dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2005) ,  (Yang and al. , 2002)   (Murino and al. , 2004). So, this work can be related to the "small scale" domain definition  (Kudo and Sklansky, 2000) ,  (Zhang and Zhang, 2002)  due to the small number of features. Working with several classifiers makes it possible to integrate their discriminatory aspects to improve the recognition step  (Melnik and al. , 2004). Classification methods are generally built separately. Their combination may induce positive interactions, because their goal is to achieve the same result and they are based on the same learning dataset  (Littewood and Miller, 1989) ,  (Ho 2002 ). The dependence between data is another problem, even if approaches like Adaboost, arcing  (Breiman 1996 ) and boosting  (Schapire and al. , 1998)  try to limit it by reinforcing their diversity.
As real data are used, it is not easy to consider all the paths of the lattice. The values of the not taken paths are modified  (Grabisch 1995a)  to check the monotonicity of the lattice, by considering both the fathers and children of the current nodes. Experimentally we process with about 100 epochs and keep the lattice providing the weaker error considering the whole dataset. We propose in the next section to study two Multi-Scale versions of training steps. The first one, called FRIFS-MS1, is based on the presented training method and the second one, based on redundant path removing, is called FRIFS-MS2.
Feature Selection Application in a Wood Singularity Recognition context This study takes place in the context of a companyuniversity collaboration. The proposed feature selection method was checked and compared on real wood data provided by our industrial partner. In this section, the applicative singularity recognition system is first described. Then, the FRIFS-MS enhancements were compared with the original FRIFS method and three other feature selection methods. The comparison is based on the analysis of the singularity recognition rates obtained with the set of selected parameters. The influence of the chosen classifier was also checked. And finally, the relevance of the proposed methods FRIFS-MS1 and 2 was highlighted considering other defect datasets from the same wood specie but also from different species.
The databases used to apply and validate our method are provided by the manufacturer. They are constituted with samples of real singularities named by a human operator. More than 20 features are computed by the low-level step of the vision system. The first database DW1 contains 877 samples issued of nine classes of wood singularities (nudo muerto, nudo suelto, peca, medula, resina …). This DW1 database is relatively heterogeneous and the nine classes are not well balanced. This database was divided into two sets. The first, called "training set", contains 250 samples (1/3) and the second one, called "generalization set", is made up of 627 samples (2/3). All the results presented in the following section were obtained with the cross-validation method. The training rates are the mean of the three rates calculated on three permutations of the training database.
The iterative Ishibushi method (Ishibushi and al. , 1992 & 1997) is used to automatically generate each rule. The delivered rule set is used to classify the unknown samples and the output class is determined by the rule of maximal answer. For more explanation about the Fuzzy Rule Classifier, readers can refer to  (Bombardier and al. , 2010)  and  (Schmitt and al. , 2009). Six feature selection methods are used SBFS, SFFS  (Pudil and al. , 1994) , SVM  (Grandvalet and Canu, 2002) , vanilla FRIFS method and the proposed extensions FRIF-MS1 and FRIF-MS2. These methods, except FRIFS, are applied on the industrial database DW1 using, at the beginning, more than 20 features as input of the recognition system. They almost provide the same set of 10 remaining features. The recognition accuracy reaches 94% for training and 74.
It is computed on the whole database, independently of the classes. Several tunings have been checked for each classifier. Only the one giving the best results is presented. For Nearest Neighbor methods, 3, 5 and 7 neighbors have been used and k=5 gives the best accuracy. So, 3 hidden layers and 20 neurons per layer for the Neural Networks classifier are retained. Then, we use trapezoidal membership functions with 5 terms for the FRC. In this case, the configuration is not the best and the reader can refer to  (Bombardier and al. , 2010)  to see the effects of the term number and the form of the membership functions. Accuracy varies from 73. 4% with 10 features to 73. 37% with three features. A maximum of 79. 43% is reached with eight features using the k-NN classifier. Classifier tuning: 1 Euclidean distance, 2,3 k=5, 4 hidden layers=3, neurons/layer=20, 5 #terms=5 (regularly distributed), 6 Gaussian kernel.
These results are quite similar despite the fact that the fuzzification step of FRC is not optimized such as shown in  (Schmitt and al. , 2007). However, such recognition accuracy is not significant because it considers the whole database even though the number of samples is not equal for each class. The rate of 79. 27% is only reached with NN when almost all the samples of the classes having the greatest number of samples are recognized. Nevertheless, we can see that all the classifiers have the same behaviour except for the Bayesian and to a lesser degree the SVM that are not well suited to the industrial context. Conversely, the soft computing classifiers (NN or Fuzzy Classifiers) give the best results. It can be noted that the FRC and FARC-HD classifiers give the best results with the least number of features (respectively 5 and 6).
Generalization test on other Dataset with the same tree species Other datasets, DW2 and DW3, consisting of samples issued from the same tree species were used to check the selected features. These datasets are respectively composed of 311 samples (80 for learning set, 231 for generalization set) and 1042 samples (134 for learning, 908 for generalization). They are taken from two different sensors: DW2 is defined using sensors RIGHT/LEFT and DW3 is defined using sensors TOP/BOTTOM (see figure  3 ). Different segmentation step adjustments yielded more features. The results obtained for DW1 by using six and seven parameters considering the best configuration for each method are provided in table  4. It is worth noting that the remaining feature sets are coherent because the achieved classification is quite similar to those obtained with DW1.
Tests on another tree species The aim of this new series of tests is to show the genericity of the proposed methods. Here another dataset of species, denoted by DW4, is processed. This dataset is composed of 133 samples for the learning step and of 192 samples for the generalization step. The number of output classes (  9 ) is the same as that of DW1, DW2 and DW3, but the defects are quite different. The system segmentation step differs too. Thus, the experimental results show that previous feature sets are not relevant and produce bad results with generalization rates lower than 60%. Then new sets were performed the initial set which is composed of more than 20 features. A remaining set of nine features was selected using six methods (FRIFS, FRIFS-MS1, MS2, SBFS, SFFS and SVM). For these tests, we added an EXPERT method, which means that the parameters were selected by an industrial expert on the existing recognition system.
Concluding Remarks The aim of this paper is to present an enhancement of a previous feature selection method (FRIFS). This "multiscale" improvement allows to easily process with more than 10 features to be selected. The partitioning of the set of features is performed through the definition of a criterion based on both significance and interaction indices. The basic method was improved by unifying the scanned paths of the capacity, which makes it possible to decrease the learning time and to focus on relevant samples. Finally, the improved automatic selection process makes it easier to use the method in an industrial context. Such algorithm makes it possible to efficiently decrease the number of features until providing an interpretable dataset to be used with a Fuzzy Rule Classifier  (Bombardier and al.
, 2010). This method is robust and the remaining sets seem to be relevant according to several classifiers. Furthermore, Mc and Ic indices attest of the good behavior of our method, which aims at providing remaining features, representative of all the classes. Tests performed on several industrial databases confirm the interest of reducing the number of features while both increasing the recognition rate and improving the interpretability of the system. Results obtained in this wood industry context with four different databases underline the generic aspect of the method. Although our study involved a larger number of parameters, results are in agreement with those previously obtained in a fabric pattern recognition context  (Schmitt and al. , 2008). The overall accuracy is better than the one provided by using the feature set selected by other methods and the accuracy is also better for each class.
The distance from the plots to the nearest hedges on the field border was equal to, or larger than, five times the height of the hedges, which should assure that the measurements calculated using the IDM is reliable  [23, 24]. by a nearby (<350 m) weather station and recorded as hourly averages (Figure  2 ) by the national Danish Meteorological Institute. Moreover, to calculate the emission, wind speed was measured at 5 min average at the HZINST heights, 1. 0 m for the R20 plot and 0. 57 m for the R5 plots, by means of 3cup anemometers coupled with Wind101A Data Loggers (MadgeTech, Inc. , Warner, NH, USA). Emission fluxes from all plots were estimated using IDM combined with wind speed and NH3 concentration measurements, while also directly by means of flux samplers in Plot1. In Plot 1 in fact, emission was assessed with the IHF method set-up with the Leuning passive flux samplers placed at heights of 0.
50 m height) and temperature (2. 0 m height) were acquired by a nearby (<350 m) weather station and recorded as hourly averages (Figure  2 ) by the national Danish Meteorological Institute. Moreover, to calculate the emission, wind speed was measured at 5 min average at the H ZINST heights, 1. 0 m for the R20 plot and 0. 57 m for the R5 plots, by means of 3-cup anemometers coupled with Wind101A Data Loggers (MadgeTech, Inc. , Warner, NH, USA). Emission fluxes from all plots were estimated using IDM combined with wind speed and NH 3 concentration measurements, while also directly by means of flux samplers in Plot1. In Plot 1 in fact, emission was assessed with the IHF method set-up with the Leuning passive flux samplers placed at heights of 0. 6, 1. 0, 1. 8, and 2. 45 m from the soil surface.
All participants had to give their consent before they had access to the questionnaires. At the end of the questionnaires, contact data of the investigators were given once again. Participants were also invited to seek psychological support from their local public mental health services if they felt any discomfort in answering the questionnaires. The study protocol was approved by the local ethical committee of Paris Descartes University (IRB: 20162900001072). The design of the study and the data collection took place between February 2016 and April 2016. The questionnaires were completed online by mothers in approximately 30 min and a forced choice procedure prevented missing data. Inclusion criteria were: being a French-speaking mother aged more than 18 years old and having at least one child living at home regularly.
Data Analysis Descriptive statistics for quantitative measures (mean, variance, standard deviation) and for qualitative measures (percentage) were first calculated. Analyses of variances (ANOVAs) with Tukey post hoc were then performed to estimate group effects of three selected variables (mother's employment, working time, and parent status) on the three maternal burnout dimensions (emotional exhaustion, depersonalization, and decrease of personal accomplishment). Employment data was coded into three groups: mothers who declared being at home (housewife), mothers who were employed, and mothers who were unemployed. Regarding working time, employed mothers were separated into four groups: mothers working full time, mothers working more than part-time, mothers working part-time, and mothers working less than part-time.
The parent status sample was divided into three groups: mothers living without a coparent, mothers living with a coparent, and mothers having a coparent but living apart. Additionally, multivariate linear regressions were carried out to test the predictive effect of depression and anxiety symptoms, problem-focused coping strategies, emotion-focused coping strategies, social support seeking strategies, parenting stress, and perceived general stress scores on the three dimensions of maternal burnout (emotional exhaustion, depersonalization, and decrease of personal accomplishment). Three distinct models were calculated for each MPI-P dimension score as a dependent variable. All analyses were performed with SPSS-21 software and hypotheses were tested with a two-sided significance level of 0. 05.
Sample Characteristics Data were collected from a sample of 304 mothers aged from 18 to 60 years old (M = 34. 8, SD = 6. 7). Sociodemographic and psychometric characteristics of the participants are described in Table  1. Regarding parent status, 89. 1% of mothers lived with a coparent (n = 271), 5. 9% had a coparent but lived apart (n = 18), and only 4. 9% had no coparent (n = 15). The mean duration of cohabitation was 11. 13 years (SD = 5. 9). Children's mean age was 6. 9 years old (SD = 6. 0, [0; 37]), and mothers had on average 2. 15 children (SD = 1. 3). Regarding work status, most mothers (63. 5%) were employed (n = 193), 11. 2% were unemployed (n = 34), and 25. 3% were housewives (n = 77). More than half of employed mothers had a full-time job (52. 5%; n = 96).
Rainfall regime scenarios and simulation design In order to describe the current rainfall regimes, we used historical rainfall data from conventional weather stations near the study sites that have reported daily rainfall since at least 1972: Cayenne (WMO-81405), located 80 km east of GYF, and Belterra (WMO-82246), located 25 km north of TNF. The probability distribution of annual rainfall (P, mm yr À1 ) at each site was characterized by a skew-normal distribution p SN (P|ξ P ;x P ;a P )  (Azzalini, 2005) , whose location (ξ P ), scale (x P ) and shape (a P ) parameters govern the mean (l P ), standard deviation (r P ) and skewness (c P ) of the rainfall distribution (Fig. 2a ; Notes S4). The historical rainfall data were used to develop a series of regimes with higher probability of extreme droughts.
Each rainfall scenario is defined by a parameter S whose values (0, À0. 2, À0. 4,. , À1. 6) indicate the change in the location parameter in units of the scale parameter (i. e. ξ S = ξ P À SÁx P ). Because neither site showed any statistically significant autocorrelation in annual rainfall (Fig. S4 ), the rainfall regime scenarios were created by randomly sampling, with replacement, complete years taken from the historical datasets, using altered distribution functions p SN (P|ξ S ;x P ;a P ) (Fig. 2b ). Simulations under scenario S = 0 use parameters of the present-day rainfall regimes plus a simulation using the observed rainfall time series. Note that because we only sampled years from historical records, even the driest scenario at GYF was still rainier than the current climate at TNF (Fig.
The water-deficit calculations used actual evapotranspiration (ET) predicted by ED2, which accounts for the spatial and temporal variation in response to temperature, vapor pressure deficit, the canopy's leaf area index and the canopy response to levels of water stress. We developed two metrics associated with one aspect of the forest's resilience, namely the precariousness  (Folke et al. , 2004) , defined in this manuscript as the climatological proximity of the current rainfall regime to critically dry rainfall regimes that would cause significant biomass loss. First, we characterized current regional rainfall regimes using a skew-normal distribution, based on precipitation data from six long-term, gridded datasets (Table  1 ; Fig. S5 ). We then used the fitted curves (Eqn 1) to define the ED2-based critical return interval of year-long droughts (s c ).
The precariousness metrics were calculated for each rainfall dataset and combined using a weighted average that accounts for their accuracy and time span (Notes S6; Figs S5, S6; Table  S2 ). For both metrics, zero means that the current   New Phytologist (2018) 219: 914-931  www. newphytologist. com drought recurrence is shorter than s c (high precariousness), whereas higher values indicate that the current rainfall regime is further away from the ED2-based criticially dry regime (low precariousness). In order to explore the risk of Amazonian forest's reaching the critically dry regime predicted by ED2 by the end of the 21 st century, we obtained the output of 40 climate models of the Phase 5 of the Coupled Model Intercomparison Project (CMIP5,  Taylor et al. , 2012). For each climate model, we selected one ensemble member, reprojected all models to a 1-degree grid, and used the last 40 yr of 21 st century simulations under the RCP8.
3 ), we calculated the ratio between ET 0 and the total rainfall (calculated over 12-month periods) for every drought event on each grid cell that occurred between 1969 and 2008. The ratio approached one for droughts longer than 12 months (Fig. 7b ), therefore we assumed    ET 0 to be a proxy for P c. The critical return interval (s c ) was obtained directly from Table  2 , using the most similar soil texture for each grid cell and the values from drought deciduous phenology for a more conservative estimate. The annual rainfall distribution calculated for each gridded rainfall dataset is shown in Notes S6. Modest changes in the current rainfall regime could bring large fractions of the Amazon to critically dry rainfall regimes according to ED2. Nearly 23% of the Amazon region (1. 8 million km 2 ) would experience return intervals of year-long droughts below s c if the mean annual rainfall decreases, or if the standard deviation of annual rainfall increases by more than twice the current standard deviations (Table  3 ).
However, widespread d r values below 2 at some of the driest areas in Central-Eastern Amazon, Northern Santa Cruz (S) and Pando (N) suggest that these regions could experience aboveground biomass loss through increased inter-annual variability, even if average annual rainfall remains similar. The results also indicate that a large area in Southern Par a (dashed blue area in Fig. 8 ) would require major changes in rainfall to reach the critical rainfall, despite being close to the forest-savanna transition zone and not particularly wet. The high distance metrics result from a combination of lower ET associated with moderate altitudes (250-400 m above sea level) and cooler temperatures, and low rainfall inter-annual variability, particularly in the PGMF dataset. The bias-corrected CMIP5 (RCP8.
One limitation of our analysis is that it does not account for regional variation in traits: plants in drier parts of the Amazon may be more adapted and less vulnerable to droughts  (Esquivel-Muelbert et al. , 2017). However, in case changes in the rainfall regime are rapid, their adaptation may be limited and insufficient to prevent major changes in carbon stocks and forest structure and composition  (Allen et al. , 2015). It is important to note that the model predictions that parts of the Amazon could reach critically dry regimes (Fig. 8 ) are contingent on the accuracy of the rainfall datasets and other climatic variables (temperature, radiation, humidity) used in the model simulations and subsequent analyses. Also, the coarse resolution (25-250 km) of the datasets means that local-scale topography and distance from large rivers, both known to affect the distribution of total annual rainfall  (Fitzjarrald et al.
Notes S3Review of the ED2 model evaluation. Notes S4 Description of the rainfall regime scenarios. Notes S5 Description of the soil texture datasets. Notes S6 Weighting factors for each rainfall dataset. Notes S7 ED2 source code, input data information and script to generate rainfall regime scenarios. Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office. See also the Commentary on this article by Lapola, 219: 845-847. Ó 2018 The Authors New Phytologist Ó 2018 New Phytologist Trust New Phytologist (2018) 219: 914-931 www. newphytologist. com.
Acknowledgements We thank  Michael Keller ,  Robinson Negr  on-Ju arez, the editor and two anonymous reviewers for suggestions that improved the manuscript. M. L. was supported by  CNPq  ( 200686/2005-4 ),  NASA/NESSF  ( NNX08AU95H ) and  NSF  ( OISE-0730305 ,  Amazon-PIRE). N. M. L. was supported by a  NOAA Climate and Global Change  fellowship award. N. M. L. , N. R-C. , S. R. S. , K. Z. and P. R. M. were supported by the  Gordon and Betty Moore Foundation Andes-Amazon Initiative. This work was partially supported by grants  NASA/NNX08AP68A  and  NASA/ NNX10AR75G , the  CNPq Millennium Institute of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA)  and  FAPESP  as part of the  BARCA  project. The  Investissement d'Avenir grants of the French ANR (CEBA :  ANR-10-LABX-0025 ) and the  Syst eme d'Observation et d'Exp erimentation sur le long terme pour la Recherche en Environnement 'Forêt ' funded data acquisition at Paracou.
The LHCb detector is a single-arm forward spectrometer designed for the efficient reconstruction decays of c-and b-hadrons. LHCb has introduced a novel real-time detector alignment and calibration strategy for LHC Run II. Data collected at the start of the fill are processed in a few minutes and used to update the alignment, while the calibration constants are evaluated for each run. This is one of the key elements which allow the reconstruction quality of the software trigger in Run-II, which fully includes the particle identification selection criteria, to be as good as the offline quality of Run-I. This approach greatly increases the efficiency, in particular for the selection of charm and strange hadron decays. We discuss strategy and performance of this novel approach, followed by a presentation of the recent developments implemented for the 2017 run of data taking, and with the performance and reconstruction quality achieved by the LHCb experiment in LHC Run-II.
1 )  [6]. A schematic view of the LHCb detector and its sub-systems is shown in Fig. 1. In Run-II (2015-2018) of data taking the LHCb experiment collects data with the bunch crossing rate of 30 MHz. The hardware trigger, based on information from the calorimeter system and muon chambers, reduces the rate of accepted candidates to about 1 MHz, at which the full detector can be read out. The candidates accepted by this stage are processed by the high-level software trigger, with two split stages (HLT1 and HLT2). The real-time reconstruction needs to be achieved within 35 (650) ms in the HLT1 (HLT2). All events passing the first stage are Schematic view of the LHCb detector. The Vertex Locator indicates the vertex detector (VELO). The labels RICH1 and RICH2 denote the Ring Imaging Cherenkov detectors, TT and T1-T3 are the tracking stations, the latter composed of IT and OT detectors in the inner and outer region respectively, M1-M5 indicate the muon chambers, SPD (PS) is the scintillator pad (preshower) detector.