dataesr/scientific-paragraphs-categorization huggingface dataset:
The size constraint and qualitative support of the standard jet formation paradigm argues for the presence of a horizon. Together, our results strongly support the hypothesis that the central object in M87 is indeed a Kerr black hole, and provide new evidence for the long-believed connection between AGNs and SMBHs. The authors of this Letter thank the following organizations and programs: the Academy of Finland (projects 274477, 284495, 312496); the Advanced European Network of E-infrastructures for Astronomy with the SKA (AENEAS) project, supported by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement 731016; the Alexander von Humboldt Posterior in the fractional difference δ between the inferred angular gravitational radius of the black hole in M87 based on the EHT data and the values measured using stellar and gas dynamics.
043. 513 ) and  Spinoza Prize SPI   78-409 ; the  New Scientific Frontiers  with  Precision Radio Interferometry Fellowship  awarded by the  South African Radio Astronomy Observatory (SARAO) , which is a facility of the  National Research Foundation (NRF) , an agency of the  Department of Science and Technology (DST) of South Africa ; the  Onsala Space Observatory (OSO)  national infrastructure, for the provisioning of its facilities/observational support (OSO receives funding through the  Swedish Research Council  under grant  2017-00648 ) the  Perimeter Institute for Theoretical Physics  (research at  Perimeter Institute  is supported by the  Government of Canada  through the  Department of Innovation, Science and Economic Development Canada  and by the  Province of Ontario  through the  Ministry of Economic Development, Job Creation and Trade) ; the  Russian Science Foundation  (grant  17-12-01029 ); the  Spanish Ministerio de Economía y Competitividad  (grants  AYA2015-63939-C2-1-P ,  AYA2016-80889-P ); the  State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa " award for the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ); the  Toray Science Foundation ; the  US Department of Energy (USDOE)  through the  Los Alamos National Laboratory  (operated by  Triad National Security, LLC , for the  National Nuclear Security Administration of the USDOE  (Contract  89233218CNA000001 )); the  Italian Ministero dell'Istruzione Università e Ricerca  through the  grant Progetti Premiali 2012-iALMA  ( CUP C52I13000140001 );  ALMA North America Development Fund ; Chandra  TM6-17006X.
The SMA is a joint project between the SAO and ASIAA and is funded by the  Smithsonian Institution  and the  Academia Sinica. The JCMT is operated by the  East Asian Observatory  on behalf of the NAOJ, ASIAA, and  KASI , as well as the  Ministry of Finance of China, Chinese Academy of Sciences , and the  National Key R&D Program  (No. 2017YFA0402700 ) of China. Additional funding support for the JCMT is provided by the  Science and Technologies Facility Council (UK)  and participating universities in the UK and  Canada. The  LMT  project is a joint effort of the  Instituto Nacional de Astrófisica, Óptica, y Electrónica (Mexico)  and the  University of Massachusetts at Amherst (USA). The IRAM 30-m telescope on Pico Veleta, Spain is operated by IRAM and supported by  CNRS  ( Centre National de la Recherche Scientifique, France ),  MPG  ( Max-Planck-Gesellschaft, Germany)  and  IGN (Instituto Geográfico Nacional, Spain).
The SMT is operated by the  Arizona Radio Observatory , a part of the  Steward Observatory of the University of Arizona , with financial support of operations from the  State of Arizona  and financial support for instrumentation development from the  NSF. Partial SPT support is provided by the  NSF Physics Frontier Center award  ( PHY-0114422 ) to the  Kavli Institute of Cosmological Physics at the University of Chicago (USA) , the  Kavli Foundation , and the  GBMF  ( GBMF-947 ). The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA. The SPT is supported by the  National Science Foundation  through grant  PLR-1248097. Partial support is also provided by the  NSF Physics Frontier Center  grant  PHY-1125897  to the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Kavli Foundation  and the  Gordon and Betty Moore Foundation  grant  GBMF 947.
There the focus is on cortical areas taken from the Paxinos-Watson rat brain atlas. An older alternative integrated onto the DPABI (http://rfmri. org/dpabi) analysis platform has been built from a dataset of ninety-seven Sprague Dawley rats acquired in vivo. It provides an anatomical T 2 -weighted brain template associated with a set of brain tissue priors, and a coregistered version of the Paxinos-Watson rat brain atlas  21. Each of these resources is characterized by significant limitations. The use of the Waxholm resources may be hindered by large file sizes (resulting from the very high spatial resolution) when conducting analyses with 4D datasets such as functional MRI, as well as from having been created from a single subject, thus leading to the incorporation of specific-subject/acquisition related anatomical details and artefacts.
For the Waxholm template, the GM and WM are defined from the labeled regions in the corresponding atlas. Source data are provided as a Source Data file hippocampus we found similar results. With both templates stressed animals demonstrated a global decrease of gray matter concentration (GMC) within the hippocampus in accordance with previously reported observations  26. The observation was not dependent on the priors set used for the analysis, whether SIGMA (Fig. 3a,   ). Since the exposure to stress is expected to induce reduction in hippocampal volume  26  , it would appear that the VBM analysis using the Tohoku priors gives a more conservative estimate of the effect, while a similar analysis using the SIGMA priors is more sensitive. SIGMA anatomical atlas of the rat brain. Using the SIGMA anatomical template as a reference, we registered two MRI compatible atlases (Tohoku and Waxholm) of the rat brain into a common space.
In addition, we describe a brain mesh suitable for data visualization with common neuroimaging software (such as SPM, FSL, BrainVisa/Anatomist). Using an ex vivo imaging approach based upon high spatial resolution MGE acquisitions, we were able to achieve high contrast between the GM, WM, and CSF in the rat brain samples and a high level of anatomical detail. Our protocol avoided the deformation which would have accompanied brain extraction from the skull. The parametrization of echoes ensured a high signal-to-noise ratio, while simultaneously restricting the influence of macroscopic magnetic susceptibility artefacts present in T Ã 2 -weighted images that are known to reduce the MRI signal within the entorhinal cortices and the amygdala. To these images, we applied the unified segmentation approach of SPM, which relies on a generative model combining bias correction with deformable tissue priors, and a Gaussian mixture model, to generate an anatomical template of the rat brain and corresponding GM, WM and CSF TPMs.
The creation of a second anatomical template, in the same space as the ex vivo SIGMA template but built using in vivo T 2 -weighted acquisitions provides another resource for the registration of in vivo anatomical and functional MRI data such as is required with BOLD sensitive EPI acquisitions. We recognize and point out a peculiarity of our TPMs, which is that certain regions of sub-cortical gray matter (thalamus, PAG) are included in the white matter prior. We believe this is due to a high fibber density within these structures which results in intensity range similar to that of white matter. This is a common phenomenon, one also found in other standard resources (Fig. 1 ). To confirm the potential of the SIGMA anatomical template of the rat brain and its corresponding tissue probability maps, we tested their use as a registration reference by analyzing the MRI data acquired on a cohort of animals previously characterized from a behavioral (elevated plus maze) and neuro-endocrine (corticosterone assay) points-of-view  26.
When we compared the results obtained with the Tohoku and SIGMA templates using the same anatomical MRI dataset and the same SPM data analysis pipeline, similar findings in the expected direction were obtained. It is of interest that the use of SIGMA resources resulted in more prominent effects. Recent studies suggest that interpolation introduced by nonlinear normalization procedures promotes over-regularization. To address these issues, alternative methodologies such as minimal deformation templates have been employed in both human and primates studies  [28] [29] [30]. It would thus appear reasonable to investigate using these alternativeprocessing strategies in rats to improve spatial normalization, particularly in longitudinal studies, by expanding the available resources to include age and strain-specific templates.
The present work was further motivated by the recognition of the need to develop resources having resolution parameters and ROI sizes appropriate for rat fMRI studies. Many anatomical atlases, such as the Waxholm or Tohoku atlases, are characterized by a large number of regions defined with a high spatial resolution. However, standard functional MRI acquisitions are unable to reach such high spatial resolutions while preserving an appropriate contrastto-noise ratio or repetition time. As a consequence, either an upsampling of the functional MRI datasets or a down-sampling of the atlas resolution is necessary when processing the data. Both these scenarios present drawbacks, so having appropriately designed and optimized resources can greatly improve and facilitate the analysis of functional MRI data.
Using our rs-fMRI dataset, we validated the use of both our atlases for functional connectivity analysis by building whole brain connectomes. We demonstrated not only that the small-world metrics, degree distribution and nodal modularity for both networks fit the expected parameters  31  , but also are comparable for both approaches. Users thus have the flexibility to choose and adapt the unique requirements of their investigations. Moreover, the availability of highly consistent anatomical and functional atlases within the same reference space should allow their concomitant use and encourage testing of more complex hypotheses. As a limitation, the SIGMA functional atlas has been built from resting-state fMRI acquisitions performed on anesthetized animals, using adapted concentrations of isoflurane permitting to keep animals in the same physiological conditions of breathing and temperature.
The projects main goal was to study the impact of stress upon the rat brain revealing imaging biomarkers of resistance and susceptibility to stress. All the data herein presented were acquired on control animals. In the Results section, we describe two examples of the application of the SIGMA resources on the analysis of data resulting from this project. Acquisition of in vivo MRI data. In vivo MRI data were acquired on an 11. 7 Tesla BioSpec preclinical scanner running on Paravision 6. 0 and equipped with a 72 mm diameter volume transmission coil and a 4-channel phased-array surface coil, optimized for rat brain, for reception (Bruker, Germany). Animals were anaesthetized using an air/O 2 mixture (50:50) and isoflurane (5% for induction, with maintenance concentrations varying from 0. 75 to 2%, depending on the animal).
They were then positioned in a dedicated cradle and placed within the magnet. During the functional acquisitions the breathing rate and body temperature were continuously recorded (using the SAM-PC software from SAII (SA Instruments, USA)). Body temperature was kept near 37 °C and breathing rate as close as possible to 70 bpm. The isoflurane concentration was adjusted for each animal to both control respiratory rate and thus limit as much as possible the potential influence of anesthesia on the animal's physiology. A Rapid Acquisition with Refocused Echoes (RARE) sequence with the following parameters was used to obtain a T 2 -weighted anatomical image for each rat brain: RARE factor = 16, effective TE/TR = 33/1500 ms, field-of-view = 28. 8 × 28. 8 × 14. 4 mm 3 , acquisition matrix = 192 × 192 × 48, spatial resolution = 150 × 150 × 300 µm 3 , 1 average, total acquisition time = 14 min 24 s.
The resting-state functional MRI dataset (rs-fMRI) consisted of a series of T 2 -weighted Blood-Oxygen-Level Dependent (BOLD) sensitive images, acquired using a multi-shot Spin-Echo Echo Planar Imaging (SE-EPI) sequence with the following parameters: 3 segments, TE/TR = 17. 5/2000 ms, field-of-view = 24 × 24 × 20 mm 3 , acquisition matrix = 64 × 64 × 20, spatial resolution = 375 × 375 × 750 µm 3 , inter-slice gap = 0. 25 mm, 450 repetitions, total acquisition time = 15 min. To reduce the artifacts resulting from movement associated with breathing, a trigger was used to ensure that functional images were only acquired during the most stable portion of the breathing curve. The trigger times were recorded by Paravision to be used during data post-processing. The rs-fMRI scan was performed at least twice, in two separate sessions, for each animal.
Acquisition of ex vivo MRI data. Ex vivo MRI data were acquired at the end of the in vivo experiments on 6 randomly chosen animals. Rats were euthanized with an overdose of sodium pentobarbital (100-150 mg. kg -1 , intraperitoneal injection), intracardiacally perfused with NaCl 0. 9% to remove blood and then fixed with paraformaldehyde 4% in phosphate buffered saline (PBS, 0. 01 M). After perfusion, animal heads were collected and post-fixed within paraformaldehyde 4% solution at 4 °C. 48 h prior to the MRI session, the paraformaldehyde solution was replaced by PBS (0. 01 M) for tissue rehydration. To avoid any possible damage and deformation of the brains, they were kept inside the skulls during the entire procedure. For scanning, the head was placed into a custom-built MRI compatible tube filled with Fluorinert FC-40 (3 M, Belgium), a magnetic susceptibilitymatching fluid.
Ex vivo MRI acquisitions were performed on the same 11. 7 Tesla BioSpec preclinical scanner as was used for the in vivo studies, using a 40 mm diameter transmit/receive volume coil (Bruker, Germany). The MRI protocol consisted of the acquisition of one T Ã 2 -weighted anatomical image using a Multiple Gradient Echo (MGE) sequence having the following parameters: TE/TR = 3/100 ms, 10 echoes spaced by 4 ms, field-of-view = 28. 8 × 21. 6 × 14. 4 mm 3 , acquisition matrix = 320 × 240 × 80, spatial resolution = 90 × 90 × 180 µm 3 , 16 averages, total acquisition time = 8 h 32 min (Supplementary Fig. 1 ). Processing of anatomical MRI data. All in vivo and ex vivo anatomical MRI data (T 2 -weighted and T Ã 2 -weighted images) were reconstructed using homemade Matlab routines (MathWorks, USA, Matlab R2016b) and were saved to NIFTI format.
For the ex vivo data, the sampled k-space was zero-filled  37, 38  ultimately to finally reconstruct T Ã 2 -weighted images with a spatial resolution of 90 × 90 × 90 µm  3. Building of the ex vivo SIGMA anatomical brain template. For each rat, we computed the sum of the 10 reconstructed T Ã 2 -weighted magnitude images (one per echo). Then, the sum images obtained for the 6 scanned animals were manually reoriented using the SPMmouse toolbox (http://www. spmmouse. org) of SPM8 software (Statistical Parametric Mapping, https://www. fil. ion. ucl. ac. uk/spm) and co-registered to the Tohoku brain template. Then, each co-registered image was segmented using the priors of the Tohoku brain template and the "New Segment" procedure of SPM8, to generate the corresponding probability maps of GM, WM and CSF using the Tohoku priors.
Each co-registered image was then segmented as previously described, using the GM, WM and CSF probability maps of the ex vivo SIGMA template. After segmentation, the normalization parameters were applied to produce normalized T 2 -weighted images that were segmented again to obtain more accurate GM, WM and CSF priors. Each set of priors was used to generate the final template of GM, WM and CSF probability maps using the same DARTEL approach, and the Jacobian image of each GM prior was applied to the corresponding normalized T 2 -weighted image. During the last step, the resulting 32 warped T 2 -weighted images were averaged to create the final in vivo anatomical template of the rat brain (Supplementary Fig. 6 ). Processing of functional MRI data. All in vivo functional MRI data (EPI images) were reconstructed using homemade Matlab routines and saved in NIFTI format.
After reconstruction, the trigger timing of respiratory rate was used to resample the data to a constant rate, in order to correct for the fact that breathing rhythm varied during the acquisition period. To minimize the amount of interpolation used while simultaneously avoiding loss of information, the new TR was set to the minimum across animals, which was found to be 2. 3 s. This resampling procedure was followed by two pre-processing steps applied on FSL (FMRIB Software Library, https://fsl. fmrib. ox. ac. uk/fsl/fslwiki). These were: (i) slice timing correction with an odd order of slices; (ii) motion correction, using the mean EPI image as reference, and motion scrubbing for identification of outlier volumes in motion. The mean SE-EPI image was then registered, using a linear registration with 7 degrees of freedom, to its corresponding anatomical T 2 -weighted image and then normalized, using a combination of affine and non-linear registrations, to the in vivo anatomical brain template.
The GM, WM and CSF tissue probability maps of the T 2 -weighted images were summed to calculate a brain mask. This brain mask and the corresponding inverted brain mask were registered to the native space of EPI images and subsequently used to isolate both the brain tissue signal and the mean signal external to the brain, as well as to skull strip the EPI data. The 6 motion parameters, outlier volumes and global brain signals were regressed from the motion-corrected data and the residuals considered as signals of interest. The concatenation of all spatial transformations was applied to the residual data, in order to spatially normalize data onto the in vivo brain template. We followed this step by spatially smoothing the data using a Gaussian filter (1. 5 voxels) and filtering the temporal signal using a band-pass filter between 0.
No non-linear registration was applied as it was found that these factors severally limited the performance of the algorithm, while the result of the linear registration was found to be satisfactory for the current purpose. For the Tohoku atlas, the FLIRT software of FSL was used to calculate a linear affine registration to our template with 12 degrees of freedom. This was followed by a non-linear registration performed with the FNIRT command of FSL (FMRIB's Nonlinear Image Registration Tool) using the previous affine registration as the initial approximation. The final registration transformations were applied to the corresponding atlases using a nearest neighbor interpolation method to preserve the label intensities. All the registered labels of interest were then merged into a common image.
stress). For each cluster, the significance of the peak voxel was set as p < 0. 05. Results presented as surface results were obtained with the BrainNet Viewer 47 (https://www. nitrc. org/projects/bnv, Version 1. 6), which generates the hippocampus mesh from anatomical atlas images. All statistical tests performed for the template comparison were two-sided tests. Atlas comparison-Functional connectivity analysis. To further validate the use of the SIGMA anatomical and functional atlases, we also tested the properties of functional networks built using both atlases. Functional networks were obtained using the full rs-fMRI processed dataset (n = 80) by extracting the mean timeseries of each ROI, using either the anatomical or functional atlas. We then calculated the Pearson correlation coefficient between each pair, creating for each subject a connectivity matrix.
We described the degree distribution, smallworldness and modularity of both kinds of networks using the Brain Connectivity Toolbox (https://sites. google. com/site/bctnet)  31. To understand if the use of ROIs having functional origin would represent more accurately the true signals, we compared the within-ROI signal variation at the voxel level to the ones obtained with the anatomical atlas using the non-parametric permutation testing randomize software of FSL. This analysis was restricted to voxels that belong to a ROI in both atlases. Results were considered significant at p < 0. 05 (non-parametric permutation-based group comparison with a Student t-test), corrected for the Family Wise Error rate and using threshold-free cluster enhancement. In order to allow comparison of the two atlases, and specifically to address the unequal number of ROIs and spatial resolution, regions of the anatomical atlas were merged according to their description and systems, thereby creating a new anatomical atlas having 82 ROIs.
Acknowledgements This work is part of the  SIGMA  project with the reference  FCT-ANR/NEU-OSD/0258/ 2012 , co-financed by the  French public funding agency ANR (Agence Nationale pour la Recherche ,  APP Blanc International II 2012 ), the  Portuguese FCT  ( Fundação para a Ciência e Tecnologia ) and the  Portuguese North Regional Operational Program (ON. 2-ONovo Norte)  under the National Strategic Reference Framework (QREN), through the  European Regional Development Fund (FEDER)  as well as the Projecto Estratégico cofunded by  FCT  ( PEst-C/SAU/LA0026-/2013 ) and the  European Regional Development Fund COMPETE  ( FCOMP-01-0124-FEDER-037298 ). D. A. B. and A. N. were funded by grants from  FCT-ANR/NEU-OSD/0258/2012. R. M. was supported by the  FCT  fellowship grant with the reference  PDE/BDE/113604/2015  from the  PhDiHES program.
A. C. was supported by a grant from the foundation NRJ. P. M. was funded by  Fundação Calouste Gulbenkian  (Portugal; 'Better mental health during ageing based on temporal prediction of individual brain ageing trajectories TEMPO') with Grant Number  P-139977. France Life Imaging  is acknowledged for its support in funding the  NeuroSpin platform of preclinical MRI  scanners. The authors also acknowledge and thank  Edward Ganz, MD , for proof reading our work.
Introduction Today, the NLP community can enjoy an evergrowing list of embedding techniques that include factorization methods (e. g. GloVe  (Pennington et al. , 2014) ), neural methods (e. g. word2vec  (Mikolov et al. , 2013) , fastText  (Bojanowski et al. , 2017) ) and more recently dynamic methods that take into account the context (e. g. ELMo  (Peters et al. , 2018) , BERT  (Devlin et al. , 2018) ). The success of these methods can be arguably attributed to the availability of large generaldomain corpora like Wikipedia, Gigaword  (Graff et al. , 2003)  or the BooksCorpus  (Zhu et al. , 2015). Unfortunately, similar corpora are often unavailable for specialized domains, leaving the NLP practitioner with only two choices: either using 1 Python code for reproducing our experiments is available at: https://github.
Static Embeddings First, we use word2vec 5  to train embeddings on a small corpus built from the task data: i2b2 (2010) 394 documents the training set to which we added 826 more files from a set of unlabeled documents. This is a small (1 million tokens) in-domain corpus. Similar corpora will often be available in other specialized domains as it is always possible to build a corpus from the training documents. Then, we also train embeddings on each of two general-domain corpora: Wikipedia (2017) encyclopedia articles from the 01/10/2017 data dump 6. This is a large (2 billion tokens) corpus from the general domain that has limited coverage of the medical field. Gigaword (  2003 ) newswire text data from many sources including the New York Times. This is a large (2 billion tokens) corpus from the general domain with almost no coverage of the medical field.