dataesr/scientific-paragraphs-categorization huggingface dataset:
Agreement within uncertainties between pp, p-Pb, and the PYTHIA 8. 2 Monash Tune is observed. By measuring associated hadrons, correlations can be observed for much larger angles than would otherwise be possible for hadrons within a reconstructed jet. A χ 2 test between pp and p-Pb data and a p-value is calculated in each z T bin for the null hypothesis that pp and p-Pb data follow the same true correlation function. In each bin, the null hypothesis cannot be rejected, indicating that there is no significant difference between the correlation functions in the two collision systems. The correlation functions from Fig. 4  are then integrated in the region |∆ϕ| > 7π 8 for each z T bin to obtain the γ iso -tagged fragmentation function shown in Fig. 5. This range roughly corresponds to the azimuthal angle consistent with the commonly used radius of R = 0.
Conclusions We report a measurement of azimuthal correlations between isolated photons and associated charged hadrons in p-Pb and pp collisions at 5. 02 TeV per nucleon. We observe no difference in the z T distribution between pp and p-Pb data within a z T -integrated statistical uncertainty of 13% on the ratio. PYTHIA 8. 2 Monash Tune describes both data sets within the current precision. This measurement provides a constraint on the impact of cold nuclear matter effects on parton fragmentation, and indicates that modifications in the z T distributions observed in Pb-Pb collisions larger than the overall uncertainty on this measurement of approximately 25% must be due to hot medium modifications. Analysis of isolated photon-hadron correlations in Pb-Pb collisions will allow hot nuclear matter effects to be quantified.
Five patients and as many cognitively healthy controls underwent formal neurocognitive and neuropsychiatric assessment at the CHU de Québec Memory Clinic (cliniq uedememoire. ca). The other affected participants did not wish to collaborate beyond the standard clinical evaluation. Controls were family caregivers and were age-and educationmatched to participants. The Mini-Mental State Examination was performed with each participant. Data derived from the Montreal Cognitive Assessment  12  and additional standardized and local cognitive measures were summarized according to composite scores for the main cognitive domains: attention (digit span and numeric substitution), executive functions (abstract thinking, lexical verbal fluency, Go/No-Go, Luria, Trail Making B, and Rey Complex Figure  Test  [RCFT] copy), visuospatial skills (block copy and clock drawing), memory (5-word list free and cued recall), and language (naming, comprehension, and sentence repetition tasks) (appendix e-1, links.
6)  13  and annotated using ANNOVAR13. 14 The exomes were analyzed using step-by-step filtering strategies. Basic filters were used to remove low-quality variants: minimum position coverage >6, minimum reads supporting the variant >3, and minimum mutation frequency >0. 15. Genetic variants were then filtered to retain only variants respecting the following criteria: (1) shared by all 5 affected family members sequenced by WES, (2) nonsynonymous single nucleotide variants, splicing site variants, or coding indels, (3) with a minor allele frequency <0. 1% in Exome Aggregation Consortium, and (4) not present in 186 controls from our inhouse data set. To validate our results and examine segregation of the identified ELOVL4 DNA variations with the disease in the family, we used specific PCR primer pairs (forward: CATTGCTTTCCACTGAACACA; reverse CATGCCTTG-TACATTTTGTGC) to amplify DNA from 18 family members and sequenced them by Sanger sequencing (Applied Biosystem's 3730xl DNA Analyzer technology).
Hence, perfect segregation of the identified c. 504G>C mutation with the disease status in the family was observed (figure e-1, links. lww. com/NXG/A228). This variant had already been reported to cause SCA34 in another French-Canadian family,  3  but no common ancestor could be identified by family history and the haplotype was not reconstructed from the exome data. With agreement of the authors, exomes from the previously described family were retrieved, and kinship coefficients were calculated using KING. 17 All pairs of sample between the 2 studies had a phi score of <0. 03, which corresponds to a third-degree relationship or second-degree cousins. Although calculating relatedness coefficients using exome sequencing data can be biased due to sparse and unevenly distributed data, this suggested that the patients from the 2 families were not close relatives.
Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version (version 4; V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter and ice or liquid water path estimates. Dedicated retrievals for water clouds were added in V4, taking advantage of the high sensitivity of the IIR retrieval technique to small particle sizes. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results will be presented in a companion (Part II) paper.
As shown in Part II, this improvement reduces the low biases at large optical depths that were seen in V3 and increases the number of retrievals. As in V3, the IIR microphysical retrievals use the concept of microphysical indices applied to the pairs of IIR channels at 12. 05 and 10. 6 µm and at 12. 05 and 08. 65 µm. The V4 algorithm uses ice look-up tables (LUTs) built using two ice habit models from the recent "TAMUice2016" database, namely the single-hexagonal-column model and the eight-Published by Copernicus Publications on behalf of the European Geosciences Union. element column aggregate model, from which bulk properties are synthesized using a gamma size distribution. Four sets of effective diameters derived from a second approach are also reported in V4. Here, the LUTs are analytical functions relating microphysical index applied to IIR channels 12.
The results are shown for 6 months of nighttime data in 2006 (from July through December) between 60 • S and 60 • N to ensure that the dataset is not contaminated by sea ice. The number of clear-sky IIR pixels used for this analy-sis is plotted in Fig. 2b. Even though the ancillary data are from GMAO GEOS 5. 10 in V3 for this time period and from MERRA-2 in V4, the differences between V3 and V4 are mostly due to the changes in the radiative transfer model. The amplitude of the variations of median BTDoc with IWVP is drastically reduced in V4 compared to V3 and the interchannel differences are significantly smaller. Between IWVP of 1 and 5 g cm -2 , where most of the samples are found, V4 median BTDoc is between -0. 2 and 0. 2 K for the three channels. Using the V4 surface emissivities compensates for a residual 10-12 inter-channel BTDoc bias of -0.
org/10. 5194/amt-14-3253-2021 Atmos. Meas. Tech. , 14, 3253-3276, 2021  Both in V3 and in V4, the first step into the computation of the radiative temperature is to determine the centroid altitude, Z c , of the cloud system. The centroid altitude of each layer is reported in the CALIOP 5 km layer product, together with the 532 nm integrated attenuated backscatter (hereafter IAB) of each layer. IAB is corrected for the molecular contribution and for the attenuation resulting from the overlying layers, noted T 2 overlying. Following the rationale presented in Appendix B, the centroid altitude of a multi-layer cloud system composed of N layers is computed as Z c = l=N l=1 Z c (l) • IAB(l) • T 2 overlying (l) l=N l=1 IAB(l) • T 2 overlying (l). ( 4 ) For single-layer cases (N = 1), Z c is obviously the centroid altitude reported in the CALIOP data product.
V4 look-up tables The difference between the V4 and the V3 ice LUTs is twofold: the ice habit models are different and a particle size distribution (PSD) is introduced in V4. Three ice habit models were used in V3. These were taken from the database described in  Yang et al. (2005)  and represented three families of relationships between β eff 12/10 and β eff 12/08: solid column, aggregate, and plate (G13). In practice, the plate model was rarely selected by the algorithm. In V4, the LUTs are computed using state-of-the-art ice crystal properties referred to as "TAMUice2016" by  Bi and Yang (2017) , which were updated with respect to TAMUice2013 reported in 2013  (Yang et al. , 2013). These optical properties determined by the Texas A&M University group are now widely used by the scientific community  (Yang et al.
Summary and perspectives The IIR Level 2 algorithm has been modified in the V4 data release to improve the accuracy of the microphysical indices in clouds of very small (close to 0) and very large (close to 1) effective emissivities. In addition, a new set of LUTs is used to retrieve ice cloud microphysical properties, and the retrievals have been extended to liquid water clouds. Improving the microphysical indices at emissivities typically smaller than about 0. 2 required improvements in the accuracy of the simulated background radiances. In this paper, the first of two describing the V4 IIR Level 2 updates, we focused on retrievals above the oceans. The changes in the new radiative transfer calculations using sea surface and atmospheric data from MERRA-2 were evaluated through comparisons with IIR measurements in clear-sky conditions.
Data availability. The version 3 IIR Level 2 track products used in this paper are available at https://doi. org/10. 5067/IIR/CALIPSO/ L2_Track-Beta-V3-01 (NASA, 2011) (last access: 14 September 2020) and the version 4 IIR Level 2 track products are available at https://doi. org/10. 5067/CALIOP/CALIPSO/CAL_IIR_L2_ Track-Standard-V4-20 (NASA, 2020) (last access: 14 September 2020). The IIR Level 2 track products are also available from the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille. fr, AERIS/ICARE, last access: 22 April 2021) (AERIS/ICARE, 2021). Author contributions. AG and JP defined the changes implemented in the V4 IIR algorithm and wrote the original draft. AG performed the data analysis and prepared the figures. NP was in charge of software development and provided V4 IIR test data.
MAV provided assistance for the use of the CALIOP data. PD provided the FASRAD and FASDOM radiative transfer models and bulk scattering properties. PY provided the ice habit models from the TAMUice2016 database. DLM provided the analytical functions derived from in situ measurements. All authors contributed to the review and editing of this paper. Competing interests. Jacques Pelon is a co-guest editor for the "CALIPSO Version 4 Algorithms and Data Products" special issue in Atmospheric Measurement Techniques but did not participate in any aspects of the editorial review of this paper. All other authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "CALIPSO version 4 algorithms and data products". It is not associated with a conference.
Acknowledgements. The authors are grateful to  NASA LaRC, SSAI (Science Systems and Applications, Inc. ), the  Centre National d'Etudes Spatiales (CNES) , and  Institut National des Sciences de l'Univers (INSU)  for their support. Melody Avery ,  Bob Holz , and  James Campbell  are warmly acknowledged for fruitful discussions about the ice LUTs. We thank the  AERIS infrastructure  for providing access to the CALIPSO products and for data processing during the development phase. We thank  Brian Getzewich  and  Tim Murray  for the processing of the version 4 IIR Level 2 data at  NASA LaRC. Review statement. This paper was edited by  Vassilis Amiridis  and reviewed by  Bryan A. Baum  and three anonymous referees.
Prediction phase sample generation based on in-plane shrinkage In the prediction stage for LPBF, systematic deviations based on specific shapes are not included as the manufacturing process is relatively new, and there is no detailed literature/data on the systematic deviation modes for metal AM concerning various geometric shapes. Moreover, the systematic deviations vary with changes in the machine architecture and process parameters as well. Therefore, the prediction phase is launched by populating the deviation fields with quantitative bounds from expert knowledge and previous experience that serves as initial seeding data. The data may be obtained from previous metrology results from other samples tested with similar systems, generic factors, such as shrinkage factors reported from standards or best practices, or simulated data from a given model.
In this specific example, a shrinkage factor is applied globally as per the in-plane deviation methodology and then updated as per the observation phase results. The shrinkage factor is added to the nominal point cloud (generated from the input STL file using MATLAB function stlread), and the new point cloud is used for further modeling. The mathematical relationship for adding systematic deviations including the shrinkage factor as described above to the nominal point cloud has been described in detail in  [50]  and are given as: where 's' is the in-plane shrinkage factor X s = Resultant point with added in-plane shrinkage, i. e. (x si ,y si ,z si ) Following this, random field theory-based random deviations is generated using an assumed correlation function. A squared exponential correlation function is used, which calculates correlation based on the Euclidian distance between adjacent points and an assumed correlation length.
GD&T extraction from deviated STL files The deviated STLs obtained from observation phase samples and the updated prediction phase samples are now used for GD&T characterization and further geometric analysis. The STLs are converted to point clouds using MATLAB function stlread. The point cloud is fed into MATLAB routines, which are programmed as per the GD&T definitions from ISO 1101. For example, for calculating flatness, the point cloud of the flat feature is first separated from the point cloud of the complete sample. Now, flatness is the distance between two parallel planes containing all points on the given feature. So, the points on the feature are subjected to linear regression to generate the datum plane and then the maximum and minimum distance of the points on the feature is calculated from the new plane, which leads to the flatness tolerance zone.
Results and discussions Skin model shapes and experimental results are considered for a comparative analysis for validation. After the generation of the observation phase and updating the deviation parameters in the prediction phase, a total of 1,000,000 skin model shapes were generated for each phase for both the pin and the hole ABTA components. The samples were then processed in MATLAB for extracting the dimensional and geometric tolerances as per the GD&T. A comparison of the functional size dimensions (diameter values) was conducted and is shown in Fig. 9. The nominal values of both the hole and the pin diameter are 15. 055 mm and 14. 840 mm, respectively and are shown with a red line on the results plot. Both of these values were overestimated with respect to the experimental measurements on the printed samples.
Even though this number should be identical, small differences exist due to the training criteria allowing for a 5% deviation and the limited number of thermo-mechanical deviation datasets. The diameter values and the GD&T characteristics of the prediction phase skin model shapes are in close agreement with the observation phase results and especially with the experimental results with a maximum standard deviation of around ±0. 010 mm. It validates the notion that the assemblability can be estimated based on the geometric tolerances before printing the part. Moreover, most of the quantifiers are overpredicted by the prediction phase results but are within the given specifications and assemblability conditions. It is also reconfirmed with the actual assembly of the all three printed hole-pin pairs (one of the actual assembled components shown in Fig.
Prediction phase variables are updated as per the observation phase results, and then samples are generated. These samples are then used to extract geometric tolerances such as flatness, cylindricity, perpendicularity and true position. The method is validated by comparison with the experimental results. Close agreement with the experimental results and the actual assemblability of the printed samples confirm the applicability of the method and further use in other metal AM processes. The proposed methodology can be applied to a variety of process parameters and materials by training the model, using thermo-mechanical simulations, to capture the deviation information from new data and provide geometric tolerance and assemblability information. However, using a better systematic deviation model could improve the prediction phase results as compared to the experimental results.
Acknowledgement Authors acknowledge the following organizations for providing funding for this research work:  • Natural Sciences and Engineering Research Council of Canada (NSERC) ,  Government of Canada , under strategic partnership grant number  494158-16 :  Holistic Innovation in Additive Manufacturing (HI-AM ). •  International Incoming Scholarship  by  ENS Paris-Saclay , for the research visit of the first author at  Laboratoire Universitaire de Recherche en Production Automatisée (LURPA), ENS Paris-Saclay, Cachan, France. • Globalink Research Award by Mitacs International, Canada , for developing research collaboration between French and Canadian institutions for higher education. • Sualp Ozel and  Jill Devlin  at  Autodesk Inc. for providing the education version of  Netfabb Ultimate  and Local simulation.
The spectra of six free-floating analogues from the same association are analyzed for comparison and to explore the potential physical differences between these substellar objects found in different configurations. We also aim at looking and analyzing hydrogen emission lines at UV and optical wavelengths to investigate the presence of ongoing accretion processes. Methods. The X-Shooter spectrograph at VLT was used to obtain the spectra of the nine young brown dwarfs over the 0. 3 -2. 5 µm range simultaneously. Performing a forward modelling of the observed spectra with the ForMoSA code, we infer the T eff , log(g), and radius of our objects. The code compares here the BT-SETTL15 models to the observed spectra using the Nested Sampling Bayesian inference method. Mass is determined in using evolutionary models and a new analysis of the physical association is proposed in using the Gaia-DR2 astrometry.
Spectro-photometric standard stars were observed as part of the observatory calibration plan. They are not reported in the log as they were not used for our reduction and analysis. We used the ESO reflex data reduction environment  (Freudling et al. 2013)  to run the X-Shooter pipeline version 2. 9. 3 on the raw data  (Modigliani et al. 2010). The pipeline produces two-dimensional, curvature-corrected, and flux-calibrated spectra for each target and epoch of observation (trace). The spectra were extracted from the traces using a custom IDL script. The flux in each wavelength channel at the position of the source was averaged within 720 mas aperture in the UVB and VIS arms, and a 1120 mas aperture in the NIR arm. The script computed the noise at the position of the source into each spectral channel following the procedure described in  Delorme et al.
(2017a). The residual non-linear pixels in the spectra were removed using the kappa-sigma clipping method. The telluric correction were evaluated and removed using the molecfit package  (Smette et al. 2015; Kausch et al. 2015). The spectra at each epoch were corrected from the barycentric velocity and re-normalized using the epochs when the sky transmission was photometric as an anchor point. Our flux-calibration was checked computing the 2MASS or MKO synthetic photometry from the spectra and comparing the values to published ones  (Lodieu et al. 2008). Comparison between our spectra (black from X-Shooter), the ones from  Lachapelle et al. (2015)  in red (R = 900) and  Aller et al. (2013)  in green (digitized) and the one from  Lafrenière et al. (2011)  in yellow (R = 5300-6000).
Description of the atmospheric models We used a forward-modeling approach to determine the atmospheric parameters of the young BDs observed in this study. Forward modelling codes enable comparison of the object spectrum to pre-computed grids of models which include our best knowl-edge of atmospheric physics. We used grids of synthetic spectra produced by the BT-SETTL15 model  (Allard et al. 2012). This model handles the radiative transfer using the PHOENIX code  (Hauschildt et al. 1997; Allard et al. 2001). It accounts for convection using the mixing-length theory, and works at hydrostatic and chemical equilibrium. The opacities are treated line by line (details on each elements are given in  Rajpurohit et al. 2018). The code models the condensation, coalescence, and mixing of 55 types of grains.
The ForMoSA code For this work, we chose to develop our own forward modelling code relying on the Nested Sampling procedure  (Skilling 2006 ). The method explores and recursively isolates different patches of likelihood levels in the parameter space. It offers several advantages over classical Markov-Chain-Monte-Carlo algorithms also used for the forward modelling (e. g. Bonnefoy et al. 2018; Samland et al. 2017). The approach avoids missing local minimum within vast and degenerate parameter spaces while ensuring the convergence of the exploration. The code ForMoSA (for FORward MOdeling for Spectral Analysis) takes as input an observed spectrum with associ-ated error bars and any grid of synthetic spectra (here the BT-SETTL15 grid). The Nested Sampling is handled by the nestle Python module 2.
Results As a safety check, the ForMoSA code was applied to our three companions HIP 78530 B, HIP 77900 B, and USco 1610-1913 B and two well known young BDs, TWA 26 and TWA 29, with similar spectral types and ages (∼ 8 Myr) and observed by  Manara et al. (2013). We considered wavelengths from 1. 0 µm to 2. 5 µm in all our fits to avoid biases related to the residual contamination of HIP 78530 B and run a homogeneous analysis on all objects. The results are shown in Figures  7 8 9 10 11. The numerical values are given in Tables 6 -7. -Panels (a), (b), (c) and (d) of Figures 7 -11 show the X-Shooter spectra (black) with the resulting best fit (blue) considering different spectral windows (J + H + K, J, H and K, respectively), and a fixed-extinction assumption from the Gaia DR2 extinction maps and values at the location of our targets  (Lallement et al.
Consequently, they are affected by the choice of the spectral range chosen to estimate the best fit. For instance, the T eff is generally higher from a fit using the J-band compared to fit using H or K-band. The surface gravity is also affected as it remains sensitive to the shape of the VO and H 2 O absorption bands. Notably, the radius, which is linked to the flux dilution factor, seems to be mostly consistent when using three different fitting bands. In addition to the best fitting solution, the ForMoSA code provides the errors on the posterior solutions. The errors on the posterior solutions are tightly connected to the er- rors bars on the spectra themselves by the likelihood function. Given the high-S/N of the X-Shooter spectra for HIP 78530 B, HIP 77900 B, USco 1610-1913 B, TWA 26 and TWA 29, the resulting fitting error bars are about one to two orders of magnitude smaller than the systematics associated with the choice of the spectral fitting window, and the non-perfect match of the BT-SETTL15 models with our spectra at all observed wavelengths (see Table  6 7 ).