dataesr/scientific-paragraphs-categorization huggingface dataset:
In a Gold Standard data set, the necessary data and metadata for processing a reasonable range of use cases is recorded in the data set. This allows the data set to be moved freely to other filesystems in other facilities and still be processed, without the need to return to the original facility to recover information that had been left behind. Although the data set will normally consist of multiple files, these files should be packaged together in an appropriate container, for example a single folder in the file system at the collecting facility or under a single DOI in a data-set repository. The specification of which metadata need be retained with the data depends on the experiment being performed and the software that will be used for processing, i. e. the 'use case'. The Gold Standard being discussed here is intended to be adequate for single-axis rotation experiments at synchrotrons and stills collected at XFELs and synchrotrons, and to work properly with the data-reduction programs DIALS  (Waterman et al.
, 2013; Winter et al. , 2018) , XDS  (Kabsch, 2010a,b) , MOSFLM  (Battye et al. , 2011) , HKL-2000 (Otwinowski & Minor, 1997), xia2  (Winter, 2010)  and autoPROC  (Vonrhein et al. , 2011) , as well as future versions of OnDA  (Mariani et al. , 2016). The more complex the design of the experiment and the more varied the non-default choices permitted by the software, the more different metadata may be required to ensure correct processing at a wide range of facilities. The Gold Standard is the minimum set of metadata upon which we have agreed. To date, the applicability of the Gold Standard has been demonstrated both for single-axis rotation data at a synchrotron (https://doi. org/10. 5281/zenodo. 3484187) and for serial crystallography data at an XFEL (https://doi. org/10. 5281/ zenodo. 3352357).
The former is a 'Small example Eiger2 X 16M data set from Diamond Light Source I04 revised for HDRMX Gold Standard Discussion' collected by Graeme Winter and revised by Graeme Winter, Aaron Brewster and Herbert J. Bernstein to conform to the Gold Standard. On Zenodo this is described as a Revised useful small (488 frame) Eiger data set recorded during routine testing, useful for software testing as it is small. [The recorded data are] from a thaumatin crystal by Graeme Winter, The original dataset is https://doi. org/10. 5281/zenodo. 3385862 which contains two Eiger2 X datasets, Therm_6_1 and Therm_6_2, each with a data file and two versions each of the metadata -a ". _master. h5" file and a ". nxs" file. The former are the usual Eiger metadata files using exposed external links to connect the metadata to the data, and the latter are HDF5-1.
10 VDS (virtual dataset) files. This revision has the same data as the original Therm_6_2 data, but now includes with the "master. h5" file a ". _master_rev. h5" file and with the ". nxs" file a ". _rev. nxs" VDS file. The latter is a '68 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file' by Aaron Brewster, Karol Nass, Dmitry Ozerov and Filip Leonarski. Minor changes for Gold Standard compliance were made by Herbert J. Bernstein. The Zenodo reference at https://doi. org/10. 5281/zenodo. 3352357, in addition to the data set described here, contains instructions on how to generate the NeXus master files using a Python script. The data set was collected with a JUNGFRAU 16M hybrid pixel chargeintegrating detector  (Redford et al. , 2018)  during SwissMX fixed-target instrument commissioning at the SwissFEL Bernina endstation  (Ingold et al.
, 2019). The detector is composed of 32 independent modules, each of roughly 500 000 pixels, arranged in a non-square geometry and mounted in a single metal frame. To simplify refinement of the module positions, we grouped the detector modules into a hierarchy of four quadrants with eight modules per quadrant. Each module consists of eight panels in a 4 Â 2 arrangement. In order to represent this arrangement in NeXus, we used a series of NXtransformations nodes linked by the depends_on attribute to represent (i) the position of the detector as a whole relative to the crystal, (ii) the location of the quadrants relative to the detector center, (iii) the location of the eight modules in each quadrant relative to their quadrant center and (iv) the location of each of the eight panels in a module relative to their module center.
We included pixel offsets for each panel into the raw data following the NXmx specification. The JUNGFRAU 16M (JF16M) represents a complex detector geometry and so an illustrative example is helpful. The following example shows the depends_on chain for the JF16M SwissFEL data set for the zeroth (first) panel. The chain starts with the NXdetector_module group at /entry/instrument/ELE_D0/ARRAY_D0Q0M0A0. The group is named after its hierarchy, referring to the fact that it is ASIC (application specific integrated chip) zero, of module zero, of quadrant zero, of detector zero. It has two fields that define the orientation of the panel by specifying the fast and slow readout directions for the raw pixel data: Both the fast and slow pixel directions depend on the AXIS_D0Q0M0A0 field of the NXtransformations group at /entry/instrument/ELE_D0/transformations, and this field continues the dependency chain of detector groups until the final group, which is the detector rail: (see Fig.
For example, we may well have one X axis for the coordinate frame, another for the entire detector face and several more for each of the detector modules. Giving each use a distinct name, such as X_nx, X_detector, X_module_1 etc. , can help to avoid confusion. In a NeXus/HDF5 NXmx file the axis chain descriptions begin with the depends_on field and NXtransformations group in each NXdetector group and in each NXsample group. In addition, the axis of the beam direction, which we call 'Beam', and of the downward direction of gravity, which we call 'Gravity', will be specified because they are needed in the coordinate system used by NeXus, which is called McStas (see Section 4. 2. 1). The axes pointed to from each depends_on field should be placed in appropriate NXtransformations groups. Each axis has a dimensionless unit vector and an optional offset vector specifying the direction cosines of the axis and the offset from the previous axis in the chain to the base of the new axis.
Summary and conclusions Built on the NeXus/HDF5 NXmx application definition and the International Union of Crystallography (IUCr) imgCIF/ CBF dictionary, the new Gold Standard for MX diffraction data collected at synchrotrons and XFELs is compatible with major data-processing programs and pipelines, and will make it faster and easier to process MX data and will help the community to share data and metadata conforming to FAIR principles. A tree diagram of the NXmx Gold Standard and the Gold Standard NeXus NXmx application definition are provided as supporting information.
Acknowledgements The work reported is the result of the efforts of many people for the past several decades. Particular credit is due to  Andrew P. Hammersley , who made the initial CBF proposal in 1995, and to the patient work of all of the participants in the many HDRMX workshops. Careful copy-editing and corrections by  Frances C. Bernstein  are gratefully acknowledged. Our thanks to  Jean Jakoncic  and  Alexei Soares  for helpful conversations and for access to data and facilities at  Brookhaven National Laboratory. Helpful comments by  Robert M. Sweet ,  Tom Caradoc-Davies  and Thomas White are appreciated.
IUCrJ (2020). 7, 784-792 Herbert J. Bernstein et al. Gold Standard for MX data 789 Figure 1 Schematic of the JF16M detector viewed from the source side, showing the hierarchical arrangement of panels. The quadrants are outlined in blue, the modules in green and the ASICs in gray. The offset components of the NeXus transformations are shown as arrows for the quadrant zero, sensor zero and ASIC zero. Note that the arrows point in the directions of the offsets, which are in the X-Y plane, not in the directions of the axes themselves, which are in the Z direction.
ACKNOWLEDGMENTS The authors are grateful to  V. Bernard  for interesting discussions, useful explanations and remarks. This work was partially supported by the  Istituto Nazionale di Fisica Nucleare  under project  MANYBODY , by the  University of Turin  under contract  BARM-RILO-17 , by the  Spanish Ministerio de Economia y Competitividad and ERDF (European Regional Development Fund)  under contract  FIS2017-88410-P , by the  Junta de Andalucia  (grant No. FQM160 ). M. B. Barbaro  acknowledges support from the  "Emilie du Châtelet " programme of the  P2IO LabEx  ( ANR-10-LABX-0038 ). G. D. M. acknowledges support from a  P2IO-CNRS grant  and from  CEA ,  CNRS/IN2P3 , France; and by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No.
41 Å from the (002) reflection of a highly oriented pyrolytic graphite monochromator. A thermodiffractometry experiment was performed in this configuration with data acquisitions of 2 min each with the stationary 153. 6 per data acquisition, in order to follow the temperature dependence of the magnetic moment. Particularly long acquisitions were performed at 1. 8 and 76 K (just above the magnetic ordering temperature), with 10 h of counting time each, using a detector scan for more precision (excluding parasitic signals and faulty detector efficiencies), two scans for each temperature, covering 3 • with a step width of 0. 05 • with 5 min counting time per step.
E. Computational methods In order to characterize the electronic structure and magnetic properties, density functional theory (DFT) based investigations were carried out using the low-temperature (1. 8 K) neutron-diffraction crystal structure, within the planewave basis set based on a pseudopotential framework as implemented in the Vienna ab initio simulation package VASP  [51, 52]. The exchange-correlation functional was chosen to be the generalized gradient approximation (GGA) following the Perdew-Burke-Ernzerhof prescription  [53]. The missing electronic correlation beyond the standard GGA, which is necessary for a transition-metal ion such as Ru, was accounted for by using Hubbard on-site coulomb correlation as implemented in the GGA + U method  [54, 55] , with a choice of the Hubbard (U = 2.
9 eV) and Hund's coupling (J H = 0. 9 eV) at the Ru site, thus considering values commonly applied for ruthenate compounds. The results shown are for U = 2. 9 eV and J H = 0. 9 eV. We also crosschecked the influence on our results by varying the U eff values in the range of 1 to 4 eV. We optimized the experimental crystal structure by relaxing the position of each atom up to 0. 005 eV/Å in the FIG. 2. Magnetic susceptibility (empty squares and circles) and inverse susceptibility (filled squares and circles) measured in an applied field of 70 kOe (green squares) and 35 kOe (orange circles). The solid lines represent fits of the data in the range from 75 to 400 K calculated with a model for isolated chains of classical spins with refined parameters 2J ≈ -74 K and g ≈ 1. 8. The dotted lines represent fits with the Curie-Weiss law.
This implies a propagation vector of the magnetic structure of κ = (000). The confirmation of the propagation vector was performed using the search routine K-SEARCH which is part of the FULLPROF suite  [58]. This propagation vector, together with the known crystal symmetry of the chemical cell, and the positions of the Ru 6+ magnetic ions, serve as an input for the symmetry analysis of the possible magnetic ordering schemes. The representation analysis of the low-temperature magnetic structure was carried out with the Program BASIREPS (part of the FULLPROF suite). All predicted symmetry-allowed spin configurations have been checked with Rietveld refinements by FULLPROF  [59]. Very clearly, only one out of the eight irreducible representations ( 6 for the Ru 6+ ions in the notation of BASIREPS), which has two basis vectors, (100) and (001), provides sets of calculated diffraction peaks consistent with our experimental data.
ACKNOWLEDGMENTS The authors acknowledge  Dr. Walter Schnelle  and  Mr. Ralf Koban  for magnetic, heat capacity, and resistivity measurements. S. K. thanks  Department of Science & Technology, Government of India , for providing an  INSPIRE faculty research grant  (Grant No. DST/INSPIRE/04/2016/000431 ) and  IUAC-New Delhi, India  for providing computational facilities. S. S. thanks  IIT Goa, Ministry of Human Resource Development, Government of India  for providing a research fellowship.
Data description We use high-frequency data (five-minute intervals) to reveal the inner dynamics of the forex market efficiency under the impacts of COVID-19 pandemic. Due to improved continuity in the distribution function, the high-frequency data more accurately accounts for long memory and structural changes. The high-frequency data sample consists of six major currencies traded in forex markets, including the Australia dollar (AUD), the Canadian dollar (CAD), the Swiss Franc (CHF), the Euro (EUR), the British Pound (GBP), and the Japanese Yen (JPY). All prices are quoted in the indirect quotes of US dollars (e. g. , amounts of foreign currency per one unit of US dollar). The data from 01 October 2019 to 31 March 2020 is collected from TrueFx and converted into fiveminute windows using the 'high-frequency' toolkit in R developed by  (Boudt et al.
Similar spectra are recorded at 100 K and 200 K, yet with shifts and broadening of the latter emission bands. A maximum of all CL bands is found at ∼600 keV electron energy for the polycrystalline sample regardless of temperature, and for the single crystal at 300 K. In contrast, a continuous increase versus electron energy is observed for the 4. 2-eV band of the single crystal at 100 K and 200 K above a threshold corresponding to the oxygen displacement energy. The dependence of CL intensities on the primary electron energy is analyzed on the basis of the interplay of the luminescence cross section and oxygen displacement cross section. The electron energy dependence of ionization cross sections is addressed by relying on secondary electron spectra computed with the PHITS code which is benchmarked against other computer codes such as PENELOPE, as implemented in Geant4.
Each spectrum was generally taken in 30 s under electron beam. Several spectra were recorded 5 times to check for the reproducibility of data. More experimental details can be found in previous papers  [14] [15]. Irradiation features such as the total inelastic stopping power ((-dE/dx)inel) and range of electrons computed with the ESTAR code in the continuous slowing down approximation (CSDA)  [24]  are displayed in Table  I. The CL Spectra of both kinds of samples were recorded for a similar electron flux and fluence at ∼100 K, 200 K, and 300 K, and for electron energies ranging from 400 keV to 1,250 keV. CL spectra were also recorded at ∼300 K in a SEM for an accelerating voltage of 20 kV.
IV) DISCUSSION The light emission process of CL upon electron bombardment of solids (so-called cathode rays)  [25]  differs from the classical photo-excitation process of photoluminescence (PL)  [26]. It is generally admitted that the CL signal in semiconductors is proportional to the volume density of excess minority carriers  [9, 10, 12]. Alternatively, excitation of CL spectra in oxides originate from the secondary electrons (and holes) generated by elastic and inelastic collisions induced by electron irradiation  [15]. The CL signal is produced during trapping of these thermalized secondary electrons by electronic levels of defects and impurities in the band gap of the insulating oxide  [4] [5]. It is important to note that CL is not due to the fast primary electrons which are mostly transmitted through the targets, as indicated by simulations by the ESTAR code (Table  I ).
IV. 1) Analysis of CL spectra The main emission bands observed in this work are centered at photon energies of ∼1. 7 eV, 2-2. 5 eV, ∼3 eV and ∼4 eV. To interpret these CL bands, it is useful to give a brief survey of literature on PL data in CeO2, which seem to show divergent interpretations as the same bands were attributed either to F-type centers (oxygen vacancies, VO) or to 5d-4f transitions of Ce 3+. For example, Aškrabič et al. assigned the 1. 7-eV band and the 2-2. 5 eV to charge transfer (CT) transitions from F + centers (VO. ) to the oxygen 2p states and to F 0 centers (VO x ) and F + centers, respectively  [22]. Alternatively the same bands at 2-2. 5 eV were associated to 5d 1 → 4f 1 transitions of Ce 3+ ions in non-stoichiometric nano-crystalline ceria (nc-CeO2) by Maksimchuk et al.
Cooling of those hot 1-keV electrons to thermal energy was followed by MC simulations in a water medium with the RITRACKS computer code  [62]. A cooling time of 37. 8 ± 8. 4 fs was found for 50 events. Little deviations in cross sections were found at low Es values for the various primary electron energies  [15]. This corresponds to small differences in (-dE/dx)inel (Table  I ) which is mainly dominated by the contribution of low-energy secondary electrons  [63]. Note that the maximum in CL intensity versus primary electron energy (Fig. 3 ) cannot be accounted for by the small variations in (-dE/dx)inel by only ∼10% (Table  I ), as (-dE/dx)inel has its minimum value in this energy range. As a result, the number of electron-hole pairs generated inside the sample is about the same between 400 keV and 1,250 keV  [64].
As was done before, a cut-off energy (Ecut) was used to select the range of efficient secondary electrons giving a maximum light emission yield: Ecut is the energy corresponding to an electron range equal to the sample thickness, as computed with the ESTAR computer code  [24]. This value is obviously larger for the 0. 5-mm thick single crystal (Ecut = 600 keV) than for the 150-µm thick sintered sample (Ecut = 250 keV) (Fig. 6 ). This rough approximation was confirmed by simulations of the survival probability of secondary electrons inside a 150-µm thick YSZ target versus Es for E = 400 keV and E = 1,250 keV, on the basis of spatial distributions of secondary electrons  [15]. Although electron-electron scattering is dominated by forward collisions, there is actually a large contribution for θ ∼90° after integration on Ω, even though (d 2 σi/dEsdΩ) is lower  [15].
The double differential cross sections for Ce atoms with a width of 18° are integrated over 2π sr. solid angle from the lower energy boundary of 1 keV to the maximum Es value for a given primary electron energy. Simulations with the PHITS code are benchmarked against other computer codes such as PENELOPE  [65]  by checking the effect of the selection of cross sections. A rather good agreement on the integrated differential cross section (dσi/dθ) is found between the various data banks as a function of θ for E = 400 keV and E = 1,250 keV (Fig. 8 ). The maximum (dσi/dθ) values for θ ∼0° and θ ∼70° are almost equal, and no strong differences are found as a function of E, between 400 keV and 1,250 keV, in contrast to our previous discussion based on (d 2 σi/dEsdΩ) for Es = Ecut (Fig. 6 )  [15].
Acts is based on the well-tested and highly functioning components of LHC track reconstruction algorithms, implemented with modern software concepts and inherently designed for parallel architectures. Multithreading becomes increasingly important to balance the memory usage per CPU core. However, a fully multithreaded event processing framework blurs the clear border between events, which has in the past often been used as a clearly defined validity boundary for event conditions. Acts is equipped with a full contextual conditions concept that allows to run concurrent track reconstruction even in case of multiple detector alignments, conditions or varying magnetic field being processed at the same time. It provides an experiment and, in particular, framework-independent software toolkit and light-weight, highly optimised event data model for track reconstruction.
Apart from this, computing workloads also need to be able to run in the environments that are available for execution. Making sure to maximize utilization of available resources is therefore crucial. One such optimization is to make sure that the applications run within the memory limit of the execution environment. ATLAS, for instance, is migrating most of their software  [8]  towards a multi-threaded execution model  [9]. This can enable new techniques to reduce the memory usage of the computations by sharing memory between threads. However, software needs to be explicitly written to be thread-safe in order to ensure error free and correct execution. To achieve this, all parts of the ATLAS software and the track reconstruction software  [10]  in particular, are undergoing development to remove any code that is not thread-safe, and build alternative implementations.
Goals and project description The Acts project  [11]  is an experiment independent tracking framework containing algorithms and tools. It is originally based on the ATLAS tracking software, and many of the concepts and methods are influenced by it. The goal is to enable building potentially experiment-specific applications using the components provided by Acts. These components are designed from scratch to be thread-safe by default. The components provide extensibility and flexibility, to allow injection of experiment specific configuration and workflows where necessary. One example is a generic geometry module, that can be adapted for a specific experiment. Compile-time calculations and optimizations are extensively used, which enable high performance at runtime. At the same time, virtual inheritance is avoided where possible.
Extraction of the code into an experiment independent environment has the benefit that changes can be made without having to adjust potential clients immediately. This facilitates shorter development cycles. Additionally, it enables the possibility to use the software in the context of other experiments. While initial developments in Acts focus on R&D, an eventual deployment as part of the production reconstruction software in ATLAS is foreseen. This contribution presents progress that has been made towards deployment, by connecting Acts to the ATLAS geometry, and providing thread-safe access to information required during processing.
Modeling of the ATLAS geometry The long term goal of deployment of Acts components into the ATLAS software stack requires implementing the current and future ATLAS geometry using Acts' geometry libraries. To this end, Acts is built as part of the ATLAS externals project, which delivers libraries for use within the Athena code. Dedicated geometry building code reads the geometry from AT-LAS' internal geometry representation, GeoModel  [12]. This information is then processed and converted into the Acts geometry description. The geometry description is optimized for fast navigation, by preparing its structure during construction. To this end, elements are logically ordered and collected to efficiently retrieve potential navigation targets during propagation. Sensors are grouped into layers which, in turn, are grouped into volumes.