dataesr/scientific-paragraphs-categorization huggingface dataset:
Results A total of 20 patients were included in the study, yet the pharmacokinetic data was available from 19 patients to 5 females and 14 males, aged 36-79 years. One patient was excluded from the study due to complication during dialysis caused by dysfunction of cannula. For 9 patients samples were collected after the first dose of antibiotic, whereas for 10 patients during the consecutive days of antibiotic therapy. In the case of patients receiving meropenem at the moment of inclusion to the study, the samples were collected after the median time of 4 days (IQR 1-19) following initiation of the therapy. A summary of the patients' characteristics is presented in Table  1. The analyzed data consisted of 256 observations of meropenem concentration obtained from 19 patients. The raw concentration data is shown in Fig.
Selection of the case study village and farm sampling This study focuses on fertilizer management in mature rubber plots. Therefore, a main criterion for the selection of the study area was the presence of farms with mature rubber plantations. Secondary data from relevant organizations such as the Office of the Rubber Replanting Aid Fund (ORRAF; now the Rubber Authority of Thailand, RAOT), provincial and district Agricultural Extension Offices, and published literature were used as sources to identify long-established rubber planting areas in the province. According to the RAOT, the most concentrated and longest established rubber farming was in Doonsard sub-district, Kranuan district. Therefore, we chose this sub-district as our main study area. To contrai for the effects of biophysical, distance to the market, and access to places to buy fertilizers variables, we decided to select one village for data collection.
After interviewing the District Agricultural Extension officers, the head of a Kranuan District rubber farmers' group, committees of Kranuan rubber farmer groups, members of Kranuan rubber farmer groups, and the heads of two or three villages within the sub-district, we chose Subsomboon village as our study site. Subsomboon had the oldest trees and highest percentage of mature rubber trees in the area. We surveyed all households (76 households) in the village to identify rubber farmers, using a short questionnaire. Fifty-four households were rubber farmers with rubber plantations ranging in age from 1 to 25 years. We made extensive efforts to survey all rubber farmers. The first author, stayed in the village for a month to collect data but unfortunately we cou Id not reach all farmers for two reasons.
First some farmers were away from their plots throughout our survey, and second some farmers did not want to participate. These farmers posed a potential threat to the safety of the interviewer. Ultimately, we interviewed 45 of the 54 (83%) households that grow rubber. To allow an analysis of returns from investment in fertilizer with no variation in yields due to the age of the rubber trees, we selected farmers who had at least one plot with 10-13 year old rubber trees. This age represented the majority of the rubber trees in the study area, where the rubber boom started in about 2004. Thirty-two farmers had rubber plots that met this criterion. However, three of these farmers were not included in the study due to unreliable data on rubber yields. Therefore, the final sample consisted of only 29 farmers.
Data collection We conducted the survey between September 2016 and September 2018 and emphasized agricultural practices in the 2016-2017 crop year. We interviewed the head of each farm by using a structured questionnaire with three sets of questions. The first part of the questionnaire aimed to understand farmers' and farms' characteristics as they may be related to farmers' fertilization practices. The second part consisted of open-ended questions designed to describe the farmer's practices at all steps of rubber production during the trees' maturing stage, focusing on fertilizer and farmers' rationale for choosing particular fertilizer management practices, as well as their perceptions of the effects offertilizer use on crop growth, yield, and production costs. The third part collected economic data on fertilizer inputs and sources of knowledge for fertilizer use.
Data analysis We entered and analyzed all data in Excel. We classified fertilization practices by adapting the typology developed by Chambon et al. {2018). We used rate of fertilizer application instead of nutrient content as a criterion, due to the high number of farmers in our sample who used organic fertilizer with unknown nutrient content, as well as a lack of consistency in the chemical fertilizers they used and in the information about its nutrient content. As shown in Table  1 , four aspects of fertilizer management were used as criteria to classify the farmers' practices as high, medium, or low intensity: rate of use of chemical fertilizer {i. e. , amount used each year), rate of use of organic fertilizer {i. e. , a mou nt used per year), frequency of fertilizer application, and method of application.
Acknowledgments This research was funded by the  Graduate School ,  Khon Kaen University , and Jeunes  Equipes Associées à l'IRD program  (JEAI), a co-program of IRD, France and  Khon Kaen University, Thailand. The authors would like to thank all participating rubber farmers and officers in the study site for their hospitality and full support. The authors very much appreciated the manuscript review and suggestions offered by  Dr. Russell Yost  ofthe  University of Hawai'i at Manoa , and  Dr. Jefferson Fox  of the  East-West Center, Honolulu, Hawai' i.
FTIR Spectroscopy Infrared spectra of the membranes were determined by using a Perkin-Elmer spectrum 65. Films were analyzed with 16 scans and a resolution of 2 cm -1. The baseline of the FTIR spectra was corrected and the data tuned up. Each spectrum containing NFC was normalized at 1105 cm -1 , which corresponds to the peak of the C-O stretching vibration of the glucopyranose ring that is not impacted by the chemical modification, and the absorbance value of this peak is set to 1 in order to qualitatively compare the intensity of the peaks from one FTIR spectrum to another.
Energy-Dispersive X-ray Spectroscopy Elemental analyses were performed by energy-dispersive X-ray spectroscopy (EDX) with a silicon drift detection from Bruker (SDD-30 mm 2 ) equipped with a detection window of light elements (5 < atomic number < 11) and using FEG-SEM microscope. Spectra of the intensity of X-radiation emitted by the sample under an electron beam were recorded in function of the energy on one point or a delimited area. Samples were metallized with 5-10 nm carbon layer to ensure their electron conductivity (Evaporateur Baltec with Carbon braiding). Spectra and X mapping were acquired with an accelerating voltage of 5 kV. The X mapping was carried out on one image and at low magnitude (x1000) for each sample to have an overview. Image processing was applied on the mapping pictures with Image J software.
Elemental Analysis Elemental analysis was carried out by the "Institut des Sciences Analytiques (Villeurbanne, France)" of the "Centre National de la Recherche Scientifique" (CNRS). Carbon, Hydrogen, Nitrogen, Oxygen and Silicon contents were measured for neat and modified NFC. The data collected has allowed to determine the degree of substitution (DS a ) which is the number of grafted hydroxyl groups per anhydroglucose unit according to the following Equation (3) from Missoum et al. [80] DS a = 6 × M C -%C × M AGU M gra f ting × %C -M C gra f ting , (3) where M C is the molecular mass of Carbon (g mol -1 ), %C is the corrected relative carbon weight content in the sample and M AGU , M grafting and M C grafting correspond respectively to the molecular mass of anhydroglucose unit, the molecular mass of the grafted moieties and the carbon mass of the grafted moieties (all in g mol -1 ).
Gas Permeability Measurements Gas permeability measurements were performed with a mixed gas-continuous flow permeation cell designed in our laboratory (Figure  2 ). Pure gases (CO 2 or N 2 ) (120 Ncc/min) were fed to the system with mass flow controllers (Bronkhorst Company, Ruurlo, The Netherlands). The stream was humidified (RH = 75% and 95%) using a controlled evaporating and mixing system (Bronkhorst Control Evaporator Mixer W-202A-221-K). The total pressure of the humid feed and retentate streams were controlled by a back pressure controller (Bronkhorst Company, Ruurlo, The Netherlands) and the permeate stream was collected using a helium sweep gas with a controlled flow rate (10 Ncc/min, Bronkhorst Company, Ruurlo, The Netherlands). The permeation cell was placed in a heating cabinet (Carbolite PF030-230SN) in order to maintain a constant temperature (40 • C).
Conducted experiments on FashionMNIST show that the regularization term effectively controls the trade-off between quality of the generated images and the conditioning. Experimental evaluation on the CIFAR-10 and CelebA datasets evidences that our method achieves accurate results both visually and quantitatively in term of Fréchet Inception Distance, while still enforcing the pixel conditioning. We also evaluate our method on a texture image generation task using fully-convolutional networks. As a final contribution, we apply the method to a classical geological simulation application.
To make the generated images honoring the prescribed pixel values, we use a reconstruction loss measuring how close real constrained pixels are to their generated counterparts. We show that minimizing this loss is equivalent to maximizing the log-likelihood of the constraints given the generated image. Thereon we derive an objective function trading-off the adversarial loss of GAN and the reconstruction loss which acts as a regularization term. We analyze the influence of the related hyper-parameter in terms of quality of generated images and the respect of the constraints. Specifically, empirical evaluation on FashionM-NIST  [9]  evidences that the regularization parameter allows for controlling the trade-off between samples quality and constraints fulfillment. Additionally to show the effectiveness of our approach, we conduct experiments on CIFAR10  [10] , CelebA  [11]  or texture  [12]  datasets using various deep architectures including fully convolutional network.
We also evaluate our method on a classical geological problem which consists of generating 2D geological images of which the spatial patterns are consistent with those found in a conceptual image of a binary fluvial aquifer  [13]   [14]. Empirical findings reveal that the used architectures may lack stochasticity from the generated samples that is the GAN input is often mapped to the same output image irrespective of the variations in latent code  [15]. We address this issue by resorting to the recent PacGAN  [16]  strategy. As a conclusion, our approach performs well both in terms of visual quality and respect of the pixel constraints while keeping diversity among generated samples. Evaluations on CIFAR-10 and CelebA show that the proposed generative model always outperforms the CGAN approach on the respect of the constraints and either come close or outperforms it on the visual quality of the generated samples.
Datasets We tested our approach on several datasets listed hereafter. Detailed information on these datasets are provided in the Appendix A. FashionMNIST  [9]  consists of 60,000 28 × 28 small grayscale images of fashion items, split in 10 classes and is a harder version of the classical MNIST dataset  [23]. The very small size of the images makes them particularly appropriate for large-scale experiments, such as hyper-parameter tuning. CIFAR10  [10]  consists of 60,000 32×32 colour images of 10 different and varied classes. It is deemed less easy than MNIST and FashionMnist CelebA  [11]  is a large dataset of celebrity portraits labeled by identity and a variety of binary features such as eyeglasses, smiling. We use 100,000 images cropped to a size of 128 × 128, making this dataset appropriate for a high dimension evaluation of our approach in comparison with related work.
Texture is a custom dataset composed of 20, 000 160 × 160 patches sampled from a large brick wall texture, as recommended in  [12]. It is worth noting that this procedure can be reproduced on any texture image of sufficient size. Texture is a testbed of our approach on fully-convolutional networks for constrained texture generation task. Subsurface is a classical dataset in geological simulation  [13]  which consists, similarly to the Texture dataset, of 20,000 160×160 patches sampled from a model of a subsurface binary domain. These models are assumed to have the same properties as a texture, mainly the property of global ergodicity of the data. To avoid learning explicit pairing of real images seen by the discrimination function with constraint maps provided to the generative network, we split each dataset into training, validation and test sets, to which we add a set composed of constraint maps that should remain unrelated to the three others.
Network architectures We use a variety of GAN architectures in order to adapt to the different scales and image sizes of our datasets. The detailed configuration of these architectures are exposed in Appendix B. For the experiments on the FashionMNIST  [9] , we use a lightweight network for both the discriminator and the generator similarly to DCGAN  [25]  due to the small resolution of FashionMnist images. To experiment on the Texture dataset, we consider a set of fully-convolutional generator architectures based on either dilated convolutions  [26] , which behave well on texture datasets  [27] , or encoder-decoder architectures that are commonly used in domain-transfer applications such as CycleGAN  [28]. We selected these architectures because they have very large receptive fields without using pooling, which allow the generator to use a large context for each pixel.
To extract these features, a pre-trained Inception v3  [35]  classifier is used to compute the embeddings of the images at a chosen layer. Assuming these embeddings shall follow a normal distribution, the quality of the generated images is assessed in term of a Wasserstein-2 distance between the distribution of real samples and generated ones. Hence the FID writes F ID = ||µ r -µ g || 2 + T r(Σ r + Σ g -2(Σ r Σ g ) 1/2 ), ( 15 ) where T r is the trace operator, (µ r , Σ r ) and (µ g , Σ g ) are the pairs of mean vector and covariance matrice of embeddings obtained on respectively the real and the generated data. Being a distance between distributions, a small FID corresponds to a good matching of the distributions. Since the FID requires a pre-trained classifier adapted to the dataset in study, we trained simple convolutional neural networks as classifiers for the FashionMNIST and the CIFAR-10 datasets.
For the Texture dataset, since the dataset is not labeled, we resort to a CNN classifier trained on the Describable Textures Dataset (DTD)  [36] , which is a related application domain. However, since we do not have labels for the Subsurface dataset, we could not train a classifier for this dataset, thus we cannot compute the FID. To evaluate the quality of the generated samples, we use metrics based on a distance between feature descriptors extracted from real samples and generated ones. Similarly to  [27] , we rely on a χ 2 distance between the Histograms of Oriented Gradients (HOG) or Local Binary Patterns (LBP) features computed on generated and real images. Histograms of Oriented Gradients (HOG)  [37]  and Local Binary Patterns (LBP)  [38]  are computed by splitting an image into cells of a given radius and computing on each cell the histograms of the oriented gradients for HOGs and of the light level differences for each pixel to the center of the cell for LBPs.
Quality-fidelity trade-off We first study the influence of the λ regularization hyper-parameter on both the quality of the generated samples and the respect of the constraints. We experiment on the FashionMNIST  [9]  dataset, since such a study requires intensive simulations permitted by the low resolution of FashionMnist images and the used architectures (see Section 4. 2). To overcome classical GANs instability, the networks are trained 10 times and the median values of the best scores on the test set at the best epoch are recorded. The epoch that minimizes: F ID -F ID min F ID max -F ID min 2 + M SE -M SE min M SE max -M SE min 2 on the validation set is considered as the best epoch, where F ID min , M SE min , F ID max and M SE max are respectively the lowest and highest FIDs and MSEs obtained on the validation set.
Texture generation with fully-convolutional architectures Fully-convolutional architectures for GANs are widely used, either for domaintransfer applications  [28][4]  or for texture generation  [12]. In order to evaluate the efficiency of our method on relatively high resolution images, we experiment the fully-convolutional networks described in Section 4. 2 on a texture generation task using Texture dataset. We investigate the upscaling-dilatation network, the encoder-decoder one and the resnet-like architectures. Our training algorithm was run for 40 epochs on all reported results. We provide a comparison to CGAN  [3]  approach by using the selected best architectures. The models are evaluated in terms of best FID (visual quality of sampled Figure  5 : An example of a loss of diversity when generating Texture samples with a trained UNetRes network using two different random noises z and a single constraint map y.
Changing the discriminator for a PacGAN discriminator with 2 samples in the encoder-decoder based architectures allows to restore diversity, while keeping the same performances as previously or even increasing the performances for the UNetRes (see Table  1 ). Table  2  compares our proposed approach to CGAN using fully convolutional networks. It shows that our approach is more able to comply with the pixel constraints while producing realistic images. Indeed, our approach outperforms CGAN Table  2 ) by a large margin on the respect of conditioning pixels (see the achieved MSE metrics by our UNetPAC or UNetResPAC) and gets close FID performance on the generated samples. This finding is in accordance of the obtained results on FashionMnist experiments. 2 : Results obtained by the selected best fully-convolutional architectures on the Texture dataset for both the CGAN approach and our approach.
Extended architectures We extend the comparison of our approach to CGAN on the CIFAR10 and CelebA datasets (Table  3 ). We investigated the architectures described in Section 4. 2. All reported results are obtained with the regularization parameter fixed to λ = 1. We train the networks for 150 epochs using the same dataset split as stated previously in order to keep independence between the images constraint maps. The evaluation procedure remains also unchanged. We use the PacGAN approach to avoid the loss of diversity issues. The experiments on both datasets show that though CGAN provides better results in terms of visual quality, our approach outperforms it according to the respect of the pixel constraints.
Application to hydro-geology Finally, we evaluate our approach on the Subsurface dataset. We use the UNetResPAC architecture, since it performed the best on Texture data as exposed in Section 5. 2. As previously, we simply set the regularization parameter at λ = 1 and, the network is trained for  40   protocol. To evaluate the trade-off between the visual quality and the respect of the constraints, instead of FID we rather compute distances between visual Histograms of Oriented Gradients (see Section 4), extracted from real and generated samples. We also evaluate the visual quality of our approach with a distance between Local Binary Patterns. Indeed, Subsurface application lacks labelled data in order to learn a deep network classifier from which the FID score can be computed. The obtained results are summarized in Tables  4  and 5.
D. Additional samples from the Texture and Subsurface datasets In this section, we show some samples generated with the UNetResPAC architecture, which performs the best in our experiments (see Section 5) compared to real images sampled from the Texture (Figure  8 ) and Subsurface (Figure  9 ) datasets. For the generated samples, the enforced pixel constraints are colored in the images, green corresponding to a squared error less than 0. 1 and red otherwise.
Acknowledgements We thank the  University of Zurich S3IT  and  CSCS Lugano  for providing the computational resources for NNLOJET. The work related to NNLOJET was supported in part by the  UK Science and Technology Facilities Council , by the  Swiss National Science Foundation (SNF)  under contracts  200020-175595  and  200021-172478 , by the  ERC Consolidator Grant HICCUP  (No. 614577 ), by the  Swiss National Supercomputing Centre (CSCS)  under project  ID   UZH10 , by the  Research Executive Agency (REA)  of the  European Union  under the  ERC Advanced Grant MC@NNLO(340983 ) and by the  Fundação para a Ciência e Tecnologia (FCT-Portugal) , project  UID/FIS/00777/2019. This research was supported by the  U. S. Department of Energy  under contract  DE-AC02-76SF00515. It used resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U.
We use the efficient Lewiner implementation of the Marching Cubes algorithm  [S1 ]  provided by Python scikit-image library  [S4 ]. This essentially performs an iteration over all the voxels of the considered volume in which a new surface element is drawn for all the voxels where there is at least one vertex with f > f 0 and one with f < f 0. As a third step we compute the mean curvature H and the Gaussian curvature Γ at each vertex of the mesh using the algorithm of  [S2 ]. Mean curvature basically corresponds to the sum of all edges connecting the considered vertex to its neighbors, while Gaussian curvature is essentially the sum of the angles between these edges and those connecting the neighbor vertices weighted by the area of the corresponding face. Finally both H and Γ are averaged over the three vertices of a surface element.
e. H = 0 everywhere) called gyroid. Subsequently, we present the same data in the two dimensional diagram (H, Γ) and estimate the probability density ρ kde with a Gaussian kernel which is defined as below: ρ kde (x) = 1 nh n i=1 e |x-x i | 2h 2 , ( S2 ) where x is one generic point in the plane (H, Γ) while x i=1,n are all the surface elements. This tool is provided by the Python scipy library and the bandwidth h is estimated with the rule-of-thumb  [S3 ]. The cumulative probability density associated with ρ kde is shown in figure  6 , S4, S5 and S6 where each contour corresponds to the successive ten percentiles from the most frequent area (black) to the least frequent area (grey). Dots correspond to the raw data before the kernel density estimation is realised. One observes that raw data are confined to remain below the parabola Γ = H 2 which is coherent with the definition of H and Γ given in equation  (5).
Video surveys and imagery analysis The extensive dataset used in this study was obtained from 68 underwater dive is provided in Table  1 , with reference to the video platform used. Data obtained during the PROMARES cruise (dives 37 to 44) was partly published in  Tubau et al. (2015) , and considered in this study given its relevance to better determine spatial patterns in litter distribution along Cap de Creus submarine canyon. All underwater dives were performed from the mid and outer continental shelf (80-90 m water depth) down to the submarine canyon floor at 1600 m (Figure  1 ; Table  1 ). Dive length was extremely variable (from 80 m to 3 km) and dependent on weather conditions, underwater visibility and bottom current speed. The total seafloor distance covered was 47. 5 km, which represents an area visually examined of about 7.