dataesr/scientific-paragraphs-categorization huggingface dataset:
If the slope is smaller than unity, the dataset on the vertical axis is the one which satisfies that condition. Although it is not clear from the left plot of Fig. 3  which dataset is better, the slope m = 0. 686 ± 0. 004 indicates that the MFI+K/Ka+PR4 dataset provides a better fit. Moreover, the K/Ka+PR4 dataset has larger uncertainties which can mask model inconsistencies. On the other hand, from the middle plot of Fig. 3 , we observe that the inclusion of the K and Ka WMAP bands to the MFI+PR4 dataset improves the goodness of the fit. Finally, comparing MFI+K/Ka+PR4 with MFI+K/Ka+PR3, we see that PR3 provides a better fit in the Galactic plane, while PR4 10 The χ 2 -distribution with N dof divided by N dof. fits better outside the Galactic plane (Fig. 2 ). Since the fit in the Galactic plane is bad in both cases we have chosen the MFI+K/Ka+PR4 as our default dataset as it retrieves better fits within the 95% confidence regions (pixels outside the Galactic plane, Fig.
Synchrotron Curvature We have also explored a synchrotron model with curvature, i. e. , the model presented in equation (3), using the MFI+K/Ka+PR4 dataset. Fig. 8  shows the estimation and uncertainty maps of the curvature parameter as well as the χ 2 red map and the cs signal-to-noise map. We observe from the signal-to-noise map that curvature is detected at more than 3σ in the Galactic plane, in regions where the fit is not good as it can be seen from the χ 2 red map. Even though the inclusion of a curvature parameter is not able to explain the complexity of this region, this parameter can account for some effects along the Galactic plane, e. g. , Faraday rotation. Outside the Galactic plane the estimated cs values are close to zero and their uncertainties are around 0. 1, which are the expected value and the spread of the prior set on cs.
Recovered Amplitudes and Comparison with Planck results We have compared our baseline results, i. e. , using the MFI+K/Ka+PR4 dataset and a power law as the synchrotron model, to those obtained from the Commander pipeline  (Eriksen et al. 2008 ) applied to PR4 data 14. We have only considered this pipeline among those used by Planck, since it is the reference method with regard to the recovery of foreground components. In Figs. 12 to 14, we show a comparison of the CMB, the synchrotron emission at 30 GHz, and the thermal dust emission at 353 GHz between Commander and our results. In order to perform a direct comparison we have filtered Commander results with FDEC. The left column shows the Q and U Commander amplitudes, the center column our amplitudes and the right column the corresponding uncertainties.
Dust Spectral Parameters Although the frequencies of QUIJOTE-MFI do not overlap with the spectral range where the thermal dust is more dominant, we have studied whether the inclusion of this data set in the analysis can help with the thermal dust characterization due to an improvement on the determination of the rest of the polarized foreground parameters. Fig. 17  shows the thermal dust spectral index β d recovered with the default data set, modelling the synchrotron emission as a power law, in two cases: • T d is included as a model parameter. • T d is fixed to Commander's estimation of the thermal dust temperature from the component separation analysis in intensity (Planck Collaboration 2016) like Commander did in their polarization analysis. Fixing T d helps breaking its degeneracy with β d in the Rayleigh-Jeans part of the thermal dust spectrum, which is the one observed with Planck in polarization.
That excess appears since there are pixels where the model is not able to track the true sky emission, mainly in the Galactic plane. Thus, those pixels are highly inconsistent with this χ 2 distribution. Fig. 22  shows the Q and U residuals maps of every frequency channel from the MFI+K/Ka+PR4 dataset. We find that Planck and WMAP residuals maps are reasonably consistent with the expected noise except along the Galactic plane. The residuals in this region are a consequence of an incorrect modelling of the sky as we saw in the χ 2 red maps. For the MFI channels we observe that the largest residuals are located in compact regions along the Galactic plane. We observe in the 11 GHz U channel a redder region in the NPS's closest part to the Galactic centre. This region overlaps with the area where we obtain a better goodness-of-fit if Faraday Rotation effects are taken into account, see Fig.
DATA AVAILABILITY The parameter maps obtained from the component separation analysis in the default case, i. e. , with the MFI+K/Ka+PR4 dataset using a power law to model the synchrotron emission, are included in the released data products associated to the QUIJOTE-MFI wide survey. These data products as well as the maps can be freely downloaded from the QUIJOTE web page 18  , as well as from the RADIOFOREGROUNDS platform 19. They include also an Explanatory Supplement describing the data formats. Any other derived data products described in this paper are available upon request to the QUIJOTE collaboration. In order to test the assumption of having the same βs in both Q and U , we fit Q and U signals independently. Fig. A1  shows the spectral index, the uncertainty of the spectral index as well as the reduced χ 2 maps obtained from the three independent fits using the MFI+K/Ka+PR4 dataset.
Ingenio project CSD2010-00064 (EPI: Exploring the Physics of Inflation). We acknowledge support from the ACIISI, Consejeria de Economia, Conocimiento y Empleo del Gobierno de Canarias and the European Regional Development Fund (ERDF) under grant with reference ProID2020010108. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 687312 (RADIOFOREGROUNDS). EdlH acknowledges financial support from the Concepción Arenal Programme of the Universidad de Cantabria. DT acknowledges the support from the Chinese Academy of Sciences (CAS) President's International Fellowship Initiative (PIFI) with Grant N. 2020PM0042. FP acknowledges support from the Spanish State Research Agency (AEI) under grant number PID2019-105552RB-C43.
ACKNOWLEDGMENTS We thank the staff of the  Teide Observatory  for invaluable assistance in the commissioning and operation of QUIJOTE. The QUIJOTE experiment is being developed by the  Instituto de Astrofisica de Canarias (IAC) , the  Instituto de Fisica de Cantabria (IFCA) , and the  Universities of Cantabria, Manchester and Cambridge. Partial financial support was provided by the  Spanish Ministry of Science and Innovation  under the projects  AYA2007-68058-C03-01 ,  AYA2007-68058-C03-02 ,  AYA2010-21766-C03-01 ,  AYA2010-21766-C03-02 ,  AYA2014-60438-P ,  ESP2015-70646-C2-1-R ,  AYA2017-84185-P ,  ESP2017-83921-C2-1-R ,  AYA2017-90675-REDC  (co-funded with  EU FEDER  funds),  PGC2018-101814-B-I00 ,  PID2019-110610RB-C21 ,  PID2020-120514GB-I00 ,  IACA13-3E-2336 ,  IACA15-BE-3707 ,  EQC2018-004918-P , the  Severo Ochoa Programs   SEV-2015-0548  and  CEX2019-000920-S , the  Maria de Maeztu Program   MDM-2017-0765 , and by the Consolider-.
Thus, the error introduced by not considering this weak neutron-γ cascade was much smaller than the statistical uncertainties in the analysis. The result showed about 7. 0(5)% of total neutron emissions going to the 132 Sn excited states. Their contribution was subtracted from the total neutron activity to ensure the neutron intensities feeding the ground state were extracted properly. The neutron TOF spectra in Figs. 2(b ) and 2(f) were fit by a template neutron response function. The procedure to determine the response function of individual peaks is explained as follows: First, the TOF spectrum was simulated for monoenergetic neutrons with GEANT4  [25] , which took into account all the neutron-scattering material at IDS and the time resolution of INDiE modules. Second, the simulated profile was convolved with a Breit-Wigner style distribution  [26]  if a state had a sizable width in energy (broad resonance) greater than our resolution.
ACKNOWLEDGMENTS We acknowledge the support of the ISOLDE Collaboration and technical teams. This project was supported by the  European Unions Horizon 2020  research and innovation programme Grant Agreements No. 654002  ( ENSAR2 ) and the  Marie Skłodowska-Curie Grant  Agreement No. 101032999  (BeLaPEx), by the  Office of Nuclear Physics, U. S. Department of Energy  under Awards No. DE-FG02-96ER40983 (UTK ) and No. DE-AC05-00OR22725  (ORNL), by the  National Nuclear Security Administration  under the  Stewardship Science Academic Alliances program  through  DOE  Award No. DE-NA0002132 , by the  Romanian IFA  project  CERN-RO/ISOLDE , by the  Research Foundation Flanders (FWO, Belgium) , by the  Interuniversity Attraction Poles Programme  initiated by the  Belgian Science Policy Office (BriX network   P7/12 ), by the  German BMBF  under Contracts No.
05P18PKCIA  and No. 05P21PKCI1  in Verbundprojekte  05P2018  and  05P2021 , by the  UK Science and Technology Facilities Research Council (STFC)  of the UK Grants No. ST/R004056/1 , No. ST/P004598/1 , No. ST/P003885/1 , No. ST/V001027/1 , and No. ST/V001035/1 , by  National Natural Science Foundation of China  under Grant No. 11775316 , by the  Polish National Science Center  under.
For CESAM/CESTAM with microscopic diffusion, it is fixed at 10 13 cm 2 s -1 , which allows for very efficient mixing and a good numerical stability. In this work, we use the CESTAM code without microscopic diffusion to model stars with an instantaneous mixing, along with the MESA code to model stars with a diffusive mixing. We ensured that the two codes use similar physics, and (especially) the same nuclear cross-sections. The rates are taken for both code from the NACRE compilation  (Angulo et al. 1999 ) except for the 14 N + 1 H reaction, for which the rate is taken from  Imbriani et al. (2005). Moreover, the two codes use the same step overshooting distance, d ov , prescription, namely, d ov = α ov min(H p , R cc ), with α ov as a free parameter, H p as the pressure scale height, and R cc as the radius of the convective core.
Acknowledgements We are grateful to  Manuel Cordova  ( EPFL ) for help in compiling the numerical DNP simulation package. This work was supported by  Swiss National Science Foundation  Grant No. 200020_212046 ,  EU H2020-INFRAIA  Grant No. 101008500 , and by an  H2020 Marie Skłodowska-Curie Individual fellowship  (grant number  101024369 ) (AV). Open Access funding provided by  École Polytechnique Fédérale de Lausanne.
Acknowledgments This project was started when the second-named author was visiting the first and third-named authors at the  LPSM, Sorbonne Université , whose hospitality is thankfully acknowledged. The first-and third-named authors are partially supported by the  DIMERS  project  ANR-18-CE40-0033  funded by the  French National Research Agency. The second-named author is partially supported by the  Swiss National Science Foundation. We would like to thank  Vladimir Fock  for valuable discussions and inspiration.
In this analysis, we search for CRDM from MeV=c 2 to GeV=c 2 with recoil protons at the Super-Kamiokande (SK) experiment  [38]. We use the data collected at SK during the 1996-2018 period (SKI-IV phases). The large fiducial volume and the directional reconstruction ability of SK, a water Cherenkov detector, enables a sensitive search for CRDM. The parameter space we explore extends by more than one order of magnitude beyond the existing limits  [35, 36]. Super-Kamiokande is a cylindrical 50 kton water Cherenkov detector located in Kamioka, Japan, under a 2700 meter water-equivalent rock overburden  [38]. The detector consists of an inner detector (ID) and an outer detector (OD) optically separated at 2 m from the detector's outer wall. There are 11 129 inward-facing 20-inch photomultiplier tubes (PMTs) viewing the 32 kton target volume of the ID, and the OD is viewed by 1885 outward-facing eight-inch PMTs.
The ID is used to reconstruct the energies, vertices, and to perform the particle identifications (PID) of the physics events, while the OD is primarily used as a veto for charged particles entering from outside the detector or identifying particles that exit the ID. This analysis uses the fully contained fiducial volume (FCFV) dataset composed of events that have activity only in the ID (FC) and are reconstructed with vertices more than 2 m from the ID wall, corresponding to the 22. 5 kton fiducial volume (FV). The total live time of the dataset is 6050. 3 days, corresponding to an exposure of 0. 37 megaton × years. The visible energy, corresponding to the energy of an electron that would cause the same amount of light in the detector, of the events is required to be above 30 MeV to remove spallation backgrounds induced by the cosmic-ray muons.
In summary, we report a directional search for the CRDM using a newly constructed proton sample selected from the data collected at Super-Kamiokande during the period of 1996-2018 (SKI-IV phases). In the absence of an excess from dark matter signals above the expected background, we derived new limits on the dark matternucleon interaction cross section, which are the most stringent constrains on hadronic coupling of sub-GeV dark matter so far. This result benefits from the large fiducial volume and directional reconstruction ability of SK, which motivates further exploration of CRDM and boosted dark matter in general from the next generation large neutrino detectors with directional capabilities, such as Hyper-Kamiokande  [54]  and DUNE  [55]. The reported proton sample efficiency and direction distribution can also be interpreted by any theory that predicts an excess of proton recoils from the direction of the GC.
We thank  Dr. Yohei Ema  for providing the CRDM flux and insightful discussions. We gratefully acknowledge the cooperation of the  Kamioka Mining and Smelting Company. The Super-Kamiokande experiment has been built and operated from funding by the  Japanese Ministry of Education, Culture, Sports, Science and Technology , the  U. S. Department of Energy , and the  U. S. National Science Foundation. Some of us have been supported by funds from the  National Research Foundation of Korea   NRF20090083526  (KNRC) funded by the  Ministry of Science, ICT, and Future Planning  and the  Ministry of Education  ( 2018R1D1A3B07050696 ,  2018R1D1A1B0 7049158 ), the  Japan Society for the Promotion of Science , the  National Natural Science Foundation of China  under Grants No. 11620101004 , the  Spanish Ministry of Science, Universities and Innovation  (Grant No.
TREXIO is an open-source file format and library developed for the storage and manipulation of data produced by quantum chemistry calculations. It is designed with the goal of providing a reliable and efficient method of storing and exchanging wave function parameters and matrix elements, making it an important tool for researchers in the field of quantum chemistry. In this work, we present an overview of the TREXIO file format and library. The library consists of a front-end implemented in the C programming language and two different back-ends: a text back-end and a binary back-end utilizing the HDF5 library which enables fast read and write operations. It is compatible with a variety of platforms and has interfaces for the Fortran, Python, and OCaml programming languages. In addition, a suite of tools has been developed to facilitate the use of the TREXIO format and library, including converters for popular quantum chemistry codes and utilities for validating and manipulating data stored in TREXIO files.
The Unix philosophy recommends the use of text files because they are architectureindependent, readable in any language, and can be read as a stream, which is useful for making programs communicate over a network. However, storing data in a text format can result in large file sizes and conversion from ASCII to binary format can be computationally expen-sive for large data sets. To address this concern, domainspecific binary formats have been developed, such as the Joint Photographic Experts Group (JPEG) format  3  for digital images and the Moving Picture Experts Group (MPEG) format  4  for videos. These binary formats are utilized through standardized application programming interfaces (API). In the field of wave function theory such a standard format and API is still lacking, and the purpose of the TREXIO library presented in this article is to fill this gap.
II. RELATED WORK It is worth mentioning that there have been several efforts to unify the data formats within different subdomains of quantum chemistry. Probably one of the earliest works in this direction was the definition of the Crystallographic Information File (CIF) for establishing databases of crystal structures. 6 A few years later, the Chemical Markup Language (CML)  7, 8  was introduced. It is a format based on the Extensible Markup Language (XML) which is used to describe chemical data: molecules, chemical properties, reactions, spectra, materials, etc. With formats like CIF or CML, the burden of following a standard is placed on the code writing the data. As a consequence, any tool that can read the format will be able to interpret the data without needing to understand the specific code that was used to produce it.
This means that data can be easily shared and reused across different programs, and new tools can be developed to work with the format without needing to know anything about the code used to produce the data. Recently, the cclib Python package  9  , originally developed for performing computational chemistry calculations, has accumulated several internal converters capable of parsing and transforming the output of different programs into the internal representation called ccData. A similar approach has been taken by the developers of IOData 10 , who have implemented converters and parsers for commonly used programs and their output files. However, there is currently no unified data representation or API that can be integrated into quantum chemistry codes to improve interoperability. Consequently, each time a given program modifies its input/output formatting, the IOData package must be adapted accordingly and promptly, which poses an additional challenge for maintainers.
More recently, consolidated efforts have given rise to QCSchema 11 , which provides an API-like access to data generated by existing quantum chemistry codes, thereby addressing the issue of dependence on the output file's formatting style. In this case, the responsibility for adhering to conventions falls on the code reading the data, as it must be aware of the conventions chosen by the code that generated the data. With the Electronic Structure Common Data Format (ESCDF)  12  and its associated library, codes that write data can supply metadata to assist codes that read data in comprehending the organization of the data in the files. Hence, ESCDF aims to provide low-level tools and flexibility to facilitate the exchange of large datasets between codes with highperformance I/O. While this greatly reduces the difficulty of understanding conventions for developers reading the data, they may still need to apply different conversions depending on the code that generated the data.
mod file), that depended on the compiled Fortran modules provided by the HDF5 library. As the format of the compiled Fortran modules is specific to the compiler vendor and even to the version of the compiler, the Q5Cost library could not be simply linked as an external library to any code. Using the Q5Cost library in a Fortran code imposed that the user's code was compiled with the same Fortran compiler as the one that was used to compile both the HDF5 Fortran modules and the Q5Cost library. This contamination of dependencies could lead to some important impact on the performance of the user's code, and the only solution to solve that problem was to compile many different versions of the HDF5 Fortran interface and Q5Cost library with multiple compilers and compiler versions. The TREXIO initiative, heavily influenced by the Q5Cost project, aims to propose a standard format and library for wave function calculations.
III. THE TREXIO FORMAT The TREXIO format (version 2. 3. 0) is designed to store all the necessary information to represent a wave function, including: the number of up-and down-spin electrons, nuclear coordinates and charges, basis set and effective core potential (ECP) parameters, atomic and molecular orbital parameters, Slater determinants and CI coefficients, configuration state function (CSF) definitions, and metadata related to the description of excited states. It is also capable of storing data required for the computation of the wave function, such as one-and two-electron integrals, numerical integration grids used in DFT calculations, and one-and two-particle reduced density matrices. One notable feature of TREXIO is that it is selfcontained, meaning that all the parameters needed to recreate the wave function are explicitly stored within the file, eliminating the need for external databases.
A. Data types So that TREXIO can be used in any language, we use a limited number of data types. It is important to keep in mind that these types are abstract in the sense that they are defined independently of their implementation, and are not tied to any specific representation on a computer. The main data types are int for integers, float for floating-point values, and str for character strings. The real and imaginary parts of complex numbers are stored separately as floats. To minimize the risk of integer overflow and accuracy loss, numerical data types are stored using 64-bit representations by default. However, in specific cases where integers are bounded (such as orbital indices in four-index integrals), the smallest possible representation is used to reduce the file size. The API presented in the next section handles any necessary type conversions.
There are also two types derived from int: dim and index. dim is used for dimensioning variables, which are positive integers used to specify the dimensions of an array. In the previous example, nucleus. num is a dimensioning variable that specifies the dimensions of the nucleus. charge array. index is used for integers that correspond to array indices, because some languages (such as C or Python) use zero-based indexing, while others (such as Fortran) use one-based indexing by default. For convenience, values of the index type are shifted by one when TREXIO is used in one-based languages to be consistent with the semantics of the language. Arrays can be stored in either dense or sparse formats. If the sparse format is selected, the data is stored in coordinate format. For example, the element A(i,j,k,l) is stored as a quadruplet of integers (i, j, k, l) along with the corresponding value.
Basis set In the basis group, the atomic basis set is defined as a list of shells. Each shell i is centered at a center A i , has a specified angular momentum l i , and a radial function R i. The radial function is a linear combination of N prim i primitive functions, which can be Slater type orbitals (STO, p = 1) or Gaussian type orbitals (GTO, p = 2). These primitive functions are parameterized by exponents γ ki and coefficients a ki : R i (r) = N i |r -R Ai | ni Nprim i k=1 a ki f ki (γ ki , p) e -γ ki |r-R A i | p. (1) Different codes have different normalization practices, so it is necessary to store normalization factors in the TREXIO file to ensure that it is self-contained and does not rely on the client program having the ability to compute overlap integrals. Some codes assume that the contraction coefficients are applied to normalized linear combinations of primitives, so a normalization constant f ki for each primitive must also be stored.