements. The authors would like to thank  Henning Löwe  and the anonymous reviewer for their in-depth peer reviews and for constructive comments and suggestions on how to improve the paper. Furthermore, the authors would like to thank  Tingting Li ,  Yuta Izumi ,  Simone Jola , and  Michael Arnold  for their support during fieldwork in Davos;  Christian Mätzler  and  Rosemary Willatt  for valuable feedback and discussion;  Emmanuel Trouvé  for proofreading the manuscript;  Laurane Charrier  for valuable input regarding applications of the CBOE; and  Thomas Busche  for support with the TanDEM-X acquisitions. The authors would also like to thank  Reto Müller  for access to his property during acquisitions and the staff of  Bergbahnen Rinerhorn AG  for assistance with equipment transportation.
The TanDEM-X data were provided by the  German Aerospace Center (DLR)  via proposal XTI_GLAC6780. Financial support. Silvan Leinss  was supported by the  French Ministry for Europe and Foreign Affairs , within the framework of the third " Make Our Planet Great Again" (MOPGA) program  (mopga-postdoc-3) for postdoctoral researchers. The MOPGA grant has been issued by  Campus France  under grant no. 0821369222. Review statement. This paper was edited by  Carrie Vuyovich  and reviewed by  Henning Löwe  and one anonymous referee.
Regions affiliations were based on the Desikan-Killiany atlas  (Desikan et al. , 2006). Epileptiform activity was generated in the epileptogenic and propagation subnetworks, while background activity was assigned to the remaining cortical regions. A detailed description of the model along with along with all simulation parameters relative to the data are provided in the Supplementary Materials and the generated source signals are provided in the GitHub repository. All sources belonging to a single patch were synchronized at a zero lag, while a delay of 30 ms was introduced between the two subnetworks to reflect the propagation of spikes between relatively distant regions in the brain. A timeseries of ̴ 6 min at 2048 Hz was simulated, and was segmented into 10-second epochs. A total of 30 epochs was selected for the subsequent analysis.
EEG Electrodes density and direct problem Five different electrode montages were used to generate scalp EEG signals. We selected the GSN HydroCel EEG configuration (EGI, Electrical geodesic Inc) for the 256, 128, 64 and 32 channels density, as well as the international 10-20 system  (Klem, Lüders, Jasper, & Elger, 1999)  for the 19 channels array. For each electrode configuration, the lead field matrix describing the electrical and geometrical characteristics of the head was computed for a realistic head model using the Boundary Element Method (BEM) using OpenMEEG  (Gramfort, Papadopoulo, Olivi, & Clerc, 2010)  implemented in the Brainstorm toolbox  (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011)  for Matlab (The Mathworks, USA, version 2018b). To generate EEG scalp simulations, we solved the forward problem as follows: 𝑋(𝑡) = 𝐺.
Statistical analysis Statistical analyses were performed using R (R Core Team, 2020). We used linear mixed model analyses to investigate the effects of electrode number, inverse solution method, and connectivity measure on the accuracy of the estimated networks. Mixed models have several advantages, such as the ability to account for the dependence between the different measures, and to model random effects (see  (Gueorguieva & Krystal, 2004) ). We used the lmer function of the {lme4} package  (Bates, Mächler, Bolker, & Walker, 2015)  with the following model that includes, epoch, electrode number, inverse solution method and connectivity measures as interacting fixed effects, and also a random intercept for epochs: 𝑚𝑜𝑑𝑒𝑙 = 𝑙𝑚𝑒𝑟(𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 k 𝐸𝑝𝑜𝑐ℎ * 𝐸𝑙𝑒𝑐𝑡𝑟𝑜𝑑𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 * 𝐼𝑛𝑣𝑒𝑟𝑠𝑒 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 𝑚𝑒𝑡ℎ𝑜𝑑 * 𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑚𝑒𝑎𝑠𝑢𝑟𝑒 + (1 𝐸𝑝𝑜𝑐ℎ) 𝑑𝑎𝑡𝑎 = 𝑑𝑎𝑡𝑎) ⁄ (10) We applied a square root transform to the data, since this led to a better compliance of the model with the assumptions of normality and homoscedasticity of model's residuals than for raw data.
Acknowledgments This work was financed by the  Rennes University, the Institute of Clinical Neuroscience of Rennes  (project named EEGCog). Authors would also like to thank the  Lebanese Association for Scientific Research (LASER)  and  Campus France ,  Programme Hubert Curien CEDRE (PROJECT  No. 42257YA ), for supporting this study. The authors would like to acknowledge the  Lebanese National Council for Scientific Research (CNRS-L) , the  Agence Universitaire de la Francophonie (AUF)  and the  Lebanese university for granting Ms. Allouch a doctoral scholarship.
Code availability Data and Codes supporting the results of this study are available at https://github. com/saharallouch/comp-epi. git. We used Matlab (The Mathworks, USA, version 2018b), Brainstorm toolbox  (Tadel et al. , 2011 ), Fieldtrip toolbox ((Oostenveld, Fries, Maris, & Schoffelen, 2011); http://fieldtriptoolbox. org), OpenMEEG  (Gramfort et al. , 2010)  implemented in Brainstorm, and BrainNet Viewer  (Xia, Wang, & He, 2013).
In this paper, we propose a new algorithm to estimate all the parameters of a Self Exited Threshold AutoRegressive (SETAR) model from an observed time series. The aim of this algorithm is to relax all the hypotheses concerning the SETAR model for instance, the knowledge (or assumption) of the number of regimes, the switching variables, as well as of the switching function. For this, we reverse the usual framework of SETAR model identification of the previous papers, by first identifying the AR models using array clustering (instead of the switching variables and function) and second the switching conditions (instead of the AR models). The proposed algorithm is a pipeline of well-known algorithms in image/data processing allowing us to deal with the statistical non-stationarity of the observed time series.
This questionnaire-based survey was conducted during May and June 2019. Data were collected face to face, by targeting the general population, and about 20 min was needed to complete each survey. The Sardinian sample is composed of 45 % male respondents (the male population comprises 49 % of the total in the 2011 census  (Istat, 2013) , so our sample under-represents the male population). The average participant is 46. 4 years old (44 is the average age of the population, 2011 census). Secondly, the use of interviews in the north Moroccan study was two-fold: they served to introduce the problem of risk management in the region and to construct the questionnaire. Regarding the Sardinian case study, the secondary literature on risk management at the regional and national level was analysed.