dataesr/scientific-paragraphs-categorization huggingface dataset:
1 • Satellite retrievals depend heavily on surface and subsurface properties such as soil texture, humidity and vegetation. These properties lead to spatially inhomogeneous datasets. • The sampling of satellite observations is not continuous. In addition, the ESA CCI SM product has a multi-instruments constitution where each independent dataset has been rescaled in order to be merged together. Combining different instruments from different periods adds temporal inhomogeneity to the final dataset and thus complications for model evaluations. Some studies have already developed methodologies to compare satellite products and model outputs in order to assess their potential for assimilation (  [11] ,  [12] ,  [13] ). Few studies have taken the next step and assimilated satellite products into the model.
Secondly, this period provided an overlap with the meteorological data used to drive the model. The top plot in Figure  1  shows the correlation between the ESA CCI SM product and ORCHIDEE simulated water content integrated over the first 4 layers i. e, the soil moisture in top first 2cms which, as mentioned previously, should be the closest theoretically. Temporal correlations tend to be high over areas where remote sensing methods are known to be efficient. Poorer results are found over desert, mountainous and frozen areas. This may to be due to the fact that it is difficult to measure SSM by remote-sensing over these regions. Over these regions, we may be seeing the effect of mixing different sensors with different sensing depths to make up the ESA CCI product. Areas with low correlations between the model and observations may also be due to issues with the model itself.
Description of PASTIS Model The PASTIS (Prediction of Agricultural Solute Transformations In Soils) model simulates the movement of water and the transformation of solutes in soil and was further described by  Garnier et al. (2003)  to simulate decomposition in straw-amended soils. The model consists of a 1-D transport module (using Richards' equation for water flow and the convection-dispersion equation for solute movement) and a transformation C/N module, CANTIS  (Carbon and Nitrogen Transformations In Soil). The model simulated the mulch decomposition  (Coppens et al. , 2007)  and pesticide transport  (Saffih-Hadadi, 2003) , independently. In this study we linked the mulch module and the pesticide module in order to simulate the pesticide dynamic in mulch.
A C C E P T E D M A N U S C R I P T The CANTIS module was slightly modified to account for changes in mass and composition due to soluble carbon that leaches during rains. When feeding occurs, the composition of the new decomposable layer was calculated according to its own previous composition and the composition of the added organic matter coming from the non-decomposable layer. In the CANTIS module, decomposition rates obey first-order equations, with rates depending on water potential and temperature. Hence, water potential and temperature of the mulch contact layer (Mc) must be simulated. -Hdadi et al. (2003)  implemented a pesticide module in PASTIS. This module considers pools of pesticide and its metabolites such as water soluble (P w , M w ), weakly or reversibly adsorbed (P ads , M ads ), and irreversible adsorbed or non-extractable residues, NER in the following (P NER , M NER ).
b) Pesticide transformation parameters Parameters of glyphosate were taken from  Aslam et al. (2014) , who performed incubation experiments for mulch that was amended with 14 C-labelled glyphosate. Experimental data corresponding to the mineralized, soluble fractions and extractable and non-extractable residues were calibrated by coupling the mulch decomposition and pesticide degradation modules (see § 2. 1. 3). Dissipation parameters of the parent molecule and metabolites were obtained from the mulch compartment. In the soil, only the parent molecule parameters were estimated because of the low amounts of metabolites in the soil layer. Therefore, a total of 10 parameters for the mulch layer and 5 parameters for the soil layer were obtained using 16 experimental curves  (Aslam et al. , 2014).
b. Calibration of hydraulic parameters of soil and mulch We estimated the hydraulic parameters of mulch and soil from HI-R treatment using the experimental data for matric potentials and volumetric water contents registered at 3 and 15 cm depths and the gravimetric water content of the mulch. Data from the LF-R treatment were used to test the model with the estimated parameters. The estimated parameters of the hydraulic properties of soil that were described by Van Genuchten's model  (van Genuchten, 1980)  and were presented in Table  S3  of Supplementary Material. Maximum water content, minimum water content and the density of mulch particles were adopted from  Iqbal et al. (2013). Comment citer ce document : Aslam, S. , IQBAL, A. , Lafolie, F. , Recous, S. , Benoit, P. , Garnier, P. (  2018 ).
c. Calibration of pesticide leaching parameters Parameters related to pesticide wash-off were estimated in order to simulate the remaining pesticide in mulch after each rainfall event. These parameters, namely, the diffusion coefficient (D p ) and the saturation index (S i ), were first calibrated for the HI-R regime and for Smetolachlor. Optimization was done using the amount of available pesticide (in solution and weakly adsorbed) that was measured from mulch residues after each rain. These data were compared to the simulated fractions P w and P ads that were calculated by the model on the same days. After obtaining the parameter values, we tested them for the LF-R regime. Similarly, parameters were tested for the glyphosate molecule for the two rain regimes.
A C C E P T E D M A N U S C R I P T Rainfall regime had a strong effect on mulch decomposition and on the subsequent quality of mulch organic matter  (Iqbal et al. , 2015). The organic carbon content of the mulch decreased during decomposition for the two rain treatments. However, it decreased more in the case of frequent rainfalls (LF-R) compared to infrequent rainfalls (HI-R) because of the more humid conditions at the soil-mulch due to smaller evaporation periods in the LF-R treatment compared to the HI-R treatment. PASTIS simulated the two different decomposition dynamics well, with efficiency coefficients of 0. 16 and 0. 85 (Table  5 ) for light and frequent and heavy and infrequent regimes, respectively. The evolution of mulch biochemical composition was also simulated with the Cantis model (the C/N module of PASTIS), which was calibrated using the parameters presented by  Iqbal et al.
Conclusions To our knowledge, very few studies have been done to simulate pesticide dissipation in mulch residues and the underlying soil. We proposed a new model for predicting pesticide dissipation in mulch and soil that combined "pesticide" and "mulch" modules. The model accounted for both transport and biological processes involved in pesticide fate during mulch decomposition. The study was conducted to test the model in soil columns under different rainfall regimes. The pesticide module in PASTIS was satisfactory for simulating the dynamics of two different types of herbicides. Parameters estimated from laboratory incubations under static conditions were useful for predicting pesticide transformation in soil columns. Satisfactory simulation results indicated that pesticide wash-off is dependent on pesticide characteristics (39-56% for smetolachlor and 45-57% for glyphosate).
Data Collection and Production An interaction in the ZnSe crystal results in an amplified signal with amplitude ranging from tens to hundreds of mV, a rise time of 10 ms and a decay time of 40 ms. Due to the smaller detector sizes, the LD signals are usually faster, with rise-time of a few ms and decay-time of about 8 ms. Each channel can be biased, amplified and filtered using a dedicated read-out chain, which allows to optimize the amplification gain and the cut-off frequency of the anti-aliasing filter  [45, 46, 47, 48, 49, 50, 51, 52]. Due to the slow time-development of the recorded pulses and the low detector rate (2 mHz in physics runs), the data are digitized with sampling frequencies of 1 kHz for ZnSe and 2 kHz for LD, and the continuous data stream is transferred to disks for the off-line analysis.
To prevent the loss of small (or noisy) light signals, when a signal is detected in the ZnSe we also associate to it the corresponding waveforms in the LD. In the following, we will use only this trigger for the LD. The derivative trigger is still run on the light detectors for future analyses (for example, to study events that are not produced by scintillation of ZnSe). The complete data-stream of all channels recorded by the DAQ, as well as the trigger positions, are saved in NTuples based on the ROOT software framework. At the first stage, we convert the continuous data into acquisition windows of 5 seconds for the ZnSe crystals (4 s after the trigger and 1 s before) and 1 s for the LD (800 ms after the trigger and 200 ms before). The pretrigger window is used to compute the baseline value, and thus the detector gain before the interaction occurred  [55, 56].
Other informations to be accessed during the off-line analysis, such as the geometrical configuration of the array, the correspondences between ZnSe and LD, the run type (physics, calibration, test. ), possible time intervals that have to be rejected because of known problems (earthquakes, electronics problems, major underground activities) are stored in a PostgreSQL database. Each physics run lasts about 2 days, and it is followed by a stop of a couple of hours to allow the liquid helium refill of the cryostat and the subsequent stabilization of the detectors. Approximately every month, we perform a calibration of 4 days with 232 Th sources. Since the most energetic γ line produced by 232 Th (2. 6 MeV) is below the 82 Se Q-value, we exploit also other sources to characterize the energy region of the 0νDBD.
To study the energy dependency of the shape parameters in the region of interest (Sec 5), we use an Am:Be neutron source, emitting a broad distribution of γ rays up to several MeV. This calibration was made every time we modified the working parameters of the detectors, in order to prevent possible changes in the shapedependency on the energy. In the first year of data taking, we performed three Am:Be neutron source calibrations: one during the detector commissioning, one between the physics runs presented in this paper, and one immediately after (as a cross-check). Furthermore, we validated the 232 Th calibration with a 56 Co source, producing γ peaks well above the 82 Se Q-value (Sec 7). The collection of the initial plus final calibration and all the physics runs in between forms a DataSet.
The average noise power spectrum is constructed by averaging hundreds of waveforms acquired during the entire DataSet with a random trigger. A further off-line analysis allows to discard acquisition windows characterized by the presence of pulses. The signal template is obtained by averaging hundreds of high amplitude events collected during the 232 Th calibrations, and aligned by the pulse maxima. In such a way, the random noise contributions are suppressed (see Fig  1 left ). In Sec. 8 we describe how the production of the signal template was improved to match the needs of scintillating crystals. After the matched filter we extract also some parameters related to the pulse shape: rise-time (time difference between the 90% and the 10% of the leading edge), decay-time (time difference between the 30% and 90% of the trailing edge), slope of the baseline before the pulse, delay of the position of the maximum of the filtered pulse with respect to the maximum of the template, and two shape parameters called Test Value Left (TVL) and Test Value Right (TVR), that correspond to the χ 2 value between the filtered signal template and the filtered pulse com-puted on the left and right side of the signal maximum, respectively: T V L = 1 Aω L i M -ω L i=i M (y i -As i ) 2 T V R = 1 Aω R i M +ω R i=i M (y i -As i ) 2 (1) where y i is the pulse, A and i M its amplitude and maximum position, s i the ideal signal pulse scaled to unitary amplitude and aligned to y i , ω L (ω R ) the left (right) width at half maximum of s i.
Then, we interpolated both the median and MAD points with a polynomial functions obtaining, in such a way, their trends in the whole energy range (up to 4 MeV), including the region where we would expect the 0νDBD signal. Finally, we use the parameters extracted from the fits to correct for the energy dependency in all the physics and calibration runs. Each shape parameter is scaled in such a way to be centered around zero (Fig. 2 ). To monitor the effect of this normalization, we developed a software tool which checks that the scaled parameters do not depend on the energy, on the channel and on the measurement time. This analysis led to discard some of the shape parameters (such as the TVR) that are not stable enough to perform uniform cuts over a dataset, as they show a time dependency related to the natural thermal drift of the detectors that can not be corrected.
Selection of Heat Pulses and Efficiency Evaluation We perform a first selection of thermal pulses by exploiting the shape parameters listed in Sec. 5. At this stage, we do not use the information provided by the light detectors, as the purpose is to reject spurious events, such as those barely affected by pile-up or electronics noise. First, we exclude all the time-intervals that are marked as bad in the database because of known problems (see Sec. 4). The effect of this selection is a reduction in livetime by 1%. Moreover, we require the pulses to be triggered only by a single ZnSe, as expected from 0νDBD events. As explained before, we exclude from the analysis the shape parameters that are not robust enough because of fluctuations in time, and we use only the decaytime, rise-time, baseline slope, delay and TVL.
3 ) and choose the cut in which r reaches the plateau. As explained before, we evaluate the total efficiency of these cuts on the even events belonging to the γ peak of 65 Zn (Fig. 4 ). Even if we keep the same shape-cuts during the entire analysis, we compute the efficiency separately on each DataSet to account for possible timevariations of the shape parameters. Weighting the efficiencies by the DataSet exposure, we obtain an average efficiency of 95±2%, with a maximum variation of 6% across all DataSets. This value is cross-checked using events in which two crystals triggered that, given the negligible amount of random coincidences, can be considered as an almost pure sample of signal-like events. We obtain an effi- Fig. 4  γ peak of 65 Zn recorded in half a DataSet (even events). Top: events that pass the pulse-shape cuts and the anti-coincidence cut.
Validation of the 232 Th Calibration Before introducing the information on the light detectors, it is worth observing that the energy calibration of the heat channels was cross-checked in a dedicated measurement. Indeed, since the Q-value of 82 Se exceeds the largest γ ray produced by the 232 Th source, we usu-ally extrapolate the calibration function and the energy resolution in the region of interest. This procedure was used in the analysis of the 0νDBD reported in Ref. [63] , in which the extrapolation at high energies resulted in an uncertainty of 3 keV on the Q-value, and an energy resolution of (23. 0 ± 0. 6) keV FWHM. In this paper we validate the 232 Th calibration by using a 17 days-long measurement with a 56 Co source (T 1/2 ∼ 77. 2 days) emitting γ rays up to ∼3. 5 MeV. We apply to these runs the calibration coefficients derived for each ZnSe from the 232 Th calibrations, as done in a "standard" DataSet.
6. The events that pass all Fig. 6  The light recorded by the LD placed on top of the ZnSe is reported as a function of the heat released in the crystal. The heat axis is energy-calibrated using the most intense γ peaks produced by the 232 Th source. On the contrary, the light axis is not energy-calibrated (broadening the β/γ and α bands). Red: pulses that pass the selection criteria to produce the signal templates, both for the heat and the light detectors. In this plot, data from all crystals are shown. the selection cuts (a few hundreds for each ZnSe) are finally averaged to suppress the random noise contributions. For each ZnSe, we construct 3 signal templates: the template of the ZnSe itself, and the templates of the light recorded by the top/bottom LD. We stress that now, in contrast to the past  [32] , each LD has two different signal templates, corresponding to the light emitted by the top/bottom ZnSe (see Fig.
Event Selection Background Index Efficiency [ counts/(keV • kg • y) ] [%] Heat (3. 6±0. 5)×10 -  95±2% Heat + α rejection (1. 5±0. 3)×10 -  95±2% Heat + veto with 212 Bi 5. 1 +2. 4 -2. 0 ×10 -3 94. 5±2% Heat + veto with all α's 3. 6 +1. 9 -1. 4 ×10 -3 93±2% Total signal efficiency 3. 6 +1. 9 -1. 4 ×10 -3 75±2% Table  1  Summary of the Background Index (counts/keV/kg/y) and signal efficiency averaged on the DataSets exposure, measured in the region 2800 -3200 keV with a ZnSe exposure of 3. 44 kg•y (1. 34 × 10 25 emitters•y). Uncertainties are reported at 68% C. L. First row: events that pass the cuts on the heat described in Sec. 6. Second row: the events are further selected requiring that the shape parameter of the light is consistent with interactions of electrons (α rejection) as described in Sec.
Acknowledgements This work was partially supported by the LUCIFER experiment, funded by  ERC  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement n. 247115, funded within the  ASPERA 2nd Common Call for R&D Activities. We are particularly grateful to  M. Iannone  for his help in all the stages of the detector construction,  A. Pelosi  for constructing the assembly line,  M. Guetti  for the assistance in the cryogenic operations,  R. Gaigher  for the mechanics of the calibration system,  M. Lindozzi  for the cryostat monitoring system,  M. Perego  for his invaluable help in several fields, the mechanical workshop of LNGS ( E. Tatananni ,  A. Rotilio ,  A. Corsi , and  B. Romualdi ) for the continuous help in the overall set-up design. A. S. Zolotarova is supported by the IDI 2015 project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-0.
This kind of approach is interesting as it is costly to have human annotators drawing bounding boxes around objects in dense datasets. Global Average Pooling (GAP), a mathematical operation performing the average of a matrix (described in Section 3), was first presented as a structural regularizer in NiN  (Lin et al. , 2013)  and later used in GoogLeNet  (Szegedy and Liu, 2015). More recently, it was used in ResNet  (He et al. , 2016)  and GoogLeNet-GAP  (Zhou et al. , 2016)  before a fully connected layer to perform object localization. In this latter approach, it was preferred to max-pooling to find all the discriminative parts of a class instead of the most discriminative one. In this work, we intend to increment the classification and localization research based on the GAP methods by proposing a modified architecture and some naive regularizations.
ACTION DATASET AND ASSOCIATED EVALUATIONS This section presents both quantitative and qualitative results obtained with our models applied on "The Stanford 40 Action"  (Yao et al. , 2011)  dataset. Inception-GAP5 built on top of InceptionV3 with 5 maps per class was preferred to Inception-GAP10 as we noticed that increasing the amount m of maps per class (10 instead of 5) did not improve results in our classification task. Inception-GAP5 achieved an accuracy of 75. 9% when Inception-GAP10 scored 1. 3 points lower and Inception-GAP5-L1 achieved an accuracy of 75. 5% when Inception-GAP10-L1 scored 0. 7 point more. For fair comparison, we also implemented the method proposed in  (Zhou et al. , 2016)  on-top of InceptionV3 and trained it using the same optimizer as the one used for our model, referred as Inception-GAP-Zhou.
First the dataset is presented then comes the comparison of two one-shot and semi-supervised training methods, one based on a fully shared GAP layer (GoogleNet-GAP-Zhou  (Zhou et al. , 2016)  and our implementation of Inception-GAP-Zhou) and the other based on an unshared GAP Layer (our 3  CHOLLET, Francois. Keras (2015). http://keras. io. Inception-GAP5). Section 4. 3 is a quantitative evaluation of the regularization introduced in Section 3. 2 and Section 4. 4 assesses the localization abilities of some of the models used up until then. Hereafter, 5 metrics are used : accuracy, precision, recall, Mean average Precision (MaP), and Intersection over Union (IoU). Precision and recall, are computed such that we only consider a label to be true if the probability of its prediction is over 50% (as in the Keras1 implementation).
Action 40 Dataset The Stanford 40 Action  (Yao et al. , 2011)  dataset has been used to perform training and testing of the networks. This dataset is composed of 9532 images (4000 used for training, and 5532 for testing) of people performing one of 40 actions such as drinking, cooking, reading, phoning, or brushing teeth. We split the test images into two subsets : one with 3532 images used for validation and 2000 (50 images per class) for the test stage. In the dataset, all images are provided with a class label and a bounding box around the person performing the corresponding action.
Comparing Inception-GAP5 and Inception-GAP-Zhou This section describes our comparison of Inception-GAP5 and Inception-GAP-Zhou, and reports the results given in  (Zhou et al. , 2016)  with GoogLeNet-GAP. In Table  2 , our model shows better performance than Inception-GAP-Zhou with respect to the the first three of metrics aforementioned (accuracy, precision and recall), meaning that Inception-GAP5 is better at classifying the dataset and that its classification is more reliable.
Evaluation of Action Localization The localization properties of our model are evaluated and shown in Table  4  and Figure 4. On the one hand, the table reports the results of the center of mass of the CAMs being in the ground-truth bounding-box, on the other hand, the Figure  4  reports how accurate we are in defining a bounding box around a class. To draw a bounding box around the predicted class, we threshold the CAM of the prediction by a given percentage of its maximum value and consider the bounding box to be smallest rectangle surrounding all these points (as in  (Zhou et al. , 2016) ). It is important to observe that, in the Stanford 40 Action  (Yao et al. , 2011)  dataset, the discriminative parts of an action are mostly located next to the human performing the action (e. g. the fishing action is mostly determined and localized, with our method, by the presence of a fishing rod; instead of a person).
CONCLUSION AND FUTURE WORK This work presented semi-supervised image classification and localization on RGB images using an unshared GAP layers. Based on evaluations, we improve upon existing approaches in terms of the performance for both image classification and localization, in the context of human action localization, we hypothesize that the increased performance is due to the unshared GAP layer and to the reduced attention field in the model, which makes our model similar to  (Oquab et al. , 2015). This increased performance also exists even though the amount of parameters is reduced and the visualization method needs less computation. As our next step, we are going to asses this modification on a larger dataset such as Imagenet, then, explore the use of shallower models (models with less convolutional layers) to tackle this problem.