dataesr/scientific-paragraphs-categorization huggingface dataset:
Sentinel-2 The Sentinel-2 (S2A-S2B) images were processed to level 2A with the MAJA processing chain  [44, 45] , allowing to obtain surface reflectances at top of canopy (TOC). This process presented in Hagolle et al. and Baetens et al. [44, 45] , allows the detection of clouds and their shadows, the correction of the atmospheric effects of the images from an estimation of the aerosol optical thickness (AOT) and water vapour. This process can be applied to time series of images. 100 images were processed in 2017 and 124 in 2018, corresponding to the tiles T30TYN and T30TYP, on Theia website [https://theia. cnes. fr/]. The average number of cloudy pixels was 31% in 2017 and 37% in 2018 over the whole time series, with cloudiness reaching over 85% in June (spring). The obtained images were temporally aggregated to regular 10-day interval composite datasets using the gapfilling module Orfeo Toolbox 6.
Sentinel-1 The Sentinel-1 images came from the C-SAR instrument onboard two Sentinel 1 satellites (S1A-S1B) (Interferometric Wide Swath mode, IWS-GRD  [26] ). This mode provides dual-polarisation (VV and VH) imagery, at resolution of 20 m, with a swath of 250 km. The images were radiometrically (Multilooking filter 2 × 2) and geometrically corrected using the OTB software  [46]. Only Sentinel-1 images acquired in ascending mode (acquisition time: 6. 00 pm) and between April and November were used (i. e. , one image every 6 days, i. e. , 79 images in 2017 and 100 in 2018 ). The ascending mode (orbit n • 30) was used in the study, to limit the impact of morning dew and freezing dew, which can lead to artefacts in the SAR signal. Like Sentinel-2 images, the SAR images were orthorectified on the S2 grid and with the same pixel size.
Meteorological Data Rainfall data from the SAFRAN database  [40]  were used in the classification process. It is defined in mm/day. SAFRAN is a system for atmospheric analysis of surface meteorological variables based on the use of homogeneous climatic zones and capable of taking into account altitudinal variations. These data are spatially interpolated to cover all of mainland France with a regular grid of (8 × 8 km). Version September 8, 2020 submitted to Remote Sens. (NDV I > 0. 8 and NDW I < -0. 40). This difference is stronger in 2017. It decreases when entering the senescent phase. The growing cycle is quite synchronous for all crops, as shown in the table 1. For sunflowers, the growth cycle is different than for other crops, with lower growth peaks.
Cumulative Indices The use of multispectral (optical and radar) and multi-temporal images products leads to an increase in the number of features, to be processed, and consequently increase the redundancy of spectral information and the computation time of the classification process. To avoid these effect, we computed cumulative monthly indices, correspond to the sum of the spectral information of each feature (rainfall, optical and radar). We used gap filling for simplicity. The use of cumulative indices sounds pertinent as they are related to the plant functioning. Indeed, many authors have demonstrated the link between plant development of the plant and the cumulative spectral indices provided by remote sensing data  [57] [58] [59]. For this study, we assumed that the irrigation have an impact of several irrigation events (4 to 5 irrigations) on crop development could be captured using the cumulative indices over the growing season.
Classification The algorithm used for the classification process is the Random Forest (RF)  [60]. It was selected for its robustness, because it is easier to parametrise and has good performances  [29, 30, [61] [62] [63] [64]. The Random Forest library used is the one provided by Shark  [65]  available through the supervised classification framework of Orfeo Toolbox 6. 7  [46]. The number of trees was set to 100, the maximum depth to 25 and max-features to number f eatures. The pixel classification procedure has been fully automated by the Iota2 processing chain developed by CESBio and available as free software [https://framagit. org/iota2-project/iota2. git]. Each classification was evaluated using the reference data set, which was randomly divided into two parts: 50% of the plots were used for the training phase and 50% were used for validation.
Validation Each metric was averaged over the five runs to obtain a mean value and a confidence interval per scenario and to measure robustness against the sample selection noise. The global performance was evaluated using the Kappa coefficient (1) and the Overall Accuracy (OA). The Kappa coefficient expresses a relative difference between the observed agreement Po and the random agreement which can be expected if the classifier was random, Pe. The Overall Accuracy (OA), is the total number of correctly classified pixels divided by the total number of validation pixels. Kappa = P o -P e 1 -P e (1) with P o = 1 n ∑ r i=1 n ii and P e = 1 n 2 ∑ r i=1 n i n i , The performance of the different scenarios was evaluated using the reference datasets. Table  5  shows the number of pixels used for learning and validation of the classification model for each class.
In view of the strong imbalance between the two datasets (Training and Validation), we know that the overall accuracy may be biased by the majority classes. To limit this bias, we decided to use another metric at the class level. The performance of each class was evaluated using the Fscore (2). Fscore = 2 × Accuracy × Recall Accuracy + Recall (2) where • Accuracy is the ratio between the correctly classified pixels and the sum of all pixels classified as this class, and.
Thus so-called "single-source" scenarios are highly dependent on the number and quality of the dataset. Figure  5  reveals that the synergy optical and radar data permits to limit the impact of cloud cover, with a gain in all metrics for both years. Similar results were found in the literature with a significant gain in performance on the detection of irrigated areas in India (more than 74% gain on the Fscore and 0. 20 on the kappa coefficient, when the synergy of the two sensors is used)  [35] , in Northern Spain, with a 5% increase in overall accuracy compared to using Sentinel-1 data alone  [37].
Impact of Cumulative Indices Dealing with the huge number of images available requires optimised computing methods. To avoid this constraint, we evaluated the performance of classifications with a reduced number of indices. Results show that cumulative indices lead to similar performances for maize (irrigated/rainfed) than the overall 10-day features (Figure  5 ), meaning that these is redundant information when using all the full spectral bands and dates. The lowest Fscore observed for the minority classes (Soybean and rainfed maize) is due to a deficit of learning samples. Indeed, the low number of reference dataset for theses classes does not allow to characterise the whole spatial and temporal variability of these classes, and consequently leads to a high degree of confusion, as shown in Figure  7.
However, Scenario 5 is seems less sensitive to the size of reference dataset. The addition of the learning data for the soybean and rainfed maize in 2018, the results were improved with lower confusion (Figure  7 ) and higher Fscore. This link between performance of the classification methods and the number of reference data has been raise by Pelletier et al. [64]. For sunflowers, there is a decrease in Fscore and confidence between 2017 and 2018, which is explained by a smaller sample area in 2018, with only 40 ha, compared to 120 ha in 2017. Nevertheless, this small sample area does not have a significant impact on the results, as the phenology of this crop is very different from that of the other crops studied, as shown in Figure  3. Much less computational resources was needed for the cumulative approach, as shown in Table  6.
Contribution of Rainfall Features The addition of rainfall data into the process slightly enhances the performance of the cumulative method, as illustrated by Fscore (Figure  6 ) and confidence (Section 4. 4 and Table  7 ). Indeed, adding these data improves the separability of the classes (Figure  7 ), and the possible noise of labelling present in the reference dataset. Nevertheless, they seem to lose importance during rainy years, as illustrated by the results on irrigated maize, which show a decrease in the Fscore between 2017 and 2018. This loss of efficiency can be explained by similar canopy growth dynamics between irrigated and rainfed crops, as rainfed crops are not subject to water stress limiting their phenological development during drought periods. These data also show that they can be used to discriminate rainfed and irrigated crops for near-real-time approaches  [37].
60), especially in 2017, which is explained by the lack of in situ reference data, partly due to the low representativeness of this crop on this territory (9%), making in situ collection difficult. In order to overcome this data constraint and to be able to extend the study area, the use of an eco-climatic spatial stratification could be envisaged as illustrated by Inglada et al. [47]. The use of spatial stratification would allow an improvement in performance as well as a better sampling of the minority classes. In the course of this work, the contribution of rainfall data was also evaluated. The addition of these data allows a significant improvement of the Fscore for the irrigated and rainfed classes and a reduction of confusion between classes during dry years. However, the low spatial resolution of rainfall data used in our study  (8 km) , can lead to high uncertainties, especially in areas with strong rainfall heterogeneity.
Acknowledgments: This work is funded by the  French space agency (CNES) ,  Adour Garonne Water Agency  and the  Compagnie d'aménagement des coteaux de Gascogne , and it was carried out at the  CESBIO laboratory. The authors would like to thank the  Hautes Pyrénées Chamber of Agriculture  for the data collection and T the development team of the iota 2 chain and especially  Arthur Vincent  for his help in the computer development of the method.
However, in the context of neurofeedback, using simultaneous recording of EEG-fMRI to estimate neurofeedback scores computed in real time from features of both modalities (NF-EEG-fMRI) is a recent application that was first introduced and its feasibility demonstrated by  Zotev et al. (2014 ), Mano et al. (2017 ), and Perronnet et al. (2017). The recent methodology synchronizing both signals for real-time neurofeedback  (Mano et al. , 2017)  allows the construction of a new kind of data named NF-EEG-fMRI data, such as the dataset presented by  Perronnet et al. (2017) , which we used for the present study. Furthermore, it has been shown in  Perronnet et al. (2017)  that the quality of a neurofeedback session is improved when using both modalities simultaneously in NF-EEG-fMRI sessions. Thus, being able to reproduce an NF-EEG-fMRI session in real time when using EEG only would reduce the use of fMRI in neurofeedback while increasing the quality of NF-EEG sessions.
, 2017)  and improve the correlation with NF-EEG-fMRI of NF scores using the EEG signal only. The motivations of this are multiple. Since we are considering a new kind of data, we want to provide a simple method of characterizing NF-EEG-fMRI in EEG, leading to an understandable model to confirm existing relations between EEG and fMRI in neurofeedback scores or to discover new relationships. Neurofeedback features in fMRI come from BOLD activation in one or more regions of interest. We propose an original alternative to source reconstruction in the context of neurofeedback by taking advantage of a recent and unique dataset of NF-EEG-fMRI that we have access to. Indeed, we intend to predict NF scores directly without dealing with source reconstruction or spatial filtering to estimate the BOLD-fMRI signal first in a specific region of interest, as proposed by a previous study  (Noorzadeh et al.
In section 2, we present the proposed model and the methods used to solve it. We then detail experiments with our learning model in neurofeedback sessions with a motor imagery task, from which unique data are presented in section 3. Section 4 presents results for a dataset of 17 healthy subjects with three NF sessions of motor imagery each; one is used to learn the model, and the other two are used to test the model. Section 5 provides a discussion of the proposed framework.
λ Parameter Selection The parameter λ is important in the optimization problem, and we decided to estimate it automatically. The following process chooses the best λ among a list of = {λ 1 ;. ; λ l } sorted in increasing order. First of all, the data must be split into two sets. In our case, subjects have three NF-EEG-fMRI sessions: one session is used as the learning set, and the other two NF-EEG-fMRI sessions are used as the testing sets (see Figure  2 ). For each value λ i of , the learning set, formed by T neurofeedback scores with their associated design matrices, is divided K = 50 times into a training set of indices R k , representing 90% of the T data, and a cross-validation set CV k composed of the remaining 10% of the learning set. A model α(k,i) is estimated on the training dataset k composed by R k neurofeedback scores y(j) and the associated design matrices X c (j) with λ i , i.
e. : α(k,i) = arg min j y(j) -q(X c (j), α(k,i) ) 2 + λ i α (k,i) 21 + ρ α (k,i) 1 (7) with j ∈ R k and X c (j) ∈ R M×B. For the current λ i evaluation, we stop the process when k | α(k,i) | 0 /K < 2. There is no need to investigate the next λ i ; the current one is sparse enough, and the next one might lead to null models. We then apply the model α(k,i) to the corresponding crossvalidation set of CV k NF scores to obtain estimated values of y(s), ỹ(s) = q(X c (s), α(k,i) ) with s ∈ CV k. For each one of the 50 partitioned into training and cross-validation sets, we computed the normalized mean squared error NMSE for a given set of data {y, X c } for the training sets and the cross-validation sets.
DATA ACQUISITION AND PRE-PROCESSING We used an existing dataset presented in  Perronnet et al. (2018)  composed of 17 healthy subjects who were scanned using the hybrid Neurofeedback platform from Neurinfo (Rennes, France) coupling EEG and fMRI signals  (Mano et al. , 2017). Data are now available online in BIDs format on OpenNeuro: https://openneuro. org/datasets/ds002338 (Lioi et al. , 2019). A 64-channel MR-compatible EEG solution from Brain Products (Brain Products GmbH, Gilching, Germany) was used, the signal was sampled at 5 kHz, and FCz was the reference electrode and AFz the ground electrode. For the fMRI scanner, we used a 3T Verio Siemens scanner with a 12-channel head coil (repetition time (TR)/echo time (TE) = 2,000/23 ms, FOV = 210 × 210 mm 2 , voxel size = 2 × 2 × 4 mm 3 , matrix size =105 × 105 with 16 slices, flip angle = 90 • ).
ACKNOWLEDGMENTS Data acquisition was supported by the  Neurinfo MRI  research facility at the  University of Rennes I. Neurinfo  receives grants from the  European Union (FEDER) , the  French State , the  Brittany Council ,  Rennes Metropole ,  Inria ,  Inserm , and the  University Hospital of Rennes. This work has received French government support granted to the  CominLabs excellence laboratory  and managed by the  National Research Agency  in the  Investissements d'Avenir program  under reference  ANR-10-LABX-07-01. It was also funded by the  Brittany region  under the  HEMISFER  project.
Major-element mineral compositions were analysed in two labs by electron probe microanalysis (EPMA): a first batch was analysed with a JEOL JXA-8800 Superprobe system at Kanazawa University, using an accelerating voltage of 15 kV and beam current of 20 nA, using a 3-μm beam. JEOL software using ZAF corrections was employed, analytical details are reported in  Sanfilippo et al. (2015). A second batch was analysed using the automated CAMECA-CAMEBAX electron microprobe of the CAMPARIS micro-analysis centre (University of Paris 6). A 5 μm beam size was used for all minerals, with an accelerating voltage of 15 kV and a beam current intensity of 40 nA. Elemental maps were obtained by recombining single major element density channels produced on 256 × 256 and 512 × 512 acquisition grids at 15 keV -45 nA with grid steps of 7 and 5 μm respectively.
Acknowledgments This study was supported by the  Italian Programma di Rilevante Interesse Nazionale  ( PRIN_2017KY5ZX8 ) and by  Accordo Bilaterale   CNR/RFBR 2018-2020. AC is supported by  Programma di Rilevante Interesse Nazionale   PRIN 20178LPCPW. We are grateful to  M. Fialin  and  N. Rividi  for their help at the CAMPARIS analytical service, Paris. The manuscript has benefited from the precious reviews of  Henry Dick ,  Akihiro Tamura  and two anonymous reviewers and the careful editorial support of  Theo Ntaflos  and  Costanza Bonadiman. I gratefully thankyou Jurgen's firm opposition to immiscibility and Mike's infinite chocolate reservoir that warmly accompanied us across the Atlantic.
Introduction The purpose of this paper is to show how dynamic causal modelling can be used to make predictions-and test hypotheses-about the ongoing coronavirus pandemic  (Huang et al. , 2020; Wu et al. , 2020; Zhu et al. , 2020). It should be read as a technical report 1  , written for people who want to understand what this kind of modelling has to offer (or just build an intuition about modelling pandemics). It contains a sufficient level of technical detail to implement the model using Matlab (or its open source version Octave), while explaining things heuristically for non-technical readers. The examples in this report are used to showcase the procedures and subsequent inferences that can be drawn. Having said this, there are some quantitative results that will be of general interest.
Finally, it would be fairly straightforward to use increasingly finegrained outcomes, using regional timeseries, as opposed to country timeseries (these data are currently available from: https://github. com/CSSEGISandData/COVID-19). Another plausible extension to the hierarchical model is to include previous outbreaks of MERS and SARS (Middle East and Severe Acute Respiratory Syndrome, respectively) in the model. This would entail supplementing the timeseries with historical (i. e. , legacy) data and replicating the general linear model for each type of virus. In effect, this would place empirical priors or constraints on any parameter that shares characteristics with MERS-CoV and SARS-CoV. In terms of the model parameters-as opposed to model structure-more precise knowledge about the underlying causes of an epidemic will afford more precise posteriors.
In other words, more information about the DCM parameters can be installed through adjusting the prior expectations and variances. The utility of these adjustments would then be assessed in terms of model evidence. This may be particularly relevant as reliable data about bed occupancy, proportion of people recovered, etc becomes available. A key aspect of the generative model used in this technical report is that it precludes any exogenous interventions of a strategic sort. In other words, the things that matter are built into the model and estimated as latent causes. However, prior knowledge about fluctuating factors, such as closing schools or limiting international air flights, could be entered by conditioning the DCM parameters on exogenous inputs. This would explicitly install intervention policies into the model.
Software note The figures in this technical report can be reproduced using annotated (MATLAB) code that is available as part of the free and open source academic software SPM (https://www. fil. ion. ucl. ac. uk/spm/). This software package has a relatively high degree of validation; being used for the past 25 years by over 5000 scientists in the neurosciences. The routines are called by a demonstration script that can be invoked by typing >> DEM_COVID. At the time of writing, these routines are undergoing software validation in our internal source version control system -that will be released in the next public release of SPM (and via GitHub at https://github. com/spm/).
Acknowledgements work was undertaken by members of the  Wellcome Centre for Human Neuroimaging ,  UCL Queen Square Institute of Neurology. The  Wellcome Centre for Human Neuroimaging  is supported by core funding from  Wellcome  [ 203147/Z/16/Z ]. A. R. is funded by the  Australian Research Council  (Refs:  DE170100128  and  DP200100757 ). A. J. B. is supported by a  Wellcome Trust  grant  WT091681MA. CL is supported by an  MRC   Clinician Scientist award  ( MR/R006504/1 ). The authors declared no conflicts of interest. References Berger , J. O. , 2011. Statistical decision theory and Bayesian analysis. Springer, New York;  London. Birkhoff , G. D. , 1931. Proof of the ergodic theorem. Proc Natl Acad Sci USA 17,  656-660. Bressloff, P. C. , Newby, J. M. , 2013. Stochastic models of intracellular transport.
Patients The molecular profiles of both liquid and tissue biopsies from 433 consecutive eligible patients with different cancer types, seen at the University of California San Diego Moores Cancer Center starting in June 2014 were analyzed. We included patients who had no prior treatment as well as those with prior systemic therapies. The last ctDNA specimen was obtained on 09/2017. Eligibility implied adequate follow up and demographic data availability and patient meeting UCSD IRB guidelines for consent or waiver. Demographics of each of these patients were provided by chart review, including, but not limited to age, gender, cancer diagnosis, tumor origin, date of biopsy report or blood test, date of diagnosis, and survival time (Figure  1 ). The study was performed in accordance with the UCSD internal review board-approved protocol (NCT02478931) and for any investigational therapies for which the patient gave written consent.
Statistical analysis Statistical analysis was performed using the R programming language version 3. 5. 1  (25). Cox proportional hazards regression model and multivariable analysis was performed using the survival(26) R package. Pairwise comparisons for the continuous time interval variable were performed by pairwise Wilcoxon test with false discovery rate (FDR) correction for multiple comparisons. Survival was examined by Cox regression; patients still alive at the last follow up were censored at that time. Survival time was calculated from time of diagnosis and the defined event was death. Quartile thresholds were created using R ntile command; in case of quartile borders value overlap, the algorithm assigns the first samples to the lower quartiles. Mutations type annotation and description of their landscape of TP53 gene were performed using MutationMapper tool in cBioPortal (https://www.
cbioportal. org/mutation_mapper )  (27)  and Seshat (https://p53. fr/tp53-database/seshat )  (28). We defined T125M , Q331R and Q331H as splice mutations despite the fact that cBioPortal defined them as missense mutations because despite the fact that they appear in exons , they influence splicing  (29). (However, even if defined as missense mutations, the statistical relationships do not change. ) Visualization was performed by the R packages of ggplot2  (29) , forest plots by the R package of forestmodel  (30). Balloon plots representing the residuals of chi square test were performed using the R package of corrplot  (31). Data availability: Dataset is available as Supplementary Table  2.