dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The authors acknowledge  N. Brunel ,  F. Farkhooi ,  G. Mato ,  S. Ostoijc ,  A. Roxin , and  M. di Volo  for useful discussions. One of us (AT) has been supported by the  French government  under the  Excellence Initiative I-Site Paris Seine  (No  ANR-16-IDEX-008 ) and under the  Labex MME-DII  (No  ANR-11-LBX-0023-01 ). The work has been mainly realized at the  Max Planck Institute for the Physics of Complex Systems (Dresden, Germany ) during the Advanced Study Group 2016/17 " From Microscopic to Collective Dynamics in Neural Circuits ".
MIRO data provide an average water loss rate of 220 ± 80 kg s -1 , with a dustto-gas mass ratio of Q V ρ C /(220 ± 80) = 18 ± 5. These two inconsistent results can be solved by two alternative end-case scenarios, i. e. many and few distributed water sources: 2. 3. 4 The difference is explained in terms of distributed water sources. MIRO, a remote sensing instrument, observes the water coma (in optically thin IR-lines) close to the nucleus surface, measuring the water loss mostly from the nucleus surface. ROSINA, an in-situ instrument, observes at Rosetta location, i. e. at hundreds km from the nucleus around perihelion, measuring the water loss from the nucleus but also from the chunks. This scenario suggests that the average perihelion water loss rate from the chunks is Q W C = 550 kg s -1 , consistent with the Q W C prediction above.
KUIPER BELT OBJECTS Kuiper Belt Objects (KBOs hereinafter) are not comets. However, according to the collisional models of comet formation  (Rickman et al. 2015) , comets may be fragments of KBOs. Following other formation scenarios, comets and KBOs formed in the same outer regions of the protoplane-tary disc  (Blum et al. 2017) , so that they should have the same average Refractory-to-Ice mass ratios. Eqs. 8 to 11 can be applied to all KBOs with sizes < 100 km, where the lithostatic pressure and melting are negligible  (Blum et al. 2017; Fulle & Blum 2017). Unfortunately, data on the bulk density of KBO's smaller than 100 km, not yet visited by spacecrafts, are unreliable (Brown 2013) as recently shown by Haumea observations  (Ortiz et al. 2017). Stellar occultation data have updated the size from 1300 to 1600 km, the geometric albedo from (75 ± 6)% to (51 ± 2)%, and the bulk density from 2600 to 1820 kg m -3 (Brown 2013;  Ortiz et al.
ACKNOWLEDGEMENTS We thank the referee for the constructive comments, and the  Rosetta Science Ground Segment at ESAC , the  Rosetta Mission Operations Centre at ESOC  and the  Rosetta Project at ESTEC  for their outstanding work enabling the science return of the  Rosetta Mission. Part of this research was supported by the  ESA Express Procurement (EXPRO)   RFP for IPL-PSS/JD/190. 2016and by the  Italian Space Agency (ASI)  within the  ASI-INAF  agreements  I/032/05/0  and  I/024/12/0.
II. EXPERIMENT AND DATASET A detailed description of the PHENIX detector system can be found elsewhere  [25]. The principal detectors used in this analysis are the beam-beam counters (BBC), the muon-piston calorimeter (MPC) and the electromagnetic calorimeter (EMCal). The BBCs are located north (BBCN, 3. 1< η <3. 9, d-going) and south (BBCS, -3. 9< η <-3. 1, Au-going) of the interaction point, covering the full azimuth and are sensitive to charged particles. In d+Au collisions, the Au ions are accelerated in the Au-going direction. The MPCs, which are high resolution electromagnetic calorimeters, are also located north (MPCN, 3. 1< η <3. 9) and south (MPCS, -3. 7< η <-3. 1) of the interaction point, in front of the BBCs, and cover the full azimuth. The south (north) MPC comprise 192 (220) PbWO 4 crystal towers with 20.
2 X 0 or 0. 89 λ I  [26]. The EMCal is located in the central (CNT) arms with pseudorapidity range |η| <0. 35  and covering two π/2 segments of the full azimuth. Figure  1  shows the acceptance of each relevant PHENIX detector subsystem in φ-η coordinates. The d+Au and p+p collision data used in this analysis were recorded in 2008 at RHIC. The events triggered by a high energy deposit in a 4x4 tower region of the EMCal in coincidence with the minimum bias (MB) requirement were selected in both the p+p and d+Au data sets. The MB trigger was defined as the coincidence of at least one hit in the BBCS and BBCN. A z-vertex cut of |z| < 30 cm is applied, using the vertex calculated from the BBC timing information. The energy threshold of the 4x4 towers is set to be 2. 8 GeV, however, due to the energy smearing effect, the towers also sample hits with lower energies but with lower efficiency.
The c 2 are small and decreasing as a function of p T , but nonvanishing in the available p T range, proving that the quadrupole component is present. To gauge the magnitude of characteristic-structure correlations as a measure of a bulk property of the system, we calculated -c 2 /c 1 , the ratio of c 2 (quadrupole) to -c 1 (dipole), for all p+p and d+Au systems, as shown in Fig. 7(c ). For the CNT-MPCS correlations [Fig. 7(a) ] the data exhibit a well-defined ordering with system centrality, within errors, from the most central d+Au down to the most peripheral (60%-88%) which is consistent with the p+p. We then see a smooth evolution from the most central collisions observed at lowest p T , where the near-side correlations are most prominent and which would be expected to have the largest contribution from a collective source, to the more peripheral and higher p T limit, where the near-side correlation vanishes and elementary processes are expected to dominate.
5 GeV/c and 5-6 GeV/c. The values for both CNT-MPCS and CNT-MPCN are shown. The smooth decrease of c 1 with N part is clearly seen for both p T selections, but the decrease of c 1 for the CNT-MPCS is more rapid compared to that of CNT-MPCN. In contrast, the c 2 is flat or exhibits little increase (decrease) as a function of N part for CNT- MPCN (CNT-MPCS) correlations, except for the lowest N part. In -c 2 /c 1 , where individual -c 1 and c 2 trends are combined, the data for CNT-MPCS show a smooth rising trend, stronger for the lower p T selection, while the -c 2 /c 1 for CNT-MPCN correlations displays no evolution with N part at all from p+p to the most central d+Au collisions. This observation clearly shows again that the characteristic structure is clearly seen in Au-going direction, rather than in d-going direction, and ceases at high p T which is a similar characteristic as the hydrodynamical particle flow in A+A collisions.
ACKNOWLEDGMENTS We thank the staff of the  Collider-Accelerator and Physics Departments at Brookhaven National Laboratory  and the staff of the other PHENIX participating institutions for their vital contributions. We acknowledge support from the  Office of Nuclear Physics in the Office of Science of the Department of Energy , the  National Science Foundation ,  Abilene Christian University Research Council, Research Foundation of SUNY, and Dean of the College of Arts and Sciences, Vanderbilt University (U. S. A) ,  Ministry of Education, Culture, Sports, Science, and Technology  and the  Japan Society for the Promotion of Science (Japan) ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado de São Paulo (Brazil) ,  Natural Sci-ence Foundation of China (People's Republic of China) ,  Croatian Science Foundation and Ministry of Science and Education (Croatia) ,  Ministry of Education, Youth and Sports (Czech Republic) ,  Centre National de la Recherche Scientifique ,  Commissariat à l' Énergie Atomique , and  Institut National de Physique Nucléaire et de Physique des Particules (France) ,  Bundesministerium für Bildung und Forschung ,  Deutscher Akademischer Austausch Dienst , and  Alexander von Humboldt Stiftung (Germany) ,  J.
Experimental data set The data set was first published and analyzed in  [32]. It consists of the activity of neural populations (46-99 neurons) recorded using calcium imaging in the auditory cortex of mice. Animals were isoflurane anesthetized (1 %). Signals were obtained from neurons labeled with the synthetic calcium indicator OGB1. Fluorescence was measured at 30 Hz sampling rate, and firing rates inferred from temporal deconvolution of the fluorescence signal. Up to 6 neural populations in each of 14 animals were recorded. The data points we use are the average firing rates over a window of 250 ms after presentation of each of 65 different sound stimuli, each for 15 trials. Responses were measured relative to spontaneous activity. Hence, negative responses occurred, if the stimulus-evoked firing rates were smaller than the spontaneous ones.
Although the intrinsic noise from the spike-generating Poisson process is dynamical, its effect is that of an additive contribution to the external variance (see also  [35] ). The dependence on the rate vector, in Eq. (  12 ), results from the Poisson character of the noise, in which the variance equates the mean. In the experimental data set, firing rates are measured relative to spontaneous activity. To take this into account, an offset, a, can be added to the rates in Eq. (12). Henceforth, we examine the model through Eqs. (7)  and  (12)  for the mean neural activities and their covariances, respectively.
Estimation of network model parameters We extract parameters of the recurrent and feed-forward network models from the data, both to test the consistency of the models with the data and to interpret the observed variability. Because rates and covariance matrices were measured for many different stimuli and thus provide a large number of constraints, one approach would be to infer as much information as possible about the full connectivity matrices B or F from the data. However, due to the relatively small number of trials for each stimulus, we use a model with few parameters. The set of parameters consists of the network parameters B and var(B) ( F and var(F ), respectively) as well as the parameters of the input ensemble, r ext , var(r ext ) and c in. In particular, we want to infer the ratios ρ E = var(r ext )/ r ext 2 and ρ = var(B)/ B 2 (and correspondingly for F ).
3 mm)i nd egassed CDCl 3. A ll spectra were acquired at 11. 7 Tand 25 8C, using aB ruker AVIIIHD500 FT-NMR spectrometer. Spin-lattice relaxation times T 1 were estimated using the saturation-recovery pulse sequence. The 908 pulse was calibrated for each sample using Bruker To pSpin and the saturation-recovery sequence employed a1 6-point delay list (0. 01, 0. 05, 0. 1, 0. 2, 0. 3, 0. 5,  0. 75, 1, 1. 5, 2, 2. 5, 5, 7. 5, 10, 15, 30 s. ). Signals of interest from the saturation-recovery experiments were integrated using Bruker To p-Spin, and the data were fitted using Mathematica. Signal amplitudes was normalized to the last data point. The fitted curves have asingle-exponential form.
Acknowledgements This research was supported by the  EPSRC (UK)  ( EP/1029451 , E P/ N002482 ,  EP/L505067/1 ,  EP/M001962  and  EP/M001970 ) including core capability ( EP/K039466 ). The work in Tallinn was supported by institutional research funding IUT23-3 of the  Estonian Ministry of Education and Researcha nd the European Regional Development Fund  project  TK134. S JE  acknowledges support from  Bruker Biospin (UK).
X-ray photoelectron spectroscopy. XPS spectra were collected on a SPECS GmbH PHOIBOS 100-1D delay line detector photoelectron spectrometer, using a monochromated AlKα (hν = 1486. 6 eV) radiation source having a 250 W electron beam power. The sample was analyzed under ultra-high vacuum conditions (1 × 10 -8 Pa). After recording a broad range spectrum (pass energy 100 eV), high resolution spectra were recorded for the C 1s , S 2p , Te 3d , Cd 3d core XPS levels (pass energy 20 eV). The peak areas were determined after subtraction of a Shirley background. The atomic ratio calculations were performed after normalization using Scofield factors. Spectrum processing was carried out using the Casa XPS software package. The sputtering of the QDs were performed in the XPS chamber using a 2 keV Ar + beam.
Acknowledgements Research leading to these results has received funding from the  International Scientific Cooperation Program of the CNRS (Centre National de la Recherche Scientifique)  under Grant agreement number  PICS07339. The authors acknowledge Assistant Engineer  B. Rieul  ( University of Paris-Sud, Université Paris-Saclay, Laboratoire de Chimie Physique, CNRS, 91405 Orsay, France ) for his technical assistance on the pilot control development of the SFG spectroscopy setup, and  Research Engineer P. Beaunier  ( Sorbonne Université, CNRS, Laboratoire de Réactivité de Surface, 75005 Paris, France ) for her precious help in the realization and interpretation of EDX measurements.
Acknowledgements. We thank the entire  MMS  team for providing high-quality data. This work was supported by the  National Natural Science Foundation of China  grants  41474145 ,  41574159 ,  41731070 , and  41504114 ; the  Strategic Pioneer Program on Space Science grant  no. XDA15052500 ; and in part by the  Specialized Research Fund for State Key Laboratories of China. The French involvement (SCM instruments) in MMS is supported by  CNES ,  CNRS-INSIS  and  CNRS-INSU. The topical editor,  Minna Palmroth , thanks two anonymous referees for their help in evaluating this paper.
Data availability. All data used in this study are publicly accessible. MMS data are available at the MMS Science Data Center (https: //lasp. colorado. edu/mms/sdc/public/, last access: 25 May 2018). The OMNI data can be downloaded from the NASA Goddard Space Flight Center Coordinated Data Analysis Web (CDAWeb; http://cdaweb. gsfc. nasa. gov/, last access: 25 May 2018).
Acknowledgements The authors are grateful for the support of the teams from the neutron facility GELINA of  JRC-Geel  and from the reactor Orphée of the LLB in CEA-Saclay. We also thank the  CERN's laboratory EP-DT-EF  for advice and the production of the MicroMegas detectors. The initial prototype was co-funded by the grant  JIUZ-2013-CIE-02  of the  University of Zaragoza. This work was supported by the  French Labex   P2IO , by the collaboration  RD51 , and by the  European Commission 's programs EUFRAT 2014-2017 and Enhanced Eurotalents 2014-2018 ( Marie Curie COFUND program  managed by the  French CEA).
Acknowledgements We thank  M. T. Gray  for assistance with depositions as well as  M. Gabay  and  X. Qi  for insightful discussions. This work was funded by the  Vannevar Bush Faculty Fellowship of the Department of Defense  under Contract No. N00014-15-1-0045. M. J. V. acknowledges a National Science Foundation Graduate Fellowship. Work at the  National High Field Magnetic Laboratory  was funded by the  state of Florida , the  Department of Energy , and the  NSF  Cooperative Agreement No. DMR-1157490. This work was granted access to the HPC resources of the CALMIP supercomputing center under the allocation p1229. R. A. and R. P. acknowledge funding by the  German Science Foundation  within  SFB/TRR 80  (projects  C03  and  G03 ) and computational time at the  Leibniz Rechenzentrum Garching , project  pr87ro.
Acknowledgments We thank  Gerardo Adesso  and  Bartosz Regula  for a helpful discussion on entanglement monotones and  Masahiro Hotta  for bringing the work in [5,6] to our attention. SK thanks the  University of Nottingham and CPT, Aix Marseille Université ,  Université de Toulon ,  CNRS  for hospitality and the  Department of Science and Technology, India , for partial financial support. JL was supported in part by the  Science and Technology Facilities Council  (Theory Consolidated Grants  ST/J000388/1  and  ST/P000703/1 ).
work highlights a new combination of real and momentum space topology. These results are promising for the development of a new field of vortextronics, where the information will be carried by vortices. The possibility to create chiral pathways for vortices and to automatically sort them according to their winding is crucial for information treatment. We acknowledge the support of the project " Quantum Fluids of Light"  ( ANR-16-CE30-0021 ), of the  ANR Labex Ganex  ( ANR-11-LABX-0014 ), and of the  ANR Labex IMobS3  ( ANR-10-LABX-16-01 ). D. D. S. acknowledges the support of  IUF (Institut Universitaire de France ).
White beam neutron tomography was carried out at the IMAGINE beamline  [38] , the cold neutron imaging facility at the Laboratoire Léon Brillouin CEA/CNRS (FR). The respective values for the instrumental set-ups are reported in Table  1. Although meteoritic iron contains cobalt as a major element, the neutron investigation caused no significant activation of the sample, and the radiation intensity fell below the safety thresholds in about 1 week. In fact, the naturally occurring Co isotope, 59 Co, is characterised by a very high neutron absorption cross section σ abs (σ abs ≈ 82. 7 barns for neutrons of 4 Å); by neutron capture 59 Co transforms into radioactive 60 Co, which decays to 60 Ni with a half-life of T 1 2 = 5. 2714 years. X-ray CT volume reconstruction was performed using the X-TEK 3D CT Pro developed by Nikon Metrology, while neutron projections were processed using the Octopus software package  [39].
Image Registration When a single imaging method cannot provide sufficient contrast to resolve the composition of the imaged object from its histogram, it is possible to take advantage of the spatial correlations of the intensity distributions from multiple registered data-sets under different modalities. Plotting the attenuation coefficients given by the two modalities in a bivariate histogram may aid in the separation of phases with distinct compositions, forming 'clusters' of intensity within the bivariate histogram plot. To achieve this, we have manually pre-aligned the two data sets (through rotations and reslice functions) in FIJI  [41]  and registered them (scaled and aligned on a common coordinate system) in the Statistical Parametric Mapping SPM8 software package  [42]  using a rigid transform and the normalised mutual information similarity metric  [43, 44].
Rigid body registration only allows for rotations and translations, and therefore does not deform the objects. Normalised mutual information is one of the most common image similarity metrics for multi-modal registration  [45]. The X-ray data set was used as fixed volume (Reference Image) to which the neutron volume (Float Image) was co-registered. An example slice from the two datasets is shown in Figure  2 , before and after the registration transformation. While the transformation applied to the Float Image does not deform the object, the difference in the appearance of the slices from the Float and the Co-Registered images is due to the fact that the transformation is applied in 3D.
Rigid body registration only allows for rotations and translations, and therefore does not deform the objects. Normalised mutual information is one of the most common image similarity metrics for multi-modal registration  [45]. The X-ray data set was used as fixed volume (Reference Image) to which the neutron volume (Float Image) was coregistered. An example slice from the two datasets is shown in Figure  2 , before and after the registration transformation. While the transformation applied to the Float Image does not deform the object, the difference in the appearance of the slices from the Float and the Co-Registered images is due to the fact that the transformation is applied in 3D. Because of the higher resolution achieved in the X-ray scan (see Table  1 ), the neutron volume (float image) was up-sampled in order to match the voxel dimensions of the X-ray volume (reference image).
These colour schemes provided the best combination for this particular dataset. The false colour images were used to visually identify features of interest in the impactite, such as corrosion areas (highly attenuating for neutrons but not for X-rays) surrounding some of the Fe-Ni spherules (high attenuation in both datasets). All X-ray attenuation False colour images are an effective way of simultaneously visualizing images from multiple modalities; some of the possible combinations obtained using JET and RGB colour models are shown in Figure  6 , alongside the respective colour map. These colour schemes provided the best combination for this particular dataset. The false colour images were used to visually identify features of interest in the impactite, such as corrosion areas (highly attenuating for neutrons but not for X-rays) surrounding some of the Fe-Ni spherules (high attenuation in both datasets).
Acknowledgments X. B. thanks  M. Grigoriev  for illuminating discussions on the field-versus group-theoretical description of shadow fields. X. B. is also grateful to the organizers of the conference " Gauge/Gravity Duality 2018 " (Würzburg, 30 July -03 August) where some of the present results were announced. The research of T. B. and E. J. was supported by the  National Research Foundation (Korea)  through the grant  2014R1A6A3A04056670. The research of T. B. was supported by  Korea Research Fellowship Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science  and  ICT 2018H1D3A1A02074698. The research of X. B. was supported by the  Russian Science Foundation  grant  14-42-00047  in association with the  Lebedev Physical Institute.
Acknowledgments The authors thank  Miguel Pino ,  Jakob Salzer  and  Ricardo Troncoso  for helpful conversations and comments. O. F. would like to thank  Claudio Bunster  and  Alfredo Pérez  for useful discussions. M. C. acknowledges financial support given by  Becas Chile ,  CONICYT. O. F. thanks the  Institute for Theoretical Physics, TU Wien , for the hospitality during his stay funded by the grant  CONICYT PCI/REDES 170052. The work of H. G. is supported by the  Austrian Science Fund (FWF) , project  P 28751-N2  and  P 27182-N27. C. V. was partially supported by  CAPES  post-doctoral scholarship. D. V. was supported by the grant  2016/03319-6  of the  São Paulo Research Foundation (FAPESP) , by the grants  401180/2014-0  and  303807/2016-4  of.
Spectroscopy Spectroscopic data for SDSS-IV are acquired in a similar manner as for SDSS-III  (Dawson et al. 2016). Targets identified by the various selection algorithms are observed with the BOSS spectrographs whose resolution varies from ∼1 300 at 3 600 Å to 2 500 at 10 000 Å  (Smee et al. 2013). Spectroscopic observations are obtained in a series of at least three 15-minute exposures. Additional exposures are taken until the squared signal-tonoise ratio per pixel, (S/N) 2 , reaches the survey-quality threshold for each CCD. These thresholds are (S/N) 2 ≥ 22 at i-band magnitude for the red camera and (S/N) 2 ≥ 10 at g-band magnitude for the blue camera (Galactic extinction-corrected magnitudes). The spectroscopic reduction pipeline for the BOSS spectra is described in  Bolton et al. (2012).
Summary of the sample The DR14Q catalog contains 526 356 unique quasars, of which 144 046 are new discoveries since the previous release. This dataset represents an increase of about 40% in the number of SDSS quasars since the beginning of SDSS-IV. Spectroscopic observations of quasars were performed over 9 376 deg 2 for SDSS-I/II/III. New SDSS-IV spectroscopic data are available over 2 044 deg 2. The average surface density of 0. 9 < z < 2. 2 quasars prior to the beginning of SDSS-IV is 13. 27 deg -2 , and reaches 80. 24 deg -2 in regions for which SDSS-IV spectroscopy is available. The overall quasar surface density in regions with SDSS-IV spectroscopy is 125. 03 deg -2 , which corresponds to an increase by a factor of 2. 4 times compared to the previous quasar catalog release. The redshift distribution of the full sample is shown in Fig.