dataesr/scientific-paragraphs-categorization huggingface dataset:
In these C -based methods, the data vector is composed of all the auto-and cross-power spectra (in this case B-power spectra) between sky maps at different frequencies, which is then fitted to a model that incorporates the correlations between all sky components, Galactic and cosmological. Current state-of-the-art constraints from the BICEP-Keck collaboration have been obtained using this methodology. In its simplest incarnation, the method assumes foregrounds are perfectly correlated in frequency (i. e. they have a homogeneous spectrum across the observed sky), and a functional form is assumed for the power spectrum of the foreground amplitudes (generally in the form of a power law, [e. g. 4]). The model is then characterised by a set of constant spectral parameters (e. g. spectral indices, dust temperature), and by the parameters controlling the amplitude power spectra.
JCAP03(2023)035 From T BF in eq. (2. 8) we can get a best-fit estimate of the foreground contribution to our observations as m FG BF = SPT BF , where P ≡ diag(1, 1,. , 1, 0) is an N c × N c diagonal matrix that selects only the non-CMB components of T BF (we have assumed the CMB to be the last component). We can then subtract m FG BF from the data to obtain the residual maps: r ≡ m -m FG BF = Qm, (2. 9) where the matrix Q is Q ≡ 1 -SP ST N -1 S -1 ST N -1. (2. 10) The matrix Q is a projector (Q 2  = Q), and satisfies the nice property that Q S = S(1-P), where 1-P selects only the CMB component. Note also that, since the spatial and frequency dependence of N is factorisable, Q is constant across the sky. Finally, it is straightforward to show that Tr(Q) = N ν -N c +1. Thus, in our case, with N c = 3, Q projects the N ν -dimensional dataset onto a subspace of dimension N ν -2, eliminating two independent modes corresponding to synchrotron and dust assuming constant spectral indices (i.
(3. 3) As in  [18] , we model the power-spectrum of the spectral index variations C βc as a power-law of the form C βc = B c 0 γc , ( 3. 4) with 0 = 80 in all cases. This model is in reasonable agreement with constraints from current measurements of the synchrotron and dust spectral index  [44]. The spectral index maps are generated as Gaussian realizations of C βc in eq. (3. 4) with varying values for the amplitude B c. Instead of varying B c directly, we generate maps of δβ c ( θ) with an arbitrary amplitude and then renormalize them to enforce a given per-pixel standard deviation σ(β c ). We generate simulations for σ(β c ) = {0, 0. 1, 0. 2, 0. 3} for both synchrotron and dust, which covers. We fix the spectral tilt γ c to (γ D , γ S ) = (-3. 5, -2. 5). We then add the mean spectral indices βc to δβ c ( θ) and use the Python Sky Model software (PySM, 4   [45] ) to generate observed sky maps in the six frequency channels of table  1.
"d1sm" simulations We produce an additional set of 20 simulations with a higher level of complexity, including the foreground templates provided by PySM. These models include "realistic" non-Gaussian amplitude and spectral index maps that are based on existing observations. Specifically, for the dust amplitude and spectral index map we adopt the templates included in the d1 model. The spatially-varying dust spectral index map in d1 is consistent with the estimate from Planck data using the Commander component separation code  [46]. Similarly, we use the synchrotron amplitude map assumed by the s1 model. However, we use a modified version of the s1 model for the synchrotron spectral index β S. The original s1 spectral-index map was determined by combining the Haslam 408 GHz map  [47, 48]  with the WMAP 23 GHz map  [49, 50].
Conclusion Detecting the signature of inflation in the CMB polarized B-mode signal is one of the most compelling goals of cosmology, where a strict upper constraint on the large scales amplitudes has the potential to further support or disprove various inflationary models  [57]. Many sources of systematic uncertainty contaminate the faint primordial B-modes with the creation of "spurious" B-mode signal. Among these, the most significant astrophysical source is coming from Galactic polarized foregrounds  [9]. Therefore, one of the most important steps in the analysis of CMB B-mode data is the separation of the multi-frequency data into its different components. In principle, the separation of CMB radiation and foregrounds can be performed either in real space, dealing with maps of the Q/U Stokes parameters, or in Fourier space, processing the E/B modes power spectra.
Acknowledgments We thank  Carlo Baccigalupi ,  Kevin Wolz ,  Davide Poletti  for many helpful discussions. SA is funded by a  Kavli/IPMU  PhD Studentship. MHA acknowledges support from the  Beecroft Trust and Dennis Sciama Junior Research Fellowship at Wolfson College. DA acknowledges support from the  Beecroft Trust , and from the  Science and Technology Facilities Council  through an  Ernest Rutherford Fellowship , grant reference  ST/P004474. This publication arises from research funded by the  John Fell Oxford University Press Research Fund. Some of the results in this paper have been derived using the HEALPix package [42]. The contour plots were generated using the GetDist package [58]. We made extensive use of the numpy [59,60], scipy [61], healpy [62], and matplotlib [63] python packages.
Statistical analyses Quantitative variables were described by their mean and standard deviation. Qualitative variables were described by frequency and percentages. Normality of data distribution was assessed by the Shapiro-Wilk test. Correlations between quantitative variables were measured using Pearson or Spearman coefficients according to the distribution of variables. Qualitative variables were analyzed using the chi-squared or Fisher's exact test based on the size of the various categories. Associations between qualitative and quantitative variables were determined using Student's t-test or the Mann-Whitney-Wilcoxon test depending on the normality of the quantitative variable, or using an analysis of variance (ANOVA) or Kruskal-Wallis test when the qualitative variable had more than 2 categories.
Linear regression was used to evaluate the effect of age and sex (independent variables) on each score from each questionnaire. In order to evaluate overlaps between PROMs, a Principal Component Analysis (PCA) was performed on the overall, PwT1D and PwT2D sample. The number of components was chosen according to eigenvalues (>1). An oblimin rotation was performed to facilitate interpretation. Bartlett's test of Sphericity was used to determine the suitability of the correlation matrix between the different PROMs for a PCA. Kaiser-Meyer-Olkin test was used to assess if the common variance of the PROMs was sufficient for a PCA. Hypotheses were tested bilaterally and considered significant when the p-value was <0. 05. XLSTAT V2020. 5. 1 software was used for the statistical analyses. 1. There were 3245 PwD enrolled in the database.
Contribution of the authors Nicolas Naïditch conducted this study, collected the data, participated in the statistical analysis of the data and wrote the first version of this article. Coline Hehn conducted this study, organized the scientific committee, collected the data and participated in drafting the first version of this article. Amine Ounajim participated in interpreting the data. Guy Fagherazzi and Ben Braithwaite participated in interpreting the data and discussing the article. Jean-François Th ebaut and Antonia Gasch-Illescas participated in interpreting the data and discussing the article.
Acknowledgements This research was supported by the  ITI Innovec  ( IdEx  ( ANR-10-IDEX-0002 ),  SFRI  ( ANR-20-SFRI-0012 )), the  French Infrastructure for Integrated Structural Biology  ( FRISBI ANR-10-INBS-05 ), and the  ITMO Cancer (Col-orME ,  22P096-00 ). N. G. received a Ph. D. fellowship from the  IdEX Unistra (Université de Strasbourg and Investissements d'Avenir ). V. H. received a fellowship from the  CNRS. Robert Drillien  styleedited the text.
Patient characteristics Institutional review board approval and waiver of informed consent were obtained for this dual-center retrospective analysis. Clinical and MRI data of women who underwent MRI before surgery between January 2011 and July 2015 were obtained from two independent institutions in two different countries, McGill University Health Centre, Montreal, Canada (Institution 1), and Hôpital Lariboisi ere, Assistance Publique-Hôpitaux de Paris, Paris, France (Institution 2). Data from patients recruited at the first institution (training set) were used to train the SPHARM predictive models. Their performance was validated on independent data from the second institution (validation set). Fig. 1  presents a flowchart that summarizes the patient inclusion/ exclusion criteria. These criteria are identical to those previously reported elsewhere  [10] , with one exception: the present study increased the minimum tumor diameter from 1 cm as used in  [10]  to 2.
Computational methods overview Our computational methods were based upon the creation of a 2D descriptor (matrix of SPHARM coefficients) that encoded a 2D space/ frequency decomposition of the 3D image signal over a tumor VOI. This representation was compact, robust to signal noise and rotationally-invariant, that is, the representation remained the same regardless of the spatial orientation of the tumor. To perform classification on such SPHARM descriptors, a regularized tensor logistic regression (TensorReg MATLAB toolbox, version 1. 0; https://hua-zhou. github. io/ TensorReg/) technique was selected  [23]. The logistic regression model was trained on VOIs extracted from precise manual tumor segmentations. Its diagnostic performance was evaluated using both precise tumor segmentations, as well as segmentations that consist of simple spheres centered on the tumor.
Funding information This work was jointly funded by the  Fonds de recherche du Qu ebec -Sant e (FRQS)  and the  Fondation de l'association des radiologistes du Qu ebec (FARQ). TLL would like to acknowledge financial support from the  Natural Sciences and Engineering Research Council of Canada (NSERC)   Alexander Graham Bell Canada Graduate Scholarship. K. Siddiqi  and  P. Savadjiev  are grateful to  NSERC  for research support.
Context. GX 301-2 is a high-mass X-ray binary (HMXB) with strong stellar outflows. The evolution of these binaries can be closely related with the interstellar environment due to strong wind interactions. Aims. We try to constrain the energy injected in the interstellar medium by GX 301-2 through stellar wind using HAWK-I and Herschel data. Methods. We analysed HAWK-I images in four different filters (Brγ, H 2 , J, and Ks) and tried to retrieve signatures of the impact of GX 301-2 on its environment. We used Herschel data to outline the interstellar medium and the Gaia DR3 catalogue to infer the proper motion of GX 301-2. Finally, we estimated the energy injected in the interstellar medium since the first supernova event of the HMXB. Results. Using both HAWK-I and Herschel images, we deduce an approximation of the total mass injected from GX 301-2 in the interstellar medium of M inj = 3.
Huthoff & Kaper (2002)  discovered an arm-like structure north of the source (referred to below as the arm) and hypothesized that it is in fact a bow shock coming from the interaction of GX 301-2 with its environment that sweeps the inner material toward this arm. We here quantify the energy exchange between GX 301-2 as a donor and its environment. We search for infrared (IR) emission from gas and interstellar dust, and compute the energy budget of the interaction of the binary with the arm. In Sect. 2, we review the photometric data we used in the study. Section 3 is an analysis of these observations, while in Sect. 4 we infer the mass transfer from the HMXB to the arm. These calculations are discussed and summarized in Sect. 5.
Herschel data Herschel  (Pilbratt et al. 2010 ) observations with the PACS 2   (Poglitsch et al. 2010)  instrument in imaging mode were carried out on August 2, 2011. Observations were made in the three available energy bands (blue 60-85 µm, green 85-130 µm, and red 130-210 µm) under Obs. ID 1342225124, 1342225125, 1342225126, and 1342225127 (PI Chaty). A summary of the observation properties is given in Table  2. The data were reduced using the standard scripts dedicated to the reduction of PACS imaging mini-scan mode data, and implemented in the HIPE 3  software.
Data analysis The HAWK-I data were processed to detect features of interaction between GX 301-2 and the gas arm structure located north of the HMXB. In the science images, we computed the instrumental Using PyRAF 5  , we applied PSF subtraction to each detected star using the findstar routine to detect stars above a given threshold, referenced in Table  3 , along with the number of stars that was subtracted for each filter. To remove the stars, we used the crowded field photometry method following  Massey & Davis (1992). A selection of the brightest nonsaturated and sufficiently isolated stars was made by hand, and a first instrumental PSF was retrieved for the stars using the psf routine. We removed the close neighbors of the bright stars by clustering regions using the group command and subtracted the faintest stars grouped with the bright stars that were used for the first PSF using the substar routine.
Discussion and conclusions Our analysis of HAWK-I images did not allow us to conclude about the interaction of GX 301-2 with its environment because we did not detect the ISM shell as expected: the SNR between the ISM and the sky background is too low. The idea of using HAWK-I comes from the wide-field capabilities of the instrument (7. 5 ′ × 7. 5 ′ ), and from its narrow and wide filters. The arm is less bright in the detection range of HAWK-I than for Herschel or Spitzer, however. The sensitivity of the detector would have required an extended observation time of 2 h per narrow filter and 1h30 per broadband filter to detect the arm because it A88, page 5 of 7 A&A 670,  A88 (2023)  is about three times less luminous at 4. 5 µm than the dust clump that is detected. We retrieved useful data for the shape of the ISM shell using Herschel images, which we modeled as a cylinder to facilitate the calculations.
It would also allow us to understand its surrounding environment better. We used IR images from different instruments and surveys to investigate the impact of GX 301-2 on the surrounding ISM shell. We reduced data from the HAWK-I instrument and Herschel/PACS instrument, and used them to detect the arm structure for the remaining analysis. In order to answer the question of the interaction between GX 301-2 and the shell through stellar winds, we computed an estimate of the total mass lost by GX 301-2 and injected in the arm. Different models were considered, from a fixed source to a moving source. We used simple energy considerations with a convective model to verify whether energetic contribution of the stellar wind to the arm was relevant. We then computed the total energy injected by the stellar winds of GX 301-2 in the ISM, and compared it to some known jets that are produced by galactic binaries whose interaction with the ISM has previously been proven by different studies.
Acknowledgements. This research has made use of Aladin sky atlas and the SIMBAD database developed and maintained at  CDS ,  Observatoire de Strasbourg ; Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration 2013); IRAF, distributed by the  National Optical Astronomy Observatory , operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation ; and  NASA 's Astrophysics Data System bibliographic Services. S. C. acknowledges the  CNES  ( Centre National d'Études Spatiales)  for the funding of  MINE (Multi-wavelength INTEGRAL Network ). The authors are grateful to the  LabEx UnivEarthS  for the funding of  Interface  project  I10  " From binary evolution towards merging of compact objects ".
Statistical Analysis Descriptive statistics (means ± standard deviation) were used to summarize all data. The normality of the data was assessed by the Shapiro-Wilk test. A two-way ANOVA repeated measures test was used to determine group X time interactions. One-way ANOVA and Fisher LSD post-hoc tests were used for evaluation of baseline data for the four groups. When a significant difference was detected by ANOVA, mean differences were determined by pairwise comparisons. The sample size needed to detect a statistical difference between study variables with a 95% confidence interval (CI) and equal to or greater than 80% of the power value was calculated. Effect sizes (ES) were reported as partial eta-squared, where ES were considered trivial (<0. 2), small (0. 2-0. 6), moderate (0. 6-1. 2), large (1.
KELT-9 b For the analysis of KELT-9 b, we used the light curves for this target from  Gaudi et al. (2017) , who supplied them to us in a private communication 6. In total, there are 23 primary transits and 7 secondary eclipses observed from 2014 to 2015 using different ground-based telescopes and filters; further details can be found in the filenames of the light curves at the ExoFOP website. There are TESS data from Sectors 14, 15, and 41 available for this target at the MAST portal with a cadence of 120 s, which were also used in the study of  Ivshina & Winn (2022). In addition to the transit described in Sect. 2. 2, several observations of this target were taken with the CHEOPS space telescope, with a total of nine observed occultations and four phase curves. These were observed from July to September 2020 and July to August 2021.
(2018). These were acquired between November 2016 and October 2018 using the telescopes and filters stated in Table  1  of their publication. Two more light curves were supplied to us by  Patra et al. (2020)  in a private communication. These were observed on 10 and 11 June, 2017, with the 1. 2 m telescope at the Fred Lawrence Whipple Observatory located in Arizona using images from the KeplerCam detector and a Sloan r ′ -band filter. We also analysed the public 7  light curves from the second data release of the ExoClock project  (Kokori et al. 2022). This data release contains 32 KELT-16 b transit light curves observed with different telescopes and filters, acquired between July 2018 and November 2020. Details regarding the observation setups can be found at the project homepage under 'ExoClock Observations'.
Additionally, we used the 36 public transit light curves from  Mancini et al. (2022). These light curves were obtained between June 2016 and June 2021, using the telescopes and filters stated in Table  1  of their publication. In total, 30 planetary transits were observed, with three of them having been observed with two telescopes, and one with three telescopes. TESS data for this target were also obtained from the MAST, including data from Sectors 15 and 41 obtained with a cadence of 120 s and already used in earlier publications. For this target, we also re-fitted all light curves homogeneously.
Transit and secondary eclipse fitting For the analysis of photometric CHEOPS data, we first use the PYTHON package PYCHEOPS  (Maxted et al. 2022). PYCHEOPS is a publicly available 8  PYTHON module for the analysis of data from the ESA CHEOPS mission. This package can help to deal with the systematic effects present for the photometric data of this space telescope, which are induced by its nadir-locked orbit  (Maxted et al. 2022). Moreover, PYCHEOPS offers the ability to detrend the data of other effects, such as correlated noise using Gaussian process regression, fit transit and eclipse models to the light curves to retrieve certain parameters, and more. Fitting can be done using least-squares minimisation and also Markov chain Monte Carlo algorithms. More detailed information can be found in  Maxted et al.
(2022). In addition to the above and data visualisation, this module also offers a built-in client for data handling, access, and retrieval from the DACE website 9  (Data & Analysis Center for Exoplanets) hosted by the University of Geneva. General users can access the public data from CHEOPS, with CHEOPS science team members also having the possibility to access proprietary data. We use this to download the data using the respective file keys. In all cases, we use the 'OPTIMAL' aperture and also use the 'decontaminate' option, which performs a subtraction of the contamination from nearby sources. We plot the light curve using MATPLOTLIB  (Hunter 2007 ) and perform a 2σ outlier clipping using the 'clip_outliers' function from PYCHEOPS. In the case of phase curves, we cut out the individual transits or occultations and separate them.
Afterwards, we make use of the PYCHEOPS 'flatten' function with the centre value of the mask being the centre of the transit or occultation from the plot. The mask width value is chosen to fit the transit or occultation width. Next, we save the individual transit or occultation light curves, consisting of time, flux, flux errors, and roll angle values, into two separate files, one including the roll angle information and the other one not. The data handling is carried out using NUMPY  (Harris et al. 2020). These two data files will be read in from the Transit and Light Curve Modeller (TLCM, version 97)  (Csizmadia 2020). TLCM is a free software tool 10  that can be used to analyse, fit, and simulate light curves and radial velocity curves of transiting exoplanets and detached eclipsing binaries.
After running TLCM, we extracted the transit epochs, as well as their error bars from the results files using the median solution values. In the cases where we created combined models using several transits, for example from TESS, we did a TLCM run while fitting for the parameters as stated above using the epoch of one of the transits near the centre of the data set. After completion of the run, we use the resulting parameter values for the individual transits and fix them, except for the epoch, which is determined visually for each transit as before. As in the case of individual transit fits without a combined model, the resulting epochs are then used in the TTV fits of our three models. For the occultation fitting, we use the same procedure as before to cut out the occultations from the light curves.
Afterwards, we phase-fold the eclipses from one observing season -with the exception of Spitzer data -using the PYASTRON-OMY package  (Czesla et al. 2019 ) and fit them using a BATMAN  (Kreidberg 2015)  box model together with LMFIT  (Newville et al. 2014)  and EMCEE  (Foreman-Mackey et al. 2013)  for the Markov chain Monte Carlo (MCMC) fitting. Our box model uses an epoch near the centre of the data set, together with the period, planet radius, semi-major axis, inclination, eccentricity, and uniform limb darkening. The values for these parameters, as before, were taken from TEPCAT. First, we do a preliminary fit to get the combined shape of all occultations in the data set -by leaving all parameters free with reasonable error bars -and the best-fit period from the linear model of the TTV fit for each target.
KELT-9 b Transit fitting. For the transit light curves publicly available from  Gaudi et al. (2017) , we fitted each transit individually with TLCM. The reason for this is that the observations were made using different telescopes and filters, making it too computationally expensive to create a combined model from all light curves at once and achieve convergence, because of all of the extra parameters associated with the implementation of a different noise model per instrument and also different limbdarkening parameters per filter. On the other hand, for the TESS and CHEOPS data, a combined model was created by jointly fitting both data sets with TLCM to improve the transit fits especially for the CHEOPS observations, because some of those lack parts of the ingress or egress, leading to uncertain fits.