dataesr/scientific-paragraphs-categorization huggingface dataset:
Crops may take benefits from silicon (Si) uptake in soil. Plant available Si (PAS) can be affected by natural weathering processes or by anthropogenic forces such as agriculture. The soil parameters that control the pool of PAS are still poorly documented, particularly in temperate climates. In this study, we documented PAS in France, based on statistical analysis of Si extracted by CaCl 2 (Si CaCl2 ) and topsoil characteristics from an extensive dataset. We showed that cultivation increased Si CaCl2 for soils developed on sediments, that cover 73% of France. This increase is due to liming for non-carbonated soils on sediments that are slightly acidic to acidic when non-cultivated. The analysis performed on non-cultivated soils confirmed that Si CaCl2 increased with the < 2 µm fraction and pH but only for soils with a < 2 µm fraction ranging from 50 to 325 g kg -1.
We implemented a spatial statistical approach based on an extensive dataset extracted from the French Soil Quality Monitoring Network database  43. This dataset contains data from over 2200 sites in the France mainland, all of which were analysed for major chemical, physical, pedological, climatic, and geological parameters including Si CaCl2 concentrations. We focused on the Si CaCl2 soil concentrations of the surface horizons (0-30 cm), considered to be the horizon most explored by plant roots.
However, Fig. 5   28, 29, 31  for soils cropped in wheat, obtained by crossing the 'arable land' pixels, 'permanent crop' and 'heterogeneous agricultural' areas with the exception of 'agroforestry' of Corine Land Cover classes  54  with municipalities for which type of farming is primarily cereal crop, according to French OTEX classification (Orientation Technico-Economique des Exploitations). We removed the municipalities from 7 departments (Ain, Haut-Rhin, Bas-Rhin, Gironde, Pyrénées-Atlantiques, Hautes Pyrénées, Landes) where corn is the main cereal under production. Maps were generated using ArcGIS software version 10. 7. 1 (ESRI: https ://www. esri. com/en-us/home). shows that soil pH and clay nature were linked, with acidic soils being mainly kaolinitic and basic soils being more smectitic or rich in vermiculite.
Soil data from RMQS program. The soil data used in this study were provided by the French Soil Quality Monitoring Network (RMQS). The RMQS network consists of observation sites situated at the centre of a regular grid (16 * 16 km) covering the French territory. It provides 2111 sites in metropolitan France, almost half of which are cultivated (permanent crops or field crops) and the remaining of which consist of pastures, natural vegetation, or urban soils. The dataset used in this study corresponds to the first sampling campaign of the RMQS carried out from 2000 to 2009. Soil type, parent material, climate, and land use were described in the field. At each site, 25 core samples were taken within a 20 m × 20 m plot and combined into a composite sample. Sampling depth generally consisted of the 0-30 cm layer  43.
Si was then analyzed, after filtration of the supernatant at 0. 45 µm, by Inductively Coupled Plasma Atomic Emission Spectroscopy (axial ICP-AES; 720 ES, Varian)  29. The limit of quantification of the method was of 0. 5 mg kg -1 with an uncertainty, U, (with a confidence level of 0. 95) evaluated as follow: with Si CaCl2 and U expressed in mg kg -1. To decipher the impact of agricultural use on Si CaCl2 concentration, we stratified the database by (i) parent material, based on the European Soil Information System (EUSIS) classification  57  available at the French territory scale as in Landré et al. 29  ; and by (ii) land use. The group of soils developed on sediment was cut into two subgroups (carbonated and non-carbonated depending on their carbonate content (> 1% and < 1% respectively).
We considered soils under forests, pastures, parks, natural vegetation, and wetlands to be non-cultivated soils. We did not make any distinction of land use for soils on igneous extrusive rocks (volcanic) and podzols, because cultivation of these soil types was limited to two and three sites, respectively. Our statistical analysis was based on a subset of 1986 data points (subset 1, see Supplementary Table  S1 ) due to the removal from the initial 2091 point dataset of any sites with missing geological information and three sites with peat soils. Digital soil mapping approach. To map and define the spatial distribution of Si CaCl2 concentrations, we implemented a digital soil mapping (DSM) approach based on the scorpan model framework proposed by McBratney et al. 58  , a spatial prediction function utilizing quantitative relationships between soil properties and soil forming factors as follows: where Soil is a soil property at position x.
The s refers to soil information derived from prior soil maps or from remote or proximal sensing data; c refers to the climatic properties of the environment at a given point; o refers to organisms, including vegetation, fauna, or human activity; r refers to relief; p refers to the parent material or lithology; a refers to the soil age; n refers to space or spatial position; and e is the spatially correlated errors. To apply this model, we retained a set of spatial covariates describing scorpan factors to predict the spatial distribution of Si CaCl2 at 90 m resolution. All covariates were previously resampled at the same resolution. The selection was carried out from a larger set of covariates using a combination of expert knowledge and statistical approaches. The latter was based on the boruta package in R  59.
This algorithm uses the classification of the covariate importance implemented in the randomForest package and compares the importance with random variables based on the Z score value. This step allows the identification of the most relevant covariates before fitting a prediction model. The initial 18 spatial covariates represented the scorpan factors as follows: soil (map of soil type and available water capacity (AWC)), climate (map of climate type, precipitation, and evapotranspiration), vegetation (land use, forest type), parent material (parent material types and gravimetry), and relief (shuttle radar topography mission (SRTM), compound topographic index (CTI), slope cosine, erosion, slope, and slope position). A final covariate corresponding to the normalized difference vegetation index (NDVI) was derived from remote sensing data.
This spectral index is widely used to describe the photosynthetic capacity of vegetative cover. The underlying assumption driving this method holds that changes in vegetation may reflect various plant responses to climate, land management, or soil properties  47. This covariate was computed from a large time series dataset. To summarise the temporal data, Loiseau et al. 47  performed a principal component analysis that yielded 3 covariates corresponding to the 3 first components: NDVI_1, NDVI_2, and NDVI_3. For the scorpan model, we developed a regression kriging (RK) model using the selected covariates and soil observations. The RK model is a hybrid technique a Random Forest approach with a geostatistical approach. Random Forest 60 consists of an ensemble of regression trees built from covariates and point data.
The final prediction is the mean of the individual tree predictions. This technique is applicable in R and requires two main parameters: the number of randomly selected splitting variables at each tree (mtry), and the number of trees (ntree). We used the default parameters provided by the package (mtry = 6 and ntree = 500). The final predictions are the sum of the Random Forest predictions and the residuals computed through an ordinary kriging procedure  61. The RK model was fitted on the on a subset of the RMQS sites for which covariate information were available (subset 2: 1987 points, see Supplementary Table  S1 ). This procedure was implemented with the package GSIF 62 , which also allows fitting of residuals in a variogram. To assess the covariates importance in the model, we used the function varImp() implemented in the Random Forest package.
The function calculates the difference in MSE (Mean Squared Error) when the values of each predictor are shuffled. This indicator is then normalized by the standard deviation of the differences to obtain the Increase in MSE noted %IncMSE. The highest values are attributed to the more important variables. The validity of the fitted prediction model was evaluated by a 30-fold cross validation. At each step, the soil dataset was split into two datasets: 2/3 for calibration, and 1/3 for validation. To obtain an estimate of the final map accuracy, the validation indicators were calculated at each iteration and then averaged over the 30 repetitions. We used different indicators for validation, including the Root Mean Square Error (RMSE), the coefficient of determination (R 2 ), the concordance  63  , and the bias.
Acknowledgements This work was performed in the frame of the  French ANR   BioSiSol  project ( ANR-14-CE01-0002 ). RMQS soil sampling and physico-chemical analyses were supported by the  GIS Sol , which is a scientific group of interest on soils involving the  French Ministry for ecology and sustainable development and Ministry of agriculture , the  French National forest inventory (IFN) ,  ADEME ,  IRD  and  INRAE. The authors are grateful to  Annie Guérin  ( LAS, INRAE, Arras, France ) and  Amélia Landré  ( INFOSOL, INRAE, Orléans, France ) for Si CaCl2 analysis and database preparation respectively.
With the emergence high-throughput technologies, it is possible to measure large amounts of data relatively low cost. Such situations arise in many fields from sciences to humanities, and variable selection may be of great help to answer challenges that are specific to each of them. Variable selections may allow to know, among all measured variables, which are of interest and which are not. A lot of methods have been proposed to handle this issue, with the Lasso and other penalized regression as special cases. These methods fail in some cases and linear correlation between explanatory variables is the most common, especially in big datasets. In this article, we introduce AcSel, a wrapping algorithm to enhance the accuracy of any variable selection method.
We then use the von-Mises Fisher law to create replacement of the original variables by some simulations (see Algorithm 1) to create B new design matrices X (1) ,. , X (B). The AcSel algorithm then applies the variable selection method select to each of these matrices and returns a vector of length P with the frequency of apparition of each variable. The frequency of apparition of variable x p. , noted ζ p is assumed to be an estimator of the probability P(x p. ∈ S) for this variable to be in S. Nevertheless, both the grouping method and the choice of c 0 are crucial. When this constant is too small, the model is not enough perturbed. On the other hand, when this constant is too large, variables are chosen at random. Algorithm 1 Pseudo-code for the AcSel algorithm with c 0 fixed Require: gr c0 , select, B, c 0 , P ζ ← 0 P for b = 1,.
Application to a real dataset We decided to apply our algorithm to the diabetes dataset used by Efron et al. [EHJT04]. This dataset contains 10 variables which are age, sex, body mass index, average blood pressure and six serum measurements and a quantitative response of interest that is a measure of the evolution of the diabetes disease one year after baseline. As proposed, we included interactions term, resulting in an 64 explanatory variables dataset. We use a wide range of the c 0 parameter, starting from 1 to 0. 35 by step of 0. 05 (see Figure  5  right). For each step, the probability of being included in the support is calculated with 500 simulations as described in the Algorithm 1. We set the threshold to 0. 95 to avoid numerical instability. We used our algorithm with the Lasso and selected the regularization parameter with the AICc.
Discussion and conclusion We introduced the AcSel algorithm which uses intensive computation to select variables with high precision. The user has the choice between using this algorithm to produce an confidence indicator, or choosing an appropriate precision-emptiness trade-off to select variables with confidence. The main idea of our algorithm is to take into account the correlation structure of the data and thus use intensive computation to select the reliable variables. We prove the performance of our algorithm through simulation studies. In some situations, where many regressions have to be made (in network reverse-engineering in which we have a regression per vertex) our algorithm may be used in an experimental design approach. We apply our algorithm on a real dataset and we found some interesting informations.
At the moment, the rapid innovations toward artificial intelligence seem to create a new paradigm. These methodological approaches are indeed very powerful in image analysis and in linking heterogeneous datasets. However, associated algorithms only give us more geoinformation but still fail in providing easily accessible, robust, and reliable outputs of guiding decisions of Digital Object Identifier 10. 1109/JSTARS. 2020. 3046096 urban actors such as urban planners. During these three rich days of exchanges, we observed this increasing importance of topics related to the possibilities of recent neural networks approaches, but we also noticed an important contribution of new sensors (UAV, LiDAR, etc. ) to provide a finer understanding of urban environments. In addition to these methodological aspects, we noticed particular focuses on the urban thermal patterns and the detection of complex morphologies, associated with poverty and slums in the Global South in particular.
" Their study shows how crowdsourced data (i. e. , citizen weather stations) can be used together with EO public data, such as Landsat imagery and Urban Atlas, in order to train supervised prediction models able to reach a degree-level precision. In their article "Monitoring seasonal and diurnal surface urban heat islands variations using Landsat-scale data in Hefei, China, 2000-2017," Lu et al. aim to a better understanding of the surface urban heat island with a focus on their diurnal and seasonal variations, and a study of their relationships with landscape patterns. MODIS and Landsat data are used to generate land surface temperature time series and to assess the roles played by the different land cover classes (impervious, water, vegetation), highlighting the specific mitigation effect of vegetation.
Generally speaking, the rise of artificial intelligence seems to create a new paradigm. And yes, these methodological approaches are very powerful in image analysis and in linking heterogeneous datasets. But, and we should keep this in mind despite all the technical euphoria, these algorithms require large and well-designed repositories of training data, have unresolved issues such as related to biases and transferability, and only give us more geoinformation. They do not give us any clear instructions for action. The ordered world of bits and bytes is undercomplex compared to our world and especially to the complexity of our urban environment. Against this background, the JURSE conference series tries to do exactly this balancing act. Scientists, practitioners, industry, and ultimately all of us as citizens come together to direct data and methods in such a way that, in the best case, they can support courses of action, decisions on urban development.
ACKNOWLEDGMENT The editors would like to thank the authors for their contributions to the Special Issue. The editors are grateful to the reviewers who committed their valuable time and knowledge to ensure that they maintain the high quality of the journal. The editors also would like to thank Prof. Jenny Du for welcoming our Special Issue in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING and continuously supporting us. Finally, their warmest thanks go to the organizing team and sponsors of JURSE 2019. They strongly participated in making this event a great success.
These include image segmentation and object detection (using U-Net (4-6), StarDist  (7, 8)  and YOLOv2 (  9 )), image denoising and restoration (using CARE  (10)  and, Noise2Void (  11 )), super-resolution microscopy (using Deep-STORM (  12 )) and image-to-image translations (using Label-free predictionfnet (13), pix2pix  (14)  and CycleGAN (  15 )) (see Supplementary Note 2, Supplementary Fig. 2 3 4 5 6 7 and Supplementary Video 2-11). In practice, ZeroCostDL4Mic is a collection of selfexplanatory Jupyter Notebooks, featuring an easy-to-use graphical user interface (GUI) (Supplementary Fig. 8 ) that requires only a web browser and a Google Drive account for a user to run any of our DL-based tasks. Jupyter Notebooks can efficiently and interactively run Python code, currently the default language to deploy DL applications.
All calculations are performed in the cloud, circumventing the need to purchase or install graphical processing units (GPUs) and associated software. Using ZeroCostDL4Mic does not require prior knowledge in coding. Researchers can, in a few mouse clicks and aided by a simple workflow, install all needed software dependencies, upload their imaging data and run networks for training and prediction (Supplementary Video 1). While the underlying code is hidden by default, it remains accessible, allowing users to learn, explore, and edit the notebooks' programmatic structure. ZeroCostDL4Mic notebooks share a common workflow to promote easy adoption and good practice in DL, encouraging users to use models trained on their data. Indeed, before deploying trained models on unseen data, users can easily test their quality (see Supplementary Note 3 and Supplementary Fig.
9  and 10 ). This is fundamental in optimizing the network performance for a particular application, determining its limitations and preventing the significant introduction of artifacts, a commonly raised concern for DL applications in microscopy  (1, 2). In practice, we implemented a quantitative quality control step in all notebooks, which allows the assessment (discussed in Supplementary Note 3) and improvement of model performance (Supplementary Fig. 11 ). Additionally, we enabled several important functionalities that facilitate and improve the applicability of our DL approaches. In particular, we implemented: (1) automated data augmentation which can artificially expand the image diversity of a dataset, especially beneficial when only small training datasets are available (Supplementary Note 4 and Supplementary Fig.
12  and 13 ); (2) transfer learning (3), allowing to take advantage of pre-trained networks (from so-called model "zoos") by re-using previously learned features within these models and therefore speeding up and improving the training process (see Supplementary Note 5 and Supplementary Fig. 14  and 15 ); and (3) batch processing (predictions) of unseen data to streamline analysis once a satisfactory model has been obtained. It is important to note that trained models can also be downloaded and used outside ZeroCostDL4Mic (e. g. , StarDist in Fiji(  16 )). Via Google Colab, ZeroCostDL4Mic provides free access to the high-performance computational resources needed to run the large range of DL networks implemented here (Supplementary Note 2 for networks and Supplementary Note 6 for resources).
For each featured network, we provide an example dataset that researchers can use to test and learn the basis of its workflow. For these datasets, we show that the corresponding training sessions only take a few minutes to a few hours (Supplementary Table  2 ), allowing the generation of high-performance DL models, producing the inference outputs shown in Figure  2  and the Supplementary Figures and Movies (Supplementary Fig. 2 3 4 5 6 and Supplementary Video 2-11. We also highlight the versatility and power of Zero-CostDL4Mic by sequentially combining multiple DL tasks such as image-to-image translation and tracking, and by integrating it within larger image analysis pipelines to enable, for instance, automated cell tracking(17) (Supplementary Fig. 7 ). Therefore, we envision ZeroCostDL4Mic constitutes an easily accessible and adaptable starting point to the use and deployment of DL-based bioimage analysis.
Availability. ZeroCostDL4Mic is available as Supplemental Software or can be accessed from our GitHub page. This resource is fully open-source, providing users with tutorials, Jupyter Notebooks for Google Colab, and many real-life example datasets for training and testing. The example datasets are available for download in Zenodo (links provided in Supplementary Table  1  and our GitHub page). acquisition of the test and example data; L. v. C. , R. F. L. , J. J. , C. S. , M. L. , S. H. -P. , P. K. M. , E. K. , S. H. , A. K. , T. -O. B. , C. L. , M. L. J. , D. K. , E. N. , Y. S. , M. H. , and G. J. tested the platform; L. v. C. , R. F. L. , J. J. , C. S. , G. J. and R. H. wrote the manuscript with input from all co-authors.
ACKNOWLEDGEMENTS First and foremost, we would like to thank  Dr. Martin Weigert  from the  Swiss Federal Institute of Technology (EPFL)  and  Dr. Uwe Schmidt  from the  Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG ), who pioneered a considerable portion of the technology this work is based on and whose ethos in making  Deep Learning  more accessible for microscopy helped inspire this work. Dr. Schmidt  has kindly given key feedback during the preparation of the manuscript. All the network architectures and tasks presented here originate from already published work, having been edited and prepared for  Google Colab  to simplify their uptake by novice users. When using the ZeroCostDL4Mic platform, please cite the original publications associated with each network. This work was funded by grants from the  UK Medical Research Council  ( MR/K015826/1 ) (R.
M. L is supported by  Victoriastiftelsen (FI). A. K, T. -O. B and F. J. funded by  German Research Foundation (DFG)  under the code  JU3110/1-1  and  German Federal Ministry of Research and Education  under the code  01IS18026C , ScaDS2. M. H. and C. S. acknowledge funding by the  German Science Foundation  (grant nr. SFB1177 ), C. S. additionally ac-.
Data Availability and Acknowledgements The qtpi interpreter and the examples referred to in the paper are available at https://doi. org/10. 6084/m9. figshare. 11882592. Our research was supported by UK National Cyber Security Centre through the VeTSS project "Formal Verification of Quantum Security Protocols using Coq". Nagarajan was also supported by EU Cost Action IC1405 "Reversible Computation -Extending Horizons of Computing". We thank Simon Gay for helpful discussions.
The objective of this work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Our method must be flexible to adapt to very different problems. Therefore, it has to be able to work with or without the influence of prior data and knowledge. Moreover, regardless of the success, it should be as interpretable as possible to allow for diagnosis and improvement. We propose here a new open source generation method using an evolutionary algorithm to sequentially build molecular graphs. It is independent of starting data and can generate totally unseen compounds. To be able to search a large part of the chemical space, we define an original set of 7 generic mutations close to the atomic level. Our method achieves excellent performances and even records on the QED, penalised logP, SAscore, CLscore as well as the set of goal-directed functions defined in GuacaMol.