dataesr/scientific-paragraphs-categorization huggingface dataset:
The constraints on the Υ (1S) resolution, taken from fits to the normalisation datasets, are 44. 00 ± 0. 05, 44. 307 ± 0. 035, 43. 155 ± 0. 023 and 43. 766 ± 0. 018 MeV/c 2 for the 7, 8, 13 TeV and combined datasets, respectively. The constraints on p 0 and p 1 are 1. 516 ± 0. 007 and (9. 6 ± 4. 4) × 10 -5 ( MeV/c 2 ) -1 , respectively, evaluated from a fit to the simulated data, as shown in Fig. 1. These constraints lead to typical X resolutions in the range ∼ [60, 70] MeV/c 2. The fits to the m(µ + µ -) distributions in the normalisation datasets are shown in Fig. 2. The fitted Υ (1S) yields in the range R Υ (1S) ≡ µ Υ (1S) ± 2. 5σ Υ (1S) are (0. 694 ± 0. 012) × 10 6 , (1. 562 ± 0. 028) × 10 6 , (4. 11 ± 0. 08) × 10 6 and (6. 37 ± 0. 12) × 10 6 for the 7, 8, 13 TeV and combined datasets, respectively.
The uncertainties include systematic components due to  the choice of shapes to describe the signal and background components. Only candidates in the signal dataset with m(µ + µ -) in the range R Υ (1S) are retained for the fits to the distributions of m(2µ + 2µ -), which includes a small fraction of non-Υ (1S) background. Background-only fits to the signal datasets are shown in Fig. 3. No significant signal excess is observed. The largest deviation occurs at a mass of approximately 19. 35 GeV/c 2 , above the η b η b and Υ (1S)Υ (1S) thresholds, with a local significance of 2. 5 standard deviations.
Normalisation factor Upper limits are set for different X mass hypotheses on the quantity S ≡ σ(pp → X) × B(X → Υ (1S)µ + µ -) × B(Υ (1S) → µ + µ -), (1) where σ(pp → X) is the X production cross-section, and B(X → Υ (1S)µ + µ -) and B(Υ (1S) → µ + µ -) are the branching fractions of the X → Υ (1S)µ + µ -and Υ (1S) → µ + µ -decays, respectively. To set limits on S, the signal yield is parameterised as N sig = S/f norm with  where σ(pp → Υ (1S)) is the production cross-section of the Υ (1S) meson  [37, 38]  within the same fiducial volume as the signal. The Υ (1S) → µ + µ -yield within the range R Υ (1S) is given by N norm , and sig(norm) is the efficiency with which the signal (normalisation) channel is triggered, reconstructed and selected. f norm = σ(pp → Υ (1S)) × B(Υ (1S) → µ + µ -) N norm × norm sig , (2) The relative efficiency of the reconstruction and selection requirements placed on the corresponding signal and normalisation datasets is defined as sig norm = geom sig geom norm × sel sig sel norm × PID sig × f trk sig , (3) where geom is the efficiency with which the products of the X or Υ (1S) decay all enter the LHCb geometric acceptance; sel is the efficiency of the reconstruction and selection of X or Υ (1S) candidates within the geometric acceptance; PID sig is the efficiency of the PID requirements placed on the additional muons in the signal decay; and f trk sig accounts for differences between data and simulation in the tracking efficiency of the additional muons.
The geometric and selection efficiencies are determined from simulated samples, while the PID efficiency is determined from calibration data samples. The ratio of efficiencies between the signal and normalisation samples is determined to be 31. 7 ± 0. 6 % (35. 2 ± 1. 2 %) for the 7, 8 TeV (13 TeV) dataset, where the same efficiency is used for 7 and 8 TeV collisions due to the similar performance of the LHCb detector during these operational periods. Uncertainties on these quantities give rise to systematic uncertainties in the fits to the signal datasets and enter these fits as a Gaussian function constraining the value of f norm. These systematic uncertainties are detailed further in Sec. 6. In the case of the combined dataset, averages of the efficiency ratio and normalisation cross-section, weighted by the integrated luminosity of each subset, are used to calculate f norm.
1 %, 0. 5 % and 46 %, respectively. Since these parameters are weakly correlated with the signal yield their effects on the measured cross-section upper limits are negligible. The uncertainty on the f norm parameter for each dataset is dominated by uncertainties on the normalisation cross-section (2. 8 to 6. 3 %) and the tracking efficiency correction (0. 8 to 3. 1 %). The systematic uncertainties from efficiencies related to particle identification or geometrical acceptance are at the level of 1. 0 % or less. For the 7 TeV result, a discrepancy is observed in the efficiency-and cross-section-corrected Υ (1S) yield relative to the other datasets. An additional uncertainty of 13. 5 % is assigned to account for this. This uncertainty increases the limits on the cross section at 7 TeV by < 4 % and has no effect on the quoted combined limits.
Limit setting For each signal dataset, upper limits are set on S as functions of the X mass, µ X , in the range  [17. 5, 20. 0] GeV/c 2 using the following procedure. For each fixed X mass, the likelihood profile as a function of S is integrated to determine upper limits on the crosssection at 90 % and 95 % confidence levels (CL). This procedure is applied at each of 101 values of the X mass. The 90 % and 95 % CL limits are tabulated in the Appendix. Background-only pseudoexperiments are generated at each scan point to determine the expected 95 % CL upper limit and corresponding one and two standard deviation intervals, as shown in Fig. 4. No significant excess is seen at any mass hypothesis for any dataset. The analysis is repeated with only a single candidate decay retained for each event (chosen at random), with a more stringent requirement on the pseudorapidity of the muons    as was previously used in Ref.
[39]. In addition, the effect of the assumption that the X decays according to a phase-space distribution is tested by evaluating the efficiency for both m(µ + µ -) less than 2 GeV/c 2 and m(µ + µ -) greater than 7 GeV/c 2 for the muon pairs that do not come from the Υ (1S) decay. The efficiency varies ±24 % with respect to the total efficiency under the assumption of a phase-space decay. Finally, the limits are evaluated using different ranges around the Υ (1S) mass to select the signal dataset, separately for each year of the √ s = 13 TeV dataset, and for the 7 and 8 TeV datasets combined. No significant differences are observed in the limits determined in each of these cross-checks. S)) 1 ( Υ B( × B(X) × ( ≡ σ(pp → X) × B(X → Υ (1S)µ + µ -) × B(Υ (1S) → µ + µ -) as.
Conclusions In conclusion, a search is performed for the decay of the beautiful tetraquark, X, to the Υ (1S)µ + µ -final state. No significant excess is seen for any mass hypothesis in the range  [17. 5, 20. 0] GeV/c 2. Upper limits are set on the value of σ(pp → X) × B(X → Υ (1S)µ + µ -) × B(Υ (1S) → µ + µ -) at centre-of-mass energies √ s = 7 TeV, 8 TeV and 13 TeV as functions of the X mass hypothesis (see Appendix). An upper limit is also set on the combined dataset using the average of the Υ (1S) cross-section, weighted by the integrated luminosity of each subset, resulting in upper limits of O(10 fb). Improved sensitivity for this state will be obtained using data collected during future running periods of the LHC using an updated LHCb detector  [40] [41] [42]. Table  1 : Upper limits on σ(pp → X) × B(X → Υ (1S)µ + µ -) × B(Υ (1S) → µ + µ -) for different X mass hypotheses in the range  [17.
Data acquisition Data were acquired at the beamline ID16A, ESRF (Grenoble, France). This beamline combines coherent imaging techniques and X-ray fluorescence microscopy at a spatial resolution down to about 30 nm, enabling quantitative 3D characterization of morphology and elemental composition of specimens in their native state  [27]. ID16A provides a highbrilliance beam at two specific energies (17 keV or 33. 6 keV). The incoming parallel X-ray beam is monochromatized and focused into a focal spot by multilayer coated curved reflective optics (Kirkpatrick-Baez mirrors). After passing through the sample, the X-ray beam is converted into visible light by a scintillator and recorded by a lens-coupled FReLoN (Fast Readout Low Noise) camera developed by the instrument support group of ESRF with a binned CCD size of 2048 × 2048 pixels.
X-ray phase nano-CT data acquisition consisted in recording sets of angular projections at different sample positions between the focus and the detector. After being focused, the beam turns from parallel to divergent so that the images recorded at different distances contain not only phase contrast but also geometric magnifications. In practice, the position of the detector is fixed while the sample is moved downstream of the focus  [28, 29]. The magnification was chosen to obtain an object voxel size of 30 nm. The samples were extracted from transverse cross-sections cut from mid-diaphysis in femurs from female cadavers (50-95 years old). Then, small cortical bone samples (0. 4 × 0. 4 × 3 mm 3 ) were prepared using a high precision low-speed circular saw. Each sample was positioned on the vacuum sample stage and rotated around a vertical rotation axis.
Data processing First the images are so-called flat-field corrected by subtraction of the dark current image and division with the empty beam image recorded without sample. Next, the images at different propagation distances must be registered for each rotation angle. This is done after correction of the magnification k M that depends on the focus-to-sample distance k Z and the focus-todetector distance d Z according to the expression (Fig. 1 ):. k d k M Z Z = (24) The images were rescaled at the highest magnification. Phase maps were estimated by using different retrieval methods. We used the equivalent propagation distance that can be expressed as: ( ). k k d k d D Z Z Z Z = ⋅ -     (25) Table  1  lists some specific experimental parameters such as 4 different focus-to-sample distances, the focus-to-detector distance, the wavelength of the X-ray, the voxel size and Fresnel number k F which is calculated by: 2 , k k F A D λ = ( 26 ) where A is the is the characteristic object size, λ the wavelength and k D the propagation distance.
The Fresnel numbers are provided both for the osteocyte lacunae and the canaliculi by setting A as different typical sizes, 10 µm and 200 nm respectively. The n δ β ratio was set to 645 according to the complex refractive index of cortical bone at 33. 6 keV, which was calculated from tabulated values by the software XOP. To improve image quality we also considered refining the estimated phase maps by using a non-linear conjugate gradient method (NLCG) based on the direct nonlinear image formation model. Finally, the extracted phase maps were used as the input to tomographic reconstruction to get the 3D structure of the sample. As there are a sufficient number of angular projections, the reconstruction can be performed by the Filtered Back-Projection (FBP) algorithm. We used the ESRF implementation PyHST2 for tomographic reconstruction  [30].
Other quality measures such as the recently proposed spatial SNR could be investigated to evaluate the quality of X-ray images  [31]. The phase retrieval and tomographic reconstruction were performed sequentially. This has many practical advantages as only a large number of 2D problems have to be solved. Recently some methods have been proposed to combine the two steps together  [32] [33] [34]. However, 3D approaches require extensive computer resources both in terms of computing time and memory. Therefore they are not practical for standard use on large data sets today, but they will be increasingly important in the future. In previous works, Paganin's method was compared with the modified Bronnikov algorithm and was shown to provide better reconstructions on low-attenuating samples  [35] , but only one propagation distance was considered.
Acknowledgements M. Autzen , and  J. P. Buylaert  receive funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme ERC-2014-StG 639904 -RELOS. The authors would also like to thank  Louise M. Helsted ,  Vicki Hansen ,  Gabor Ujvari  and  Warren Thompson  for help in preparing samples as well as  Arne Miller  for lending us the electron beam.
Acknowledgements The research and developments in The University of Manchester and University of Cambridge were supported by  STFC, UK  in the framework of the  SKA MFAA  project. The authors thank  Leonardo MW Ltd  for conducting the pattern measurements and  Trackwise Designs Ltd  for manufacturing the C-ORA array. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Limits from high-p T tails of pp → ¯ As shown in Refs. [57, 58] , a contribution arising from the t-channel exchange of LQs to pp → ¯ ( = µ, τ ) can be directly probed in the high-p T tails of Drell-Yan processes at the LHC. In particular, larger values of Yukawa couplings, that are often needed to accommodate the B-anomalies, could modify the tail of the differential cross section of pp → ¯. In the following we use LHC data from pp-collisions at 13 TeV to set limits on each LQ model. For this we have recast two recent searches by ATLAS at 36. 1 fb -1 for a Z decaying to µμ  [59]  and τ τ  [60] , respectively. For the di-tau analysis we focused on the fully inclusive channel with hadronic taus (τ had ) in the final state, given that these perform considerably better at high p T than the leptonic tau decay channels.
We find χ 2 min = 6. 61 for x sµ L ≈ -10 -2 , x bµ L ≈ 0. 25 , x sτ L ≈ 4. 4 × 10 -3 , x bτ L ≈ 2. 81. (43) We then perform a random scan over the values of the four left-handed couplings shown in Eq. (  39 ), and enforce perturbativity, |x ij L | ≤ √ 4π. We select only the points which satisfy ∆χ 2 (par) ≡ χ 2 (par) -χ 2 min ≤ 6. 18, i. e. within 2 σ from the best fit point. The selected points are compared with the limits deduced from the direct LHC searches in ( =µ, τ ) final states, as detailed in Sec. 4. 2. In the plots presented in this Section, the points excluded by direct searches based on current LHC data (36 fb -1 ) are shown in grey. Furthermore, the red points are those that are excluded from our projections to 300 fb -1. The blue points are those that would survive. We first show, in Fig.
Acknowledgments This project has received support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie grant  agreement  N • 674896  and N • 690575. Work at  University of Nebraska-Lincoln  is supported by the  Department of Physics and Astronomy. A. A. would like to thank  Frank Golf  for insightful discussions on leptoquark searches at the LHC. D. A. F is supported by the  Young Researchers Programme  of the  Slovenian Research Agency  under the grant  N • 37468.
4. Acknowledgments G. Boudebs and co-workers at LPhiA in Angers would like to acknowledge the organizers of the conference ICL 2017  -Brazil  for the invitation to present this work and the financial support from the  NNN-TELECOM Program ,  region des Pays de la Loire. C. B. de Araújo  acknowledges financial support from the  Brazilian agencies Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq)  and the  Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE).
Observations The 2. 5 -5 µm spectra of 145 GOALS sources were observed with AKARI pointed observations between 2006 October 29 and 2010 February 14 using the Infrared Camera (IRC;  Onaka et al. 2007). The data obtained prior to 2007 August were taken during the cold phase, and the data from after this time were taken in the warm (post-helium) phase mission. The total number of pointings was 385 with the majority obtained with the AGNUL program (54%, PI T. Nakagawa), in addition to 36% from the GOALS program (PI H. Inami), 8% from DTIRC (PI H. Murakami), and the remaining 2% from the BRSFR (PI M. Malkan), EGANS (PI H. Matsuhara), and NULIZ programs (PI H. -S. Hwang). The Np aperture with a size of 1 ′ × 1 ′ was used in order to avoid the confusion of spectra from the entire IRC field-ofview (10 ′ × 10 ′ ).
Data Reduction and Analysis The standard data reduction software package provided by JAXA, the IRC Spectroscopy Toolkit for Phase 3 Data 1 (ver-sion 20101025), was used to perform dark subtraction, linearity correction, flat correction, background subtraction, wavelength calibration, spectral inclination correction, and spectral response calibration. This package can handle both the cold and warm phase data. Although the number of bad pixels increased dramatically in the post-helium mission due to the elevated detector temperatures, the data quality was uniform enough for the purposes of this work, to measure the 3. 3 µm PAH and Brα line fluxes and the continuum emission. The wavelength accuracy was ∼ 0. 01 µm and the uncertainty in absolute flux calibration was estimated to be ∼ 10%  (Ohyama et al.
7. Ratio of 2MASS K-band to 5 µm monochromatic continuum emission as a function of 3. 3 µm PAH EQW. The EQWs are derived from the AKARI spectra extracted with an aperture covering ∼ 95% of the total AKARI flux. The K-band emission is measured in the imaging data using the same aperture size. The symbols are the same as in Figure  2 , in addition to the red diamonds, which indicate PAH(3. 3µm)EQW < 0. 04 µm and PAH(6. 2µm)EQW < 0. 4 µm. is likely produced by the stellar continuum of more massive, evolved stars rather than hot dust. Dust extinction cannot cause the discrepancy between the PAH EQW(3. 3 µm) and EQW(6. 2 µm) diagnostics, because it would also suppress the 3. 3 µm PAH flux with respect to the 6. 2 PAH flux. However, if the ∼ 3 µm continuum is dominated by stellar emission instead of hot dust and the ∼ 3 µm continuum emission is more extended than the 3.
Acknowledgements. The authors would like to thank the referee whose constructive comments helped improving the manuscript. HI thanks  Grant-in-Aid  for  Japan Society for the Promotion of Science (JSPS) Fellows  ( 21-969 ) and  JSPS   Excellent Young Researchers Overseas Visit Program  for supporting this work at the  Spitzer Science Center, California Institute of Technology, USA. YO acknowledges support from  Ministry of Science and Technology (MOST) of Taiwan   106-2112-M-001-008 -. This research is based on observations with AKARI, a  JAXA  project with the participation of  ESA. The authors appreciate the opportunity to present this work at the 4th AKARI international conference which helped to enrich this work (Inami et al. 2018). The Spitzer Space Telescope is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under  NASA  contract  1407.
Acknowledgments This work is supported by the  French RENATECH network  and the  ANR  project  DESTINEE  ( ANR-16-CE05-0022 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 720527  ( Inrel-NPower ). The authors would also like to gratefully acknowledge the company EpiGaN for the high material-quality delivery as well as the company Kyma for the thick PVD AlN deposition.
22, 0. 23, and 0. 24. Details of the sample and contact preparation can be found there. In addition, similarly prepared samples with p = 0. 12 and 0. 15 were measured. The T c values for all samples are listed in Table  I. The thermal conductivity was measured in the field-cooled state in a dilution refrigerator over the range 50 mK to 1. 0 K, using a one-heater-two-thermometers steady-state technique. The heat current was applied in T ( K ) κ / T ( mW / K 2 cm ) Nd-LSCO H = 15 T H = 0 H = 15 T H = 0 (a) (b) FIG. 2. (a) Thermal conductivity κ versus temperature plotted as κ/T vs T , for Nd-LSCO at p = 0. 22 (red) and 0. 24 (blue), in H = 0 (open symbols) and 15 T (dots). The lines are linear fits to the data over the temperature range shown. The y-intercepts of the fits are the residual electronic terms κ0/T.
This was not true for Au-H aromatic and Au-C aromatic which had to be mimicked by an ad hoc Morse term, instead. More details are reported elsewhere. 22 oth the simulation cells for FM and HM scenarios are hexagonal and their dimensions are 138 × 134 × 80 Å. The Au(111) surface is represented by six layers with the bottom one held fixed to mimic the bulk. The number of the Fe 4 Ph present in the cell is 72 (33 120 total atoms including the surface) and 36 (24 336 total atoms) for FM and HM, respectively. All MD/MM calculations are performed with the LAMMPS package  25  with Ewald summation (10 -6 accuracy in forces), time step of 1 fs, and cutoff of pairwise interactions of 20 Å with tail corrections. The equation of motion was integrated with a standard velocity-Verlet integrator. Before all simulation runs, we performed geometry optimization with the Hessian-free truncated Newton algorithm and a thermalization run of 1 ns, with temperature rescaling, up to 300 K.
Ab initio calculations CP2K  26  and ORCA  27  packages were employed for the DFT description. For all the calculations involving the metal surface the CP2K code was used with the meta-GGA TPSS functional, 28 rVV10 corrections for dispersion forces  29  and periodic boundary conditions (PBC). The CP2K code uses a GPW approach (hybrid Gaussian -Plane Wave) in which the electronic density obtained by the Kohn-Sham orbitals in a GTO basis set is mapped to an auxiliary plane wave basis set. The molecular optimized DZVP-MOLOPT-SR-GTH basis set was used along with a cutoff of 450 Ry for the plane wave expansion. To account for the metallic character of the surface, a smearing of the occupation numbers of the molecular orbitals with a Fermi-Dirac distribution with a temperature of 1500 K was considered.
The isotropic exchange coupling constants were computed within the broken symmetry  31, 32  framework with the PBE0 + rVV10 functional. 33 The calculations of anisotropic tensors for the ground state S = 5, D S=5 , were performed within the Giant Spin Approximation (GSA): H aniso ¼ P 4 i ŝ i ÁD i Áŝ i ð2Þ H GSA aniso ¼ SÁD S¼5 ÁS ð3Þ where D i is the single ion anisotropic tensor. The anisotropic intensity and its distortion from the perfect axial symmetry are conventionally described with the axial (D) and rhombic (E) parameters, where D eigenvalues are chosen as 0 < |E/D| < 1/3. Diamagnetic substitution of Fe 3+ ions with Ga 3+ ions was used to calculate the single ion anisotropic terms and the g tensors. For the calculations of anisotropic tensors (vide infra) we used the ORCA code  27  with PBE functional,  34  the def2-TZVP/J basis set for iron and oxygen atoms and def2-SVP for all the others.
Acknowledgements The authors gratefully acknowledge the support from the  European Research Council  (Advanced Grant No. 267746 -MolNanoMaS ), the  European Union 's  Horizon2020 Research and Innovation Programme  under grant agreement No. 737709  (FEMTOTERABYTE, http://www. physics. gu. se/femtoterabyte) and the  MOLSPIN COST  action  CA15128.
We have monitored the time duration (called length L) of the avalanches as well as their consistency (size S), corresponding to the total number of spikes emitted during an avalanche. The densities R L and R S per unit of time of the avalanche length L and size S are reported in Fig. 2  for various system sizes. There is a clear increase of the length and size of the avalanches with the system size N and hence (due to the massive coupling) with the in-degree K = cN. Upon rescaling the R S densities by √ N , they collapse onto a same curve (that we expect to depend on the coupling strength J) as shown in the inset of Fig. 2 (b ). Finally, no simple scaling behavior has been found for the avalanche length L. We tested different scalings assumptions but none of them yielded a convincing data collapse.