dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiments We perform several experiments to validate the theory, and also test the practical relavance of non-uniform SAGA (79) with the optimized probability distribution (132). All of our code for these experiments was written in Julia and can be found on github in https://github. com/gowerrobert/StochOpt. jl. In our experiments we test either ridge regression f (x) = 1 2n A x -y 2 2 + λ 2 x 2 2 , (136) or logistic regression f (x) = 1 n n i=1 log 1 + e -yi ai,x + λ 2 x 2 2 , (137) where A = [a 1 ,. , a n ] ∈ R d×n , y ∈ R n is the given data and λ > 0 the regularization parameter.
We used an option of repetitive scene of the DART model to eliminate boundary effects  [29]. The scene horizontal dimension was set to 4 m for default configuration (i. e. , fractional vegetation cover (FVC) = 0. 6, see Table  2 ). By adjusting the scene horizontal dimensions, the fractional vegetation cover (FVC) was modified from sparse forest (FVC = 0. 3) to a dense forest (FVC = 0. 8). Due to the computational constraints, four key input parameters of the DART model were varied to generate the simulation database (see Table  2 ), including leaf chlorophyll content (Cab), fractional vegetation cover (FVC), background spectra, and solar zenith angle (SZA). Variable values of leaf chlorophyll content were used to represent the variation of leaf optical properties. Varying values of fractional vegetation cover were used to describe the canopy structure of different forest types: 0.
Remote Estimation of Canopy Interceptance In addition to canopy reflectance, canopy interceptance (i o ) is a crucial parameter for the estimation of SIF escape probability  [22]. Although i o is not easily accessed directly from remote sensing measurements, it can be indirectly estimated from remotely sensed data using equations such that of  [25] : i • = 1 -exp(-G(θ)•LAI•CI/COS(θ)), (6) where G(θ) is the mean value of leaf projection function for direction θ. It is generally fixed to 0. 5 for global-scale remote sensing applications  [33]. LAI and CI are the leaf area index and clumping index, respectively. They are directly available from satellite remote sensing products. To explore the uncertainties of the remote sensing products on the estimation of SIF escape probability, artificial errors (-50% and +50% constant value error of LAI*CI) were generated and then were used to estimate SIF escape probability.
Remote Estimation of Canopy Interceptance In addition to canopy reflectance, canopy interceptance (io) is a crucial parameter for the estimation of SIF escape probability  [22]. Although io is not easily accessed directly from remote sensing measurements, it can be indirectly estimated from remotely sensed data using equations such that of  [25] : 𝑖 ∘ = 1 -exp (-𝐺(𝜃) • 𝐿𝐴𝐼 • 𝐶𝐼/𝐶𝑂𝑆(𝜃)), (6) where () is the mean value of leaf projection function for direction. It is generally fixed to 0. 5 for global-scale remote sensing applications  [33]. LAI and CI are the leaf area index and clumping index, respectively. They are directly available from satellite remote sensing products. To explore the uncertainties of the remote sensing products on the estimation of SIF escape probability, artificial errors (-50% and +50% constant value error of LAI*CI) were generated and then were used to estimate SIF escape probability.
The latter option was chosen. As the solar observations are concerned, a non-profit association of amateur astronomers was founded, the "observateurs associés" (OA), literally "associated observers" in French. The OA, thanks to a private sponsor, manage to purchase several solar instruments; CLIMSO (Christian Latouche IMageurs SOlaires) was born. In 2007, the French national research council for astronomy and astrophysics (CNRS/INSU) labelled the CLIMSO instruments as a national observation service for solar surveillance. This label, on top of the recognition it gives the project, allows the employment of dedicated staff and the allocation of funding at the national level (CNRS/INSU) or at the regional level (Toulouse University, Midi-Pyrénées observatory). In 2020, the CLIMSO database (CLIMSO-DB) got the SNO-5 label for data archiving and dissemination.
As inputs, those models need basic parameters such as the time of eruptions, the velocity and density of the ejected chromospheric plasma, and the magnetic field at the footprint of the structures and/or within the ejected plasma. CLIMSO is a system that can play its part in those observational efforts. CLIMSO has contributed to the on-going solar survey at Pic du Midi observatory  (Koechlin et al. , 2019)  and now, we aim at monitoring in real time the full solar disk and near environment, and thus at anticipating possible solar flares and coronal mass ejections. CLIMSO is listed as an asset by the Space Situation Awareness (SSA) service of the European Space Agency (http://swe. ssa. esa. int/web/guest/asset-database). Koechlin et al. (2019)  briefly described the CLIMSO instruments before focussing on image calibration.
To the authors's knowledge, the imaging of the FeXIII line from the ground is only performed on a regular basis at the Mauna Loa Solar Observatory (MSLO) on Hawaii and at CLIMSO. The reader should note that the two observatories are located 156°apart in longitude, which maximizes the temporal coverage of solar observations. In other words, the two sites and their instruments complement each other very nicely. 3 Operations and data archiving.
CLIMSO database All individual images and movies are sent every night to the CLIMSO database (called CLIMSO-DB) at IRAP, Toulouse (https://climso. irap. omp. eu). CLIMSO-DB is part of the Grand Sud-Ouest Virtual Observatory (OV-GSO;  Sanguillon et al. 2019)  Practically, there are several options from the portal (Fig. 10 ). The user may visualise the latest images displayed on the page and possibly select them for larger version of them. One may click the top left link "Latest videos" and access to the latest videos from the four instruments). There, at the bottom of the page, so-called combined videos, that are superimposed movies from L1 and C1 (full disk and prominences in Ha), are available. Still from the portal, the user may also access the full archive from the top left link "Full archive" in the blue top banner.
(2019)  and the corresponding files are named *_emi1. fts. Regarding C1 and C2, this photometric calibration is not done yet; the so-called calibrated images are just oriented and corrected for the dark, flat, and bias frames. They are named *_bri1. fts. For those who would like to do the calibration themselves, the raw images and the calibration files are also at disposal with the following naming (file type): -Raw images: *_b1. fts -Dark files: *_da. fts -Flat files: *_pl. fts -Bias files: *_bi. fts -Orientation files : *_ca. fts. Apart from its dedicated database and website, CLIMSO data are reachable through web services. F. Pitout et al. : J. Space Weather Space Clim. 2020, 10, 47  CLIMSO data are accessible from the Virtual Solar Observatory  (Hill et al. , 2009) : https://sdac. virtualsolar.
org/cgi/search. The CLIMSO web service is also registered on the International Virtual Observatory Action (IVOA) registry. The database is thus accessible to any client able to query the IVOA registry through the VO-compatible EPN-TAP protocol. The protocol and the underlying infrastructure are detailed by  Erard et al. (2014). To this end, the CLIMSO DB's endpoint is: http://climso. irap. omp. eu/__system__/tap/run/tap/sync where a request can be placed. For instance, if one wishes to retrieve the 10 first data the CLIMSO's EPN-TAP service finds: http://climso. irap. omp. eu/__system__/tap/run/tap/sync?LANG= ADQL&QUERY=SELECT+*+FROM+climso. epn_core+& REQUEST=doQuery&MAXREC=10. This command line returns a VO-Table with the requested information. (VO-Tables can be read with a tool such as the Tool for Operations on Catalogues And Tables (TOPCAT;  Taylor, 2005)  for instance.
CLIMSO parameters are named climso. epn. core. *, the star (*) being any of the available EPN-TAP parameters (https://voparis-wiki. obspm. fr/display/VES/EPN-TAP+V2. 0 +parameters). Any database or service making use of the EPN-TAP protocol, like the Automated Multi Dataset Analysis (AMDA;  Jacquey et al. , 2010; Génot et al. , 2020)  or the Virtual European Solar and Planetary Access (VESPA;  Erard et al. 2018)  for instance can thus access the CLIMSO database. From AMDA, one should use the link "Interoperability" on the bottom right of the work space (Fig. 12 ) while from VESPA, CLIMSO is suggested in the main page as a possible "EPN resource". All VO applications can be found on the IVOA web page: http://www. ivoa. net/astronomers/applications. html. The reader also should note that CLIMSO-DB can be accessed directly through, and the VO-tables handled with, commands written in Astronomical Data Query Language (ADQL; http://www.
Data input for and from the STORMS national service STORMS is the Solar Terretrial ObseRvation and Modelling Service developed by and hosted at IRAP (http://stormsservice. irap. omp. eu/). The goal of the service is, among other things, to study the propagation of solar properties with the solar wind through the heliosphere. In particular, the STORMS service will ultimately predict the IMF and solar wind properties at 1 AU and also predict geomagnetic indices. To achieve this, the service needs its propagation models to be fed with solar inputs, such as Carrington maps of the solar disk in Ha or Carrington maps of the limb. CLIMSO is able to provide these inputs and we are working at it. Since 2020, CLIMSO has joined the group of groundbased instruments that make complementary observations to cover the PSP perihelia passages (https://whpi.
Concluding remarks Many ground-based solar coronagraphs have closed down in recent years. Yet, they are very valuable as they can capture the very low solar corona; they are much cheaper to build and maintain than space telescopes; and they can be used easily to test new techniques. CLIMSO offers a set of instruments that prove suitable for solar physics studies and solar surveillance. The four telescopes (two refractors and two coronagraphs) image respectively the full disk of our star and its near environment. The images and films are accessible through the CLIMSO-DB database and EPN-TAP compatible web services. On-going developments tend to turn CLIMSO into a real time solar monitor for space weather applications. In parallel, we are developing a third coronagraph that, on top of becoming one of the largest in the world, will complement our solar observations with more detailed imaging of the hot corona and polarimetric capabilities to infer the velocity and magnetic field inside magnetic loops.
Acknowledgements. The Observateurs associés are funded by  Fiducial. The CLIMSO national observation service is supported by  CNRS/INSU ,  Observatoire Midi-Pyrénées , and  Université Toulouse 3 -Paul Sabatier. The authors would like thank  Jacques-Clair Noëns , the father of this project, and all the Observateurs associés for their devotion and seriousness in operating CLIMSO. Patrick Maeght  and  Martine Lafon  greatly contributed to the data archiving. CLIMSO-DB is hosted and taken care of by the OV-GSO virtual observatory (https://ov-gso. irap. omp. eu/). In memory of Laurent Parès, optician engineer at IRAP who contributed greatly to the design of CLIMSO's optics, and  Claude Frelat , the diligent and always joyful treasurer of the Observateurs associés association. The editor thanks  Yoichiro Hanaoka  and an anonymous reviewer for their assistance in evaluating this paper.
A. DATA DESCRIPTION The data used in this work was downloaded from the MICCAI website 2017 targeting for the glioma segmentation challenge  [19] ,  [20] ,  [21] , which is classified into 75 patients with LGG (astrocytoma or oligo-astrocytoma) and 210 patients with HGG (anaplastic astrocytoma and glioblastoma multiforme tumors) based on histological diagnosis. All the patients were examined with axial T1-weighted, T1-Gd enhanced and T2-weighted images. To overcome the influence of the motions of patients, the skull stripping was performed firstly, followed by image registration to make sure that the multi-contrast images are strictly matched for the same patient  [19] ,  [20] ,  [21]. The spatial resolution of the registered images is 1 mm ×1 mm ×1 mm  [22]. The regions of interest (ROIs) were drawn manually by experienced radiologists, including edema, non-enhancing solid core, necrotic core, and enhancing core.
C. PREDICTION EVALUATION The performance of the proposed wavelet scattering-based radiomics for glioma grade prediction was evaluated in terms of receiver operating characteristic (ROC) curve, area under curve (AUC), sensitivity, specificity and accuracy. ROC is obtained by plotting true positive rate (TPR) against false positive rate (FPR) at different thresholds in a classifier. AUC indicates the surface under the curve of ROC and specifies the classification accuracy. The bigger the AUC, the more accurate the classification. Sensitivity represents the correct classification rate of positive samples while specificity represents the correct classification rate of negative samples. These metrics allow reflecting the false positive and false negative errors of the prediction models. Since AUC is not sensitive to sample properties such as the unbalance of sample classes, it is often used to evaluate the performance of the classifier for unbalanced dataset.
A. EXPERIMENTAL SETUP To fairly compare the glioma grade prediction accuracy based on the proposed wavelet scattering features with that based on traditional features, experiments with different imaging modalities and different tumor regions were performed. Given all the samples (285), the training set and testing set were selected with the same ratio of HGG to LGG (3:1). That is, 57 samples were selected as testing set and 228 samples as training set. Since the number of HGG and LGG samples was not equal, sample balance was also considered by weighting the loss function of LR and SVM or the splitting criterion of RF with the class weight. Such sample equalization method was inspired by the work of King and Zeng  [25]  and implemented with Scikit-learn  [26]. For the traditional radiomics, a total of 505 radiomic features were calculated for each patient with the package of Pyradiomics  [27] , including 9 shape features, 18 first-order features, and 478 textural features such as gray level co-occurrence matrix (GLCM) features, gray level size zone matrix (GLSZM) features, gray level run length matrix (GLRLM) features, neighboring gray tone difference matrix (NGTDM) features, gray level dependence matrix (GLDM) features and multiscale wavelet features.
The detailed list of traditional features is given in Table  S1  in the supplementary file. For the wavelet scattering-based radiomics, we obtained 505 feature maps ( S 0  1, S 1  6 , S 2  498 ) with scattering direction L  8 , wavelet decomposition scale J  6 and scattering level m  2. To enhance the contrast of the feature maps and keep the local invariance, a logarithmic operation followed by an averaging operation was performed on the wavelet scattering feature maps, which results in 505 wavelet scattering features in total for each patient. The wavelet scattering feature extraction was programed with Matlab 2016a and can be found in the work of  [29]. To avoid redundancy and correlation of the large number of traditional or wavelet scattering-based features, PLS regression was used to reduce the feature dimension to 50  [30].
E. COMPARISON WITH DEEP LEARNING-BASED METHODS Besides the comparison with the traditional radiomic features, to further validate the superiority of our methods, we compared our methods with the radiomics performed by others and some deep learning-based models in terms of accuracy, sensitivity, specificity, and AUC. For the fair comparison, we use the same dataset that comes from MICCAI BraTS 2017. Table  IV  shows comparison results. It is clearly observed that, comparing the radiomics, the accuracy, sensitivity, specificity, and accuracy are all increased a lot. With respect to the deep learning-based method, the prediction accuracy is also increased. This verified that the transformation-invariant property of WS features can help to promote prediction accuracy of the glioma grading.
The experimental results showed that the use of those invariant features allows us to better represent image properties and thus distinguish more effectively glioma grades. This can be reflected in the samples clustering results, as shown in Fig. 8 , which was obtained using the Consensus Cluster Plus package in bioinformatics analysis software under R language  [37] ,  [38]. The consensus matrix M is usually used to reflect the quality and robustness of clustering. The horizontal or vertical axis of M represents the sample index. If we have N samples, the size of M is N×N. The value of each matrix element M ij is between 0 and 1, representing the probability that the sample i and sample j are clustered into the same class during multiple clustering tests. In our case, the blue color indicates the probability value of 1 and the white color the probability value of 0.
The cleaner the consensus matrix, the better the clustering results. Considering the distribution of datasets used in this work, with 75 patients presenting LGG and 210 patients presenting HGG, the ratio of positive (high-grade) to negative (low-grade) samples is about 3:1, therefore the ratio of the corresponding clusters should be also about 3:1. When using single modality T1-weighted images (Fig. 8(a) ), the ratio of cluster 1 to cluster 2 grouped with wavelet scattering features is about 3:1, whereas the ratio obtained with traditional features is about 3:2. In the case of intratumor features, the clustering accuracy of traditional radiomics for LGG and HGG is respectively 50. 2% and 50. 5%, and that of wavelet scattering-based radiomics for LGG and HGG is respectively 50. 0% and 67. 7%.
The combination of T1-enhanced and T2-weighted images was the best if two modalities were used, and the combination of T1-enhanced, T2-and T1-weighted images was the best if three modalities were required. Comparing Tables II and III show that the number of modalities used in the predictions does not influence greatly the wavelet scattering-based radiomics but impacts more the traditional radiomics, which implies that with the wavelet scattering-based radiomics, we are able to grade glioma accurately just with one single modality. This is beneficial for reducing acquisition time. Although the AUC, sensitivity and specificity obtained with the proposed method are greatly increased, there are still several limitations in the present work. Firstly, the dataset used is a public open source dataset, and the training cohort and testing cohort come from the same group.
Demographic data We measured the impact of stricter vehicle emission standards on all residents of France and all residents of Italy as separate arms of the model. In our Markov model, the average age, population size, and age-specific mortality rates for each country were retrieved from Eurostat statistics. 27 We also applied different regulatory standards in France, Italy, and the US to a uniform, hypothetical cohort. This allows us to provide estimates of the impacts of regulatory standards on health that are independent of sociodemographic differences between the three nations.
Costs We included health careerelated costs, the costs of implementing new test facilities, and fuel savings from stricter PM 2. 5 emission standards as direct costs. 11 We quantified indirect costs through health-related productivity gains associated with reduced PM 2. 5 levels. 3, 6, 7, 35 All the costs associated with this policy change were taken from the literature. To determine the direct costs of introducing US standards to both France and Italy, we used data from the US EPA's 2014 Regulatory Impact Analysis. 11 Costs included a one-time implementation cost for PM 2. 5 regulation test facilities, the unit cost of upgrading vehicles with additional hardware, the indirect cost of additional labor, and the annual fuel savings after the upgrade for gasoline vehicles. 11 To compute the total cost of vehicle upgrades, we multiplied the unit costs per vehicle by the annual number of light-duty vehicles sold in each country.
Acknowledgments This research has been performed as part of the  Energie Campus Nürnberg  and is supported by funding of the  Bavarian State Government. The authors thank the  DFG  for their support within project  A05 ,  B08 , and  Z01 in CRC TRR 154. Fränk Plein  thanks the  "Fonds de la Recherche Scientifique" (F. R. S. -FNRS)  for financial support. Martine Labbé  has been partially supported by the  Fonds de la Recherche Scientifique -FNRS  under Grant(s) no PDR  T0098. 18.
Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an F1000Research 2020, 9(ELIXIR):278 Last updated: 11 JUN 2020 Any reports and responses or comments on the article can be found at the end of the article. generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives.
Major European contributions in structural bioinformatics Structural bioinformatics is a well-established scientific activity, which started in the 1970s following the establishment of the Protein Data Bank  1  which provides open access to macromolecular structure models. Figure  1  illustrates major themes in structural bioinformatics. Structure data can give deeper insights into the mechanism of proteins, the functions of biomolecules (proteins, nucleic acids, carbohydrates, lipids, etc. ) and their interactions with each other and with chemical modulators of their functions (inhibitors, activators, co-factors, etc. ). This enables the design of new experiments to study the function of macromolecules as well as rational design of proteins and drugs, to modify their function and properties.
Activities and action plans prioritized by 3D-Bioinfo Following their formation in March 2018, the 3D-Bioinfo Executive and Steering Committees held regular conference calls to determine the highest priority areas, which would become the main Activities to be first undertaken by the community. These 3D-BioInfo Activities, outlined below, were further discussed and refined at the launch meeting, and involve multiple participating groups across the nodes. Structural Bioinformatics is well established, as the field began more than 40 years ago following the establishment of the protein structure databank in the 1970s, so most of these Activities represent mature areas of research, some of which had already received sustained node funding to develop their tools and resources. Each Activity will have the major goals, listed above, as the core of their mission.