dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008) ; therefore, the start and rate of senescence of flag leaves in wheat can be used as an indicator of stress resistance for determining resistance to stress  (Larbi and Mekliche, 2004). PhénoField R sensors and the actual processing data are not allowing us to look at flag leaf alone but rather the plot canopy. Here, nitrogen deficiency had a negative effect earlier than drought as shown on GPAI (Figure  11B ) but also on plant height (Figure  11A ). Strangely, at early stage (end of winter), the WD modality had a GPAI higher than that of the WW modality (Figure  11B ). A lack of nitrogen probably link to irrigation on WW and a loss due to nitrogen leaching could explain the effect on GPAI of the latter modality. The spectroradiometer data are currently used to extract common vegetation indexes (as NDVI, MCARI2, or MTCI).
CONCLUSION AND OUTLOOK As shown with 2017 dataset, the automated movement of the 8 rainout shelters demonstrated its performance to control water and nitrogen deficiency on bread winter wheat field trials without other significant impact on the trial environment. This data set is limited on genotype number to perform 3 replicates and 4 stress conditions. PhénoField R platform, with its large plot capacity, could also provide genetics' field trials with more than 300 genotypes under sole drought conditions and enhance knowledge on physiological analyses, varieties tolerance evaluation or genomic regions controlling these complex traits. 2017 was the 1st year using field HTP with a data-processing pipeline that still has to be improved. However, it showed promising results, especially with the dynamics of sensor traits allowing the calculation of relevant indicators of abiotic stresses.
The actual set of sensors allows testing of many traits and new parameters to select the best way to discriminate modalities as varieties under nitrogen or water deficiency. The possibilities to analyze the 2017 data set are really important and we are aware that only a small part was explored in this paper. Nevertheless, our first objective was to demonstrate the PhénoField R platform's capacity to efficiently conduct the test protocols (intensity and duration of stress). Conducting trials on field HTP induced significant soil environment genotype interactions and, mapping the spatial variation of soil characteristics (as well as WHC soil resistivity) is essential for incorporating field variability into crop management and for the interpretation of experimental results. Acquisition of field phenotyping data is now a well-established process, allowing weekly data registration from spectroradiometers, LiDARs and RGB cameras.
ACKNOWLEDGMENTS We would like to thank  L. Boulard  for his commitment as a farmer,  Y. Flodrops ,  E. Tremblay ,  J. C. Gapin ,  T. Joie ,  F. Savignard , and  C. Fontaine  for their contribution during the field crop management;  P. Poix  for the preventive and curative maintenance of PhenoField R equipments; the  UMT CAPTE  (lead by  INRA  and ARVALIS) and  F. Baret  research team for his research on data processing tools. We also thank  L. Inchboard  for revision of the English version.
FUNDING This manuscript is a contribution to the research conducted under the  ANR-PIA  projects  PHENOME FPPN EMPHASIS/France  ( ANR-11-INBS-0012 ) and  BREEDWHEAT  ( ANR-10-BTBR-03-14 ). The authors gratefully acknowledge the financial support provided by the  National Research Agency (ANR)  in the framework of Investments for the Future.
ART is an easy-to-implement method which relies on only two signal features (i. e. magnitude or amplitude range). By contrast, SVM is a supervised machine learning method that may use several signal features for classification purposes. SVM first "learns" the classification using a set of labelled data and defines a classification model. During the testing stage, SVM uses the model and its parameters to detect debondings in the test data. SVM sensitivity analysis is performed to observe the robustness of the method and also to analyze its dependency on the parameters such as kernel type, signal features, training data size etc. This article is organized as follows: Section. 2 presents the experimental data that are used in this paper. In Section. 3, refined signal analysis is performed to characterize the debonding and non-debonding signals.
X Y Z Figure  5 : Experimental setup for data collection (surrounding blue cones are dampeners to avoid stray reflections)  [27]  In Fig. 5 , an automatic bench which is controlled by a computer can move the Tx and Rx antennas in both X (scanning direction) and Y axes (elevation). The scanning direction along the X-axis is about 150 cm at the most. The bench moves the antennas step-wise every 1 cm to 2 cm to collect data vectors, namely, A-scan profiles over the pavement. The set of vectors form a 2D B-scan image of the subsurface, as illustrated in Fig. 6. Dampeners on either sides on the Tx-Rx shield the receiver from the surrounding stray reflections from the bench. For the data collection, raw B-scan images were taken at each loading stage over each defective patch, each Bscan having 80 to 160 traces (A-scans) per image.
3) Besides, taking advantage of the radar data base collected on the fatigue carousel, the decision threshold is established from the statistical distribution of the ART values over both healthy and defective areas. The Tack-free area is selected as the defective pavement structure because it leads to the smallest ART values compared to sand and geotextile defects. For illustration, the Probability Density Function (PDF) of ART norm values is plotted in Fig. 9. The decision threshold for classification is determined by finding the point of intersection of the two PDFs; it is found slightly above 1 owing to noisy conditions. Another explanation to this behavior could be due to the over-compaction of the pavement layers due to the traffic in the initial loading stages  [11]. The decision threshold depends on the dielectric contrast between pavement layers but also on the interface roughness and the radar bandwidth.
Avoiding overfitting One of the major problems encountered in machine learning is the concept of over-fitting. Over-fitting is generally associated to the oversampling of data. To avoid this, various methods such as as regularization, ensembling, early-stopping, leave-out, hold out, crossvalidation etc. have been discussed in the literature. Indeed, cross validation (CV) can also be used in this context; to simply identify over-fitting  [41]. So, in this paper, we use CV to do it. To detect the over-fitting, we use CV constrained by the root-mean square error of positive identification. The learning dataset is initially divided into k = 5 equal folds. Then, the classification method is used with the optimal hyper-parameters obtained with CV and on both training and validation databases. The behavior of the classification method from both databases allow to determine the presence of over-fitting.
Pseudo-ground truth estimation The performance of the data processing methods in Section. 5 relies on a reference benchmark dataset, which is detailed in this section. The latter benchmark dataset is called Ground Truth (GT) when the operator has full information about the spatial distribution of debondings. Otherwise, Pseudo Ground Truth (PGT) is used instead when the operator infers the spatial location of debondings from in situ measurements. The generated PGT is a binary vector consisting of '1' (to indicate debonding) and '0' (to indicate non-debonding). For the experiment data collected using SFR at the carousel, the operator manually creates the PGT vector from radar B-scan images, for which debonding and non-debonding are labeled by '1' and '0', respectively. The debonding area is visually easy to locate owing to the larger magnitude of the second echo, as illustrated in Fig.
Results The classification results obtained by the conventional reference method (ART) and Supervised machine learning method (SVM) are presented in Sections. 5. 1 and 5. 2, respectively and compared to each other as mentioned in Fig. 20. ART is a conventional debonding detection approach that was developed in MATLAB R. SVM was implemented using the well-known LIBSVM  [40]  library in MATLAB R. The selected strategy has been to optimize the methods for each type of defects separately, namely, tack-free, sand and geotextile. The two formulations of the SVM method shown in Fig. 20 , i. e. , from raw data samples and from either global or local signal features, are used to perform sensitivity analysis of SVM later in Section. 5. 3. The performance of methods are evaluated using DSC similarity index, S and P rates as mentioned in Section.
Model-based sensitivity analysis For classification applications, SVM can use various mathematical functions called 'kernels'. As discussed in Section. 4. 2, a linear kernel is used for linearly separable data whereas for non-linearly separable data, there are various kernels. Some of the non-linear SVM kernels include: Gaussian RBF, Sigmoid, Polynomial kernel, etc. Each kernel is implemented using the LIBSVM  [40]  library. Here, we analyze the SVM performance for various kernels (as done in  [67]  for classification of Landsat and QuickBird datasets and  [68]  for diagnosis of respiratory diseases). Each iteration implements a kernel type for the local feature set (defined in Section. 4. 3. 1). Fig. 27  shows the SVM performance with each of the kernels for Tack-free defects. The training to test ratio used was 50% : 50%.
It is seen that for Tack-free defect type, the Gaussian RBF kernel presented an overall improved performance over other kernels. The optimal hyper-parameters used for each kernel function are as mentioned in Table. Since SVM uses a small part of the data for training, their sensitivity to space dimensionality is very low  [44]. However, the detection efficiency is susceptible to the number of training samples  [69]. using very few training samples, there may be the problem of under-fitting leading to a low performance rate. On the other hand, a very large number of training samples may lead to over-training which once again reduces the performance. Hence it is necessary to utilize an optimal training to testing samples ratio. We therefore analyze the SVM sensitivity w. r. t. various training dataset sizes.
The ratio of Training data w. r. t. Test data is varied and the performance is evaluated. The global feature set (discussed in Section. 4. 3. 2) along with a Gaussian RBF kernel is used here. Fig. 28b  plots the variation in DSC score w. r. t. the training dataset size at 50k, 100k and 300k cycles loading stages for Tack-free based defects using the global feature set (discussed in Section. 4. 3. 2). From Fig. 28b  and Fig. 28a , it can be seen that for small training data sets, the debonding detection rate is low. Additionally, for these datasets, if the training data set is too large, the performance is once again reduced. An optimum data set size for training step is as indicated in Fig. 28b  and Fig. 28a  (i. e. between ≈ 50% to ≈ 80%) for both local and global feature sets. These results are in accordance with the conclusions of m train ≈ 66% (as in  [70] ), m train = 70% (as in  [71, 72] ), m train = 75% (as in  [73] ) and m train = 75% (as in  [74] ), all where m train is the percentage of training data set w.
Table. 1). [66] monitors the performance of SVM by varying the number of features in the training dataset. However, in the feature subset-OAT, we analyze the performance of SVM w. r. t. each subset. As mentioned in Section. 4. 3, two feature types namely local and global time domain features are used in this article. Under local features, we have the statistical, PQRST and Morphological feature subsets (as indicated in Table.
A limited number of RS cycle models can thereby provide a sufficiently large database of moderate-to-large earthquakes. This dataset is used to investigate the dynamic rupture characteristics of each single event through spontaneous rupture modeling and their sensitivities to initial input models such as the critical distance, Dc. Dynamic rupture models give access to rupture properties that may be poorly resolved by source inversion, e. g. spatial correlation of high slip and high slip-rate areas, source time functions, and rupture velocities  (Schmedes et al. 2010; Field 2019). Most importantly, individual events are not the result of ad hoc tuning of stress and strength heterogeneities; they are the result of the spatio-temporal evolution of the governing parameters on the frictional interface in response to steady tectonic loading.
Geometry and Mesh The Landers fault model used in our study is composed of 5 segments. The 1992 M w 7. 3 Landers To extend these segments to a length of 200 km (M w 7. 8), the Eureka-Peak fault and Graves Hills-Harper fault have been added at the ends, as shown Fig. 1a. Figure  1b  shows a 3D view of our fault model. In order to mesh the complex fault system, we made use of CUBIT, a state-of-the-art hexahedral mesh generation software. The mesh and the faults used in this study are shown in Fig. 2a. Minimal Dc values available for modeling depend on the minimum mesh size. In order to allow smaller Dc values and thus smaller nucleation area and event magnitude, we refine the mesh size in the inner box containing the fault domain (see Fig. 2b ). This refinement allows accurate modeling of the rupture without a large increase in demand for computer resources, while inducing only minor disturbances in the wave propagation modeling.
Method of Validation Among our 30 simulated events we selected events that satisfy the following criteria. (1) Magnitude should be nearly equal to that of the 1992 Landers earthquake, M w 7. Rupture initiation should be close to the hypocentre of the 1992 Landers earthquake. Using these criteria, we selected two simulated events, a M w 7. 3 event at time 1245. 2 years and M w 7. 31 event at time 1831. 3 years. The slip distributions of the selected events are shown in Fig. 6  and compared with the multi-time-window source inversion result of  Wald and Heaton (1994). Data for this figure are downloaded from the Finite-Source Rupture Model Database (http://equake-re. info/SRCMOD/). Other inversion results (e. g. Cohee and Beroza 1994 , Cotton and Campillo 1995 , Hernandez et al. 1999 ) have roughly similar slip distributions.
Recordings of the 1992 Landers earthquakes were downloaded from the Center for Engineering Strong Motion Data (https://www. strongmotioncenter. org/). Only processed records were used. A map of the selected stations is shown in Fig. 7. The sites are divided into basin and non-basin sites using the SCEC community velocity model (version 4,  Lee and Chen 2016, Small et al. 2017). In order to avoid additional uncertainties related to uncertainties in the velocity model, only non-basin sites were used for validation. The recording at Lucerne was downloaded from the PEER Ground Motion Database (https:// ngawest2. berkley. edu). This record has no absolute time. We aligned simulated and observed records by fitting their S-wave arrivals. A somewhat similar alignment of the S-wave was used by  Wald and Heaton (1994)  for this record.
While a K i block requires O(m 9 n) storage, its H-matrix approximation requires only O(r(m ? n)) storage. We implemented the use of the H-matrix module ''hmmvp'' developed by  Bradley et al. (2014)  in the QDYN solver for earthquake cycle modelling in complex fault systems. This module makes use of the M approximation, which is a modification of Lowrank approximation (LRA). The M approximation allows for greater compression of the K matrix making MVP less time consuming than the LRA approximation for large N values. The hmmvp module contains C ?? routines that comprise the K matrix and a library to compute MVP. More details on this module are presented by  Bradley et al. (2014). To validate our implementation, we perform earthquake cycle simulations for the planar fault shown at Fig. 10 , top.
Acknowledgements This study was based on the 2017 research project ' Examination for uncertainty of strong ground motion prediction for the inland crustal earthquakes ' by the  Nuclear Regulation Authority (NRA), Japan. We would like to thank  Dr. Andrew Bradley  for providing the H-matrix module and his valuable assistance in the usage of this library. The  Super Computer Shaheen II at KAUST University  has been used to run the models presented in this study. Shaheen II  is a Cray XC40 delivering over 7. 2 Pflop/s of theoretical peak performance. Overall the system has a total of 197,568 processor cores and 790 TB of aggregate memory. J. P. A. acknowledges funding from the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01.
EKF adjustment After obtaining the modulated output pressure signals which are used as the EKF input, we must adjust the parameters of the EKF leak detector according to our system. Firstly, the Jacobians defined in equation (  18 ) are calculated from the process function f and the measurement function h. The matrix A k is obtained by Matlab function jacobian and the matrix H k is given by H k = [0 0 0 0 0 0 0 0 0 1]. Secondly, assuming that process and measurement noises are white, Gaussian and stationary, the covariance matrices Q k and R k are constants. Assuming also that state variables of the system are independent from each other, then Q k and R k are diagonals. Finally, it can be deducted that the Jacobian matrices related to noises W k and V k are unity matrices. In order to initialize the EKF, we must determine the initial values of the state vector x0 , the initial estimation error covariance P 0 , process noise covariance Q k , and the measurement noise covariance R k.
We mapped the sequencing reads on the Populus trichocarpa transcriptome (v3. 0) to obtain gene expression data. Sample collection extended on a 2 weeks period, with varying weather along the days, and different operators involved. We did PCA analyses on the cofactors that were presumably involved in the experience, to look whether any confounding effect could be identified (Figure  S1 ). No clear segregation was found for any of these, except for the ones associated with weather. To verify this observation, we used mixedmodels to correct effects of all these cofactors, with the breedR R package  (Muñoz and Sanchez, 2017) , and while it properly corrected the environmental effects, it also removed information from the data, making prediction quality much poorer than without cofactor correction for most of the traits (Figure  S2 ).
Clustering and network construction The classical approach to build a signed scale-free gene expression network is to use the weighted correlation network analysis, implemented in the WGCNA R package  (Langfelder and Horvath, 2008) , using a power function on correlations between gene expressions. We chose to use Spearman's rank correlation to avoid any assumption on the linearity of relationships. The scale-free topology fitting index (R 2 ) reached a maximum of 0. 85 for a soft threshold of 15, yielding a mean connectivity of 22. 9 (Figure  1A ). We detected 25 gene expression modules (Table  S2 ) with automatic detection (merging threshold: 0. 25, minimum module size: 30, Figure  1B ). Spearman's correlations between 17 traits values and expression, presented in the lower panel of Figure  1B  below the module membership of each gene, displayed a structuration when ordered following the gene expression tree.
4 To get further insight into the relationships between module composition and traits, we looked at the strongest correlations between the best theoretical representative of a gene expression module (eigengene) and each trait, in order to identify genes in relevant modules with an influence on the trait (Figure  1C ). Following a Bonferroni correction of the p-values provided by WGCNA, only 72 correlations remained significant (p ≤ 0. 05) out of the initial 425 traits by modules combinations, and 5 modules were defined as composed of genes not involved in any of the traits studied (salmon, greenyellow, brown, lightgreen and darkgrey, Figure  S4 ). In significantly correlated modules, gene expression correlation with trait was also significantly correlated with centrality in the module (represented by the kME, the correlation with the module eigengene), while no correlation was found in poorly correlated modules (Figure  1D , Figure  S5 ).
We thus decided to stick to the peripheral genes set as previously defined to contrast the core genes set. To assess the robustness of WGCNA analysis results, we compared it to a k-means clustering (R package coseq  (Rau and Maugis-Rabusseau, 2017 )) of the gene expressions (Figure  S7A ). The distribution of WGCNA and k-means' clusters showed a correlation of 0. 42 (Figure  S7B ). K-means clustering tends to force groups of comparable size  (Biernacki et al. , 2006) , which does not seem biologically credible. Furthermore, the correlations between the kmean modules eigengenes and traits were lower than with WGCNA's, with a poor repartition of the different modules on the first 2 principal component analysis space (Figure  S7C ). We thus preferred WGCNA clustering to k-means clustering for this analysis and were quite confident about its robustness given its overall concordance with k-means clustering.
Boruta gene expression selection In addition to the previously defined gene sets (full, core, random, peripheral), we wanted to have a set of genes being relevant for their predictability of the phenotype. Our hypothesis here would be that this set is the one that enables the best prediction of a given trait with a limited gene number that would be comparable to the other subsets of genes selected from their centrality within the networks. For that purpose, we performed a Boruta analysis (Boruta R package  (Kursa and Rudnicki, 2010) ) on the full genes set within the training sample (60% of all observations). This algorithm performs several random forests to analyze which gene expression profile is important to predict a phenotype. In the end, a pool of 637 unique gene expressions was found to be important to predict our phenotypes (Figure  S8 ).
Heritability and population differentiation of modules To get further insights into the biological role of core and peripheral genes at population levels, we looked at the distribution of various characteristics between gene sets: gene expression level (Figure  4B ); several classical population statistics, including heritability (h 2 , Figure  4A ), coefficient of quantitative genetic differentiation (Q ST , Figure  4C ), coefficient of genetic variation (CV g, Figure  4E ), gene diversity (Ht, Figure  4F ); and a contemporaneous equivalent to F ST for genome scans (ScoreP Cadapt  (Luu et al. , 2017) , Figure  4D ). Gene expression level, h 2 , Q ST and CV g were computed from gene expression data, while Ht, and ScoreP Cadapt were computed from polymorphism data (SNP) and averaged per gene model, for more details see the methods section.
On average, core genes were the ones predicting the most efficiently a phenotype, for any trait category, with an additive model, even if the full set still reaches the highest global prediction R 2 (0. 77 for the mean sample diameter). This might be expected from the positive and highly significant relationship observed between gene significance (correlation between gene expression and trait value) and connectivity within WGCNA modules displaying a significant correlation with traits. On the other side of networks, peripheral genes predict better with an interactive model than with an additive one and provide over both types of models the most stable predictions (interquartile ranges of 0. 19 for peripheral, 0. 27 for random, 0. 34 for core and Boruta and 0. 35 for full set). The information necessary to predict a phenotype does not seem to be particularly concentrated at any side of the network, but rather spread over it, as highlighted by the performance of random gene sets.