dataesr/scientific-paragraphs-categorization huggingface dataset:
The electron recombination of NH 4 + then produces amidogen. For acetaldehyde, a dozen reactions are listed in the KIDA and UMIST databases. Among them, the reaction O + CH 3 CH 2 → CH 3 CHO + H  (Charnley et al. 1992; Harding et al. 2005; Yang et al. 2005)  is the most efficient in the conditions appropriate for the L1157-B1 gas. Therefore, the two gaseous species necessary to synthesise acetaldehyde are atomic oxygen and ethyl radical. According to the astrochemical models, less than 20% of gaseous oxygen is in the form of atomic oxygen. In L1157-B1, the O abundance is predicted to be 5 × 10 -6 , in agreement with the bright [OI]-63 µm line observed  (Benedettini et al. 2012 ) by Herschel in L1157-B1. The case of ethyl radical is a bit more complicated. It may be the result of the partial hydrogenation of C 2 H 2 or C 2 H 4 on the grain surfaces (to be noted that no observations exist about the abundance of this species in the solid form and no computations have been carried out) and be directly sputtered from the grain mantles as such.
Acknowledgements. We are very grateful to all the IRAM staff, whose dedication allowed us to carry out the  SOLIS  project. We also thank the anonymous referee for useful suggestions. This work was supported by (i) the  French program Physique et Chimie du Milieu Interstellaire (PCMI)  funded by the  Conseil National de la Recherche Scientifique (CNRS)  and  Centre National d'Etudes Spatiales (CNES) , (ii) by the  Italian Ministero dell'Istruzione, Università e Ricerca  through the  grant Progetti Premiali 2012 -iALMA  ( CUP C52I13000140001 ), (iii) by the program PRIN-MIUR 2015 STARS in the  CAOS -Simulation Tools for Astrochemical Reactivity and Spectroscopy in the Cyberinfrastructure for Astrochemical Organic Species  ( 2015F59J3R ,  MIUR Ministero dell'Istruzione, dell'Università della Ricerca e della Scuola Normale Superiore) , and (iv) by the  French Agence Nationale de la Recherche (ANR) , under reference  ANR-12-JS05-0005.
PC, ACT, JEP, and APu acknowledge support from the  European Research Council  (ERC; project  PALs 320620 ). APo acknowledges the financial support provided by a  Canadian Institute for Theoretical Astrophysics (CITA) National Fellowship. I. J. -S. and D. Q. acknowledges the financial support received from the  STFC  though an  Ernest Rutherford Fellowship  (proposal number  ST/L004801 ) and Grant ( ST/M004139 ).
The candidate sources all lie in the declination range -85 • and +20 •. Targets in each class are combined in a "stacked analysis," assuming that most or all candidate sources in a target set are emitting photons resulting in a more significant combined signal compared to that of a single target (Section 4). The results of this analysis, including particle and energy flux upper limits of selected target directions, are given in Section 5. This study uses the same methods for hadron reduction and calculation of upper limits that were explained in the preceding paper. 2. DATA SET Air showers induced by UHE cosmic rays detected with the Pierre Auger Observatory (The Pierre Auger Collaboration 2015) are used in this analysis. The observatory is located in Argentina near Malargüe and is centered at latitude 35.
2 • S and longitude 69. 5 • W at a mean altitude of 1400 m above sea level. A SD of 1660 water-Cherenkov particle detectors covering an area of 3000 km 2 on a triangular grid with 1. 5 km spacing observes electrons, muons, and photons at the ground with a duty cycle of nearly 100%. The area is overlooked by 27 fluorescence telescopes operating on dark nights with a duty cycle of ∼15%. Events recorded between 2005 January and 2013 December in hybrid mode, i. e. , recorded by both the FD and SD, are used in this analysis. The selection criteria are the same as in the previous blind search paper  (Aab et al. 2014c ), but additional accumulated data increase the statistics by 28% to 308,676 recorded events in the present study. The energy range is between 10 17. 3 eV and 10 18. 5 eV to take advantage of high statistics at low energies and to avoid additional shower development processes at the highest energies  (Risse & Homola 2007).
1 nG. of the dataset, the attenuation length varies between 90 kpc at 10 17. 3 eV and 900 kpc at 10 18. 5 eV encompassing Galactic and nearby extragalactic sources. Requiring only a survival probability of 1% the attenuation length increases, as indicated by the dashed line in Figure  1 , extending to a distance of a few Mpc at the highest energies considered and hence also including the nearest active galactic nucleus Centaurus A, which lies at less than 4 Mpc. It should be noted that the observable distance may increase further if including also secondary photons, i. e. , UHE photons converted back from electrons via inverse Compton scattering within the electromagnetic cascading process taking place outside the Galaxy. However, to be useful for point source searches, these photons must be in the energy range and have parent electrons that have not been deflected more than 1 • in ambient magnetic fields (see Section 4).
ACKNOWLEDGMENTS The successful installation, commissioning, and operation of the  Pierre Auger Observatory  would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargüe. We are very grateful to the following agencies and organizations for financial support: Argentina -Comisión Nacional de Energía Atómica ;  Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) ;  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) ;  Gobierno de la Provincia de Mendoza ;  Municipalidad de Malargüe ;  NDM Holdings  and  Valle Las Leñas , in gratitude for their continuing cooperation over land access; Australia -the  Australian Research Council; Brazil -Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ;  Financiadora de Estudos e Projetos (FINEP) ;  Fundação de Amparo à Pesquisa do Estado de Rio de Janeiro (FAPERJ) ;  São Paulo Research Foundation (FAPESP)  Grants No.
2010/07359-6  and No. 1999/05404-3 ;  Ministério de Ciência e Tecnologia (MCT) ;  Czech Republic -Grant  No. MSMT CR LG15014 ,  LO1305 , and  LM2015038  and the  Czech Science Foundation  Grant No. 14-17501S ;  France -Centre de Calcul IN2P3/CNRS ;  Centre National de la Recherche Scientifique (CNRS) ;  Conseil Régional Ilede-France ;  Département Physique Nucléaire et Corpusculaire  ( PNC-IN2P3/CNRS );  Département Sciences de l'Univers (SDU-INSU/CNRS) ;  Institut Lagrange de Paris (ILP)  Grant No. LABEX ANR-10-LABX-63  within the  Investissements d'Avenir Programme  Grant No. ANR-11-IDEX-0004-02 ;  Germany -Bundesministerium für Bildung und Forschung (BMBF) ;  Deutsche Forschungsgemeinschaft (DFG) ;  Finanzministerium Baden-Württemberg ;  Helmholtz Alliance for Astroparticle Physics (HAP) ;  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ;  Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen ;  Ministerium für Wissenschaft, Forschung und Kunst des Landes Baden-Württemberg; Italy -Istituto Nazionale di Fisica Nucleare (INFN) ;  Istituto Nazionale di Astrofisica (INAF) ;  Ministero dell'Istruzione, dell'Universitá e della Ricerca (MIUR) ;  CETEMPS Center of Excellence; Ministero degli Affari Esteri (MAE) ;  Mexico -Consejo Nacional de Ciencia y Tecnología (CONACYT)  No.
Global Cancer Incidence The estimated cancer incidence for 27 cancer types was obtained from 184 countries in the world and for the aggregated category 'all cancers excluding nonmelanomatous skin cancers' for the year 2012 from the GLOBOCAN 2012 online database developed by the International Agency for Research on Cancer (IARC). The methods and data sources used to develop GLOBOCAN have been previously described  [1, 10]. Cancer incidence projections for the years 2015, 2020, 2025, 2030 and 2035 were obtained by country across the 27 cancer types from GLO-BOCAN, as previously described  [11].
Such estimations were therefore considered beyond the scope of this study. We do note that our radiotherapy demand estimates included both palliative and curative indications. The other limitations of our study pertain to the quality of the available incidence data. Details of the data sources and methods of estimation used in GLOBOCAN to compile national incidence are available online (http://globocan. iarc. fr) and an alphanumeric scoring system gives a broad Our findings highlight the potential gains in terms of lives saved and local control attained for patients in LMICs if radiotherapy resource supply was optimal. However, the current supply of radiotherapy in LMICs is far below the demand. We have previously estimated this gap in supply in LMICs to be about 5500 megavoltage machines  [9].
Clustering based on symmetric Hölder divergences Given a set of fixed densities {p 1 , • • • , p n }, we can perform variational k-means  [28]  with respect to the Hölder divergence to minimize the cost function: E(O 1 , • • • , O L , l 1 , • • • , l n ) = n i=1 S H α,γ (p i : O li ), (94) where O 1 α = γ = 1. 1 α = γ = 1. 5 α = γ = 2 (CS) α = γ = 10 n = 50 95. 6% ± 7. 6% 92. 4 ± 9. 8% 92. 2% ± 9. 9% 92. 2% ± 9. 7% n = 100 97. 3% ± 5. 3% 94. 9 ± 8. 8% 94. 0% ± 10. 1% 94. 2% ± 9. 3% We made a toy dataset generator which can randomly generate n 2D Gaussians which have a underlying structure of two clusters. In the first cluster, the mean of each Gaussian G(µ, Σ) has the prior distribution µ ∼ G((-2, 0), I); the covariance matrix is obtained by first generating σ 1 ∼ Γ(7, 0. 01), σ 2 ∼ Γ(7, 0.
003), where Γ means a gamma distribution with prescribed shape and scale, then rotating the covariance matrix diag(σ 1 , σ 2 ) so that the resulting Gaussian has a "radial direction" with respect to the center (-2, 0). The second cluster is similar to the first cluster with the only difference being that µ ∼ G((2, 0), I) is centered around (-2, 0). See Fig. 4  for an intuitive presentation of the toy dataset. To reduce the number of parameters that has to be tuned, we only investigate the case α = γ. If we choose α = γ = 2, then S H α,γ becomes the CS divergence and Algorithm 1 reduces to traditional CS clustering. From Fig. 4  we can observe that the clustering result does vary with the settings of α and γ. We performed clustering experiments on two different settings of the sample size. Table  1  shows the clustering accuracy measured by the percentage of "correctly clustered" Gaussians, i.
Those two families of distances can be symmetrized and intersect at the unique Cauchy-Schwarz divergence. Since the Cauchy-Schwarz divergence is often used in distribution clustering applications  [16] , we carried out preliminary experiments demonstrating experimentally that thesymmetrized Hölder divergences improved over the Cauchy-Schwarz divergence for a toy dataset of Gaussians. We briefly touched upon the use of these novel divergences in statistical estimation theory. These projective Hölder (pseudo-)divergences are different from the recently introduced compositive scored-induced Hölder di-vergences  [20, 19]  that are not projective divergences and do not admit closed-form expressions for exponential families in general. We elicited the special role of escort distributions  [2]  for Hölder divergences in our framework: Escort distributions transform distributions to allow one: • To reveal that Hölder pseudo-divergences on escort distributions amount to skew Bhattacharyya divergences  [22] , • To transform the improper Hölder pseudo-divergences into proper Hölder divergences, and vice versa.
, • • • , O L are the cluster centers, and l i ∈ {1, • • • , L} is the cluster label of p i. The algorithm is given by Algorithm 1. Notice that one does not need to wait for the CCCP iterations to converge. It only has to improve the cost function E before updating the assignment. We have implemented the algorithm based on the symmetric HD. One can easily modify it based on HPD and other variants. Hölder variational k-means Input: A list of probability distributions p 1 , • • • , p n ; number of clusters L; α > 1; γ > 0 Output: A clustering scheme p i → {1, • • • , L}, ∀i ∈ {1, • • • , n} 1 Randomly assign p i to l i 2 while not converged do Assign l i = arg min l S H α,γ (p i : O l ) Clustering accuracy of the 2D Gaussian dataset (based on 500 independent runs).
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands);  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MinES  and  FASO (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF  (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
Acknowledgements We profited from numerous discussions with  T. Adamo ,  I. Bandos ,  B. Eden ,  P. Heslop ,  E. Ivanov ,  L. Mason  and  D. Skinner. We are particularly grateful to  G. Korchemsky  for making comments on the draft. We acknowledge partial support by the  French National Agency for Research (ANR)  under contract StrongInt ( BLANC-SIMI-4-2011 ). The work of D. C. has been supported by the " Investissements d'avenir, Labex ENIGMASS " and partially supported by the  RFBR  grant  14-01-00341.
The image formed on each of the focal planes (see the right panel of Fig. 2 ) is an interference pattern formed by peaks and lobes. This pattern actually works like a beam pattern that convolves the sky signal. Therefore, if X is the signal from the sky (either in intensity, I, or polarization, Q,U), then the measured signal on the pixel p is the convolution between the signal and the synthetic beam pattern of pixel p. In mathematical form: S X (p) = X(n)B p synth (n)dn. This means that QUBIC data can be analyzed exactly like the data obtained from a normal imager, provided that we build a window function of the synthetic pattern for each pixel. A feature that is unique in QUBIC compared to normal imagers is the systematic error control provided by the possibility to calibrate independently each interferometric baseline.
2015). In a third paper (paper III;  Kiefer et al. 2016) , we derived projected masses (M sin 3 i) with precision better than 1. 2% for the two components of 10 binaries, and the masses of the binary HIP 87895 with an accuracy of ∼1% thanks to additional astrometric data. Here, we present the accurate orbits measured for 14 SB2s (Table  1 ) with periods ranging from 33 to 4185 days. After 8 years of observations with SOPHIE, we collected a total of 203 spectra of these stars. A large number of previously published measurements is also available for each of these binaries in the SB9 catalog  (Pourbaix et al. 2004 ). Four of these targets were identified as new SB2s in paper I. We combined the radial velocity (RV) measurements and existing interferometric data for HIP 61100, HIP 95995 and HIP 101382, to derive the masses of their components.
THE SB2 ORBITS The orbits derived from the RVs in Table  3  have too large residuals in relation to uncertainties. This is clear when the F2 indicator of the goodness-of-fit (GOF) is calculated (see paper II, equation 1): the values are too large to obey a normal distribution, because the uncertainties were underestimated. This results in underestimating the uncertainties of the parameters of the orbit, but also in assigning erroneous weights to the RVs of each component. Deriving an SB2 orbital solution necessitates attributing realistic errors to each dataset properly. The correction process was already used in paper III, we refer the reader to that paper for explanations. The corrected errors express as follow with respect to initial errorbars σRV : σ corr RV,1 = ϕ1 × σ 2 RV,1 + ε 2 1 (1) σ corr RV,2 = ϕ2 × σ 2 RV,2 + ε 2 2 (2) The correction terms ϕ1, ϕ2, ε1 and ε2 are given in Table  4.
INITIAL STELLAR PARAMETERS OF HIP 61100 AND HIP 95995 Having derived very accurate masses for HIP 61100 and HIP 95995 allowed us to characterize the two components of these binaries in terms of initial helium content and age. For that purpose, we modeled the two components following the stellar model optimisation method described in  Lebreton & Goupil (2014). We adopted the reference set of stellar input physics described in that paper and the Cesam2k stellar evolution code  (Morel & Lebreton 2008). The observational constraints considered for the models are the masses of the two components herebefore determined, their effective temperatures and luminosities, and the present metallicity of the primary component. We point out that we decided not to model HIP 101382 because this binary system is enriched in αelements, with [α/Fe]=0.
The choice of the metallicity is more delicate. We therefore considered three possible values of the metallicity ([Fe/H]=-0. 13, -0. 18, -0. 33 dex) covering the range reported in the literature displayed in the SIMBAD database  (Wenger et al. 2000). We further assumed that the stars have a common origin and therefore share the same initial metallicity, helium abundance and age. Their initial helium abundance in mass fraction should be higher than the primordial value Yp∼0. 245 (see e. g. Peimbert, Peimbert, & Luridiana 2016; Izotov, Thuan, & Guseva 2014). Furthermore, according to  King et al. (2003) , the system is a bona-fide member of the UMA Group nucleus. Therefore, we assumed that the common age of the components is 400 Myr, i. e. the age of the UMa group  (Jones et al. 2015). The model optimisation provides the initial helium abundance for each star.
SUMMARY AND CONCLUSION Thanks to new SOPHIE spectra of 14 SB2s, four of which are newly identified SB2 (paper I), and the use of the todmor code, we derived new better accurate orbital solutions to the RV measurements of these binaries. The projected masses M sin 3 i were calculated for all 14 SB2s, with an average accuracy of 1. 0±0. 2 %, with extreme cases such as the rapid rotator HIP 48898 with σ(M sin 3 i)∼4 %, or HIP 101382 with σ(M sin 3 i)∼0. 12 %. For HIP 61100, HIP 95995 and HIP 101382, archival interferometric measurements allowed us to fully constraint the systems and derive masses for components (A,B) with accuracies respectively (2. 0, 1. 7) %, (3. 7, 3. 7) %, and (0. 2, 0. 1) %. The stellar evolution code Cesam2k  (Morel & Lebreton 2008)  applied to HIP 61100 and HIP 95995, led to constrain their age, metallicity and initial helium content.
ACKNOWLEDGMENTS We sincerely thank the anonymous reviewer for his careful reading of our manuscript and valuable comments. This project was supported by the  french INSU-CNRS  " Programme National de Physique Stellaire", "Action Spécifique Gaia ", and the  Centre National des Etudes Spatiales (CNES). We are grateful to the staff of the  Haute-Provence Observatory , and especially to  Dr F. Bouchy ,  Dr H. Le Coroller ,  Dr M. Véron , and the night assistants, for their kind assistance. We made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research has received funding from the  European Community 's  Seventh Framework Programme  (FP7/2007-2013) under grantagreement numbers 291352 (ERC).
We describe an approach to calibrate SEE-based detectors in monoenergetic fields and apply the resulting semi-empiric responses to more general mixed-field cases in which a broad variety of particle species and energy spectra are involved. The calibration of the response functions is based both on experimental proton and neutron data and considerations derived from Monte Carlo simulations using the FLUKA code. The application environments include the quasi-monoenergetic neutrons at RCNP, the atmospheric-like VESUVIO spallation spectrum and the CHARM high-energy accelerator test facility.
8 and a relative 2σ deviation of 7%. This spread was assumed to originate from the sensitivity differences among the detectors, as the count statistics error was significantly smaller in every case (at least 5 • 10 3 SEU counts were accumulated during each run, corresponding to a 2σ spread of ∼ 3%). Moreover, this spread was assumed to be constant for the different test conditions considered. In addition, a 10% error in the fluence value provided by the facility is assumed as typically reported. σ HEH = (2. 63 ± 0. 32) • 10 -14 cm 2 /bit (8) The ESA Monitor SEU cross section results were normalized to the 230 MeV value and fitted to a threeparameter Weibull function ω p (E) such as that shown in Eq. 7. The normalized experimental data and the respective response function ω p (E) are plotted in Fig.
ACKNOWLEDGMENTS We acknowledge fruitful discussions with  Philippe Allard Guérin ,  Flavio Baccari , and  Časlav Brukner. This work was funded by the  DAAD , the " Retour Post-Doctorants" program  ( ANR-13-PDOC-0026 ) of the  French National Research Agency , the  Brazilian ministries MEC  and  MCTIC , and the  FWF  (Project: M  2107 Meitner-Programm ).
1  The new xFitter 2. 0. 0 program 2 links to the APFEL code  [18]  which has implemented generalized matching 1 The perturbative coefficients of M j i at NLO are available in Refs. [12, 13] , and at NNLO in Ref. [14]. m H is the mass of the N F +1 flavor quark. For α S (µ), the c n k coefficients are available in the Particle Data Group review of Quantum Chromodynamics  [15]. 2 Information on the xFitter program can be found at www. xFitter. org, and in Refs. [16, 17]. conditions that enable the switch from N F to N F + 1 at an arbitrary matching scale µ m. This allows us to introduce the heavy quark PDF at any scale-not just at µ m = m H ; this flexibility provides a number of advantages. For example, as the matching scale moves to higher scales, the theory at the lower scales effectively becomes a Fixed Flavor Number Scheme (FFNS); yet we still retain a VFNS at the higher scales.
Therefore, a careful choice of the matching scales could help formulate a matching prescription between FFNS and VFNS able to achieve this goal in a very simple fashion  [19]. This study will examine the combined HERA data set and evaluate the impact of the matching scale on the features of the fit of PDFs. In Sect. 2, we review the key elements of the VFNS used in this study. Sect. 3, shows the impact of the matching scale µ m on the PDFs. In Sect. 4, we perform a fit of the combined HERA data sets at both NLO and NNLO, and investigate the effect of the matching scale µ m. Sect. 5 presents an example of how the µ m flexibility can be used as a tool to evaluate a recent suggestion for a N F dependent PDF. Sect. 6 summarizes the general characteristics and conclusions of this study. m charm m bottom m top μ Scale N F Sub-schemes N F =3 N F =4 N F =5 N F =6 f(x,μ) a) m charm m bottom m top μ Scale N F =3 N F =4 N F =5 N F =6 f(x,μ) N F Sub-schemes b).
xFitter, APFEL, and data sets To study the effects of varying the matching scales for the charm and bottom quark we will perform a series of fits to various data sets. Since we are varying the matching scales in the vicinity of m c and m b , we want data that constrain the PDFs in this region. For Here, we have chosen µ = Q. For details on the FONNL calculation see Ref. [6]. this purpose, we include the very precise combined HERA data sets as these provide strong constraints in the region µ ∼ m c,b , and also extend up to higher scales  [34] [35] [36] [37]. In particular, the HERA measurement of the charm and bottom cross sections are included as they are sensitive to the choice of µ c and µ b. These fits are performed with the xFitter program using the APFEL evolution code  [18, 38, 39]. The DIS calculations use the FONLL-B scheme for the NLO calculations, and the FONLL-C scheme for the NNLO calculations; these are both O(α 2 S ) prescriptions, and the details are specified in Ref.