dataesr/scientific-paragraphs-categorization huggingface dataset:
Value of the Data -the referenced land cover maps provide an unprecedented overview of the entire territory of Reunion Island, with a significant potential impact in various tasks related to land, agriculture and environmental monitoring. -These maps can be used in GIS to monitor changes in the territory and help managers make decisions about urbanization, natural and agricultural land management. -The reference database can be used by remote sensing specialists to assess new methods for land cover mapping and other classification algorithms. -All data provided is georeferenced and in vector format for use in GIS tools in future projects. supervised classification process to train an algorithm to recognize land use classes on a set of variables from high and very high spatial resolution satellite images.
A part of the polygons (20%) is dedicated to the validation of classification results (Cf. Fig. 1 ); Three land use maps produced starting from a dataset including a Very High Spatial Resolution (VHRS) Pl eiades image, a time series of HRS Sentinel-2 and Landsat-8 images and a digital terrain model. These maps correspond to three levels of land use nomenclature (from 4 to 34 classes) and are distributed in vector format (shapefile). Each geometry corresponds to an object provided by the segmentation of the Pleiades image, attributed using reflectances, radiometric and textural indices computed on the different remote sensing images available plus topographic information (altitude and slope). Such objects are individually classified using a supervised classification algorithm trained using the reference database described above.
The validation of the maps gives overall accuracies ranging from 86% for the most detailed level to 98% for the least detailed level. These maps were produced as part of the GABIR project, for which we needed precise information on crop location. The nomenclature is therefore detailed on this type of land use but we have also detailed the natural areas so that these maps can be used in different themes. The three maps produced are illustrated in Figs. 2e4. Final maps in ESRI shapefile format are delivered in the local UTM projection (WGS 84 UTM 40 South, EPSG code 32740). Data are referenced in the CIRAD Dataverse and are hosted on CIRAD's Aware Geographic Catalog (http://aware. cirad. fr). The advantage of distributing geographical data with this system is that it is possible to simply visualize them, to use them directly on GIS software with web services (WFS: Web Feature Service or WMS: Web Map Service) or simply download them.
Reference database and nomenclature The reference database is organized according to a multi-level nomenclature (Cf. Table  1 ). For class representativeness purposes it is important that the number of polygons in each class meets the minimum requirement of 60 entities at the most detailed level of the nomenclature. To build this database we use data disseminated by the French Institute of Geography (IGN) and available on condition of eligibility. These are the products: RGE Alti®, "BD Topo®" and "RPG". They are available on this website: http://professionnels. ign. fr. The reference database for 2017 consists of 6256 plots. We will give a brief description of the sources and techniques used to build it according to the various land use groups:   For agricultural areas, we have information on certain cultivated plots.
Field surveys were conducted using GPS for market gardening crops. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD TOPO" product, and selected among different height levels to keep a sufficient diversity of greenhouse types. Each of the polygons was verified by photointerpretation using the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was deleted. For natural areas, there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of the State services that manage these areas (ONF: National Forestry Office and DEAL: Regional Office of the Ministry of Environment, Development and Housing). The validity date of this data is not known. We therefore checked the coherence of each selected polygon against the VHSR image.
This selection was made in the "nature" field of the layer (i. e. the following types: silo, industrial and livestock). These different polygons were finally merged into a single built up class grouping all types of buildings while maintaining a good representativeness. The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on the 2017 Pleiades image The contours of each polygon have been checked to ensure that they are perfectly overlapping on the THRS image used for the classification process and slightly eroded (negative buffer) in order to limit edge effects in the training process. The reference database was used in the works described in Refs. [1, 2].
Images ➢ Very High Spatial Resolution (VHSR): We use VHSR images acquired in the framework of the Kalideos project, driven by CNES (Centre National d'Etudes Spatiales: government agency responsible for shaping and implementing France's space policy in Europe). Pleiades images are not free and are available under the condition of eligibility via the Kalideos project website (https://www. kalideos. fr). In 2017, we dispose of 15 Pleiades scenes covering the whole island. Several zones are acquired multiple times because of cloud coverage. Pre-processed images are provided: radiometric correction to Top-Of-Atmosphere (TOA) level, precise orthorectification using high quality IGN's Pl eiades database as a spatial reference (see Fig. 7 , image mosaic).
The Moringa processing chain The TETIS laboratory is developing an automated soil classification method based on the Moringa chain that minimizes interactions with users by automating most image analysis and processing processes  [4]. The Moringa chain can be downloaded with this link: https://gitlab. irstea. fr/raffaele. gaetano/moringa. The methodology jointly uses a Spot6/7 or Pleiades Very High Spatial Resolution (VHSR) image, time series of Sentinel-2 and Landsat-8 High Spatial Resolution (HRS) optical images and a Digital Terrain Model (DTM) for Object Based Image Analysis (OBIA) classification (use of the Random Forest algorithm) driven by a learning database combining in situ and photo-interpretation measurements. The chain is built upon the Orfeo Tool Box (OTB) applications, orchestrated by python scripts.
HSR pre-processing Pre-processing applied to HRS images is automated in the Moringa chain. For S2, all bands are resampled at 10 m. For L8, pansharpening processing is applied to bring the spatial resolution as close as possible to S2 images and a cloud cover mask is calculated with the F-Mask tool  [6]. An automatic co-registration onto the THRS image is performed automatically by a procedure based on homologous point extraction. This processing is conceived to improve overlapping among the different remote sensing dataset, and is mostly important for the characterization of smaller scale objects. Cloud and shadow masks are then used to fill gaps due to cloud coverage into the time series by means of a multi-temporal interpolation  [7]. The chain produces, from the cloud masks, an image illustrating the number of times a pixel is not covered by clouds in the time series.
The variables used in the classification ➢ We have selected the indices that we believe are most useful for extracting our classes of interest (Cf. Table  2 ) ➢ Textures are important to detect patterns visible on the THRS image such as tree alignments in tree crops. In the Moringa chain, these texture indices are the only variables derived from the VHSR image. We use the OTB "Haralick Texture Extraction" algorithm applied to the panchromatic image (Cf. Table  2 ) ➢ Slopes were also calculated on this data using QGIS software. DTM and slopes are used as variables in the classification process.
Object based classification We use the Random Forest classification algorithm  [14]  which is adapted for heterogeneous data. This is our case since we use data from several sensors but also altitude, slopes and texture indices. RF is in our case applied to objects extracted in the image via a segmentation step. This step allows to extract objects corresponding to homogeneous groups of pixels in the THRS image. We use the method described in Ref. [15]  with OTB's GenericRegionMerging (Large Scale version) application  [16]. To obtain a segmentation result adapted to our study, we act on both the homogeneity criteria and the maximum heterogeneity threshold. We have chosen the following parameters: ➢ Scale parameter: 150 ➢ Weight parameter on the shape: 0. 1 ➢ Weight parameter on compactness: 0.
Validation We here use the k-fold cross-validation technique to evaluate the accuracy of the provided land use maps. We decide to avoid using the classical hold out strategy (i. e. , leaving a single subset aside for validation) because of the strong intra-class variability which can bias the evaluation. After dividing the field database into approximately equal k subsets (with k ¼ 5 in our case), a cross-validation is performed by training five Random Forest models with different combination of four out of five subsets and validating with using the fifth one, so that each subset is used once for validation and 4 times for training. The final precision metrics (global accuracy, Kappa, fscore, etc. ) are obtained by averaging the metrics obtained at each turn (fold) of the cross-validation.
Data source location R eunion Island is a French oversea region located in the Indian Ocean near Mauritius and Madagascar (upper left corner: 20 50 0 30. 81 00 S and 55 12 0 51. 92 00 E//lower right corner: 21 27 0 02. 43 00 S and 55 48 0 51. 90 00 E) Data are referenced in the CIRAD Dataverse and are hosted on CIRAD's Aware Geographic Catalog. The web links are in the following files.
Acknowledgments This work was supported by a financial contribution from the  "Agricultural and Rural Development" Trust Account of the French Ministry of Agriculture. This work was supported by a financial contribution from the  European Regional Development Fund (European Union) , the  French State  and the  Reunion Region. This work benefited from the  Pleiades  images of the KALIDEOS programme funded by  CNES. We would like to thank  SAFER of Reunion Island , the  Reunion Island Sugar Union , the  DEAL of Reunion Island , the  ONF (National Forestry Bureau)  and the teams of  CIRAD research units (AIDA, HortSys and PVBMT)  for their participation in the creation of the learning database. We also thank them for the data provided and which they allowed us to use for this work.
Data source location R eunion Island is a French oversea region located in the Indian Ocean near Mauritius and Madagascar (upper left corner: 20 50 0 30. 81 00 S and 55 12 0 51. 92 00 E//lower right corner: 21 27 0 02. 43 00 S and 55 48 0 51. 90 00 E) Data are referenced in the CIRAD Dataverse and are hosted on CIRAD's Aware Geographic Catalog. The web links are in the following files. Data accessibility Repository name: CIRAD Dataverse Data identification number: -Land use map: http://dx. doi. org/10. 18167/DVN1/RTAEHK -Learning database: http://dx. doi. org/10. 18167/DVN1/TOARDN Related research article Gaetano R. -Land use map: Dupuy, St ephane; Gaetano, Raffaele, 2019, "Reunion island -2017, Land cover map (Pleiades)", doi:10. 18167/DVN1/RTAEHK, CIRAD Dataverse, V2 -Reference database: Dupuy, St ephane, 2019, "Reunion Island -2017, reference spatial database", doi:10.
18167/DVN1/TOARDN, CIRAD Dataverse, V3 Direct URL to data: , Dupuy S. , Lebourgeois V. , Le Maire G. , Tran A. , Jolivot A. , B egu e A. 2019. The MORINGA Processing Chain: Automatic Object-based Land Cover Classification of Tropical Agrosystems using Multi-Sensor Satellite Imagery, in ESA Living Planet Symposium (LPS 2019), Milan, Italy.
As a consequence, one can perform structural optimizations and Langevin molecular dynamics. (iv) The code implements the newly developed Lattice Regularized Diffusion Monte Carlo (LRDMC), a stable DMC algorithm that, very recently, has shown to have a better scaling with the atomic number Z, compared with standard DMC. 37 is review is organized as follows: in Sec. II, we briefly explain the fundamental QMC algorithms implemented in TurboRVB, namely, the VMC and LRDMC; in Sec. III, we describe the RVB WF; in Sec. IV, we extend the WF to treat periodic systems; in Sec. V we introduce the DFT algorithm efficiently implemented in TurboRVB; in Sec. VI, we describe the way energy derivatives are computed (e. g. , atomic forces) by means of AAD; in Sec. VII, we list the TurboRVB, steps to optimize a many-body WF; in Sec.
VIII, we introduce the first and the second-order Langevin molecular dynamics implemented in TurboRVB; in Sec. IX, we summarize the typical QMC workflow from the WF generation to the final LRDMC calculation; in Sec. X, we show weak and strong scaling results of TurboRVB, measured on the Marconi/CINECA supercomputer; in Sec XI, we list the physical properties that can be calculated by TurboRVB; in Sec. XII, we review the major TurboRVB applications done so far; in Sec. XIII, we introduce a python-based workflow system, named Turbo-Genius.
This reweighting scheme is very important when evaluating atomic forces, as discussed in Sec. VI B. Since the evaluations of the standard deviations is nontrivial in this case, TurboRVB employs the bootstrap and jackknife methods in order to estimate the mean value and the statistical error  15  , which are also used when evaluating those of the local energy, forces, and so on. Indeed, the code outputs the history of the local energies, forces, or other properties in appropriate files (when the corresponding option is true), thus allowing the error bar estimates by simple post-processing. The user can also use the reblocking (binning) technique to remove the autocorrelation bias. 40 ne can optimize the WF based on the variational theorem by introducing a set of parameters α 1 , α 2 , • • • , α p to the WF Ψ (x, α): E VMC (α) = dxe L (x, α) π (x, α) E exact.
I. Contraction of the primitive atomic basis The contraction of atomic orbitals has been widely used in quantum chemistry and DFT codes, and was originally introduced to define a pseudo-Slater orbital by combining several primitive Gaussian orbitals. This is also important in the QMC context because it decreases the number of variational parameters by a large factor, as it will be shown in this section. Although one can obtain a contracted basis set directly from a database, TurboRVB allows us to prepare a high-quality hybrid basis set (contraction) starting from a given primitive basis, by the so-called "geminal embedding scheme". 85 To this purpose, let us decompose the genimal function in terms of atomic contribution, as far as the dependence over i (the left argument) is concerned: g (i, j) = I U I proj (i, j) = I µ,ν A I µ,ν φ µ (i) φ ν (j) , (77) where I represents an atom in a system, U I proj (i, j) is the pairing function projected on the atom I, A I µ,ν is A µ,ν if µ ∈ I, otherwise A I µ,ν = 0, where A µ,ν is assumed to be given for the system under consideration, e.
V. BUILT-IN DENSITY FUNCTIONAL THEORY (DFT) CODE Although most QMC codes load their trial WFs from available DFT/quantum chemistry codes such as Gaussian  98  , CRYSTAL  91  , and Quantum Espresso  99  , TurboRVB is a selfconsistent many-body package and does not require input from other codes. Indeed, in TurboRVB, an original DFT code is implemented that has the important feature to work in exactly the same basis and pseudopotentials (if any) used for the QMC many-body WF, and for instance can work with mixed Slater-Gaussian basis or with arbitrary contraction, i. e. , mixing angular momenta. On the other hand, the drawback is that only very few functionals are available so far because, within the TurboRVB approach, the DFT has to be used only to have a reasonable initialization of the antisymmetric part of the many-body WF.
IX. IMPLEMENTATION AND OPERATIONS Table  II  shows the main modules implemented in Tur-boRVB and a brief description of their functionalities. All programs reported in Table  II  are coded in Fortran90. The flow-chart of a typical QMC calculation (see Fig. 1 ) is detailed as follows: starting from the geometry and the chemical composition, the user first chooses the basis set for both Jastrow and antisymmetric factors, and generates a JAGP-type ansatz using makefort10. x (fort. 10 is the wave function filename in TurboRVB). The antisymmetric part of the WF is initially determined at the DFT mean-field level. Since the DFT calculation is based on a single Slater determinant, the generated JAGP WF should be converted to a JSD one. This is performed by the convertfort10mol. x module, which adds M molecular orbitals to the WF, where M = N ↑.
XIII. PYTHON MODULE:TURBO-GENIUS In general, a computational study involves many complicated operations, such as preparing input files, searching for optimal parameters, performing benchmark studies, transferring information coming from previous calculations to a subsequent simulation's input file, and analyzing output files. Automatizing these operations can significantly save researcher's burden, avoid trivial human errors, and is beneficial for accelerating the distribution of a computational package to a much wider community. We are now developing a python-based workflow system suitable for TurboRVB named Turbo-Genius, like Nexus 191 and QMC-SW  192. The workflow system has been implemented in Python 3 in an object-oriented fashion, so the modules are highly extensible. Notice that, since TurboRVB is an all-in-one package, any other commercial code is not necessary to run the code.
There are two main modules, turbo-genius-serial. py and turbo-geniussequential. py, which manage other modules/classes. The former allows one to perform a single job specified by an option (e. g. , turbo-genius-serial. py -j makefort10), and the latter does a sequential job (e. g. , makefort10. x → convertfort10mol. x → prep. x → turborvb. x). By using Turbo-Genius, one can also analyze a simulation output. For example, one can plot an optimization history, average the variational parameters after optimization, and search for an optimal number of equilibration steps and a reblocking length. 40 Turbo-Genius is still at an early stage of development. However, its main features are already there. In the near future, the Turbo-Genius modules will be capable of sophisticated TurboRVB jobs automatizations, as well as setting optimal parameters depending on target systems.
Notably, the user can freely navigate between these ansätze. Indeed, the package includes conversion modules, which allow the user to choose the most suitable WF form for the target system. All the above ansätze can be optimized by state-of-the-art algorithms, namely, the stochastic reconfiguration and the linear method, thus achieving highly accurate variational and fixed-node energies, with a computational cost remaining at the single-determinant level, thanks to efficient algorithmic developments. The stochastic optimization of many variational parameters (so far up to the order of 10 5 ) is feasible in TurboRVB thanks to an efficient evaluation of energy derivatives using the AAD. This algorithmic scheme, which gives the code an original architecture, drastically decreases the computational cost of ionic forces calculations, keeping them of order N 3.
16H06439 ). C. A. acknowledges funding from the  European Union   Seventh Framework Program  under grant agreement No. 785219 Graphene Core 2  and  COST Action TUMIEE   CA17126. M. C. is grateful to the  French grand équipement national de calcul intensif (GENCI)  for the computational time provided through these years under the project number  0906493 , for the funded PRACE projects Nos. 2012061116, 2015133179, 2016163936, and for the access to the Hokusai and K-computer granted by the  Institute of Physical and Chemical Research (RIKEN). Y. L. was supported by the  Argonne Leadership Computing Facility , which is a  U. S. Department of Energy Office of Science User Facility  operated under contract  DE-AC02-06CH11357. A. Z. 's work is sponsored by the  Air Force Office of Scientific Research, Air Force Material Command, U.
S. Air Force , under Grant No. FA9550-19-1-7007. S. S. acknowledges a financial support from  PRIN 2017BZPKSZ  and computational resources from  PRACE  project No. 2019204934. S. S is also grateful for his wife L. Urgias for bearing the too many weekends spent for the development of TurboRVB. We dedicate this paper to the memory of  P. W. Anderson , remembering him as one of the most influential contributors to physics and chemistry of the past century, and in particular for deeply inspiring this work with his RVB theory of matter.
Similar to SPARSE, the base pairing and joint in-loop probabilities need to be computed only once for each sequence. This is important for computing multiple alignments, using a pairwise aligner like Pankov in a progressive scheme (e. g. via mlocarna tool from the LocARNA software package). The Pankov implementation of the recursions keeps track of both ends of the base-pairs, while in SPARSE the tracking is relaxed and M matrices with common left-ends are combined for further speed-up, although the complexity is not affected. So in practice, compared to SPARSE, Pankov's run-time is slightly increased.
Evaluation of real ncRNAs The described evaluation procedure was repeated for the set of RNA sequences obtained from the RNAstrand database (sequence length  nucleotides, entries without ambiguous or spurious characters). Figure  2  shows the distribution of the rank correlation evaluation of the sequences. To inspect the model agreements in details, we visualized the output on a sample tRNA transcript of RNAstrand (ID: SPR_00633) in Fig. 3. An evaluation for the top 500 suboptimal structures of lowest free energies is shown. As can be seen in Fig. 3 , the Pankov's in-loopprobability-based models (bottom row of Fig. 3 ) perform best in preserving the reference free energy ranks. It is also notable that the energy scaled values are precisely scaling back to the range of reference free energies.
Our implementation of Pankov Fig. 2  The distribution of rank correlation for evaluating the energy models over around 500 RNA sequences from RNAstrand database. Spearman's rank correlations are computed for each sequence, between the reference and the models' approximations of the suboptimal energies Fig. 3  Evaluation of the agreement between the structure probabilistic and scoring models with the reference energies for a tRNA transcript. Each dot corresponds to a suboptimal structure of the tRNA. The structures are sorted according to reference free energy in the x-axis, computed by Vienna package RNAsubopt tool. The probabilities and scores are scaled to the energy dimension (Eq. 4). Annotated correlations of each panel are the Spearman's correlation coefficients between the x and y axes.
To solve this issue, we demonstrated an exact way to compute the probability of an RNA secondary structure from the decomposed loop-probabilities. To circumvent the computational complexity of multi-loop cases, we introduced an energy model to accurately approximate this loop decomposition in an efficient way using the precomputed in-loop probabilities. Our proposed energy model takes care of the nearest-neighbor thermodynamic rule. It was further empirically validated that the novel model has a much closer agreement with the full-loop energy model, based on the dataset of real noncoding RNAs. Using this energy model, we proposed the Pankov algorithm for pairwise simultaneous alignment and folding of RNAs. Benchmark results show that the implementation of Pankov outperforms its predecessors on predicting the secondary structure from the pairs of homologous RNAs.