dataesr/scientific-paragraphs-categorization huggingface dataset:
186  [19]  parton shower model. The CT10 parton distribution function (PDF) set  [20]  is used in the matrix element calculation. The AZNLO  [21]  set of tuned parameters ("tune") is used, with the CTEQ6L1 PDF set  [22] , for the modelling of non-perturbative effects. The EvtGen v1. 2. 0 program  [23]  is used for properties of the bottom and charm hadron decays. Photos++ version 3. 52  [24]  is used for Quantum Electrodynamic (QED) emissions from electroweak vertices and charged leptons. Event yields are corrected with a mass-dependent rescaling to next-tonext-to-leading order (NNLO) in the QCD coupling constant, computed with VRAP 0. 9  [25]  and the CT14NNLO PDF set  [26]. The NNLO QCD corrections are a factor of ∼ 0. 98 at m ℓℓ = 3 TeV. Massdependent electroweak (EW) corrections are computed at NLO with Mcsanc 1.
The parton shower, fragmentation, and the underlying event are simulated using Pythia 6. 428  [35]  with the CTEQ6L1 PDF set and the Perugia 2012 tune (P2012)  [36]. The top-quark mass is set to 172. 5 GeV. The EvtGen v1. 2. 0 program is used for properties of the bottom and charm hadron decays. The t t and single-top-quark MC samples are normalised to a cross-section as calculated with the Top++ 2. 0 program  [37] , which is accurate to NNLO in perturbative QCD, including resummation of next-to-next-to-leading logarithmic soft gluon terms. Resonant and non-resonant signal processes are produced at LO using Pythia 8. 186 with the NNPDF23LO PDF set  [38]  and A14 tune  [39]  for event generation, parton showering and hadronisation. In the case of Z ′ production, interference effects (such as with DY production) are not included.
However, for the production of non-resonant signal events, both the DY and CI events are generated together in the same sample to account for the significant interference effects between those two processes. Higher-order QCD corrections are computed as for the DY background and applied to both the resonant and non-resonant MC samples. EW corrections are not applied to the resonant MC samples due to the large model dependence. However, these corrections are applied to the non-resonant MC samples as they involve interference between the DY and CI processes. Moreover, including the EW corrections leads to a more conservative estimate when setting exclusion limits. The generator settings and corrections described here are also used to compute the signal cross-sections and branching ratios. The detector response is simulated with Geant 4  [40, 41]  and the events are processed with the same reconstruction software as used for the data.
The uncertainties in this background estimate are evaluated by considering differences in the estimates for events with samecharge and opposite-charge electrons as well as by varying the electron identification probabilities and changing the parameters of the extrapolation functions. As a final step, the sums of backgrounds estimated using MC samples are rescaled independently in both channels so that the estimated count of events matches the data in the Z-peak normalisation region 80 GeV < m ℓℓ < 120 GeV. This normalisation procedure is found to agree with the equivalent scaling using the expected integrated luminosity within 2% for both channels (and in the same direction), which is well within the current luminosity uncertainty of 5%. The luminosity uncertainty was calculated using the same methodology as for the 7 TeV data  [46].
Conclusions The ATLAS detector at the Large Hadron Collider has been used to search for resonant and non-resonant new phenomena in the dilepton invariant mass spectrum above the Z-boson pole. The search is conducted with 3. 2 fb -1 of pp collision data at √ s = 13 TeV, recorded during 2015. The highest invariant mass event is found at 1775 GeV in the dielectron channel, and 1587 GeV in the dimuon channel. The observed dilepton invariant mass spectrum is consistent with the Standard Model prediction, within systematic and statistical uncertainties. Among a choice of different models, the data are interpreted in terms of resonant spin-1 Z ′ gauge boson production and non-resonant contact interactions. Upper limits are therefore set on the cross-section times branching ratio for a spin-1 Z ′ gauge boson.
In addition, individual groups and members have received support from  BCKDF , the  Canada Council, CANARIE ,  CRC ,  Compute Canada, FQRNT , and the  Ontario Innovation Trust ,  Canada ;  EPLANET ,  ERC ,  FP7 ,  Horizon 2020  and  Marie Sklodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex and Idex ,  ANR ,  Région Auvergne  and  Fondation Partager le Savoir, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF ;  BSF, GIF and Minerva, Israel; BRF, Norway ;  Generalitat de Catalunya, Generalitat Valenciana, Spain ; the  Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN , the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC (Taiwan) ,  RAL (UK)  and  BNL (USA) , the Tier-2 facilities worldwide and large non-WLCG resource providers.
Data sample We have extracted 167 stars, grouped into six samples from the HARPS survey described in ?, and covering the spectral types  G0, G2, G5, G8, K0, and K2. The spectral types have been extracted from the Simbad database at the CDS 1. This survey was biased against very active and young stars, and focuses on stars with vsini lower than 3-4 km/s, as pointed out by ? in the study of cycles in stars from the same survey. The spectra have been retrieved from the ESO archive 2  : We analyse 1D spectra produced by the ESO Data Reduction Software after the interpolation of the 2D echelle spectra (one spectrum per order) over a grid with a constant step in wavelength (0. 01 Å). The original 2D spectra are also retrieved for the computation of the uncertainties. We only retained spectra with a S/N, averaged over all orders, larger than 100.
The green dotted line is the linear fit on all points. Lower panel: Distribution of the RV residual after removal of the linear fit, that is, the green dots of the upper panel (solid line). The rms RV is 84 m/s. The dashed line shows the same distribution but for the residual computed after the wavelength-dependence correction (see text), and the rms RV is 78 m/s. ESO Data Reduction Software does not change our subsequent results significantly. The dispersion of the residual RV is important. It can be due to several factors, for example: telluric lines, impact of a wavelength dependence, uncertainties on laboratory wavelength (all discussed in Sect. 2. 5), impact of blends not taken into account or other properties of the lines, such as the excitation potential ?, ]dravins81. We defined a criterion to easily study the shape of the differential velocity shifts of spectral lines as a function of various parameters: we computed the slope of RV versus F values after averaging all points corresponding to a given line (i.
Telluric lines Telluric lines caused by water may impact the line position estimation. Water wavelengths have been extracted from the HITRAN database ?, ]rothman13 available at http://hitran. org/. We listed the telluric lines which are within 0. 02 Å of the lines used in our analysis. Most of these lines are very weak and are more than one order of magnitude lower than the stronger lines. Out of the 197 final lines used to compute the TSS in our selection, 5. 5% have water telluric lines within 0. 02 Å and the percentage decreases to 3% when only considering lines that are used in all six samples (i. e. lines that are most likely to highly impact the TSS estimations). The residual of RV versus F after substracting the linear fit (used to determine the TSS) is not correlated with the position of telluric lines nor with their amplitude.
RV variability We now wish to compare the observed RV variability (derived in Sect. 5. 1) with those which can be predicted using our estimated TSS (in Sect. 5. 2). We have thus retrieved the radial velocities computed by the ESO Data Reduction Software from the headers of the archive files. These RV must be corrected for two effects: -36 stars in our sample bear planets according to the Extrasolar Planets Encyclopaedia 4. We therefore retrieved the exoplanet parameters, fitted the parameters when not published (this is sometimes the case for the time at periastron and periastron argument), reconstructed the corresponding RV time series, and finally substracted the plane-4 http://exoplanet. eu/ tary signal from the measured RV. Note that some of these stars bear several planets (22 with 1 planet, 9 with 2 planets, 3 with 3 planets and 1 with 4 planets).
Acknowledgements. This work has been funded by the  Université de Grenoble Alpes project called "Alpes Grenoble Innovation Recherche (AGIR) " and by the  ANR GIPSE   ANR-14-CE33-0018. This work made use of several public archives and databases: The HARPS data have been retrieved from the  ESO  archive at http://archive. eso. org/wdb/wdb/adp/phase3spectral/form. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. The telluric line properties were retrieved from the HINTRAN database. Exoplanets information was retrieved from the  Extrasolar Planet Encyclopaedia  at http://exoplanet. eu/. The solar spectrum from Kurucz et al. (1984) is available on-line at http://kurucz. harvard. edu/sun/fluxatlas2005/.
To this end, we conduct two sensitive, blankfield imaging surveys of the Hubble Ultra-Deep Field (HUDF, α = 03 h 32 m , δ = -27 • 47 ) using the VLA and ALMA at λ = 5 cm and 1. 3 mm, respectively, to make 0. 4 resolution images of SFGs at z ∼ 2. The 5 cm continuum traces star formation through the synchrotron emission from supernova remnants, but can be affected by AGN emission; whereas the 1. 3 mm continuum traces cold dust associated with star formation, but requires uncertain assumptions about the shapes of spectral energy distributions (SEDs) to estimate the SFR. The combination of the two surveys therefore provides complementary strengths, especially in the HUDF where the wealth of ancillary data can help, e. g. , identify AGN. By establish-ing that the VLA and ALMA trace the common extent of star formation, ALMA can serve as a morphological tracer of "pure" star formation in AGN hosts because the 1.
3-mm dust continuum is neither contaminated by AGN torus emission  (Elvis et al. 1994; Mullaney et al. 2011; Mor & Netzer 2012)  nor synchrotron emission from the jets 1. Furthermore, because ALMA significantly gains in sensitivity to star formation with the help of negative Kcorrections at z > 2. 5, whereas the VLA gradually loses star-formation sensitivity beyond this redshift, the combination of VLA and ALMA yields a sensitive probe of the morphology of star formation over the entire range of z = 1 -3. In this paper, we present first results from combining the VLA and ALMA HUDF surveys, focusing on the extinction-independent distributions of the star formation in SFGs at z = 1 -3. We discuss the VLA and ALMA surveys and ancillary data in §2 and present the size and location of star-formation in SFGs selected from VLA and ALMA, along with their implications in §3.
OBSERVATIONS AND ANCILLARY DATA To image SFGs at z ∼ 2 with the VLA, the radio continuum at 5 cm offers a good balance between angular resolution and the expected flux density, given the synchrotron radio spectrum, S ν ∝ ν -0. 7. We observed the HUDF for 177 hours in the A, B, and C configurations during 2014 March -2015 September using the full Cband bandwidth of 4-8 GHz (λ = 7. 5-3. 7 cm). The observations comprised 42 dynamically-scheduled sessions of 2. 5 -5. 5 hours. Each session observed 3C48 for flux and bandpass calibrations; J0402-3147 was observed for phase calibration every 25 minutes. Data reduction was carried out with  CASA (McMullin et al. 2007 ) using the following steps: (1) standard calibration using the VLA Data Reduction Pipeline  (Chandler et al. , in prep) ; (2) removal of any portions of the data corrupted by strong radio frequency interference; and (3) imaging with the task TCLEAN.
The imaging parameters are the following: MT-MFS deconvolver with nterms of 2, 0. 06 pixel size, and Briggs weighting with robust parameter of 0. 5. We imaged the data well beyond the primary beam radius of 3. 6 (employing the w-projection) to mitigate the imaging artifacts caused by the sidelobes from bright sources far from the pointing center. The final image has a 0. 31 × 0. 61 synthesized beam and rms noise at the pointing center of 0. 32 µJy beam -1 , consistent with the theoretical sensitivity (0. 30 µJy beam -1 ). The ALMA HUDF Deep Field is a 1. 3-mm survey of the 4. 5-arcmin 2 HUDF using a 45-pointing mosaic during 2014 July -2015 May in 13 sessions, utilizing a total of approximately 20 hours. The rms noise of the naturallyweighted ALMA map is 29 µJy beam -1 and the synthesized beam is 0.
ACKNOWLEDGMENTS We would like to thank the anonymous referee for comments and suggestions to improve the paper, as well as  E. Murphy  for helpful discussions. This work was supported by the  World Premier International Research Center Initiative (WPI) ,  MEXT, Japan  and  JSPS KAK-ENHI  Grant Number  JP15K17604. WR acknowledges support from  Chulalongkorn University's Ratchadapiseksompot Endowment Fund and CUniverse (CUAASC). JSD acknowledges the support of the  European Research Council  via the award of an  Advanced Grant  (PI J. Dunlop), and the contribution of the EC  FP7   SPACE  project  ASTRODEEP (Ref. No:  312725 ). RJI acknowledges support from the  European Research Council  through the  Advanced Grant   COSMICISM 321302. RJM acknowledges ERC funding via the award of a consolidator grant ( PI McLure ).
Abstract. The 235 U neutron-induced cross section is widely used as reference cross section for measuring other fission cross sections, but in the resonance region it is not considered as an IAEA standard because of the scarce experimental data covering the full region. In this work, we deal with a new analysis of the experimental data obtained with a detection setup based on parallel plate ionization chambers (PPACs) at the CERN n_TOF facility in the range from 1 eV to 10 keV. The relative cross section has been normalised to the IAEA value in the region between 7. 8 and 11 eV, which is claimed as well-known. Comparison with the ENDF/B-VII evaluation and the IAEA reference file from 100 eV to 10 keV are provided.
However, the 235 U(n,f) cross section data from n_TOF has been released only for a limited range of the resonance region, that are presently in EXFOR  [5]. In this work, we report on an improved release of the fission cross section in the range between 1 eV and 10 keV, where the n_TOF flux shape is smooth and very well known. These data are compared to the ENDF/B-VII evaluated dataset and to the integral values given by the IAEA reference file.
An excellent reproduction of the resonance structure is achieved thanks to our good energy resolution, while the significant statistics allow us to show the different behaviour in the dips. The excellent background conditions led to reduced counts in the dips between resonances (see in Fig. 1 ). On the other hand, it is worth to note a problem with the energy calibration of the ENDF data above 100 eV. The complete n_TOF dataset, including uncertainties, will be available in EXFOR.
Averaged integral values In Table  1  the n_TOF values are compared with those provided in the IAEA reference file, as well as with the ones taken from the ENDF/B-VII data base. All these values are averaged cross-sections (in barn) centred in the same energy intervals as given by IAEA in Ref. [6]. Those from n_TOF were normalised as previously mentioned but those from ENDF maintain its original normalisation. In order to clearly show the existing differences between the IAEA recommended values and those obtained from the ENDF evaluation, we have calculated their ratio with respect to the present results.
WONDER-2015 These ratios are shown in Fig. 3 , centred in the intervals as in Table  1 , with error bars corresponding to their statistical uncertainties. The dashed lines represent the average values below and above 2 keV. The abrupt drop in the ratio with the ENDF/B-VII evaluation denotes the influence of different experimental sources above and below 2 keV, corresponding to the transition at around 2. 25 keV from the resolved to the unresolved resonance regions in the ENDF evaluation. On the other hand, an offset appears in Table  1  as a systematic deviation of our dataset from the IAEA one. To some extent this offset could be due to the low background in the n_TOF experiment. Fig. 4  shows the new ratios after subtracting 0. 09 b from every value in the IAEA file (the error bars also include the IAEA reported uncertainties).
The ratio distribution along the whole energy range is now extremely flat, its mean value is 0. 998, and the spread is compatible with the low statistical uncertainties of the n_TOF data, endorsing so the goodness of both n_TOF and IAEA datasets. Table  1. Averaged integral cross-sections (in barn) compared to the IAEA reference file  [6]  and to ENDF/B-VII. Uncertainties included between brackets refer to the last significant digits.
In Eq. (  1 ) this amounts to the introduction of a dependence of the dielectric permittivity on a nonlinear function of φ(r) via ε ε φ → ∇ F (r) ( ( (r)) ( 2 ) where F is a nonlinear function, resulting in terms of higher powers of φ ∇ 2 in the DPBL equations as compared to the PB equation  27. This approach underlies the Marcus functional employed in studies of electron transfer  28  , which is equivalent to the DPBL-model in its linearized form  29, 30  , and was also employed in the work by Warshel and Levitt  31. The DPBL-model lies at the basis of a dedicated solver, AquaSol  27  , built on an original PB solver  32. It has previously been applied e. g. to the computation of SAXS profiles based on an extension of the AquaSol solver by the AquaSAXS module  33  , and it was also used to predict free energies of amyloid fibril aggregates  34.
Methods MD simulations. The three-dimensional coordinates of the systems were retrieved from the Protein Data Bank, entries 1BRS (Barnase/barstar, chains A:D)  15  , 3U43 (E2/Im2)  19  and 4KE2 (Maxi)  35. Missing atoms or residues were added with the Jackal package  41  or interactively with VMD in the case of Barnase by copying from another chain in the unit cell. The systems were prepared in an octahedron periodic box, using a minimum distance of 1 nm between the protein and the boundary. Solvation was achieved using the standard Gromacs solvate tool  42  , see also http://www. gromacs. org/. All ions and crystal waters were removed before solvation, including the interfacial water molecules. However, we also prepared simulation runs of Maxi where crystal waters were kept. Both systems are referred to as 'random' and 'crystal' , respectively.
Systems were made electrostatically neutral with randomly placed Na + and Cl -counterions. Due to its peculiar nature, the neutral system Maxi was charged at a concentration of 7 mmol/L. However, we found none of the counterions to interact significantly with the protein during the course of our simulations. MD simulations were performed with the Gromacs software 42 , v5. 0. 4. using the charmm27 force field  43  and the SPC water model  44. Prior to data production, the systems were minimized using the steepest descent method until the maximum force on any atom was lower than 10 3 kJ mol -1 nm -1. The systems were then equilibrated by a 100 ps NVT run to thermalize the system, followed by a 200 ps NpT equilibration run to stabilize the volume. During the equilibration, all bonds were constrained using the LINCS algorithm  45.
The diffusion constant was computed by calculating the mean square displacement of a selected number of water molecules and then using the Einstein relation, using the Gromacs suite of analysis tools. In order to calculate the residence times of protein-associated water molecules, we monitor the total time a first-shell water molecule stays in contact with a protein atom. Any water (oxygen) within a distance of 3. 5 Å from a protein heavy atom was considered to be in contact with the protein. This is a slightly simplified approach as employed in ref. 36, since we do not distinguish between different atom types. The residence time is then calculated by averaging over all residence times of associated water molecules. AquaSol. We obtained the AquaSol software  27  from the authors and installed it locally.
We use both the basic DPBL-model as well as the Yukawa functional which takes repulsion between the water molecules into account  50. Conversion from pdb to pqr format was done using the online pdb2pqr Server  51. The simulation temperature was the same as for the MD simulation (300 K and 273 K) and the implicit ion concentration was set to match the volume and number of ions in the MD simulation box. The number of points in the x, y and z dimensions (2 n + 1) was 129 × 129 × 129 for Barnase and E2/Im2, and 65 × 65 × 129 for Maxi. This ensures a resolution of around 1 Å in each dimension. Individual coordinate configurations, chosen at random, were extracted from the MD runs and used as input structure in the AquaSol software  27. The configurations with the best f w (nat) MD values are listed in Table  3.
Acknowledgements We thank  M. Delarue  for providing the AquaSol solver and  F. Poitevin  for help. This work was supported by the  ANR  project " FSCF ", grant number  ANR-12-BSV5-0009. Computer resources were provided by the  French CINES Montpellier , under contract  x2016-077225. Support from the  Research Federation FRABio (University Lille ,  CNRS ,  FR 3688 , " Structural and Functional Biochemistry of Biomolecular Assemblies ") is gratefully acknowledged.
RICAP-2014 The scintillation light is collected by 2212 photomutipliers (PMTs) that are uniformly attached to the inner surface of the SSS. All but 384 PMTs are equipped with light concentrators that are designed to reject photons not coming from the active scintillator volume, thus reducing the background due to radioactive decays originating in the buffer liquid or 's from the PMTs. The 384 PMTs without concentrators are used to study this background, and to help identify muons that cross the buffer, but not the Inner Vessel. Due to ageing, the actual number of operational PMTs during data taking was normally smaller, typically about 1800 in total. The SSS is supported by 20 steel legs and enclosed within a large tank (WT) that is filled with ultra-pure water. The WT has a cylindrical base with a diameter of 18 m and a hemispherical top with a maximum height of 16.
pp neutrino analysis The data used in this analysis was collected between Jan. 2012 and Jun. 2013, containing the first 408 live days of data of Borexino Phase-II. These data are characterised by reduced levels of some of the most relevant radiogenic backgrounds, particularly 85 Kr, 210 Bi and 210 Po, thanks to a series of successful purification campaigns based on water extractions and low Argon-Krypton nitrogen sparging  [23]. Basic selection criteria of this analysis are the same as previous Borexino solar neutrino papers and have been described in detail in  [23] , in the context of Borexino Phase-I. In particular, the criteria used in the pp analysis are: 1. No coincidence with muon events, identified both the the Cherenkov detector and with pulse-shape analysis in the scintillator; 2.