dataesr/scientific-paragraphs-categorization huggingface dataset:
We used three models from ESTIMAP (Ecosystem Service Mapping Tool): a GIS model based approach to spatially quantify ES, developed to support ES policies at a European scale  (Zulian et al. , 2013b). The research was undertaken as part of the OpenNESS EU project, which tested methods and models for operationalizing the ES concept in 27 case studies. Each case study team chose their own analytical methods during the first year of the project from a range of available methods and applied them to real-world situations with guidance from modelling experts  (Harrison et al. , 2018). Ten case study teams in Europe and South America selected one or more ESTIMAP models: recreation, pollination and air quality improvement. In this paper, we describe the model adaptation process, including the consideration of data sources and the technical or scientific efforts required.
, 2011). The influence of neighboring pixels on a locations' FN statistic depends on the distance weight function, introducing an element of subjectivity  (Hagen-Zanker, 2006; Visser and de Nijs, 2006). We used an exponential decay function with a 200 m halving distance and 400 m radius, using the same distance for all ES. We explored the effect spatial resolution has on model agreement by calculating FN indexes and maps at the original resolution of case study data (10 or 25 m pixels), the original resolution of continental scale maps (100 m) and at 250 m. We use a FN index >0. 5 to indicate reasonably good agreement between models  (Avitabile et al. , 2011). We created FN maps using Map Comparison Kit Software, version 3. 2. The SIP of spatial covariance reflects the degree of spatial correlation between two maps.
The SIP statistic is the ratio of local covariance between two maps to the product of local standard deviations  (Jones et al. , 2016). SIP statistic values range from À1 to 1, with negative values indicating pixels from the two maps have opposite predictions and positive values indicating map agreement. Because we were primarily interested in detecting the areas where continental and local models had opposing predictions, we computed the percentage of cells where SIP <0 for each comparison. We then assessed the distribution of areas with con-tradictory predictions by computing an index of fragmentation of areas where SIP <0. We used scripts provided by  Jones et al. (2016)  to calculate SIP statistics. We used the SIP approach to analyze the spatial agreement between a locally adapted model and its corresponding continental model and at spatial extents corresponding to relevant administrative levels for two case studies.
In Barcelona, we assessed local model agreement with the continental model at the Province (7818 km 2 , BARC-PR), Metropolitan Region (3246 km 2 , BARC-MR), Metropolitan Area (637 km 2 , BARC-MA) and Municipality (101 km 2 , BARC-M) levels. For the Loch Leven case, we used a 2500 km 2 region (LLEV) and a 3. 2 km buffer of land surrounding the lake (84 km 2 , LLEV-lake). We were unable to compute either FN or SIP statistics for OSLO recreation or BIOB pollination because neither Norway or Brazil are included in the EU datasets used to calculate continental scale versions of these models. To explore whether particular land cover categories disproportionally contributed to deviations between locally adapted maps and their continental scale equivalents, we cross-tabulated FN maps (100 m resolutions) to land cover maps.
We first converted FN scores into categorical data by separating them into eight equal-interval classes  (Baró et al. , 2016; Burkhard et al. , 2014). We used data from Corine Land Cover 2012 level 2 -v 18. 5. 1 (EEA, 2016), and used the following 13 dominant land cover categories: water bodies, wetlands, open spaces with little or no vegetation, scrub and/or herbaceous vegetation, forest, heterogeneous agricultural areas, pastures, permanent crops, arable land, and artificial or developed. Again, we excluded OSLO and BIOB case studies from these analyses because they are not included in EU datasets.
The recreation model components include Degree of Naturalness (DoN); Suitability of land to support recreation activi-ties (SLRA); natural features influencing the potential provision (FIPS_N); Infrastructure influencing the potential provision (FIPS_I); water elements (W); other cultural elements (CE); and green urban area elements (GUA). Pollination model components included DoN, FIPS_N, FIPS_I, W and scores for land cover categories according to either floral resource availability, nesting site availability or overall habitat quality. As described above, the air quality model's components included pollutant concentrations and spatial predictors of pollutant removal. Tables 1-4 also report the type of GIS data inputs and the number of data layers for each of the model components.
6 , upper panel). However, stakeholders' understanding varied both within and among groups. Stakeholder in the BARC and SIBB cases, for example, provided some of the lowest scores for model credibility. One respondent from BARC declared: ''. The resulting map (ESTIMAP-recreation) looks like a map of protected natural areas. The existence of recreational facilities, such as picnic areas, itineraries, etc. outside protected areas can have a higher weight in terms of recreational use than the protection of a particular area. Maybe data on these features is not available for all the case study area, but I think that some elements could be incorporated (important itineraries, trails, etc. )" Stakeholders indicated that the need for technical assistance with applying ESTIMAP constituted a considerable constraint (Fig.
6 , lower panel). The KISK case study was the only example where stakeholders expressed strong concerns that data availability constituted a constraint. Stakeholder opinion of the ESTIMAP's model usability varied between the two extremes (Fig. 7 ). While many stakeholders provided largely positive feedback, at least some individuals in many cases had low opinions on how easy the model was to use or communicate to others. The mean scores for each case indicate that stakeholders' overall impressions of ESTIMAP's usefulness were predominantly positive (Fig. 8 ). Only the BIOG case had mean scores that reflected negative perceptions of the model's utility. Stakeholder comments provided additional depth for interpreting numerical assessment of the ESTIMAP modelling approach. Of the 111 comments stakeholders provided, we received 74 comments with content amenable to analysis.
Discussion The examples from the OpenNESS case studies presented here provide insight into how context-the relevant decisions, spatial extent, stakeholder engagement, data precision and accuracy-determine model structure for mapping ES. Inspired by this work, we generated a conceptual diagram illustrating the key elements of a research agenda for ES modeling (Fig. 10 ). The primary user group of any map defines the spatial extent for the mapping exercise (vertical axis). The needs of the users for decision support and the intended policy or management application will define both the resolution necessary to capture the relevant spatial heterogeneity (depth axis) and the necessary levels of information accuracy (horizontal axis). The costs of acquiring and producing information will increase with increasing spatial extent, spatial resolution and accuracy requirements.
Creating a user toolbox to facilitate public use of ESTIMAP was not an objective of the OpenNESS project. With a clearly defined decision context, determining the spatial extent of an ES spatial model is reasonably straightforward. The local adaptations of ESTIMAP involved mapping at extents that ranged from 84 to 4500 km 2. These extents constituted scales of relevance from the property to regional levels and corresponded with end users that ranged from property owners to local governments (Fig. 10 ). The spatial extent will at least partially dictate the spatial resolution. However, adaptation of a spatial model to local contexts is not just a matter of acquiring data with the highest possible spatial resolution for a given spatial extent. What is more important is utilizing a spatial resolution that is sufficient for capturing the spatial heterogeneity that is relevant to variation in ES supply.
We use precision differential as a measure of the spatial agreement between comparable models at different spatial scales and model structures. It is important to note that precision differential values do not constitute a measure of model accuracy or reliability. Assessing accuracy would require obtaining repeated observations or using independent datasets generated from other methodsmeasures described in Fig. 10  as ''reliability costs" (also referred to as ground-truthing). ES mapping is a relatively new field, and the reliability of the information it produces may be limited as the field matures. Developing and accumulating external data sets for model validation and repeated mapping over time will ultimately allow decision-makers and researchers to assess ES spatial model reliability.
A simple increase of spatial resolution, however, is not sufficient to increase legitimacy and the ultimate utility of maps. Our results indicate that limitations in data availability hinders how end-users relied on the model results. Furthermore, in most of the OpenNESS case studies, stakeholders did not participate in selecting the ES that would be mapped and/or the selection of the modelling approach. We can expect greater credibility and uptake of ES maps if models are co-produced with active participation from their end-users: considering the decision-making question that the model aims to inform, understanding the level of spatial heterogeneity that needs to be captured, and jointly evaluating of the quality of the indicators and data available.
Acknowledgements The authors are extremely grateful to all the case study teams and case study advisory boards who provided input to the study. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no  308428 ,  OpenNESS Project (Operationalisation of Natural Capital and Ecosystem Services: From Concepts to Real-world Applications  (www. openness-project. eu).
that is 2-3 orders of magnitude faster than the previous version. This is achieved by exploiting the sparsity of the model using the SuperLU direct solver for parameter fitting, and sparse Cholesky factorization together with the sparse inverse subset algorithm for computing standard errors. Furthermore the HDF5 library is employed to store data efficiently on hard drive, reducing memory footprint while keeping I/O low. Whole-genome fits for human ChIP-seq datasets (ca. 300 million parameters) could be obtained in less than 9 hours on a standard 60-core server. GenoGAM 2. 0 is implemented as an open source R package and currently available on GitHub. A Bioconductor release of the new version is in preparation. We have vastly improved the performance of the GenoGAM framework, opening up its application to all types of organisms.
It provides base-level and region-level significance testing. An important advantage of GenoGAM over competing methods is that smoothing parameters are objectively estimated from the data by cross-validation, eliminating ad-hoc binning and windowing. It leads to increased sensitivity in detecting differential protein occupancies over competing methods, while controlling for type I error rates. GenoGAM is implemented as an R package based on the well-established and flexible generalized additive models (GAM) framework (3). On the one hand, it builds on top of the infrastructure provided by the Bioconductor software project  (4). On the other hand, it uses the mgcv package (5), a general-purpose R library for fitting GAMs (6) that provides a rich functionality for GAMs with a variety of basis functions, distributions and further features for variable and smoothness selection.
Workflow Data preprocessing consists of reading raw read alignments from BAM files, centering the fragments, computing the coverage y, and splitting the data by genomic tiles (Figure  1 ). Afterwards, normalization factors for sequencing depth variation are computed using DESeq2  (9). In the new version of GenoGAM we store the preprocessed data in HDF5 files  (10)  through the R packages HDF5Array (11) and rhdf5  (12). This allows writing in parallel as the data is being preprocessed, which reduces the memory footprint of this step. For all subsequent matrix operations the Matrix package is used, which implements routines for storage, manipulation and operations on sparse matrices  (13). Fitting GenoGAM models on tiles is achieved by the Newton-Raphson algorithm (Equation  7 ). This is done on few representative tiles during crossvalidation in order to identify optimal hyperparameters λ and θ, and subsequently when fitting the model on the full dataset.
The variance of the smooth estimates (Equation  8 ) is obtained using the sparse inverse subset algorithm as detailed in a subsection below. The implementation is based on the R package sparseinv  (14) , which wraps relevant code from the SuiteSparse software  (15). As in the previous GenoGAM model (  2 ), fitting on different tiles is conducted in parallel. The result objects for the fits, variances and parameters are initialized prior to fitting on hard drive. This allows the processes to write results in parallel on the fly, ensuring fast computation and low memory footprint. The HDF5 storage is further optimized for reading time by adjusting HDF5 chunk size to the size of the tiles (for preprocessed count data) and chunks (for fits and variance). As HDF5 is not process-safe on R level, writing is serialized by a queuing mechanism.
The parallelization backend is provided by the R package BiocParallel. It offers an interface to a variety of backends and can be registered independently of GenoGAM. Parallelization is performed over chromosomes during the read-in process. Over tuples of folds and tiles during cross-validation process and over tiles during fitting process. Because some backends have a particular long startup time, the use of many processes might end up dominating computation time. Specifically during cross-validation on small and limited number of regions, this might pose a problem. Therefore an optimal number of workers is automatically obtained and registered by the cross-validation function and reset on exit.
Hence, it applies to computing variance of the predicted value for any smooth function f k (x) or computing variance of the derivatives of any order r of any smooth d r f k (x) d r x. To obtain the elements of H -1 that are in NZ(H), we used the sparse inverse subset algorithm  (17). Given a sparse Cholesky decomposition of symmetric matrix A = LL T , the sparse inverse subset algorithm returns the values of the inverse A -1 that are nonzero in the Cholesky factor L. Since nonzero in the lower triangle of A are nonzeros in the Cholesky factor L (18), the sparse inverse subset algorithm provides the required elements of H -1 when applied to a sparse Cholesky decomposition of H. See also Rue  (19)  for similar ideas for Gaussian Markov Fields. To perform the sparse inverse subset algorithm, we used the R package sparseinv  (14) , itself a wrapper of relevant code from the SuiteSparse software  (15).
Performance on human and yeast ChIP-Seq datasets The previous version of GenoGAM could only be partially applied genome-wide for megabase-scale genomes such as the yeast genome and was impractical for gigabase-scale genomes such as the human genome. A genome-wide model fit with two conditions and two replicates each took approximately 20 hours on 60 cores (2). With computational and numerical improvement on the one side and a data model largely stored on hard drive on the other side, runtime and memory requirements have dropped significantly. Figure  4  shows the runtime performance on seven human ChIP-Seq datasets with two replicates for the IP and one or two replicates for the control. The analysis was performed with 60 cores on a cluster, the memory usage never exceeded 1. 5GB per core and was mostly significantly lower.
Conclusion We have significantly improved the implementation of GenoGAM (2) on three main aspects: Data storage, coefficient estimation and standard error computation. We showed its runtime and memory footprint to scale linearly with the number of parameters per tiles. As a result, GenoGAM can be applied overnight to gigabase-scale genome datasets on a typical lab server. Runtime for mega-base genomes like the yeast genome is within minutes on a standard PC. Finally, our algorithmic improvements apply to GAMs of long longitudinal data and can therefore be relevant for a broader community beyond the field of genomics.
We proposed a framework adapted from spectral clustering, which is general in scope and suitable for the co-parcellation of a group of subjects. We applied it to a dataset of 62 adults, optimized it and revealed a striking agreement between parcels produced by this unsupervised clustering and Freesurfer lobes (Desikan atlas), which cannot be explained by chance. Constituting the first reported attempt of spectral-based fully unsupervised segmentation of neuroanatomical regions such as lobes, spectral analysis of lobes (Spanol) could conveniently be fitted into a multimodal pipeline to ease, optimize or speed-up lobar or sub-lobar segmentation. In addition, we showed promising results of Spanol on smoother brains and notably on a dataset of 15 fetuses, with an interest for both the understanding of cortical ontogeny and the applicative field of perinatal computational neuroanatomy.
(2016) , require a preliminary labeling of some cortical sulci at the subject level. There are, however, recent attempts of unsupervised parcellations procedures, using spectral analysis  (Germanaud et al. , 2012)  or 'sulcal pits' concepts  (Auzias et al. , 2015). A full unsupervised segmentation of the cortical surface into anatomically meaningful entities remains a very challenging task and there might even be no a priori reasons to hope to obtain one, at least from a non-comprehensive input dataset. Nevertheless preliminary results of two previous studies of our group  (Lefevre et al. , 2014; Pepe et al. , 2015)  suggested an intriguing relationships between parcellations obtained by spectral clustering and what has been known for a long time as brain lobes  (Gratiolet, 1854). Brain lobes are a coarse but well-accepted anatomo-functional segmentation of the brain into 5 parts: frontal, parietal, temporal, occipital and insular.
Evaluation Metrics We propose here two different metrics to evaluate the quality of parcellations across a group of surfaces. The first one assesses the relationship between cluster boundaries and some anatomical landmarks that have been identified by neuroanatomical experts (sulcal lines). The second metric quantifies the distance between spectral clusters and reference clusters (obtained by concatenating smaller parcels obtained with the Desikan atlas in the Freesurfer software  Desikan et al. , 2006).
Spectral Boundaries and Landmarks We consider M anatomical landmarks that will be compared to cluster boundaries (M = 2 in practice). In particular, Central Sulcus (C. S. ) and Parieto-Occipital Sulcus (P. O. S. ) are very important landmarks since they are reliable borders between lobes. For each discrete sulcal line L m we can define a mean distance to the boundaries of the segmentation map B of a given subject: D m = 1 |L m | i∈L m d(i, B) (3) where d(. ,. ) is the geodesic distance from a point to a set of points. In practice we use the Fast Marching Algorithm and the Matlab implementation of  Peyré and Cohen (2008)  to obtain a distance map from B to any vertex of the mesh. The mean distance D m is also expressed as a percentage of the largest geodesic distance of the cortical mesh as approximated by extremal points of the Fiedler vector  (Lefevre et al.
Datasets and Pre-processings First we tested our method on a dataset of 62 left hemispheres previously used in  Auzias et al. (2013). Triangular meshes were obtained from T1-MRI through the morphologist pipeline of BrainVisa software. Central and Parieto-Occipital sulci were delineated semi-automatically by using Surfpaint  (Le Troter et al. , 2011). In parallel, Freesurfer was used to obtain (1) a spherical parameterization of each mesh and (2) to generate a parcellation of each surface in 35 regions based on the atlas of  Desikan et al. (2006). The 35 regions were then merged in 4 lobes (frontal, We also tested the constrained spectral clustering on a dataset of 15 fetuses previously used in  Lefèvre et al. (2015). The gestational age ranges from 21 weeks to 34 weeks. T2weighted images were acquired on axial, coronal, and sagittal planes with a half Fourier acquisition single shot turbo spin echo (HASTE) sequence.
Preprocessings consist in high resolution image reconstruction, image segmentation, and mesh generation using an adaptation of BrainVisa processes. Major sulci, when present, were manually traced with Surfpaint. Next the results are divided in five sections. In sections 3. 2, 3. 3, 3. 4, 3. 5 results involve the first dataset. Section 3. 6 illustrates the stability of the spectral partitioning with respect to surface deformation and how it can be a powerful tool to parcellate developing brains as revealed by the second dataset.
DISCUSSION AND CONCLUSION The major applicative result of our general Spanol approach is the possibility to segment a cortical surface in a few parcels that have strong similarities with brain lobes. These similarities can be precisely quantified by partition distances and a dedicated statistical procedure, which has been proposed to determine how far the resulting parcels stood from randomly segmented lobe-sized parcels. The segmentation process can be entirely unsupervised, using only the geometrical properties of the hemispheric mesh shape. However, we improved dramatically the global overlap with the four main lobes (frontal, parietal, temporal and occipital) obtained by Freesurfer software by adding limited constraints only, excluding points that belong to non-cortical regions of the mesh, namely the cingulate pole.
For example, FPGAs can be configured to accelerate portions of code that are known to perform poorly on CPU or GPU  [1]. The energy efficiency of FPGAs is also one of the primary assets of this kind of accelerators compared to GPUs, which encourages the scientific community to consider them as one of the building blocks of large-scale low-power heterogeneous platforms. Unfortunately, their programming complexity has prevented High-Performance Computing (HPC) developers from using them broadly. When the entry point to hardware design was a Register Transfer Level (RTL) description, with Hardware Description Languages (HDL) like VHDL and Verilog, FPGAs were mostly confined to hardware designers. The capabilities delivered by modern High-Level Synthesis (HLS) tools bridge the gap between software programmers and FPGA.
II. RELATED WORK Numerous proposals to ease the programmability of an FPGA enhanced platform have appeared with different features regarding the types of supported processing units, the programming abstraction (from tasks to threads), the way they manage data transfers as well as their scheduling support. OpenCL  [2] ,  [3]  is a standard framework offering both a device-side language and a host management layer to exploit multiple hardware accelerators from a single application, created initially for GPU and later on extended for FPGA. The device-side language, called OpenCL C, implements both task and data parallelism and provides ways to map data efficiently on specialized processor devices. The application programmer can attach command queues to processing elements and explicitly assign them tasks from the application.