dataesr/scientific-paragraphs-categorization huggingface dataset:
Our adopted solutions in T eff , log(g) and R are therefore derived from the average and dispersion of the solutions from the different spectral fitting windows with the extinction value given by  Lallement et al. (2019). The bolometric luminosity is calculated with the Stefan-Boltzmann law. In addition to different spectral fitting window, the ForMoSA code was also applied with the extinction A v as a free parameter. The resulting posteriors can be directly compared with the ones obtained with the Gaia DR2 values at the location of our targets  (Lallement et al. 2019 ). Surprisingly, with the simple use of an interstellar extinction law correction, we considerably improve the goodness of the fits of all targets (see Figures  7 8 9 10 11 panel (e)). Considering that our targets have no extinction (both sources are located at less than 80 pc; TWA 26 has a transition disk around it but the flux excess appears at λ > 20µm,  Riaz & Gizis 2008 ; TWA 29 has no known disk, Rodriguez et al.
5 times more luminous than freefloating analogues from the Upper Sco association. This is il-lustrated in Figure  5  using for comparison USco 1607-2211 and USco 1610-2239 which both have high quality astrometric solutions. In addition, the probability for a chance-alignment within the association is slim. Aller et al. (2013)  identified USco 1610-1913 B while searching for distant companions within 30 arcseconds from Upper Sco stars and with optical (Pan-Starrs) and near-infrared (UKIDSS) colors compatible with young, cool objects. Using the now available Pan-Starrs data  (Chambers et al. 2016) , we confirm that USco 1610-1913 B belongs to one of the three objects with the reddest i-z and i-y colors within 5 arcmins of USco 1610-1913 A. It is also the only object in this field with colors typical of M7-M9 Upper-Sco objects.
The over-luminosity of USco 1610-1913 B Evolutionary models of  Chabrier et al. (2000)  predict a T eff = 2827 ± 169 K for USco 1610-1913 B using the bolometric luminosity of the source. This corresponds to a M5 brown dwarf (see Table  5 ), incompatible with the spectral type of M9 ± 0. 5 derived from our empirical analysis. Our results using the ForMoSA code (see Section 4. 3) further confirms this over-luminosity, based on model atmosphere fits. There are several other sources in literature which are similarly overluminous, such as USco 1602-2401 B  (Aller et al. 2013 ) or 2MASS J162243. 85-195105. 7 (Dahm et al. 2012). Aller et al. (2013)  proposed that differences in the accretion history of USco 1610-1913 B could play a role in such a discrepancy as proposed by  Baraffe et al. (2012).
Conclusion Based on medium resolution spectra obtained with the X-Shooter spectrograph at VLT, we carried out an in-depth characterization of three low-mass brown dwarf companions on wideorbits, specifically USco 161031. 9-16191305 B, HIP 77900 B, and HIP 78530 B of the Upper-Scorpius association, together with six young isolated brown dwarfs of similar spectral types and ages. The X-Shooter data yield the first medium-resolution optical spectra of the companion objects. We can summarize the main results as follow: 1. the re-investigation of the spectral classification of the three companions USco 1610-1913 B, HIP 77900 B and HIP 78530 B confirms that they have spectral types M9 ± 0. 5, M9 ± 0. 5, and M8 ± 0. 5, respectively. HIP 77900 B and HIP 78530 B are identified as young, intermediate surface gravity brown dwarfs, whereas USco 1610-1913 B is confirmed as a very-low surface gravity brown dwarf.
2. the development and use of the ForMoSA forward modelling code relying on the Nested Sampling procedure enables us to infer posterior probability distributions of the physical properties (T eff , log(g), R, L and extinction) of USco 1610-1913 B, HIP 77900 B and HIP 78530 B using the BT-SETTL15 atmospheric models and the X-Shooter spectra. We find that generally, the models fail to reproduce the pseudo-continuum of the X-Shooter spectra over broad wavelengths range. Our solutions are mainly affected by the choice of the spectral range considered to estimate the best fit, and the fitting error bars remain about one to two orders of magnitude smaller given the high S/N of the X-Shooter spectra. Finally, when the extinction is considered as a free parameter, the ForMoSA fitting solutions are considerably improved at all wavelengths with extinction values of 1.
Acknowledgements. The authors thank the  ESO  staff for conducting the observations. We are grateful to  D. Lachapelle  for providing the spectra of HIP 78530 B and USco 1610-1913 B. We also thank  J. Bouvier  for advising us on accretion process and chromospheric activity effects. We acknowledge financial support from the  Programme National de Planétologie (PNP) , the  Programme National de Physique Stellaire (PNPS)  of  CNRS-INSU in France , and the  Agence Nationale de la Recherche  ( GIPSE project ; grant  ANR-14-CE33-0018 ). This research has made use of the SIMBAD database and VizieR catalogue access tool (operated at  CDS, Strasbourg, France). This research has made use of  NASA 's Astrophysics Data System and of the  Extrasolar Planet Encyclopedia  (http://exoplanet. eu/). This publication makes use of VOSA, developed under the  Spanish Virtual Observatory  project supported from the  Spanish MINECO  through grant  AyA2017-84089.
This is due to numerical computational problems, mainly because of the limited number of points in arrays which limits both the field and the sampling. The noise increases also because of the two successive propagations for (C), instead of just one for (A) and (B). Figure  3  gives an example of a radial cut |Ψ E (r)| 2 using the analytic expression of Eq. (  11 ). Here the Bessel-Hankel integrals are computed using the function NIntegrate, that is a general numerical integrator of Mathematica. The same Xeon based computer is used as for getting Fig. 2  with MATLAB but the Mathematica license we had did not allowed the machine to run in parallel on all cores (only 16 out of 56 available). Results are much more precise than the direct numerical calculation using Fourier transforms. Parameters for this system are Ω = 3 mm, ω = 2.
The programming language used is MATLAB and the computer is a machine with two Intel Xeon(R) @ 2. 3 GHz × 56 cores and 503. 8 G of RAM operated under Ubuntu. The transmission of the disks in the computation is either 0 or 1. Disk Ω has a diameter of 40% of the array. The Fresnel diffraction of the wave after the disk Ω is calculated using Eq. (2) for the distance zd = 0. 1m. Then the transmission of the second disk ω is applied to the wave, and the final result is obtained computing the propagation over a same distance d = 0. 1 m using again Eq. (2). The representation is divided in 3 parts giving (A) the diffraction of the disk Ω alone, (B) that of the disk ω alone, and (C) the diffraction for the two disks on-axis. A dark central zone appears in the right part (C) of the top figure of Fig.
Thus, Na cylindrical permanent magnets, of radius Ra, height Ha (aspect ratio AR = U F 5 F ), and magnetic remanence Br will be considered. The permanent magnets are alternated and separated by cylinders of the same height as the magnets Hf=Ha to ensure the simple bonding of the magnets and include 2×Na coils of similar height Ha, as shown in Figure  10. This arrangement is used to maximize the spatial variation of the magnetic flux when the center of the permanent magnet stack is aligned with the center of the winding, thus maximizing the electrical power. + - L b r b R i The air-gap will be noted e, Rb1=Ra+e represent the inner radius of the coil(s), and Rb2 the outer radius of the coil(s). Overall, the height of the permanent magnet stack is therefore equal to H=(2Na-1)Ha. Simulations using the finite element software (Ansys®) were performed to estimate the variation of the magnetic flux.
When preparing the samples, carbonation was hard to avoid. The absolute CO 2 binding quantity (I r ) during experiments was then defined as the difference between the CO 2 binding quantities at each carbonation date (I t ) and at time zero (I 0 ) in Eq. (3): = I I I r t 0 (3) To determine thermal energy storage capacity (TESC), the carbonated grain samples were powdered and characterised by TGA-DSC (Sensys Evo TG-DSC), which provided thermal analysis simultaneous with the loss of weight. Approximately 10 mg of specimen was heated at 80 °C for 5 h with a speed of 10 °C/min from 25 to 80 °C, while the sample was protected by a nitrogen flow of 50 mL/min. Thermal analysis data were acquired by Calisto Acquisition and processed by Calisto Processing (both software) to give loss of weight and enthalpies.
Granulometry Granulometry was executed on collected grains by stereoscopic magnifier STEMI SV 11 with a Zeiss electronic fibre optic lighting KL 1500. The 1-2 and 2-4 mm particles were randomly picked for optical observation. After observation of each sample, the Olympus Image Analysis Software carried out measurement for each grain. The approximate maximum and minimum values of 10-30 grains were used to calculate the mean diameter.
Patients and Study Design A historical cohort study was designed using 1,194 RA patients followed between 1992 and 2012 at the Clinical Immunology and Rheumatology Department in a tertiary university hospital in Lyon (Hôpital Edouard Herriot), France. Demographic and clinical data and RA-related parameters were collected during the whole follow-up for each patient and recorded in a computer database. The inclusion criteria were that the patients had to fulfill the American College of Rheumatology criteria for RA diagnosis  (18) , with available clinical and biological follow-up data collected between 1992 and 2012. In 2012, CVE status was available in 698 cases out of 1,194, and these patients were included in the study. Then, patients were excluded when a CVE preceded RA diagnosis or when follow-up duration was <3 years; 571 patients were finally included.
Outcome Measurements The primary outcome was the first occurrence of a CVE defined as: myocardial infarction, stroke, acute limb ischemia, chronic heart failure, cardiomyopathy or atrial fibrillation, deep vein thrombosis, and pulmonary embolism. Data regarding CVE were retrospectively obtained from the hospital chart by calling the general practitioner or calling the patient. Both fatal and nonfatal CVE were considered. The patients were assigned to two groups according to the presence or the absence of radiographic wrist bone destruction, defined by the Larsen score that ranges from 0 to 5. RA with scores equal to 0 or 1 were considered as non-destructive, whereas scores superior or equal to 2 were considered as destructive. The Larsen score assigned to each patient was the highest observed during the whole follow-up.
Statistical Analysis The main goal of the study was to assess the link between RA radiological severity (Larsen score) and the occurrence of CVE in 571 RA patients. Regarding the retrospective collection of CVE, data were first analyzed as a case-control study. A logistic regression model was used to test the association between joint destruction and other factors with the occurrence of CVE. Odds ratios (OR) were calculated in a univariate analysis. Then, a multivariate logistic regression was performed to calculate the same OR adjusted to all the variables significantly associated with the risk of CVE in the univariate analysis and to assess the interactions between these variables. All logistic regressions and tests were performed using R software with the glm function  (20). As logistic regression analysis cannot account for differences in disease and follow-up duration, a survival analysis was performed to confirm the results in a framework able to properly handle right-censoring.
To assess the link between RA and CVE, data for demographic and RA activity parameters were prospectively collected since 1992. This RA cohort was not different from other RA cohorts regarding sex ratio, age at diagnosis, and ACPA/RF positivity  (23, 24). The percentages of smokers and patients with high blood pressure, diabetes, and dyslipidemia were rather higher than in the general French population  (25). The multivariate analysis could then adjust for these covariates. This difference could be due to the long-term follow-up and the aging of the study population during the last two decades. While our non-Anglo-Saxon population is supposed to have a lower CV risk  (12) (13) (14) , this long follow-up study confirmed the high prevalence of CVE in RA patients, with 30. 3% (173/571) of the cohort with at least one CVE.
Study design and treatment TASCO1 was a multinational, open-label, randomized, noncomparative phase II study conducted in 52 hospital centers across 12 countries (Australia, Belgium, Brazil, Denmark, France, Germany, Italy, The Netherlands, Poland, Russia, Spain, and UK). The study was reviewed and approved by the Institutional Review Board at each participating center and performed in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. All patients provided written informed consent. An independent data safety monitoring board provided frequent oversight of the study. This study was registered with ClinicalTrials. gov (registration number NCT02743221). The final study protocol is available online. Eligible patients were randomly assigned by the minimization procedure in a 1:1 ratio via an interactive webresponse system to receive either TTeB or CeB (reference therapy).
Associated Kaplane Meier survival estimates were summarized for both groups. Secondary subgroup analyses were performed on prespecified factors. An adjusted multivariate Cox regression analysis was used to investigate the effect of prespecified potential prognostic/predictive factors on PFS. DCR and ORR were compared using the Fisher's exact test with two-tailed 95% Clopper Pearson CIs with the intention-to-treat principle. DR was analyzed according to the per-protocol principle for patients with measurable disease at baseline, at least one tumor evaluation on treatment, and presenting a tumor response. For QoL scores analyses, clinically relevant change from baseline was considered for difference of AE10 points. Statistical analyses were performed using SAS (version 9. 2; SAS Institute, Cary, NC).
Further results can be expected from the ongoing comparative phase III study (SOLSTICE, NCT03869892) which is conducted in the same population setting as TASCO1. for participation to Advisory Boards and Research Grants to his institution from Amgen, Bayer, Merck, MSD, Roche, Lilly, Servier, and Bristol Meyers Squibb. AK, AE, PA, and NA are employees of Servier. The views expressed in the submitted article are the authors' own and not an official position of the institution or funder.
ACKNOWLEDGEMENTS We thank the patients who participated in our study and their families. We also thank  Joana Fernandes, PhD  ( Scinopsis Ltd, United Kingdom ) who drafted the outline and first draft of this manuscript. Medical writing assistance was funded by  Global Medical Affairs Oncology, Servier, France. We would also like to thank  Eric Gandossi  ( Institut de Recherches Internationales Servier, France ) and  Sarah Novack, PhD  ( Global Medical Affairs Oncology, Servier, France ) for their assistance and contribution to this work.
Data analysis Quantitative analysis of each fluorescent image was performed based on the measurement of mean signal intensity in the manually selected regions of interest, using Image J software (Version 1. 6. 0, National Institutes of Health, USA). Graphs were generated using Origin Pro software (Version 9. 0, OriginLab Company, USA). The data were presented as mean ± standard deviation (SD). Statistical analysis was performed using Student's t-test. * denotes a statistical significance (* P < 0. 05, ** P < 0. 01, and *** P < 0. 001) between the experimental data of two groups.
Statistical Analysis Spearman rank correlation tests were performed between the measurements on the whole sample and each hemi-biopsy, as well as between the two adjacent hemi-biopsies. Diagnosis concordance between whole samples, and the corresponding hemibiopsies 1 and 2 was checked. We considered diagnoses to be fully concordant only when in full accordance on the whole sample, on hemi-biopsy 1 and hemi-biopsy 2. A p value <0. 05 was considered statistically significant. The statistical software GraphPad Prism version 5. 0. 3 was used for statistical analyses.
Data analysis All data were analyzed using IBM® SPSS Statistics software Version 25, with significance set at p < 0. 05. Threeway repeated measures ANOVA was performed with assessment site, cuff width and inflation pattern as the independent variables, and DDT, PDT and ΔStO2 as dependent variables. Within-subjects effects were assessed using an F-test with Mauchly's test and Huynh-Feldt correction for violation of sphericity. Multiple regression analysis was performed to predict the intensity of discomfort from cuff inflation pressure, ATT and pneumatic cuff width. Data are presented as mean and standard deviation (SD), and in plots with the means and standard error (SE).
Participants and environmental details This study included 21 healthy participants (5 female, 16 male) aged 18-57 years (median age: 20 years), recruited from the University of Limerick. Twenty participants were Caucasian, one male was African. Four females and nine males reported that they engaged in sports. Participants' anthropometric data are summarized in Table  1. Ambient temperature and humidity during testing were 23. 9°C ± 0. 3 and 41. 4% ± 7. 1 respectively. ATT -Adipose Tissue Thickness, BMI -Body Mass Index, SD -standard deviation.
Array programming provides a powerful, compact, expressive syntax for accessing, manipulating, and operating on data in vectors, matrices, and higher-dimensional arrays  [1]. NumPy is the primary array programming library for the Python language  [2, 3, 4, 5]. It plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material science, engineering, finance, and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves  [6]  and the first imaging of a black hole  [7]. Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed.
Two Python array packages existed before NumPy. The Numeric package began in the mid-1990s and provided an array object and array-aware functions in Python, written in C, and linking to standard fast implementations of linear algebra  [8, 9]. One of its earliest uses was to steer C++ applications for inertial confinement fusion research at Lawrence Livermore National Laboratory  [10]. To handle large astronomical images coming from the Hubble Space Telescope, a reimplementation of Numeric, called Numarray, added support for structured arrays, flexible indexing, memory mapping, byte-order variants, more efficient memory use, flexible IEEE error handling capabilities, and better type casting rules  [11]. While Numarray was highly compatible with Numeric, the two packages had enough differences that it divided the community, until 2005, when NumPy emerged as a "best of both worlds" unification  [12] -combining Numarray's features with Numeric's performance on small arrays and its rich C Application Programming Interface (API).
Now, fifteen years later, NumPy underpins almost every Python library that does scientific or numerical computation including SciPy  [13] , Matplotlib  [14] , pandas  [15] , scikit-learn  [16] , and scikit-image  [17]. It is a community-developed, open-source library, which provides a multidimensional Python array object along with array-aware functions that operate on it. Because of its inherent simplicity, the NumPy array is the de facto exchange format for array data in Python. NumPy operates on in-memory arrays using the CPU. To utilize modern, specialized storage and hardware, there has been a recent proliferation of Python array packages. Unlike with the Numarray and Numeric divide, it is now much harder for these new libraries to fracture the user community-given how much work already builds on top of NumPy.
NumPy arrays The NumPy array is a data structure that efficiently stores and accesses multidimensional arrays  [18] , also known as tensors, and enables a wide variety of scientific computation. It consists of a pointer to memory, along with metadata used to interpret the data stored there, notably data type, shape, and strides (Fig. 1a ). The data type describes the nature of elements stored in an array. An array has a single data type, and each array element occupies the same number of bytes in memory. Examples of data types include real and complex numbers (of lower and higher precision), strings, timestamps, and pointers to Python objects. Fig. 1 : The NumPy array incorporates several fundamental array concepts. a, The NumPy array data structure and its associated metadata fields. b, Indexing an array with slices and steps.
For example, a vector of numbers can be stored as a one-dimensional array of shape N , while color videos are four-dimensional arrays of shape (T, M, N, 3). Strides are necessary to interpret computer memory, which stores elements linearly, as multidimensional arrays. It describes the number of bytes to move forward in memory to jump from row to row, column to column, and so forth. Consider, for example, a 2-D array of floating-point numbers with shape  (4, 3) , where each element occupies 8 bytes in memory. To move between consecutive columns, we need to jump forward 8 bytes in memory, and to access the next row 3 × 8 = 24 bytes. The strides of that array are therefore  (24, 8). NumPy can store arrays in either C or Fortran memory order, iterating first over either rows or columns. This allows external libraries written in those languages to access NumPy array data in memory directly.
In broadcasting, one or both arrays are virtually duplicated (that is, without copying any data in memory), so that the shapes of the operands match (Fig. 1d ). Broadcasting is also applied when an array is indexed using arrays of indices (Fig. 1c ). Other array-aware functions, such as sum, mean, and maximum, perform element-by-element reductions, aggregating results across one, multiple, or all axes of a single array. For example, summing an ndimensional array over d axes results in a (n -d)dimensional array (Fig. 1f ). NumPy also includes array-aware functions for creating, reshaping, concatenating, and padding arrays; searching, sorting, and counting data; and reading and writing files. It provides extensive support for generating pseudorandom numbers, includes an assortment of probability distributions, and performs accelerated linear algebra, utilizing one of several Fig.
Many research groups have designed large, complex scientific libraries, which add application specific functionality to the ecosystem. For example, the eht-imaging library  [30]  developed by the Event Horizon Telescope collaboration for radio interferometry imaging, analysis, and simulation, relies on many lower-level components of the scientific Python ecosystem. NumPy arrays are used to store and manipulate numerical data at every step in the processing chain: from raw data through calibration and image reconstruction. SciPy supplies tools for general image processing tasks such as filtering and image alignment, while scikit-image, an image processing library that extends SciPy, provides higher-level functionality such as edge filters and Hough transforms. The scipy. optimize module performs mathematical optimization.