dataesr/scientific-paragraphs-categorization huggingface dataset:
Collection of Sparse STEM Images Collection of non-overlapping pixels blocks randomly distributed over the area of interest (ROI) was performed using an in-house developed Digital Micrograph (DM) script. The Digiscan II software of DM has built-in functions that allow the collection of pixel blocks which dimensions can be 16 × 4 pixels. This collection scheme was used on a carbon crossed line grating grid to collect 12. 5% of the pixels (Figure  1A ). The white background represents uncollected pixels. Inpainting was then computed to recover missing pixels (Figure  1B ). The pattern of the crossed line grating replica becomes apparent after the inpainting reconstruction and resembles the ground-truth image of the same ROI (Figure  1C ).
Since the method aims to reduce the electron dose in the ROI, a second region next to the ROI and aligned with the tilt axis is used to acquire the fully-collected images to perform both focusing and tracking tasks (Figure  3 ). These areas are later on referred as focusing and tracking areas. The whole tomography workflow has been scripted and developed in DM. The script also codes for a graphical user interface to input the data collection parameters (Figure  S2 ). The main steps of the script are the following: • Step 1-Focusing: determination of the 0 nm focus value by successive acquisitions of fully-collected images on the focusing area in a focus range.
Since the method aims to reduce the electron dose in the ROI, a second region next to the ROI and aligned with the tilt axis is used to acquire the fully-collected images to perform both focusing and tracking tasks (Figure  3 ). These areas are later on referred as focusing and tracking areas. The whole tomography workflow has been scripted and developed in DM. The script also codes for a graphical user interface to input the data collection parameters (Figure  S2 ). The main steps of the script are the following: • Step 1-Focusing: determination of the 0 nm focus value by successive acquisitions of fullycollected images on the focusing area in a focus range.
Validation of the Sparse STEM Tomography Workflow For validation, the 3D reconstruction computed on sparse images has been compared with a reference volume. To this purpose, the sparse tomography workflow (6. 25% downsampling) has been compared to a reference collection scheme (i. e. , made of fully-collected images). Sparse images and fully-collected ones were consecutively collected at the same tilt-angle on the same ROI of a crossed line grating grid so that both images represent the object in the same orientation. After collection of the sparse data, missing pixels were first reconstructed using inpainting. Secondly, both tilt-series (i. e. , sparse and reference ones) were aligned using the cross-correlation alignment parameters computed on the inpainted sparse images so that the quality of the alignment performed on inpainted data can be estimated.
Inter-block measurements have similar values, demonstrating that the beam positioning does not introduce disruption of the crystalline lattice, proving that the method is accurate enough to perform sparse imaging using pixel blocks. Figure  S2 : Graphical interface of the Digital Micrograph script to perform sparse STEM tomography. Standard parameters such as the microscope magnification, the dwell time, the angular range and the tilt-step are set up in the interface. Parameters specific to the sparse imaging are present: scan (block) size (e. g. , 16 × 4 px), the reconstructed image size (e. g. , 2000 px) and the number of collected scans (blocks) (e. g. , 512). The focus value parameter serves as an initial value around which the script will search for the 0 nm defocus value. Figure  S3 : Intensity profiles of the same ROI in 3D volumes reconstructed from variously downsampled sparse STEM images.
Acknowledgments: The author is greatly indebted to  G. Melinte  and  O. Ersen  for providing access to the CS-corrected JEOL 2100F at  IPCMS (Strasbourg, France)  to test the block-based sparse imaging at high-resolution on FeO nanoparticles. The author acknowledges the  laboratory of P. Bastin at Institut Pasteur (Paris, France)  for providing the T. brucei sample. The author acknowledges  M. Ribardière  and  J. -P. Dérouet  from  JEOL France SAS (Croissy-sur-Seine, France ) for the multiple constructive discussions about scanning process in JEOL electron microscopes. J. -P. Michel at  Institut Galien (Châtenay-Malabry, France)  is acknowledged for his critical reading of the manuscript. The author acknowledges the  PICT-IBiSA  for providing access to the cryo-electron microscopy facility.
Elastic Dislocation Modeling We use elastic dislocation modeling following  Okada (1985 Okada ( , 1992) )  for coseismic slip inversion and static Coulomb failure stress (CFS) transfer calculation. We employ the Matlab-based software package "Coulomb" by  Toda et al. (2011). The model setup for elastic modeling uses the up-scaled geometry of the analogue model and according prototype material properties (see Figure  2b ).
Acknowledgments This study has been partially funded by the  German Research Foundation  (DFG) collaborative research center  SFB1114  " Scaling Cascades in Complex Systems ," project  B01. F. C. received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant agreement   658034  (AspSync). Y. Kaneko  and three anonymous reviewers are thanked for their very valuable comments which helped in clarifying and focusing this paper. We thank  Y. Kaneko  and  J. P Avouac  for discussions and sharing their data to populate Figure 9a. We thank  Kirsten Elger  and GFZ Data Services for publishing the experimental data underlying this study. All data underlying this study are published open access in GFZ Data Services: https://doi. org/10.
Acknowledging the challenges above, there is a need for solutions that facilitate collaboration, information exchange, analysis and evaluation to support better energy efficient retrofitting projects. In this framework, the OptEEmAL project has developed a tool that aims at improving the design phase to deliver retrofitting projects with the energy efficiency as main axis. This tool has been designed to ease the process, reduce time and uncertainties and, therefore, result in reduced costs and increased confidence in the retrofitting process  [8]. OptEEmAL, as a tool to deliver these services, is based on the integration of 5 main pillars, and is conceived to provide a proper integration of these five concepts which allow providing the users with this holistic tool aimed at reducing time and costs in the design phase, and improving the whole process in terms of management, information flows, and quality of the design.
As input data, users are requested to upload Building Information Models of the buildings subject to retrofitting in IFC format  [13]  and a District Information Model containing those buildings and those in the surroundings in CityGML format  [14] , representing the geometry and properties of materials, zones and schedules into the tool. To appropriately elaborate the BIM and CityGML files, a set of guidelines has been produced that contains the main instructions to avoid errors and to insert the minimum data required by the OptEEmAL tool  [15, 16]. Considering the geo-reference inserted through the CityGML, the tool then retrieves data from existing data sources as Eurostat or climate data databases in order to complete the information with energy prices and weather conditions of the district location.
This step also covers two core functionalities of the tool, which are the matching of the IFC and CityGML files, and the insertion of information about the building and district energy systems. All this information is then mapped onto the District Data Model (DDM), which, based on OWL technologies  [17] , makes it possible to model all required data for the functionalities covered by the tool. To map this information, first an important process of data quality checking is implemented, which covers the validation of the files and information provided by the users and external data sources and ensures its quality for the purposes of OptEEmAL. This data checking evaluates geometry errors or data completion among others. A set of tools  [18]  has been designed and implemented for this purpose.
• (3). Scenarios generation and optimisation process. Considering the results from the baseline, the third steps forms the core process of the tool, which provides a set of solutions to the problem formulated by the user, understanding such problem as a district to be retrofitted to improve its energy efficiency. In this step, a set of models and tools are executed to formulate a mathematical problem that allows optimising the DPIs according to a set of rules and for a set of candidate scenarios. These scenarios are generated as combinations of Energy Conservation Measures (ECMs), which are divided into passive, active, renewable and control technologies. These solutions are catalogued within a database that is completely interoperable with the DDM and which contains geometrical, energetic, economic, technical, and maintenance information of a set of 135 measures that can be incorporated into the elements of the buildings, replacing existing assets (e.
Elaboration of Input Data As explained in Section 2, the first step is to elaborate the input data that is required by the tool. These data include the BIM models of all the buildings existing in the district, the CityGML model representing these buildings and those in the surroundings, and data related to the energy systems existing in the current conditions of the district. As mentioned in the methodology, the appropriate modelling of data is essential to ensure that the tool can perform the processes for integration and mapping. Thus, the BIM and CityGML (OGC Making location count. , MA, USA) guidelines introduced before have been used in the modelling of the 4 BIM models that represent the 5 buildings (one for each block and one for the two towers) with the use of Autodesk Revit TM (Autodesk, CA, USA)  [34] , and the CityGML that represents the district, which has been modelled using CityEditor (https://extensions.
Validation of Results against Real Performance Data To ensure the validity of the model proposed, a validation process using real data collected from the case study for the pre-and post-retrofitting situations has been carried out. The data used for the validation has been gathered from the energy bills for the pre-retrofitting situation and from monitoring systems for the post-retrofitting scenario. From the various prioritisation schemes used to evaluate the candidate retrofitting scenarios, the first scheme (priority to achieve a nearly zero energy district) was the one selected, as it is the one closest to the approach considered when designing the retrofitting project for this district. Table  10  shows the values of the indicators for the real retrofitting project implemented compared to the two scenarios proposed by the tool under the prioritisation scheme.
The production of 97 Ru, a potential Single Photon Emission Computed Tomography (SPECT) radioisotope, was studied at ARRONAX. The cross-section of nat Mo(α,x) 97 Ru reaction was investigated in the range of 40-67 MeV irradiating the nat Mo and Al stacked-foils. The activities of 97 Ru and other radioactive contaminants were measured via gamma spectroscopy technique. A global good agreement is observed between obtained cross-section results, previously reported values and TENDL-2017 predictions. Additionally, Radionuclide Yield Calculator, a software that we made available for free, dedicated to quickly calculate yields and plan the irradiation for any radioisotope production, was introduced. The yield of investigated nuclear reactions indicated the feasibility of 97 Ru production for medical applications with the use of α beam and Mo targets opening the way to a theranostic approach with 97 Ru and 103 Ru.
Stacked-Foils Irradiations Three experiments were performed at the ARRONAX facility  [23] , irradiating stacked-foils targets in vacuum with α beam of 67. 4(5) MeV for about 1 h with beam currents of 40-60 nA. The stacked-foils technique and set-up in our facility have been described most recently in  [24] [25] [26]. A typical stacked-foil target consisted of an Al monitor foil (~10 µm thick) in front, followed by the set of multiple nat Mo foils (~10 µm thick) and Al degraders (50-500 µm thick), arranged alternately. The order of the foils in the stacks were planned so that each nat Mo foil is activated with a different energy, all covering the energy range from 40 MeV to 67 MeV in about 3 MeV intervals (the projectile stopping-power in the stacks was calculated using SRIM software  [27] ).
Gamma Spectroscopy and Data Analysis After about 14 h of cooling time, the gamma ray spectra of irradiated samples were collected using a HPGe Canberra detector with efficiency 20% at 1. 33 MeV equipped with low-background lead and copper shielding. Each foil was placed at a height of 19 cm from the detector to ensure the dead-time below 10%. The detector was calibrated in energy and efficiency at 19 cm with 57 Co, 60 Co and 152 Eu calibrated sources from LEA-CERCA (France) prior to the measurements. Gamma spectra were recorded using the LVis software from Ortec© while the activity of the radionuclides produced at the End of Bombardment (EOB) were derived using the FitzPeaks Gamma Analysis and Calibration Software (JF Computing Services). For the identification and activity estimation we used the γ-line and associated branching presented in Table  1.
The errors of cross-section values were propagated from the uncertainty of thickness measurements (around 1%), uncertainty of the counts in the γ-line peaks in the spectroscopy measurements (around 5-10%) and the error of the calculated beam current (around 5-10%) while the corresponding energy errors were propagated with SRIM software  [27]  considering the beam energy straggling through the foils (the initial energy error estimated by the cyclotron operators was 0. 5 MeV). The obtained cross-section values are then compared with TENDL-2017 (TALYS-based evaluated nuclear data library)  [29]  and the experimental results from other research groups.
Radionuclide Yield Calculator Given the cross-section values, one can calculate the Thick Target Yield (TTY) with the following formula  [30, 31] : TTY(E) = H N A λ Z e M E max E min σ(E) dE/dx(E) dE where: E max and E min -maximal and minimal energy of the projectile penetrating the target (in case of TTY, E min ≤ reaction threshold), dE/dx-stopping-power of the projectile in the irradiated target. To facilitate this calculation for 97 Ru, as well as any other radioisotope and cross-section, we developed a Radionuclide Yield Calculator, later named RYC. RYC is graphical user interface software written in python programming language (version 2. 7)  [32]  using the TKinter module and compiled with PyInstaller software (version 3. 4)  [33]. It uses the cross-section and basic target data inputs to instantly calculate TTY and activity produced in any irradiation scenario.
Data points can be fitted using different type of function, gaussian-like and polynomial functions, using the least-squares method. Excitation functions from TENDL  [29]  can be easily imported to compare with experimental data and to look for potential radioactive impurities. RYC with its detailed documentation can be downloaded from the ARRONAX website  [34]. In particular, RYC uses implemented SRIM module  [27]  for stopping-power calculation. The validation of this software was performed using data from the literature. On Figure  1 , we compare the RYC-calculated TTY with the values published by IAEA  [28, 35]  based on the same cross-section values for 127 I(p,3n) 125 Xe, 64 Ni(d,2n) 64 Cu and 209 Bi(α,2n) 211 At reactions on metallic targets. Data calculated by RYC are presented as points whereas the curve published by IAEA correspond to the lines.
Funding: The cyclotron Arronax is supported by CNRS, Inserm, INCa, the Nantes University, the Regional Council of Pays de la Loire, local authorities, the French government, and the European Union. This work has been, in part, supported by a grant from the French National Agency for Research called "Investissements d'Avenir", Equipex Arronax-Plus noANR-11-EQPX-0004 and Labex IRON noANR-11-LABX-18-01.
Direction of Propagation Velocity (µm OM allows also to derive relevant information about the change of the crystal dimensions during the spin transition process. Using our own image processing software developed on Matlab, we could measure the distances AB, AC and AD in the low-temperature and high temperature phases, from which we deduced their relative variations. The obtained results are summarized in Table  3. Thanks to the good agreement with the relative variation of the lattice parameters extracted from X-rays studies  [38, 45]  and summarized in the same Table  3 , one can easily identify that the front propagation takes place in the a-b plane, where the most important changes are observed at the transition. Table  3. Relative variation of the crystal sizes derived from OM data and X-rays measurements  [38, 45]  in the HS and HS-LS phases.
The cross section for the elastic reaction Pb+Pb (γγ) → Pb+Pb γγ can then be calculated by convolving the appropriate photon flux with the elementary cross section for the process γγ → γγ. Since the photon flux associated with each nucleus scales with the square of the number of protons, the cross section is strongly enhanced relative to proton-proton (pp) collisions. The γγ → γγ reaction has also been measured in photon scattering in the Coulomb field of a nucleus (Delbrück scattering)  [19] [20] [21] [22]  and in the photon-splitting process  [23]. A related process, in which initial photons fuse to form a pseudoscalar meson that subsequently decays into a pair of photons, has been studied at electron-positron colliders  [24] [25] [26] [27]. The previous ATLAS and CMS measurements were based on the Pb+Pb dataset of 0.
Simulated γγ → γγ signal events were generated using the SuperChic 3. 0 Monte Carlo (MC) generator  [32]. This program takes into account box diagrams with charged leptons, quarks and W ± bosons. An alternative signal sample was generated using calculations from Ref. [33]. These calculations were then folded with the Pb+Pb photon flux taken from the Starlight 2. 0 MC generator  [34]. The theoretical uncertainty of the cross section is mainly due to the limited knowledge of the nuclear form factors and initial photon fluxes. This is extensively studied in Refs. [13, 35] , and the relevant uncertainty is estimated to be 10% within the fiducial phase space of the measurement. Higher-order corrections, which are not included in the calculations, are also part of the theoretical uncertainty and amount to 1-3% in the fiducial phase space  [36, 37].
7. 1  [39] , where the initial photon fluxes from pp collisions are implemented. The sample was then normalized to cover the differences in the photon fluxes between Pb+Pb and pp collisions. All simulated events make use of a detector simulation  [40]  based on GEANT4  [41]  and are reconstructed with the standard ATLAS reconstruction software. Photons are reconstructed from EM clusters in the calorimeter  [42]  and tracking information provided by the ID, which allows to identify photon conversions  [43]. An energy calibration specifically optimized for photons  [44]  is applied to account for energy loss before the calorimeter and both lateral and longitudinal shower leakage. Photons in MC samples are corrected  [43]  for known mismodeling of quantities that describe the properties ("shapes") of the associated EM showers.
Neisseria meningitidis and N. gonorrhoeae are closely related pathogenic bacteria. To compare their population genetics, we compiled a dataset of 1,145 genes found across 20 N. meningitidis and 15 N. gonorrhoeae genomes. We find that N. meningitidis is seven-times more diverse than N. gonorrhoeae in their combined core genome. Both species have acquired the majority of their diversity by recombination with divergent strains, however, we find that N. meningitidis has acquired more of its diversity by recombination than N. gonorrhoeae. We find that linkage disequilibrium (LD) declines rapidly across the genomes of both species. Several observations suggest that N. meningitidis has a higher effective population size than N. gonorrhoeae; it is more diverse, the ratio of non-synonymous to synonymous polymorphism is lower, and LD declines more rapidly to a lower asymptote in N.
Dataset All 15 genomes of N. gonorrhoeae that were present in Genbank in April 2018 (NCCP11945  (Chung et al. , 2008) , FA19  (Abrams, Trees & Nicholas, 2015) , FA6140  (Abrams, Trees & Nicholas, 2015) , 35/02  (Abrams, Trees & Nicholas, 2015), FA1090, MS11, FA19, FA6140, 35/02, 32867, 34530, 34769, FDAARGOS 204, FDAARGOS 205, FDAARGOS 207 , NCTC13799, NCTC13798, NCTC13800) and 20 randomly selected genomes of N. meningitidis (MC58  (Tettelin et al. , 2000) , Z2491  (Parkhill et al. , 2000) , FAM18  (Bentley et al. , 2007) , 053442  (Peng et al. , 2008) , alpha14  (Schoen et al. , 2008) , 8013  (Rusniok et al. , 2009) , alpha710  (Joseph et al. , 2010) , WUE 2594  (Schoen et al. , 2011) , G2136  (Budroni et al. , 2011) , M01-240149  (Budroni et al. , 2011) , M04-240196  (Budroni et al. , 2011) , H44/76  (Budroni et al.
, 2011) , M01-240355  (Budroni et al. , 2011) , NZ-05/33  (Budroni et al. , 2011) , 510612  (Zhang et al. , 2014) , NM3686, M7124, NM3682, NM3683, L91543) were downloaded from Genbank. From these all protein coding sequences were extracted. We retained those coding sequences that started NTG, terminated with TAA, TAG or TGA and had a length that is a multiple of three. We identified orthologs using reciprocal BLAST, with an e-value threshold of 0. 00001; i. e. each protein coding gene in each genome was BLASTed against the genes of FA1090, and then the best hit was BLASTed back onto the original genome, retaining only those hits in which the original query sequence was the best hit. Similar selections of genes were obtained using alternative starting genomes. The protein sequences of the orthologs were aligned using MUSCLE  (Edgar, 2004).
We selected genes where the alignments meet these criteria: the number of gaps is lower than 1% of the length of the sequence and the total number of nucleotides in gaps is lower than 10% of the total number of nucleotides in the sequence. Sequences with internal stop codons were removed. This resulted in a dataset of 1,145 genes belonging to the core genome of both N. gonorrhoeae and N. meningitidis. We used the BioPython Phylo library  (Cock et al. , 2009)  to estimate a phylogeny of the strains based on the core genome alignment. without respect for synteny. For some analyses we masked those regions inferred to be due to hLGT in the strains affected. We investigated whether linkage disequilibrium (LD) declines with the distance between sites by measuring the LD between all pairs of polymorphisms within each gene; we did not concatenate the genes or align whole genomes, because with the gain and loss of genes the distance between sites differs depending on the strains being analysed.