dataesr/scientific-paragraphs-categorization huggingface dataset:
fMRI data preprocessing Preprocessing was performed using tools from FMRIB Software Library (FSL)  (Jenkinson et al. , 2012) , Advanced Normalization Tools (ANTs; http://stnava. github. io/ANTs) (Tustison and Avants, 2013) , Human Connectome Project Workbench  (Glasser et al. , 2013)  (https://www. humanconnectome. org/software/connectome-workbench), and the Magnetic Resonance Comparative Anatomy Toolbox (MrCat; https://github. com/neuroecology/MrCat). First, T2* EPI images acquired during task performance were reconstructed by an offline-SENSE method that achieved higher signal-to-noise and lower ghost levels than conventional online reconstruction  (Kolster et al. , 2009) (Offline_SENSE GUI, Windmiller Kolster Scientific, Fresno, CA). A low-noise EPI reference image was created for each session, to which all volumes were non-linearly registered on a slice-by-slice basis along the phase-encoding direction to correct for time-varying distortions in the main magnetic field due to body and limb motion.
ACKNOWLEDGMENTS This work was funded by the  Wellcome Trust  ( 203139/Z/16/Z ,  WT100973AIA ,  WT101092MA ,  105238/Z/14/Z , and  105651/Z/14/Z ), the  Medical Research Council  ( MR/P024955/1  and  MRC   G0902373 ), the  Bettencourt Schueller Foundation , and  Agence Nationale de la Recherche  under the  Future Investments program  ( ANR-10-EQPX-15 ). We are very grateful to  Dr. Masayoshi Murakami  and  Dr. Zach Mainen  for sharing the analysis code and to  Oxford University Biomedical Services  staff for ensuring the welfare of animals.
 The MSA so obtained was further filtered by removing the genomes having too many unique (i. e. not shared by any other genome) mutations and indels. We used more stringent thresholds than in the previous stage. This resulted in an MSA of 2,426 genomes, where the 12 first and 22 last positions of the reference genome were removed due poor alignment and low signal, but all other reference positions were preserved and showed high conservation. We used HMMER to estimate our profile from this curated MSA. All details and program options are available in Supplementary Information. The resulting profile was implemented in a Nextflow (Di Tomaso et al. 2017) and Galaxy workflow combining hmmalign from HMMER to align the input genomes to the profile, GoAlign to format the input/output files (https://github.
com/evolbioinfo/goalign), and Python to compute summary statistics. These statistics help users evaluate the sequencing quality and potential evolutionary novelties of input genomes; for example: number of unique mutations and indels, number of mutations compared to the reference genome. A user-friendly interface, implemented in GO (similar to  Lemoine et al. 2019 ) allows users to launch their analyses without having to know how to use the Galaxy system. For advanced users, COVID-Align can be installed locally via Docker (https://www. docker. com). COVID-Align is also available on www. gisaid. org, using GISAID identifiers rather than the sequences themselves.
The site numbering corresponds to that of the reference, to be used to recover the ORFs and genes. In RBD region the Pangolin virus genome is closer to Human's than is Bat's, suggesting a possible recombination. On the opposite, Human viruses are highly conserved. Right: Statistics summary, displaying the number of High and Low Quality genomes, and the number of evolutionary events (mutations, gaps, gap openings, insertions, insertion openings). We distinguish the number of unique events (not seen yet and present only once in submitted genomes, possibly due to errors) and the number of new events (seen at least twice, likely corresponding to evolutionary novelties). This table was filled with GISAID sequences deposited between August 10 and September 21 2020, with unique and new statistics with respect to the database as of August 9.
While graph partitioning/clustering is a well-studied problem, it is important to note that the obtained meta-DAG (direct acyclic graph) must remain acyclic, i. e. , the dependencies between the cluster of nodes should ensure to be executable as a graph of tasks, and keep a large degree of parallelism. Hence, the usual graph partitioning methods do not work because they do not take into account the direction of the edges  (Hendrickson & Kolda, 2000). Moreover, the DAG of tasks we target can be of several million nodes, and we need an algorithm capable to process them. In the current study, we use a generic algorithm that has been proposed to solve this problem  (Rossignon et al. , 2013) , and we refer to it as the general DAG clustering algorithm (GDCA). The contributions of the paper are the following: • We provide two variants of the GDCA, which change how the nodes are aggregated and allow to have clusters smaller than the targeted size; 1 An implementation of our method is publicly available at https://gitlab.
The problem also exists with FPGA, where a complete program does not fit in the field and thus should be divided in sub-parts with the objective of minimizing the re-programming  (Purna & Bhatia, 1999). In linear algebra, the LU factorization can be represented as a tree graph that can be partitioned in linear time  (Pothen & Alvarado, 1992). The real application we used to assess our method solves the transport equation on unstructured meshes. Task-based implementations to solve the transport equation on a grid (i. e. structured and regular mesh) have already been proposed by  Moustafa et al. (2018). The authors have created a version on top of the ParSEC runtime system where they partitioned the mesh and avoided working on the graph of tasks. Working on the mesh is another way to partition the graph, but this was possible in their case because the dependencies on the mesh were known and regular.
Software configuration. We use the GNU C compiler 7. 3. 0. We parallelize using OpenMP threads and we do not use any mutex during the execution. We only use lock-free mechanisms implemented with C11 atomic variables to manage the dependencies and the list of ready tasks, which is actually an array updated using atomics. Test cases. The two test cases represent a 2D mesh that has the shape of a disk with sizes 40 and 60. The details of the two corresponding graphs are provided in Table  1  under the names Chukrut -disque40 and Chukrut -disque60. The execution of a single task takes around 1. 5 • 10 -5 s. To estimate the overhead, we take the execution time in sequential T 1 and the execution time using a third of the available threads T x and do   O = (T x × x -T 1 )/T 1. We obtained an overhead of 3 on Intel-24t and of 3.
Thalamus The parcellation of the thalamic nuclei adopted was based on that of  (Iglesias et al. , 2018). In the present work, we firstly reconstructed the high-resolution version of the single-subject T1 image (colin27: the one used in AAL) using the Freesurfer software. The T2-weighted image of colin27  (Aubert-Broche et al. , 2006)  was used as an additional MRI volume to obtain a more reliable result in the segmentation module  (Iglesias et al. , 2018). Table  1  shows the AAL3 parcellation for the thalamus, which is also illustrated in Fig. 1. The definition column shows where we have combined divisions of  Iglesias et al. , in order to combine nuclei, where useful, in order to reduce the number of very small divisions (with e. g. fewer voxels than 10), which would probably not yield reliable MRI data unless special registration for such small groups of voxels was performed.
Acknowledgements The voxels in the locus coeruleus were defined as described by (Betts et al. , 2017), and the actual template used was very helpfully provided by these authors. The voxels in the raphe nuclei were defined as described by (Beliveau et al. , 2015), and the actual template used was very helpfully provided by these authors. The authors thank  Dr Nathalie Tzourio-Mazoyer  ( GIN UMR5293 ,  CNRS CEA Universit e de Bordeaux, Bordeaux, France) , an author of previous versions of the automated anatomical labelling atlas, for her help and advice with this version. J Feng  is partially supported by the  key project of Shanghai Science and Technology Innovation Plan  (No. 15JC1400101  and No. 16JC1420402 ),  Shanghai Municipal Science and Technology Major Project  (No. 2018SHZDZX01 ) and ZJLab, and the  National Natural Science Foundation of China  (Grant No.
Acknowledgment Centre de Calcul Intensif d'Aix-Marseille is acknowledged for granting access to its high performance computing resources. ated, they must be imported in the runFiles directory with the help of the extrudeMeshDict le. The initial and boundary conditions for the velocity and the pressure are set in the U and p les. In the transportProperties the kinematic viscosity is set and the in the turbulenceProperties le the simulation type must be specied (here laminar). The pointVectorField class and the displacementLaplacian solver are included in the dictionary le dynam-icMeshDict with the diusivity model (inverseDistance in our case). The type of the object motion is chosen in pointDisplacement, here angularOscil-latingDisplacement for a pure pitching motion.
We also excluded Omega Cen, 47 Tuc, and NGC 6752 as deep X-ray source catalogs on these clusters have been recently published  (Henleywillis et al. 2018; Bhattacharya et al. 2017; Cheng et al. 2019b; Forestell et al. 2014). These criteria provide us with a sample of 38 GCs (Table  1 ). Of these 38, 10 have new data taken as part of MAVERIC: 6 (Djorg 2, M 10, M 19, NGC 4372, NGC 4833, and M 107) have single Chandra/ACIS ob-servations obtained specifically for MAVERIC follow-up, while 4 others (M 22, M 62, Terzan 1, and Terzan 5) have both previous data and newer MAVERIC followup data.
Data Reduction We used CIAO 4. 8 and higher (as there were software updates over the duration of the project), with CalDB 4. 7. 7 and higher for all Chandra data reduction 3  (Fruscione et al. 2006) , extracted data products and performed analysis using ACIS-Extract (here after AE,  Broos et al. 2010)  and Bayesian X-ray Analysis package BXA  (Buchner et al. 2014). Our overall goal was to obtain a carefully vetted, consistently analyzed Xray source catalog for the GCs in our sample. Thus, we first reprocessed the data and produced input files necessary for a high-level analysis (event files, exposure maps, aspect solutions, source catalogs etc) using CIAO and used these as input (after inspection and modification, where necessary) for analysis in AE. Finally we used the spectra produced by AE for spectral analysis in BXA ( §3).
5-10 keV band, with an image binning factor of 0. 5. A small (< 1) binning factor can improve detection of faint sources in crowded regions (like the core of some GCs) on the Chandra/ACIS detector. In many observations covering the GCs in our sample, the fields of view (FOV) covered are substantially larger than the half-light radius of the target GC. As we are only interested in sources within the GCs, we filtered all images, exposure maps and event files using dmcopy to only include the data covering the GC up to 3 We note that there have been no software/calibration updates that would impact the analysis in this work significantly. 1. 2 times the half-light radius. The factor of 1. 2 is a rather conservative upper limit to consider uncertainties in measurements of half-light radii and GC center coordinates.
Source Detection We performed source detection on all observations/GCs by running wavdetect on the under-binned stacked images (binned by a factor of 0. 5) of each cluster. Source detection was carried out with a broad range of scales  (1, 2, 4, 8, 16) , and generally liberal detection thresholds (wavdetect's threshold for identifying a pixel as belonging to a source, 10 -4 -10 - 5  , depending on the depth and noise level in the observations). Choosing a liberal detection threshold generally increases the number of possible false detections, while it allows sensitivity to marginal detections. We chose such a threshold in order to leave assessment of whether a source is real to a later stage in AE, which allows a more rigorous verification in the photon-starved regime. Merging multiple data sets generally produces deeper data; however in some cases it produced sub-optimal results.
While we excluded observations in which a bright outburst (or persistent emission) from an XRB prevented effective inspection of faint sources in the cluster, we did include observations where outbursts were sufficiently faint to allow detection of faint sources in the GC to some level. There were only two observations impacted by this issue (one for Terzan 5, and one for NGC 6388). In both cases, the impact of the contaminated observations was small, particularly as the rest of the data covering these clusters were relatively deeper. After producing observation-specific and merged source catalogs for each GC, we visually inspected the images and detected sources to evaluate the quality of the stacked images and search for clear sources that were missed by the algorithm, especially in crowded regions (where wavdetect has difficulty picking two sources close together apart).
As an auxiliary resource, for each source we provide a compilation of plots summarizing its position within the GC and localization and extraction regions, along with plots showing the three spectral fits and parameter posterior distributions. These plots are meant to facilitate manual assessment of individual sources and are avail- able as a complete figure-set in the online version of this article (see Figure  8  for an example) and are also accessible at https://bersavosh. github. io/research/goose. html.
github. io/research/goose. html) include 1-D and 2-D histograms of posterior distributions for each model fit, to allow inspection of these issues. It is also important to note that while our spectral fitting method provides a rather robust evaluation of parameter space, it will not overcome the intrinsic uncertainties in the extremely low-count statistics regime. For example, in some cases, spectral fitting at low S/N indicates an excessively high N H , coupled with a very high X-ray luminosity (while the absorbed flux is rather low). Thus, it is important to check the uncertainty bounds and the shape of the posterior distribution at low S/N, and we urge caution in interpretation of spectral modeling results and model comparison for sources with < 20 counts. Lastly it is worth noting that the models used for spectral analysis here are simple single-component models and they may not be sufficiently descriptive for some sources.
Facilities: Chandra(ACIS), VLA, ATCA Software: ACIS-Extract  (Broos et al. 2010 ), Astropy (Astropy Collaboration et al. 2013 , 2018) , BXA  (Buchner et al. 2014) , CIAO  (Fruscione et al. 2006 ), Corner (Foreman-Mackey 2016), HEASOFT (NASA High Energy Astrophysics Science Archive Research Center 2014), IPython  (Perez & Granger 2007) , Jupyter  (Kluyver et al. 2016) , Matplotlib  (Hunter 2007) , Multi-Nest  (Feroz et al. 2019) , Numpy  (Oliphant 2006; van der Walt et al. 2011 ) Pwdetect  (Damiani et al. 1997) , SAOImage DS9  (Joye & Mandel 2003) , Scipy  (Virtanen et al. 2020) , XSPEC  (Arnaud 1996). foreground flag True means that the source could be a foreground object. This is determined based on comparing the upper limit on the source NH (from the best-fit model) compared to the host cluster NH (estimated based on E(B-V)).
ACKNOWLEDGMENTS We thank the anonymous referee for their helpful comments. AB thanks  Patrick Broos ,  Diogo Belloni ,  Liliana Rivera Sandoval  and  Scott Ransom  for helpful discussions. JCAMJ is the recipient of an  Australian Research Council   Future Fellowship  ( FT140101082 ) funded by the  Australian government. LC is grateful for the support of  NASA  grants  Chandra-G06-17040X ,  Chandra-GO7-18032A ,  HST-GO-14351. JS acknowledges support from a  Packard Fellowship ,  NASA  grants  Chandra-GO3-14029X ,  Chandra-GO5-16036X , and Chandra-GO8-19122X, and  NSF  grants  AST-1308124  and  AST-1514763. COH & GRS acknowledge  NSERC   Discovery Grants   RGPIN-2016-04602  and  RGPIN-2016-06569  respectively, and COH also a  Discovery Accelerator Supplement. KLL is supported by the  Ministry of Science and Technology of the Republic of China (Taiwan)  through grant  108-2112-M-007-025-MY3.
, 2016; Milliken et al. , 2010; Thomson et al. , 2011). VRR was a separate orbitally defined unit in large part because of its association with strong features at 535 and 860 nm in CRISM data, in addition to its distinctive texture and topography  (Anderson & Bell, 2010; Fraeman et al. , 2013)  (Figures  1  and 2 ). No other spectral absorptions are present at longer wavelengths in CRISM data over the ridge. VRR has a positive topographic expression with respect to underlying strata and is composed of light toned, well consolidated bedrock (Figure  2 ). The ridge is ∼200 m wide and extends ∼6. 5 km northeast-southwest, approximately parallel with the base of Mt. Sharp. VRR is also located near the mouth of the Gediz Vallis channel, a ~10 km long channel that is one of the youngest geomorphic features on Mt.
(2000)  used the generic name "nanophase ferric oxide" for the dust ferric pigment. CRISM data show several large, isolated areas in lower Mt. Sharp that are associated with spectra that have diagnostic ferric features at 535 and 860 nm (Figure  1 ), as a well a local reflectance maximum near 750 nm  (Fraeman et al. , 2016; Milliken et al. , 2010; Seelos et al. , 2019). These spectra are consistent with hematite for reasons presented above  (Fraeman et al. , 2013). Similar absorptions are not present in CRISM data over Glen Torridon, the trough immediately south of VRR (Figure  1 ). There are a few pixels immediately to the north of VRR that are associated with CRISM spectra which also have weak 860 and 535 nm features, but these absorptions are often on the same order of magnitude as instrument noise and the individual pixels are not clearly associated with specific geologic features.
Orbital Data Sets CRISM measures reflected radiance from the Martian surface and atmosphere at 544 discrete visible short-wave infrared (VSWIR) wavelengths between 362 and 3,920 nm  (Murchie et al. , 2007). CRISM is a line scanning (cross track) spectrometer that is mounted on a gimbal that slews in the along-track direction. In full resolution targeted (FRT) mode, CRISM has a spatial resolution of 18 m/pixel. CRISM also can collect half resolution long (HRL) scenes that cover greater areas than FRTs but provide half the spatial resolution (36 m/pixel). A third observing mode, along-track oversampled (ATO), is achieved by adjusting the angular velocity of CRISM's gimbal to collect 18 m/pixels at intervals <18 m in the along-track direction. ATO data can be processed to an along-track resolution of 9-12 m/pixel using an iterative maximum-log likelihood (MLM) algorithm, which retrieves the best estimate of scene radiance in the presence of noise (L.
(2009; 2019) , and tau_dust estimates were iterated until model results produced SSA values that were similar for overlapping regions for HRL0000BABA. For this scene, tau_dust was estimated to be 0. 4 at 900 nm. All CRISM data were registered to a HiRISE base map using hand selected ground control points focused near Curiosity's traverse, and the data were warped using the first-order polynomial function available in the georeferencing toolkit that is included in ArcMap software by Esri (www.
, 2012; Wiens et al. , 2012). The violet (382-469 nm) and visible/near-infrared (474-906 nm) spectrometers on ChemCam have sufficient sensitivity to collect reflectance spectra from surfaces without the use of the laser (passive mode)  (Johnson et al. , 2015 (Johnson et al. , , 2016)). After every LIBS point, ChemCam acquired a 3 msec exposure passive measurement that was Journal of Geophysical Research: Planets converted to target radiance and relative reflectance using methods developed and described in  Johnson et al. (2015). Resulting ChemCam passive spectra have useable data from 400 to 840 nm with spectral sampling 0. 04-0. 2 nm depending on wavelength and spatial resolutions that range from 1. 3 to 4. 5 mm for typical targets distances of 2-5 m, respectively  (Johnson et al. , 2015).
, 2012). For this work, we used RMIs to select only ChemCam passive spectra from sunlit bedrock surfaces within the Mt. Sharp group; we excluded shadowed spectra or spectra that covered sands, soils, veins, other diagenetic features, drill tailings, or float rock (Table  S2 ). We also excluded ChemCam passive data from Sols 2,076-2,215 because their calibration was uncertain due the large amount of scattered light from during the 2018 planetary encircling dust event. The ChemCam and Mastcam data sets are complementary to one another. As a multispectral imager, Mastcam provides low spectral resolution information for every pixel across an entire image, which can be used to understand geologic relationships between materials with distinct spectral properties. ChemCam is a hyperspectral point spectrometer, meaning it collects higher spectral resolution data than the multispectral Mastcam, but spectra are restricted to <5 mm-size-targeted points on a single nearby target.
, 2018; Croce et al. , 2019). 2020 ), in the context of the GRAVITATE project, build a 3D semantic annotation system aimed at the virtual assembly of fragmented or broken heritage artifacts. More thoroughly in the architectural heritage domain,  Apollonio et al. (2018)  propose a web-based information system to support the restoration of the Neptune's Fountain in Bologna. The 3D model of the heritage object is at the core of the whole system, becoming the pivotal element where to store and semantically organize new and previous restoration data. With the same principle, the software tool CulTO  (Garozzo et al. , 2017)  is developed to record data within 3D models of religious historical buildings. The semantic annotation is conceived to enable automatic indexing, classification and correct archiving of texts and iconographic resources of the concerned buildings.
ACKNOWLEDGEMENTS The presented work is the first result of a collaboration between the  Department of Civil and Environmental Engineering of the University of Florence , the  Department of Civil and Industrial Engineering of the University of Pisa (Italy) , the  MAP laboratory of the Centre National de la Recherche Scientifique CNRS Marseille  and the  LISPEN Laboratory in Aix-en-Provence (France) , and it is carried out in the framework of a Ph. D. cotutelle agreement. It is partially funded by the  VINCI2019  Project, promoted by the  Université Franco-Italienne.
A commercialized measurement system Flow Watch together with WebRoc imaging software from Rocsole Ltd. is being used within the inline separation demonstrator. The Flow Watch system applies a voltage injection current measurement scheme such that one electrode is used as excitation electrode V 1 (source electrode) and the remaining electrodes (sink elec trodes) are used for measurement as shown in Fig. 7  [21], while V 0 is ground voltage. The physical sensor comprises 16 stainless steel elec trodes of 12 mm head diameter and 5 mm thread size per electrode. They are placed equidistantly on the inner surface of the pipe. In order to avoid cross talk, the electrodes are well separated by about 5. 7 mm. Electrodes were placed inside the pipe by drilling holes and are sealed using rubber sealing of 2 mm thickness.