dataesr/scientific-paragraphs-categorization huggingface dataset:
Field work for data collection by  BRT, GMK , and  SPW  was supported by the  Embassy of Nicaragua in the Hague, Netherlands. Manuscript preparation for BRT, GMK, and  SPW  was supported by the  Beatrix Farrand Fund  of the  Department of Landscape Architecture and Environmental Planning of the University of California Berkeley , and manuscript preparation for GMK was also supported by the  Collegium de Lyon -Institut des Etudes Avancees de l'Universitede Lyon , the  EURIAS Fellowship Programme  and the  European Commission  ( Marie-Sklodowska-Curie Actions -COFUND Programme -FP7 ) (no grant numbers). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Forming gas (Ar+2%H 2 ) flowed through the tube furnace to prevent sample oxidation. After annealing, the sample was cooled by a jet of forming gas and allowed to stabilize for 5 min before starting a new DCT scan. In total 14 annealing steps (with annealing time of 5 min) and 15 DCT scans were performed at room temperature. The annealing time between the first and the second scan was not determined as accurately as the rest; thus, this first scan was not used in the fitting procedure. The data was analyzed using the DCT software package  1  following the procedures summarized in  [26, 39]. The orientations of all grains and the grain boundary positions were mapped with a resolution of 0. 1 degrees and 1:5À3 mm; respectively  [37]. By inspection of the evolution of the reconstructed grain boundaries, we suspect that the spatial resolution of the relative boundary movement can be less than one voxel (see Figure  S1 ).
Low-velocity boundaries The analysis reported in Section 3. 1 and results shown in Fig. 4  are based on a set of grain boundaries, which were chosen based on certain selected criteria. Amongst these is criterion 3: boundaries should move a distance that is sufficiently large compared to the experimental uncertainty on boundary position. In a complementary search of the database of all reduced mobilities we here test to what extent boundaries with a very small velocity are correlated with crystallography. In this case, we define the set of valid boundaries by criteria: 1. The boundary is not connected to the sample surface. 2. The delta parameter Eq. (  10 ) less than 1. 3. The distance moved is less than 0. 3 voxel size. 4. The fitted reduced mobility is less than 0. 01 mm 2 /s.
Acknowledgments This study was supported by grants from the  Danish Innovation Fund  for the center  CINEMA  [ 1305-00032B ],  National Institute for Standards and Technology through the Center for Hierarchical Materials Design  contract [70NANB19H00],  Office of Naval Research and The US Naval Research Laboratory  [ N0001415WX00004 ]. We thank  Danscatt [ 7055-00005B ] for travel support and  ESRF  for beamtime [HC2933]. We thank the use of computing resources at the  DTU Computing Center.
H. -K. Kim, S. G. Kim, W. Dong, I. Steinbach, B. -J. Lee, Phase-field modeling for 3D grain growth based on a grain boundary energy database, Model. Simul. Mater. Sci. Eng. 22 (3) (2014) 34004, doi: 10. 1088/0965-0393/22/3/034004.
PanCAKE Simulations All simulations are performed using the package PanCAKE 1  (Van  Gorkom et al. 2016; Perrin et al. 2018; Girard et al. 2018) , which is based on the official JWST exposure time calculator Pandeia  (Pontoppidan et al. 2016). Given a desired input scene, PanCAKE is capable of producing corresponding 2D simulated images for all coronagraphic observations with the NIRCam and MIRI instruments aboard JWST. For every object in the sample we simulate observations using NIRCam's MASK335R with the F356W and F444W filters, and MIRI's F1140C and F1550C masks. A block diagram demonstrating the primary steps performed in throughout the simulation process is displayed in Fig. 4 , alongside example simulated images in both the F444W and F1550C filters. Any observed structure in these images is a result of the residual scattered starlight or fundamental limitations in the subtraction process, and not descriptive of the spatial profile of a true astronomical object.
In these simulations we mitigate such affects by repeating each individual simulation 10 times in order to generate a statistical sample from which the contrast curve can later be determined. When performing a default simulation, the PanCAKE package utilises a library of PSFs which are precomputed across the coronagraphic field of view. As a result, variations in the simulated images due to small offsets such as the target acquisition error will not be accurately represented. We remedy this issue in our simulations by enabling the on_the_fly_PSFs setting within PanCAKE. This setting circumvents the use of the precomputed library and instead calculates the precise PSF using the WebbPSF dependency  (Perrin et al. 2014). In order to make the simulations computationally tractable we reduce the wavelength sampling from the default of 150, to 41 through the wave_sampling setting.
Readout Specifications The NIRCam detectors host a broad range of readout modes, with nine distinct readout patterns and up to either 10 or 20 groups per integration. Whilst this provides significant versatility, finding the optimal readout mode for a desired observation can be a non-trivial task. Although it is technically possible to perform the simulation for every possible readout mode to assess which performed the best, this is very computationally intensive and hence not practical. Alternatively, we estimate the optimal readout mode using the ramp_optimize function of the pyNRC software package (Leisenring et. al, in preparation 2  ). Given a NIRCam observational setup and a desired integration time, this function can quickly estimate the achievable SNR of a target object for each possible readout mode.
We use this function to select the input readout mode for every PanCAKE simulated target by finding the corresponding ramp_optimize mode which: provides the maximal SNR of a synthetic companion object 20 magnitudes fainter than the target star, has a total integration time less than 3600 s, and does not saturate the detector. The choice of such a faint companion is made as this preferentially selects readout modes best for observing objects with the largest contrast to their host stars. For the MIRI simulations we instead perform a custom optimisation to determine the optimal readout parameter for every target in the sample. The MIRI detector only has two possible readout patterns -SLOW or FASTand the SLOW pattern is only recommended for parallel observations where the overall data volume in the FAST pattern would be too high.
to perform an accurate PSF correction and therefore will affect the achievable contrast. We include the effects of thermal drifts throughout the simulated observations using the thermal_slew function included in the webbpsf software package  (Perrin et al. 2014). By providing a desired slew start pitch angle, end pitch angle, and the elapsed time, this function can model the variation in the optical path difference (OPD) map of each primary mirror segment which in turn can be provided to the PanCAKE simulation. During these simulations the OPD map is updated after the target observation and then after every consecutive dither for the reference star observation. Selecting a slew start pitch angle is not straightforward, as the observatory pitch angle can vary between 45 • and -5 • as shown in Fig.
ACKNOWLEDGEMENTS We thank  Eric Mamajek  for valuable discussions on pre-main sequence stars, and  Camilla Danielski  for providing the MIRI coronagraphic transmission profiles. We also thank the anonymous reviewer for their useful comments on this manuscript. A significant portion of this work was performed whilst A. L. C was funded by a  UK Science and Technology Facilities Council (STFC)  studentship. M. B. acknowledges funding by the  UK STFC  grant no. ST/M001229/1. This work benefited from the  2019 Exoplanet Summer Program  in the  Other Worlds Laboratory (OWL) at the University of California, Santa Cruz , a program funded by the  Heising-Simons Foundation. This research has made use of the  NASA Astrophysics Data System and the modules NumPy, matplotlib, Astropy and SciPy.
The problem can be solved using any generic secure multiparty computation. There exist specialized solutions that combine different techniques and use the domain knowledge to develop efficient protocols. c) Generic Secure Two-party Computation Solution: Generic secure two-party computation  [21] ,  [31] ,  [55] , such as garbled circuit and secret sharing, can implement PDTE. The idea is to transform the decision tree program into a secure representation that can be evaluated without revealing private data. There exist frameworks such as ObliVM  [40]  or CBMC-GC  [28]  that are able to automate the transformation of the plaintext programs, written in a high level programming language, into oblivious programs suitable for secure computation. Their straightforward application to decision tree programs does certainly improve performance over a handcrafted construction.
6 : Dependency Graph for n = 4 and n = 5 1: for j = 1 to j = n do 2: for l = 1 to l = L(j) do 3: a j ← a j ⋅ a i l Algorithm 7: Multiplication using Dependency Lists Lemma 6. 6: Let [a 1 ,. , a n ] be an array of n integers, G = g([a 1 ,. , a n ]) be a DAG as defined above and L(j) = [a i1 ,. , a i L(j) ] be the dependency list of a j. Then Algorithm 7 computes Π n i=1 a i and has a multiplicative depth of log(n). The proofs of Lemmas 6. 5 and 6. 6 follow by induction similar to Lemma 6. 2. Before describing the improved path evaluation algorithm, we first extend our Node data structure by adding to it a new field representing a stack denoted dag, that stores the dependency list. Moreover, we group the nodes of the decision tree by level and use an array denoted level[], such that level[0] stores a pointer to the root and level[i] stores pointers to the child nodes of level[i -1] for i ≥ 1.
E. Performance of our Schemes on Real Datasets We also performed experiments on real datasets from the UCI repository  [51]. We performed experiments for both PDT-BIN and PDT-INT for the datasets illustrated in Table VII (parameters n, d, m are defined in Table  I ). For PDT-BIN, we reported the costs for HElib (single and amortized) and the costs for TFHE. Since TFHE evaluates only boolean circuits, we only have implementation and evaluation of PDT-INT with HElib. We also illustrate in Table VII the costs of two best previous works that rely only on homomorphic encryption, whereby the figures are taken from the respective papers  [41] ,  [48]. For one protocol run, PDT-BIN with TFHE is much more faster than PDT-BIN with HElib which is also faster than PDT-INT with HElib. However, because of the large number of slots, the amortized cost of PDT-BIN with HElib is better.
Large-scale research integration is contingent on seamless access to data in standardized formats. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. In small animal magnetic resonance imaging, an overwhelming proportion of data is acquired via the ParaVision software of the Bruker Corporation. The original data structure is predominantly transparent, but fundamentally incompatible with modern pipelines. Additionally, it sources metadata from free-field operator input, which diverges strongly between laboratories and researchers. In this article we present an open-source workflow which automatically converts and reposits data from the ParaVision structure into the widely supported and openly documented Brain Imaging Data Structure (BIDS).
g. , data organized by one hierarchical principle may lead to loss or obfuscation of certain category correspondence relationships), or positive restrictions of technologies (e. g. , data available in highly specific formats might require usage of and familiarity with highly specialized tools)-with the latter restriction being particularly relevant in database standards. In order to access the benefits and mitigate the pitfalls of standards compliance, data should be migrated to a form which is openly and thoroughly documented, and easily accessible for user manipulation. In the field of small animal MRI, the vast majority acquisition devices (∼80 %) are produced by the Bruker Corporation, and thus the largest segment of data is initially formatted according to the ParaVision standard. This standard is largely transparent, with most metadata stored in plain-text files.
The data, however, are stored in a binary format, which strongly diverges from the de facto standard of NIfTI  (Cox et al. , 2004) , and for which extensive documentation is not openly available. Conversion tools from the ParaVision standard to NIfTI exist  (Ferraris et al. , 2017; Rorden and Naveaum, 2018) , and have more recently also been made available from the manufacturer (presently only in closed-source form and only in ParaVision 360, without backward compatibility). Contingent on the scope limitations of the NIfTI format itself, these tools can however not repackage the majority of metadata represented in ParaVision standard plain-text files. This situation exemplifies how the utility of standards is not only contingent on their suitability for use as a common origin format, but also on their flexibility to accommodate all relevant information.
THE WORKFLOW The workflow, entitled bru2bids (Bruker ParaVision to BIDS), is distributed as part of SAMRI  (Ioanas et al. , 2019b) , a free and open source workflow package of the ETH and University of Zurich Institute for Biomedical Engineering. This repositing workflow can be used stand-alone, but also serves as a gateway to all the further workflows included in the SAMRI package (encompassing dedicated solutions for all analysis steps showcased in Figure  1 ). As such, the ParaVision-to-BIDS workflow not only permits users to convert data into a format which is more widely supported and flexible, but also easily links to reference implementations for BIDS-based small animal processing functionalities (e. g. , registration;  Ioanas et al. , 2019c). The workflow reposits data and metadata from the ParaVision standard into a BIDS-compliant form, notifying the user of BIDS validation possibilities upon completion.
The repositing process automatically handles the conversion of data from ParaVision volumetric reconstruction files (2dseq) to NIfTI files. Additionally, it assigns metadata from the specific ParaVision text files to either the NIfTI header, the BIDS metadata files, or the BIDS directory hierarchy, as applicable. A simplified overview of this process is presented in Figure  2A. A more extensive break-down of metadata sourcing-showing the actual input and output files, and highlighting metadata fields represented in the data paths-is laid out in Figure  2B. The repositing functionality described herein can be accessed from both Bash and Python, via SAMRI bru2bids or samri. pipelines. reposit. bru2bids(), respectively. Invocation variants are illustrated in Figure  3 , and link to the same code implementation.
The bru2bids function is highly parameterized, with the same parameter set available in either Bash or Python. A full list of parameters can be obtained by executing the SAMRI bru2bids -help command from the console. The current parameter listing for Bash is presented under Figure  S1. Notable parameters include the functional, diffusionweighted, and structural scan specification. These three selectors each use an input dictionary (pairs of one key, which is a BIDS metadata string, and a list of accepted values), to identify which scans are to be reposited. Examples of such dictionaries are given in Figure  3 , where scans with an "EPI" acquisition field are categorized as functional, and files with a "TurboRARE" acquisition field are categorized as structural. The repositing pipeline is run sequentially for all scan categories, as separate processes are required for each scan type (examples of the internal processing nodes are shown in Figure  6 ).
Preexisting Data Compliance Preexisting data sets-acquired with operator metadata input divergent from the recommended guidelines-can also be redressed, via a small number of plain text editing operations. Performing such edits is highly recommended, as it is still easier than manually repositing data in the BIDS standard, and also permits direct ParaVision data sharing, as shown in Figure  1. The relevant parts of a ParaVision scan directory which need to be edited are single lines in the subject and Scanprogram. scanProgram files. Editing the subject identifier in the ParaVision directory name is also advisable, though only for ease of overview-since the subject field is not read from the directory name by the repositing workflow. Before editing, value names for all available metadata fields must be chosen, such as respect the constraints of the BIDS standard.
g. , <R02> for a session identified as R02 within the study, as shown in Figure  4B. The Scanprogram. scanprogram file is used to define other BIDS metadata fields. Within this file, individual lines containing the string (E (with the open box character representing a space) are used both to record the ParaVision "Instruction Name" and to establish correspondence with the respective numbered ParaVision scan directory. On such lines, after the open <displayName> tag, and up to the space character before the open parenthesis, an arbitrary sequence of BIDS short identifiers and value pairs, separated by underscores, can be inserted-as shown in Figure  4A. Examples for manually redressed data are included in the SAMRI ParaVision testing data archive, and are in their end form indistinguishable from data acquired along the lines of the operator recommendations.
Package Management The workflow becomes available upon installation of the SAMRI software package. The dependency list of the package is documented inside the SAMRI repository and version archive  (Ioanas et al. , 2019b)  in accordance with the Package Manager Specification  (Bennett et al. , 2017). Automatic installation of the entire dependency stack has been made available for package managers conforming to this specification  (Ioanas et al. , 2017) , including the standard package manager of Gentoo Linux and its derivatives  (Ioanas et al. , 2017). The package design is based on the widely-adopted Python setuptools functionality, and is thus easily accessible to maintainers using further distributions.
RESULTS To demonstrate the capabilities of the workflow, we have compiled a versioned reference archive of multi-species small animal MRI data. The resulting package, samri_bindata FIGURE 4 | Editing operations on only up to four lines are required to render preexisting data compatible with repositing via the workflow at hand. Depicted are file differences for the 20171024_165248_MD1704_Mc285AB_P02_1_1 testing dataset scan, in a patch syntax. The line numbers identifying the position of the text segment (before and after editing) are highlighted between arobase characters and in cyan. Deleted and added lines are highlighted in red or green, and prefixed with a minus or plus, respectively. Conserved lines are printed in black. (A) A total of two lines need to be edited in the ScanProgram. scanProgram file in order to render acquisition, task, and contrast automatically interpretable.
(Ioanas et al. , 2019a) , includes multi-center ParaVision standard scans from mice, rats, and mouse lemurs, and serves as testing data for the SAMRI package, including the repositing workflow. The data in this archive is based on scans acquired with originally compliant operator input, as well as on preexisting scans rendered compliant ex post facto, as detailed in this article. In Figure  5 , we test the capability of the workflow to correctly and automatically source specified datasets from a diverse collection, and reposit them in the BIDS standard. The resulting three separate and shareable BIDS data sets pass the BIDS validator with no errors. The commands listed in this section are included in the SAMRI test suite, and monitored for continued quality assurance.
The workflow is implemented as a function in the Python programming language, and uses the Nipype package  (Gorgolewski et al. , 2011)  for workflow execution, overview generation, parallelization, and access to non-Python tools. Bash bindings are auto-generated based on the Python function definition and documentation string by the Argh package. Data conversion from the ParaVision 2dseq format to NIfTI is performed by the Bru2 function from the Bru2Nii package  (Rorden and Naveaum, 2018). Preliminary to workflow execution, ParaVision metadata parsing and BIDS metadata assignment is performed by Python utility functions implemented in the SAMRI package. These functions iterate through the lines of the relevant metadata files: subject and ScanProgram. scanProgram, falling back to <scan_number>/acqp if the scan program file is corrupted.
Metadata detection is performed via regular expressions, to afford a maximum of flexibility, and avoid dependency on more convoluted higher-level tools. The processed metadata is recorded in a Pandas Dataframe  (McKinney, 2010) , which is both used internally and written to disk in the "work directory" to permit debugging. This record is then divided along the lines of the supported scan categories (structural, functional, and diffusion-weighted), and the resulting selections are used to initiate the respective workflow iterations, as seen in Figure  6. Following successful (1) the metadata_file node, responsible for creating a JSON BIDS metadata file is supplied with (i) a bundled subject and session identifier, (ii) the exact ParaVision scan path including the numbered subdirectory, and (iii) the computed BIDS output metadata file name; (2) the s_bru2nii node, responsible for creating a BIDS-named NIfTI data file is supplied with (i) the exact scan path including the numbered subdirectory and (ii) the computed BIDS output file name; (3) the datasink node, responsible for creating a BIDS-style directory hierarchy is supplied with a bundled subject and session identifier.
(B) Functional scan category repositing graph. The get_f_scan node queries the base Pandas Dataframe for scan entries selected as functional, and computes data descriptors, which are further piped in the following fashion: (1) the metadata_file node, responsible for creating a JSON BIDS metadata file is supplied with (i) a bundled subject and session identifier, (ii) the exact ParaVision scan path including the numbered subdirectory, and (iii) the computed BIDS output metadata file name; (2) the f_bru2nii node, responsible for creating a BIDS-named NIfTI data file is supplied with (i) the exact scan path, and (ii) the BIDS output file name; (3) the events_file node, responsible for creating a BIDS-style events file is supplied with (i) the exact scan path, (ii) the task identifier, (iii) the BIDS output event file name, (iv) the metadata file (to query delay entries), and (v) the functional file path (to determine the repetition time necessary to perform eventfile adjustments in light of the metadata); (4) the datasink node, responsible for creating a BIDS-style directory hierarchy is supplied with a bundled subject and session identifier.
workflow execution, the BIDS standard * _sessions. tsv file is separately generated, recording the onset acquisition times for each session. The guidelines regarding operator input and preexisting data compliance presented in this document are based on the ParaVison 6. x release series. The parsing functionality of the workflow in the current SAMRI version (0. 4. 1) is based on the ParaVison 6. x release series and the BIDS 1. x specification.