dataesr/scientific-paragraphs-categorization huggingface dataset:
54 ). The analysis stops when reaching data saturation (i. e. , no new properties emerge from the categories). At the end of the process, answers to the questionnaire were analyzed quantitatively to confirm the emerging theory, except for items about presence (Q18-Q21). Indeed, once these questions were answered, we realized that externally assessing children's feeling of presence through practitioners' quantitative ratings was not reliable enough. Videos were only used to double-check other data in case of doubts during the analysis process (e. g. , potential mistakes in the questionnaire answers). Grounded theory and quantitative analysis were performed with the software MaxQDA 4  and Matplotlib 5. Children's views were considered, but practitioners' interpretations prevailed as children were minimally verbal.
Discussion This paper had three main research questions related to the possibility for Magic Bubbles AR environment to secure autistic children with SLN (RQ1), to prompt the child-practitioner relationship (RQ2), and to evaluate the quality of children's experiences (RQ3). To address them, we conducted a long-term field study with seven autistic children with SLN at a day hospital. Multiple data were collected: practitioners' observations (questionnaires and interviews), children's drawings, and educators' observations. Using a grounded theory approach and descriptive statistics we built three interconnected categories that describe children's experiences: Encountering the AR setting, Sharing experience over time, and Ways of getting secure. A fourth category, Conditions supporting the AR experience, represents environmental factors influencing this experience (see again Figure  4 ).
Acknowledgments We want to thank the children who participated in the protocol, and their parents who agreed with their participation. We also want to thank the entire clinical team of the day hospital  André Boulloche  who enabled us to conduct this experience. This work is part of the  AudioXR4TSA  project, funded by the  DIM RFSI Ile de France. This work was also supported by  French government  funding managed by the  National Research Agency  under the  Investments for the Future program   (PIA)  under grant  ANR-21-ESRE-0030 / CONTINUUM.
c Spontaneous switching recorded in two consecutive XMCD datasets (red/blue) in the absence of applied B-field, shown at the Mn L3 (main graph) and Mn L2 (inset) absorption edges. d Mn L3 -edge model based on 4 Gaussian functions simulating Mn s and Mn i spectral contributions. For PM the Mn s and Mn i features are aligned, with their amplitude dependent on the B-field. The FiM consists of oppositely aligned peaks with different amplitude. M and M SW refer to the reference and switched state respectively. Cd 1-x Mn x Te DMS system  23. The top-inset is a zoom-in into the Mn L3 absorption edge, showing two distinct XMCD features separated by ≈0. 5 eV. A time series of XMCD spectra for selected B-fields are shown in Fig. 1b , revealing that these features fluctuate, eventually completely and spontaneously reversing at zero B-field (Fig.
These two driving frequencies, schematically depicted in Fig. 3c , exemplify stochastic resonance in two-frequency signal systems  33. With such a combination of periodic and aperiodic driving frequencies the SR can x: [111]   y: [111]   inter Mn (i ≠ j) intra Mn (i = j) be harnessed, e. g. controllably suppressed by detuning the frequencies from their optimal setting, as schematically depicted by aperiodic gray flash symbols in Fig. 3d. We achieved this by measuring XMCD data with different photon energy ranges (grey bottom traces in 3d), thereby moving away from the optimal synchronization with driving frequency J 1,1 [meV] t 1 = t 2 = 1 2 t Ω 32 , where t 1,2 is the time needed to measure one XAS spectrum, and t Ω one full XMCD dataset (i. e. two XAS spectra measured with opposite circular polarisations).
The XMCD measurements were recorded as on-the-fly scans for a given magnetic field up to 6 T either parallel or anti-parallel to the beam direction. All data presented were measured during three independent beamtimes on fresh samples prepared in exactly the same way. The same samples were used for further NEXAFS (SuperXAS beamline at Swiss Light Source) and SQUID magnetometry. Special extra-large samples capped with Au were prepared for low energy muon spin relaxation experiment (LE-μSR) at the μE4 muon beamline at the Paul Scherrer Institut  49. LE-μSR time spectra are measured with two positron detectors placed opposite to each other for the considered samples. Temperature dependent asymmetry spectra were obtained under an external magnetic field of 5 mT applied perpendicular to the initial muon spin polarization direction.
Crystal structure and computational details Using first-principles calculations within the density functional theory, we have investigated the ground state magnetic properties of bulk Ge 0. 867 Mn 0. 133 Te. The calculations were carried out using the multiple scattering KKR Green function method as implemented in the spinpolarized fully relativistic Korringa-Kohn-Rostoker (SPRKKR) code  52. The exchange and correlation effects were incorporated within the LDA framework  53. Brillouin zone integrations were performed on a 39 × 39 × 39 dense mesh of k-points. The angular momentum expansion up to l max = 4 has been used for each atom. The energy convergence criterion and coherent potential approximation (CPA) tolerance has been set to 10 -7 Ry. The potential is constructed in full potential geometry.
Acknowledgements We thank  Stefano Rusponi  and  Valerio Scagnoli  for fruitful discussions. J. H. D. gratefully acknowledges financial support from the  Swiss National Science Foundation  project No. PP00P2_170591. J. M. and S. dS. thank the CEDAMNF with reg. no. CZ. 02. 1. 01/0. 0/0. 0/15_003/0000358  and within the project QM4ST with reg. no. CZ. 02. 01. 01/00/22_008/ 0004572 , co-funded by the  ERDF  as part of the MŠMT. G. S. acknowledges support by the  Austrian Science Funds (FWF) , Projects  P30960-N27  and  I4493-N. DK acknowledges the  Lumina Quaeruntur fellowship  of the  Czech Academy of Sciences. M. G. acknowledges the financial support under grants  APVV-SK-CZ-RD-21-0114 ,  VEGA 1/0105/20  and  Slovak Academy of Sciences  project  IMPULZ   IM-2021-42  and project  FLAG ERA JTC 2021   2DSOTECH.
Code availability In the manuscript we used two ab-initio DFT based packages. SPRKKR multiple scattering package is freely available (no costs apply) under the specific user license and the package can be downloaded upon registration (https://www. ebert. cup. uni-muenchen. de/index. php/en/ software-en). The Python based interface, ase2sprkr, is published under the MIT-license (https://github. com/ase2sprkkr/). Quantum ESPRESSO suite can be downloaded under GNU General Public License and is open source code (https://www. quantum-espresso. org/). All post-processing procedures and scripts used to evaluate experimental and theoretical data are available from the authors upon request.
Methods and results Sixty seven whole/intact Perdiz arrow points recovered from Caddo burial contexts in Camp, Nacogdoches, and Shelby counties comprise the basis of this study (supplementary materials). A standard suite of linear metrics were collected for each specimen, including maximum length, width, thickness, stem length, and stem width (Table @ref{tab:table1}). Following collection, data were imported to R  [77] , where boxplots were produced, along with a principal components analysis (PCA), followed by a permutational multivariate analysis of variance (perMANOVA) to test whether the morphology of Perdiz arrow points differs between the behavioral regions (supplementary materials). Boxplots illustrate the distribution and mean for each of the five linear variables (Figure  2a-e ), and the PCA (Figure  2f ) illustrates over 92 percent of the variation in the sample among PC1 (84.
Predictive model A support vector machine is a supervised machine learning model regularly used in classifying archaeological materials  [14, 49, 29, 37, 80, 28] , which has utility in comparing and classifying datasets aggregated from digital repositories, comparative collections, open access reports, as well as other digital assets. For this effort, linear data were imported and modeled using the scikit-learn Fig. 2  Boxplots for a, maximum length; b, maximum width; c, maximum thickness; d, stem length; e, stem width, and f, PCA [on correlation matrix] for linear metrics associated with the Perdiz arrow points. Additional information related to the analysis, including all linear data and the code needed to reproduce these results, can be found in the supplemental materials at https://seldenlab.
github. io/perdiz3/. package in Python  [51, 19]  (supplementary materials), and subsequently split into training (75 percent) and testing (25 percent) subsets. A standard scaler was used to decrease the sensitivity of the algorithm to outliers by standardizing features, and a nested cross validation of the training set was used to achieve unbiased estimates of model performance, resulting in a mean cross validation score of 86 percent (supplementary materials). The model was subsequently fit on the training set, yielding a receiver operator curve score of 97 percent, and an accuracy score of 94 percent (supplementary materials).
Geometric morphometrics Each of the arrow points was imaged using a flatbed scanner (HP Scanjet G4050) at 600 dpi. The landmarking protocol developed for this study (supplementary materials) included six landmarks and 24 equidistant semilandmarks to characterize Perdiz arrow point shape, and were applied using the StereoMorph package in R  [50]. The characteristic points and tangents used in the landmarking protocol were inspired by the work of Birkhoff  [15]. Landmarks were aligned to a global coordinate system  [40, 41, 73] , achieved through generalized Procrustes superimposition  [60] , performed in R 4. 1. 1  [77]  using the geomorph package v4. 0. 1  [5, 12]  (Figure  3 ). Procrustes superimposition translates, scales, and rotates the coordinate data allowing for comparisons among objects  [32, 60].
The geomorph package uses a partial Procrustes superimposition that projects the aligned specimens into tangent space subsequent to alignment in preparation for the use of multivariate methods that assume linear space  [59, 73]. Principal components analysis  [36, 56]  was used to visualize shape variation among the arrow points (Figure  4 ). Shape changes described by each principal axis are commonly visualized using thin-plate spline warping of a reference image or 3D mesh  [42, 72]. A residual randomization permutation procedure (RRPP; n = 10,000 permutations) was used for all Procrustes ANOVAs  [1, 22] , which has higher statistical power and a greater ability to identify patterns in the data should they be present  [7]. To assess whether shape differs by group (region), Procrustes ANOVAs  [31]  were also run that enlist effect-sizes (z-scores) computed as standard deviates of the generated sampling distributions  [23].
3  Results of generalized Procrustes analysis, illustrating mean shape (black) and all specimens in the sample (gray), as well as the difference in centroid size for Perdiz arrow points from the two behavioral regions. Additional information related to the GPA, including all data and code needed to reproduce these results, can be found in the supplemental materials at https://seldenlab. github. io/perdiz3/. the south), suggesting that between-region differences in Perdiz arrow point size may be more visually apparent than differences in shape. A comparison of mean consensus configurations was used to illustrate shape differences from the northern and southern behavioral regions. Diacritical morphology is characterized by a comparatively smaller blade and larger stem in the north, and by a comparatively larger blade and smaller stem in the south.
Further, the angle between the shoulder and base is more acute, with a base that is gen-Fig. 4  Principal components analysis plot (PC1/PC2) for Perdiz arrow points by behavioral region/community (top; gray squares, north; orange triangles, south), and results of modularity (bottom left) and blade/base morphological integration (bottom right) analyses. Additional information related to the PCA, including the full listing of results and all data and code needed to reproduce these results, can be found in the supplemental materials at https://seldenlab. github. io/perdiz3/. erally shorter and narrower in the southern behavioral region (supplementary materials). The analysis of modularity, which compares within-module covariation of landmarks against between-module covariation was significant (see Figure  4  and supplementary materials)  [3, 6] , demonstrating that Perdiz arrow point blades and bases are, in fact, modular.
Data Management The data and analysis code associated with this project can be accessed through the GitHub repository (https://github. com/seldenlab/perdiz3) or the supplementary materials (https://seldenlab. github. io/perdiz3/); which are digitally curated on the Open Science Framework (DOI: 10. 17605/OSF. IO/VZHJR). Images of all Perdiz arrow points used in this study were made available in an open access comparative collection (https://scholarworks. sfasu. edu/ita-perdiz/), with permission from the Caddo Nation of Oklahoma. These supplementary materials include all analysis data and code used in the study, providing a means for others to reproduce (exactly) those results discussed and expounded upon in this article. The replicable nature of this undertaking provides others with the means to critically assess and evaluate the various analytical components of this study, which is a necessary requirement for the production of reliable knowledge  [33, 53, 30].
Numerical methods The numerical integrations presented in this section are carried out using dNami  (Alferez et al. 2022). Explicit spatial (five point, fourth-order centred finite difference scheme) and temporal (third-order Runge-Kutta) schemes are used to discretise the governing equations. Message passing interface communication operations are used to enforce the spherical boundary conditions. All the simulations presented below are run at a grid size of 6144 × 3072 that corresponds to a ∼3 km × 3 km grid resolution at the equator following a grid convergence study of the results. All the source files required to run the TWC simulations presented below as well as documentation detailing how to reproduce the cases are available on the dNami GitHub repository (https://github. com/dNamiLab/ dNami).
This was also captured by a geostationary For the latter, the field is clipped to 10 500 km from Tonga so as to not obscure the first field. The location of selected ground pressure measurements in figure  12  are illustrated by the coloured dots. The colour indicates which group of sensors the measurement belongs to (green: Sensor Com. Archive, red: Weather News Inc, blue: NOAA/ASOS). The snapshots correspond to times when the main wave is focusing towards HTHH's antipode in the Sahara (yellow marker). The TWC model correctly predicts the wave location, including the pinches along its front. The trailing waves in the satellite data correspond to subsequent eruptions following the most energetic one, see, e. g. Wright et al. (2022). satellite and illustrated in, e. g. Wright et al. (2022)  who processed GOES-16 data to make the reflected wave visible.
A manual approach, rather than advanced signal processing, is justified due to the low number of frames (30 are retained here) and the significant noise in some regions (e. g. due to cloud cover). (iv) The obtained temporal field of wave position in (distance from  Tonga-azimuth)  coordinates is interpolated to a uniform σ grid by cubic-spline interpolation that yields the curves plotted in figure 7. (v) For representation, such as that of figure 1, the interpolated wave position is remapped to (longitude-latitude) coordinates. This data was gathered and post-processed for 15 January 2022 and time averaged over the day to obtain the base flow quantities in (D1). The geopotential field was converted to height (as per ECMWF documentation https://confluence. ecmwf. int/display/ CKB/ERA5%3A+data+documentation) that is used for vertical averaging operations.
The colour indicates which group of sensors the measurement belongs to (green: Sensor Com. Archive, red: Weather News Inc, blue: NOAA/ASOS). The snapshots correspond to times when the main wave is focusing towards HTHH's antipode in the Sahara (yellow marker). The TWC model correctly predicts the wave location, including the pinches along its front. The trailing waves in the satellite data correspond to subsequent eruptions following the most energetic one, see, e. g. Wright et al. (2022).
Declaration of interests. The authors report no conflict of interest. Data availability statement. The code and data used to make the figures in this paper can be found at https:// doi. org/10. 5281/zenodo. 7197431. The bathymetry data was obtained from  GEBCO (GEBCO Compilation Group ,  2022 , doi: 10. 5285/e0f0bb80-ab44-2739-e053-6c86abc0289c ). The international standard atmosphere details can be found at https://www. iso. org/standard/7472. html. The geostationary data were accessed on 24 January 2022 from https://registry. opendata. aws/noaa-goesfor NOAA geostationary operational environmental satellites (GOES) 16 and 17, from https://registry. opendata. aws/noaa-himawarifor JMA Himawari-8, from https://navigator. eumetsat. int/product/EO:EUM:DAT:MSG: HRSEVIRIfor EUMETSAT High Rate SEVIRI Level  1.
sensor. community/. The global run-up wave heights provided by the NOAA can be accessed at https://www. ngdc. noaa. gov/hazel/view/hazards/tsunami/related-runups/5824. The polar ice coverage for January 2022 was gathered from the National Snow and Ice Data Center at: https://nsidc. org/data. The code used to perform the simulation is open source and includes the source files and documentation required to perform the TWC simulations in this work: https:// github. com/dNamiLab/dNami.
Species occurrence data Native species occurrence records (783 species considered) at the watershed scale (i. e. , Hydrologic Unit Code 8, HUC8) were obtained through NatureServe (https://www. natureserve. org) and included both extant and extinct species to account for species historically present in a given watershed but extirpated as a potential consequence of various human activities such as species invasions. Occurrence records of the naturalized or established non-native fish species (300 species considered) were obtained through the U. S. Geological Survey (USGS) Non-indigenous Aquatic Species (NAS) database  48. Nonnative species data includes exotic species that were historically absent from the continental US, and translocated species that were native to the continental US but translocated to watersheds from which they were historically absent.
This dataset only considered the identified species that locally create self-sustaining populations, thus we excluded records of non-self-sustaining or eradicated populations, vagrant species detected in only one sampling occasion, and nonidentified or hybrid species. NatureServe and USGS data provide comprehensive species lists per watersheds, and have often been used in previous studies  [49] [50] [51] [52]. Watershed average area was 3628. 7 ± 2113. 2 km². This spatial grain was relevant to investigate species invasiveness and community invasibility, because native and nonnative species occurring in each watershed are actually encountering each other and can thus interact. The 1868 considered watersheds cover most of the continental US, and belong to 18 river basins. Several watersheds per basin were thus considered and watersheds are thus not strictly independent units, but each account for homogeneous environmental conditions, and ecosystem structure  53.
4  and S5 ). Predictors used in the invasiveness models Functional traits. We collected ten morphological traits related to fish locomotion and food acquisition and ten additional traits related to life-history and physiological functions from FISHMORPH database  54  , the Fish Traits database for North American freshwater fishes  55  , and FishBase  56. The ten morphological traits are maximum body length, body elongation, relative eye size, oral gape position, relative maxillary length, vertical eye position, body lateral shape, pectoral fin vertical position, pectoral fin size, and caudal peduncle throttling. Except for maximum body length, the other nine morphological traits are relative traits that were measured as unitless ratios. The ten additional ecological traits are longevity, fecundity, mature age (i.
e. , age of sexual maturity), trophic level, temperature range, minimum temperature, maximum temperature, euryhaline (yes, no), parental care (non-guarders1: open substratum spawners, non-guarders2: brood hiders, guarders1: substratum choosers, guarders2: nest spawners, bearers), and diet breadth (from 1 to 9). See Table  S2  for details on the 20 functional traits. Due to insufficient information on some species, some values were missing in the raw functional trait data. Overall, 18. 1% of the values were missing in the raw trait dataset of 859 fish species, and missing values distributed evenly among the native, nonnative, and taxonomic orders (Fig. S6 ). We statistically imputed these missing values (NA) with a machine learning algorithm called 'missForest'  57, 58. This method uses a random forest trained on the observed values of a data matrix to predict the missing values and automatically calibrates the filling values by a set of iterations.
md and cd were computed for all 1715 watersheds with at least one non-native species. See Fig. 1  for details about how these two metrics were calculated. Environmental and human-related variables. We also included the variables widely used in testing the three main hypotheses relevant to community invasibility  4. Variables related to biotic acceptance/resistance hypotheses were selected as the native species richness (NSR), net primary productivity (NPP) and watershed area (Area, km 2 ). NPP was taken from an online data repository (http://files. ntsg. umt. edu/), using the mean annual NPP (in g C m -2 yr -1 ) from 2000 to 2015. Area at the watershed basin scale is used as quantitative surrogate for habitat size and heterogeneity, which is known to correlate with native freshwater fish species richness  4, 65.
This anthropic disturbance is not redundant with the GDP or HFI, and has a potential important effect on non-native species establishment 2. In addition, these human activities may also indirectly affect invasibility by reducing the abundance or number of native species, and thus reduce biotic resistance of the native fauna and ease non-native species establishment. We, therefore, considered that the GDP, HFI, and DOF are the best available surrogates for propagule pressure and establishment risk. HFI is a comprehensive representation of anthropogenic threats to biodiversity. The HFI dataset (resolution: 1 km 2 ) was taken from  68  , and for any grid cell, the value can range between 0-50. GDP measures the size of the economy and is defined as the market value of all final goods and services produced within a region in a given period.
The GDP dataset (in US$, 1 square degree resolution) was taken from ref. 69. DOF measures the degree to which river networks are fragmented longitudinally by infrastructure, such as hydropower and irrigation dams  1. The DOF dataset (resolution: 500 m 2 ) was taken from 1 , and for any grid cell, the value can range between 0-100. See Table  S3  for a statistical summary of the values of these variables. If community invasibility is strongly and positively correlated to NSR, NPP, and Area, the biotic acceptance hypothesis will be supported. Otherwise, if community invasibility is strongly and negatively correlated to NSR and Area, the biotic resistance hypothesis will be supported  4. Human activity hypothesis will be supported if community invasibility is highly correlated to the HFI, GDP, and DOF.