dataesr/scientific-paragraphs-categorization huggingface dataset:
Controlled tests of variation in gfit parameters In this section, we show results of a more detailed exploration of the gfit software used by the EPFL_gfit and CEA-EPFL teams (see method descriptions in Appendices C3 and C4). In particular, we investigate the dependence of the results on choices made in the course of estimating the per-object shears, or the weighting used to estimate an average shear for the entire field). Our comparison focuses on the constant-shear branches, where we have additional diagnostics such as the multiplicative and additive biases (see Sec. 2. 3 for definitions). This comparison uses the submissions from EPFL_gfit, but the results are also applicable to CEA-EPFL submissions. The factors that were considered in the comparison are the galaxy model, the postage stamp size, precision on the total flux and centroid, maximum half-light radii of the bulge and disk, filtering of the galaxy catalog, constraints on positivity of bulge and disk flux, and occasional other experiments, such as stacking the 9 PSFs in the starfield images, or running a denoising scheme.
HST images). Thus truncation, noise, and other biases should be consistent between the two sets of results. Differences in c+ for the control and realistic galaxy experiments depend on whether the simulated data represents a space survey or a ground survey. We note the general point that, using this dataset, we cannot cleanly separate model bias in true isolation, as compounding interplays may exist between model bias, truncation bias, noise bias and other biases. This would be an interesting subject for future study. For the purpose of controlling for the effects found in this analysis of gfit results, in the general analysis in Sec. 5, we will use a set of gfit submissions with consistent postage stamp sizes (one set for ground, and another for space). These will be the same submissions used in Sec.
In some sense, the addition to the term in the χ 2 is equivalent to multiplying the likelihood, i. e. , imposing a prior on the ellipticity. It seems that this is a way to remove or reduce noise bias in all fields (with stronger impact on those that have poor seeing), eliminating the need for explicit calibration factors. For GREAT3, the best value of shape noise σs to use in the weighting scheme and the form of the penalty term to use in the χ 2 was clearly shown to do an excellent job at shear estimation for the particular p(ε) and galaxy property distributions used here. However, it is unclear whether these results would necessarily be consistently reproducible for other datasets with different intrinsic p(ε), or those with a p(ε) that correlates with other galaxy properties in a way that is not reproduced here.
Summary To summarize the MegaLUT results, we find that good results required identification and rejection of a small fraction of problematic galaxies. Use of the image autocorrelation function led to substantially worse performance than use of the adaptive moments (from a fit to an elliptical Gaussian, using code in GalSim). An attempt to use convex hull peeling led to substantial calibration biases and overall noisiness. Use of training images with 1/10 (rather than 1/2) the noise level of GREAT3 reduced the additive systematic errors. Finally, clipping the shears substantially (to a maximum value of |g| = 0. 6) led to negative calibration biases and overall worse performance. The MegaLUT method had overall better performance in variable-shear branches due to the pervasive ∼ 1 per cent calibration biases, which hurt their performance preferentially on the constant-shear branches.
5. 5. For the "ess" team, the reason for the outliers shown in Fig. 14  is fairly clear: they used a sum of three Gaussian components to describe the PSF, which makes it particularly difficult to model PSFs with defocus or trefoil. In contrast, the middle panel of that figure shows a comparable plot for the Amalgam@IAP team, which modeled the full PSF, and does not show significant outliers. Finally, the bottom panel shows images of the PSF for the eleven subfields in RGC for which the "ess" results were seriously discrepant. As shown, in about three cases the PSF has the characteristic "donut" shape of highly out-of-focus images; such data would likely be eliminated from a shear analysis in a real dataset. These subfields were problematic for several other methods. In other subfields, there is a triangular shape characteristic of trefoil, which seems to have been less problematic for other methods that have a more flexible representation of the PSF.
• CMU experimenters: Only one submission per branch. • CEA_denoise, MetaCalibration, EPFL_MLP / EPFL_MLP_FIT, EPFL_KSB, EPFL_HNN, Wentao Luo: Best submission in each branch. • GREAT3-EC (or re-Gaussianization): These results used the shear estimation example script described in Appendix B. As noted there, for several reasons the results are not science-quality shear estimates and therefore the results have no reflection on science papers that use this algorithm. However, since it is a stable algorithm in the public domain, and one of the few moments-based methods, we include it in this section to provide a basic point of comparison. The errorbars show the possible range of Q values for a submission with shear calibration biases that would nominally give a particular Q value. As shown, the sizes of these ranges depend strongly on Q, and are smaller for space than for ground branches.
Results for the following teams are not shown in this section: miyatake-test (for reasons described in Sec. 3), BAMPenn, and HSC/LSST-HSM. The BAMPenn results included some bugs that mean the results do not correctly reflect the real performance of the method. The HSC/LSST-HSM submissions used the HSC/LSST software pipeline with the same shear estimation method as in the GREAT3 example scripts purely as a sanity check of the pipeline.
Choices related to calibration of shears were quite varied, with some teams that aim for an unbiased measurement (e. g. BAMPenn, ess, MBI) and others that apply calibrations in a variety of ways. Aside from external simulationbased calibrations, which are subject to the limitation that the calibrations are only as good as the simulations, a few more sophisticated options were tried. These include iterative external simulations that get updated until the outputs match those in the dataset that is being analyzed (sFIT), analysis of a deep subset of the same data (COGS), and self-calibration using manipulations of the images themselves (MaltaOx, MetaCalibration). These alternatives appear promising, and avoid some of the objections to the most basic brute-force calibration. The utility of the deeper data to several teams, either for calibrations or deriving galaxy property distributions, suggests that future surveys may find it useful to have a deeper subsurvey, as indeed many already intend to do.
For the "shear responsivity"  (Bernstein & Jarvis 2002)  describing how galaxies with a particular distortion respond to a lensing shear, the script uses an overly-simplistic expression rather than a more accurate one (both available in the above reference). It also uses a simple but inaccurate way of estimating the RMS distortion of the galaxy population, rather than more accurated but more complicated methods that are available in the literature (e. g. ,  Reyes et al. 2012 ) as an input to the responsivity calculation. Finally, the default settings for initial guess of object size lead to convergence to a local minimum for the space branches that cuts out the outer parts of the PSF, resulting in very wrong shear estimates (but accurate cen- troid estimates). Fine-tuning the initial guesses is necessary for this script to give reasonable results on space simulations.
C3 EPFL_gfit All submissions by the EPFL_gfit team used the gfit method. A few submissions also used a wavelet-based DWTWiener denoising code from  Nurbaeva et al. (2011) , integrated into gfit. The gfit method uses a maximumlikelihood, forward model-fitting algorithm to measure galaxy shapes. An earlier version of gfit, used in the GREAT10 galaxy challenge  (Kitching et al. 2010 (Kitching et al. , 2012)) , was described in  Gentile, Courbin & Meylan (2012). The version used in GREAT3 is completely new, written in Python and relies on the NumPy, SciPy and PyFits libraries. The software has a modular design, so that additional galaxy models and minimizers can be plugged in fairly easily. The behavior of gfit is controlled though configuration files. gfit requires catalogs generated via an automated process from input galaxy and PSF mosaic images by SExtractor  (Bertin & Arnouts 1996).
C4 CEA-EPFL The CEA-EPFL team used an object-oriented framework written in Python and usable in other contexts than GREAT3 with minimal changes, including: • Galaxy shape measurement (gfit from Sec. C3). • Weight calculation (sfilter). • PSF estimation (star shape measurement, PSF interpolation, PCA decomposition and reconstruction). • Image coaddition routines. • Wavelet-based tools for deconvolution, denoising, coaddition, and super-resolution. gfit was described in Sec. C3, but the remaining pipeline elements used in GREAT3 are described below.
C6 CMU_experimenters The stacking method used by CMU_experimenters was a simple modification of the example script described in App. B. The basic steps were galaxy registration, stacking, and PSF correction of the stacked image. First, CMU_experimenters measured the weighted first moments (centroids) for all galaxy images. They used the default GalSim interpolation routines to shift each galaxy so the centroid would be at the exact center of the postage stamp. Next, they stacked all 10 4 galaxies in a single GREAT3 image using a simple unweighted average. Finally they used GalSim routines for PSF correction (re-Gaussianization) to estimate the PSF-corrected distortion ê. The shear estimate for the field is simply ĝ = ê/2, since the stacked object is effectively round in the absence of a shear.
Prior to the deadline, this treatment of variable PSF branches was not sufficiently mature to be used as a proof of concept of this novel approach. The MegaLUT team submissions do not use the deep datasets, and do not weight the per-galaxy shear estimators, aside from rejections following simple criteria. The time per galaxy listed in Table  2  for this method, 20 ms, includes the overhead involved in generating a typical-sized training dataset as well as the training of the ANN. However, once the ANN has been trained, the shear estimation per galaxy takes roughly 3 ms.
The most important parameters are the PSF properties such as ellipticity, size, kurtosis, etc. Even with perfect knowledge of PSF, galaxy ellipticities still have both additive and multiplicative bias, which increases with the size of the PSF. In their GREAT3 analysis, the sFIT team ignored kurtosis and characterize the PSF in terms of its ellipticity and FWHM. They modeled the variation of both additive and multiplicative errors as a function of PSF FWHM using second-order polynomials. Variable shear branches do require a per-galaxy correction using the PSF properties at the galaxy location to estimate the correction factors (but not using the individual galaxy-fitting results). Weighting Scheme: The ellipticity measurement code used by the sFIT team outputs ellipticity uncertainties by evaluating the Hessian matrix.
ACKNOWLEDGMENTS We thank  Gary Bernstein  and  Mike Jarvis  for providing helpful feedback on this paper,  Peter Freeman  for providing guidance on the statistical interpretation of results, and the anonymous referee for making suggestions that improved the presentation of results in the paper. We thank the  PASCAL-2 network  for its sponsorship of the challenge. This project was supported in part by  NASA  via the  Strategic University Research Partnership (SURP) Program of the Jet Propulsion Laboratory, California Institute of Technology ; and by.
Material The material is composed of data obtained from the four RhE methods, EpiSkin™, EpiDerm™, SkinEthic™ RHE, and epiCS® after obtaining authorization from the test methods' developers to use them. These data come from their respective validation studies  (Alépée et al. , 2014a; Alépée et al. , 2014b; Barratt et al. , 1998; Hoffmann et al. , 2005; Kandárová et al. , 2006; Liebsch et al. , 2000; Kandárová et al. , 2014 and Tornier et al. , 2010) , implementing their respective protocols with regard to the evaluation of skin corrosive chemicals. For the purpose of including in TG 431 the sub-categorization abilities of these four methods, additional data were generated. Results for EpiDerm™ and epiCS® have never been published and we report them here on the basis of their current PM, PMvar1 and PMvar2.
The OD serves to derive relative cell viability values. The value of 100% cell viability is set to the OD measured for the negative control. For each of the test chemicals, the relative cell viability is then obtained from their OD in proportion to the one of the negative control. For colored chemicals and/or for direct MTTreducer chemicals raw OD has to be corrected prior to deriving the final cell viability. After measurements of the OD and linear conversion into cell viability values, the results provide cell viability at 3 and 60 min for EpiDerm™, SkinEthic™ RHE and epiCS®, and additionally at 240 min for EpiSkin™. These values serve to the prediction models. The set of data considers 80 chemicals (Table  9 ) that the four test methods tested in common. Those chemicals have been tested over 3 runs by EpiSkin™, EpiDerm™ and SkinEthic™ RHE, and over 2 runs by epiCS®.
New prediction model variation 2 (PMvar2) PMvar2 linearly combines v3 and v60 to establish a composite cell viability indicator, noted vfin, that stands for "final cell viability", and that serves afterwards as a basis for providing predictions for subcategorization (Table  6 ). Combination of v3 and v60 to obtain vfin follows different modalities (Table  6 ). This vfin composite indicator of cell viability aims to facilitate sub-category predictions on the basis of one value instead of considering several ranges of cell viability values   6 ). Similarly as PMvar1, it ultimately aims to improve Cat1BC predictions as well as overall accuracy of the method. This PM assumes that, theoretically, cell viability values would be included in a range of 0 to 100%. However some values of v3 are above 100% in the dataset and therefore, although PMvar2 (Table  6 ) would be the best possible case, some values of vfin might be still overlapping in sub-categories.
Nonetheless such overlaps between sub-categories of chemicals would be reduced when compared to original prediction model. As the intervals of vfin overlap in a lower extent, discriminating between the three sub-categories may result in a greater overall accuracy. On this theoretical basis, PMvar2 was then developed for EpiDerm™, SkinEthic™ RHE and epiCS® using the composite cell viability 'vfin', thanks to the custom-made program and the boxplot graph representing the distribution of vfin over the in vivo sub-categories. Two cutoff values were thus derived from vfin distribution, y and z respectively for discriminating Cat1A chemicals from Cat1BC and for discriminating noncorrosive chemicals from all the others. This provides in the end also a classification type Cat1A versus Cat1BC versus noncorrosive (Table  6 ).
The new method jointly analyzes neural signals by extracting statistical properties, obtained by exploiting the fundamental feature of dynamical systems, the recurrence structure. Recurrences represent instances in time when the system's trajectory returns to a phase space neighborhood of a location it has visited previously, cf. [1]. Classical recurrence plots (RP)  [2]  represent symmetric binary valued matrices and characterise the system's phase space. Since neural recurrence patterns occur in particular frequency bands, we propose to build novel, frequency-selective recurrence plots from their time-frequency signal representations. To evaluate the recurrence results, we compute surrogate RPs by time randomisation of the original time-frequency representations. To obtain statistically significant recurrence information, statistics of the original RPs is pixel-wise compared with the surrogate dataset statistics.
To extend the search for a possible fourth sterile neutrino down to small ∆m 2 41 values, the likelihood has been computed using the GLoBES software  [23] , in order to take into account also matter effects and the non-zero value of ∆m 2 21. The likelihood has been profiled also on the ∆m 2 31 value. More details on the analysis are available in  [22]. In figure  4  the 90% CL exclusion plot is reported in the ∆m 2 41 vs sin 2 2θ µτ parameter space. The most stringent limits of direct searches for ν µ → ν τ oscillations at short-baselines obtained by the NOMAD  [13]  and CHORUS  [14]  experiments are also shown. Our analysis stretches the limits on ∆m 2 41 down to 10 -2 eV 2 , extending the values explored with the τ appearance searches by about two orders of magnitude at large mixing, for sin 2 2θ µτ 0.
Acknowledgments We thank  CERN  for the successful operation of the CNGS facility and  INFN  for the continuous support given to the experiment through its  LNGS laboratory. We acknowledge funding from our national agencies:  Fonds de la Recherche Scientique-FNRS  and  Institut InterUniversitaire des Sciences Nucleaires for Belgium, MoSES for Croatia, CNRS  and  IN2P3 for France, BMBF for Germany, INFN for Italy, JSPS (Japan Society for the Promotion of Science) ,  MEXT  ( Ministry of Education, Culture, Sports, Science and Technology), QFPU (Global COE programme of Nagoya University, Quest  for Fundamental Principles in the Universe supported by  JSPS  and  MEXT) and Promotion and Mutual Aid Corporation for Private Schools of Japan for Japan, SNF , the  University of Bern for Switzerland , the  Russian Foundation for Basic Research  (grant no.
19-02-00213 -a,  12-02-12142  ofim), the  Programs of the Presidium of the Russian Academy of Sciences Neutrino physics and Experimental  and theoretical researches of fundamental interactions connected with work on the accelerator of  CERN , the  Programs of Support of Leading Schools  (grant no. 3110. 2014. 2 ),and the  Ministry of Education and Science of the Russian Federation for Russia  and the  National Research Foundation of Korea Grant  ( NRF-2013R1A1A2061654 ) for Korea. We are also indebted to INFN for providing fellowships and grants to non-Italian researchers. We thank the  IN2P3 Computing Centre  ( CC-IN2P3 ) for providing computing resources for the analysis and hosting the central database for the OPERA experiment. We are indebted to our technical collaborators for the excellent quality of their work over many years of design, prototyping, construction and running of the detector and of its facilities.
FUNDING SOURCES AND ACKNOWLEDGMENTS Funding. European Union FET-Open  grant  ULTRAQCL 665158 ,  ANR-12-NANO-0014  " PhaseLock ", the  EPSRC (UK) , the  EC programme (TOSCA) ,  ERC "GEM " grant, the  Royal Society and Wolfson Foundation  and the  Conseil Général de l'Essonne. See Supplement 1 and 2 for supporting content.
Spectral energy distribution We start by modeling the 1000 -10,000 Å SED of NGC 1068 (Fig. 4 ). To represent the host galaxy contribution, we use the Sbc template presented in Sect. 2. 3. This template is consistent with the morphological classification of the host of NGC 1068, such as discovered by  Balick & Heckman (1985). The authors also highlighted the importance of starburst activity in the SED of NGC 1068 that impacts the observed fluxes at wavelengths larger than 1µm. The resulting host SED nicely fit the fluxes observed with large aperture detectors that are listed in the NASA/IPAC Extragalactic Database, see the first panel of Fig. 4. The AGN continuum is modeled using an isotropic power-law spectral energy distribution F * ∝ ν -α with α = 1. This continuum is the scattered component of the intrinsic flux that is blocked along the equatorial plane by an obscuring dust screen, usually refereed to as a "torus".
Acknowledgements. The author would like to thank  René W. Goosmann  for his comments on the text,  Ariane Lanc  ¸on for her advises on galaxy structures and physics, and  Mari Kolehmainen  for proof-reading this paper. This work was supported by the  Centre national d'études spatiales (CNES)  who funded this project through to the post-doctoral grant "Probing the geometry and physics of active galactic nuclei with ultraviolet and X-ray polarized radiative transfer".
ACKNOWLEDGEMENTS The authors thank the anonymous referee for their constructive comments. VS acknowledges the support of an  Science & Technology Facilities Council (STFC)  studentship. AAV acknowledges support from the  Swiss National Science Foundation  through an  Ambizione Fellowship. SVJ and SBS acknowledge research funding by the  Deutsche Forschungsgemeinschaft (DFG)  under grant  SFB 963/1 , project  A16. SGG acknowledges support from the  STFC  via an  Ernest Rutherford Fellowship  [ ST/J003255/1 ]. This study was supported by the grant  ANR 2011  Blanc  SIMI5-6 020 01  " Toupies: Towards understanding the spin evolution of stars " (http://ipag. osug. fr/Anr_Toupies/). This work is based on observations obtained with ESPaDOnS at the CFHT and with NARVAL at the TBL. CFHT/ESPaDOnS are operated by the  National Research Council of Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (INSU/CNRS) of France  and the  University of Hawaii , while TBL/NARVAL are operated by  INSU/CNRS.
The unparalleled photometric data obtained by NASA's Kepler Space Telescope has led to improved understanding of red-giant stars and binary stars. We discuss the characterization of known eccentric system, containing a solar-like oscillating redgiant primary component. We also report several new binary systems that are candidates for hosting an oscillating companion. A powerful approach to study binary stars is to combine asteroseimic techniques with light curve fitting. Seismology allows us to deduce the properties of red giants. In addition, by modeling the ellipsoidal modulations we can constrain the parameters of the binary system. An valuable independent source are ground-bases, high-resolution spectrographs.
Phenomenologically, the ZFC/FC behavior is similar to that seen in ferromagnets with a sizeable coercivity. The upturn in χ c (T) begins at nearly the same temperature as the peak in χ a (T). Data were taken in both field-cooled-cooling (FCC) and field-cooled-heating (FCH) modes. The observation of hysteresis in these two plots is in line with the ferromagnetic character of the transition for H ‖ [001]. The behavior of the in-field magnetization at 1. 8 K, measured in the ab-plane along [100] and [110], and along [001] (Figs. 2(a, b )), is in conformity with the χ(T) data depicted in Figs. 1(c, d ). For H in the ab-plane, the magnetisation in EuRhAl 4 Si 2 increases almost linearly, reaching a (spin-flip) saturation field near 5. 5 T, with a saturated Eu 2+ moment M 0 = 7 μ B , as expected for a g = 2, S = 7/2 ion.
Qualitatively the behavior of MR in the two compounds is similar. At low fields, below T N , it is positive and increases with field. Above the metamagnetic transition the MR increases with field, its magnitude and sign depending upon the temperature and field. In the paramagnetic state at 15 K in EuRhAl 4 Si 2 and at 20 K in EuIrAl 4 Si 2 , the MR is negative at all fields. 151 Eu Mössbauer spectra. Additional information on the two compounds was obtained by employing 151 Eu Mössbauer spectroscopy. Spectra were recorded between 4. 2 and 15 K. We show here only the data for T = Ir (the spectra for the Rh-analogue are shown in the Supplementary information). We recall that the specific heat data show a transition near 14. 7 K followed by another one at 13. 2 K. At 4. 2 K, the spectrum is a standard Eu 2+ hyperfine pattern with a single hyperfine field of 30.