dataesr/scientific-paragraphs-categorization huggingface dataset:
34) on the Wikianswers dataset even without using the paraphrase corpus. This suggests that the embedding method successfully captures the similarity between NL questions and KB queries. Our work continues this direction by further separating relations with entities. 1 http://reverb. cs. washington. edu The idea of distinguishing entities and relations in question answering can also be found in  (Yih et al. , 2014). However, they base their work by supposing that we can cut the sentence into "entity part" and "relation part" and then calculate the matching score. Our model does not need this cut and simply enforces the entity embeddings and relation embeddings (on the KB side) to be different. Orthogonality or near orthogonality is a property which is desired in many embedding techniques. In random indexing  (Sahlgren, 2005) , a near orthogonality is ensured amongst the embeddings of different contexts.
Toy example In this section, we illustrate the benefits of orthogonality via a toy example. We construct a KB containing 50 entities (E) and 50 relations (R) then generate all their cross products obtaining 2500 fact pairs. In consequence the entities and relations are independent. For every e i ∈ E, we suppose that there is a single word lexicalizing the entity noted "e i ". Similarly, we note the lexicalization of r j "r j ". We separate these 2500 pairs into training (2450) and test (50). Notice that similarly to Wikianswers, this toy dataset involves KB entities and relations whose type is known a priori. The training corpus is built using one simple generation rule : (e i , r j ) → "e i r j ". Negative examples are created by replacing with probability 1/2 both entity and relation with another one.
Wikianswers Wikianswers contains a set of possible triples for each question and we re-rank these triples to report our system's performance. This is the "reranking" setting used in  (Bordes et al. , 2014b). Table  3  compares different systems in this setting. The Embedding scores are taken from  (Bordes et al. , 2014b)   Table  3  shows that our technique improves the performance also on the larger, non-synthetic, dataset provided by  Fader (2013)  over the Bordes (2014b)'s method. In addition, Table  1  shows some examples where the two systems differ and where the orthogonality regularized embeddings seem to better support the identification of similar relations. For instance, "is the argument on" is mapped to support. r rather than be-type-of. r and "is the religious celebration of" to be-mostimportant-holiday.
Both the emissions used for the model simulations and the atmospheric concentration measurements were also decaycorrected. Modelled cesium and xenon concentrations were sampled at the location of the 11 CTBTO sites (Fig. 1 ), at times when measurements were available. This allows a direct comparison to the measurements and observation-based lifetime evaluations  (Kristiansen et al. , 2012). Modelled total atmospheric burdens as a function of time were also calculated and evaluated. The transport of the radioactive cloud across the Northern Hemisphere is illustrated in Fig. 2 , as simulated by the FLEXPART model using meteorological analysis data from the Global Forecast System (GFS) model of the National Centers for Environmental Prediction (NCEP), in the weeks following the initial release at FD-NPP.
Additionally, we use global burdens estimated from measurement data as in  Kristiansen et al. (2012)  and compare these to modelled global burdens. Several definitions of atmospheric lifetime exist. In  Kristiansen et al. (2012) , the e-folding lifetime was used as a measure of aerosol lifetime. Croft et al. (2014)  document, compare, and explain differences between global mean aerosol lifetime, the definition typically reported for aerosol and climate model simulations, and e-folding times from their GEOS-Chem transport simulations of the FD-NPP accident emissions. They show that the two definitions are not directly comparable for the FD-NPP case. The lifetime results for the former definition were heavily influenced by  initial quick removal, giving a much shorter lifetime than the e-folding lifetime for the hemispherically relatively wellmixed phase starting about 3 weeks after the main emissions.
Sensitivity of e-folding times to measurement sampling In this study we have used modelled surface concentrations at the times when observation data exist, amounting to 433 data points over all 11 stations and measurement times (March to June 2011) considered. Croft et al. (2014)  did a similar lifetime estimate but derived from modelled surface concentrations of 137 Cs from continuous time series at the 11 stations during March to June 2011 using about 19 000 hourly values. They found a lifetime for GEOS-Chem of about 16. 7 days, while in this study the lifetime estimate for GEOS-Chem is longer (20. 2 days). For five other models (GISS-modelE, FLEXPART, BCC SGCM CAM, EMAC-1, and EMAC-2) we calculated the difference in the estimated lifetime using continuous (1-hourly or 3-hourly) time series at the stations, compared to using only the 433 data points at the times of the measurements.
For all five models, the lifetime is slightly shorter (by 0. 2 to 0. 9 days) when using the continuous data than when using only data at the measurement times, but the difference is much smaller than for GEOS-Chem (3. 5 days). GEOS-Chem is sensitive to the initial scavenging and transport as apparent in Fig. 3  by the anomalous values at Wake Island in the early time period. Insufficient scavenging at midhigh latitudes may also play a role, as seen in the overestimation of aerosol concentrations in Table  4 , and this seems to contribute to the longer lifetime for the selection of points used in this study. Further discussion on the scavenging parameterization in GEOS-Chem is given in Sect. 6. 6. Another issue with the measurement sampling is how to deal with measurement values below the detection limit (BDL).
GEOS-Chem_allT Aerosol Passive Figure  10. Same as Fig. 4 , but including two revised simulations of the GEOS-Chem model. GEOS-Chem_Orig is the same simulation as in Fig. 4 ; GEOS-Chem_allT has an updated scavenging scheme relative to GEOS-Chem_Orig through processes related to cloud liquid and ice water content, cloud temperature, and using cloud fraction from the assimilated meteorological field. GEOS-Chem_T258 includes the same revisions, but restricts the large-scale in-cloud scavenging of aerosols to temperatures above 258 K; same restriction for simulation GEOS-Chem_Orig. ulation GEOS-Chem_T258 produces a significantly lower aerosol concentration bias (1. 69 vs. 5. 53) relative to observations (Fig. 10 ). In addition, the e-folding lifetimes for the station data are shorter by up to 6 days (Table  7 ).
C1 ). Second, modelled global burdens were also calculated from the full 3-D model output (light green and turquoise lines in Fig. C1 ). Comparing the burdens calculated by applying the box model to the model-simulated station data (dark green and blue lines in Fig. C1 ) with the modelled global burdens based on the full 3-D model output (bright green and turquoise lines) serves as an evaluation of the box-model assumption. For some models, the global burdens from the surface sites extrapolated with the box model compare well with the burdens from the full model output directly, while for almost half of the models (GISS-ModelE, ULAQ-CCM, CAM5, EMAC-1, ECHAM-HAM2, EEMEP, OsloCTM2, NAME), the box-model estimates do not reconstruct the full global burdens very well and a general underestimation from the box-model estimates is found, which is expected since they are based on surface measurements.
Acknowledgements. We would like to thank all the scientists who produced the CTBTO measurement data and made them available to us. The research leading to these results has received partial funding from the  Norwegian Research Council  under the  NORK-LIMA and KLIMAFORSK program  (project " AeroCom-P3 "). H. Zhang  and  S. Y. Zhao  are supported by the  National Basic Research Program of China  (grant no. :  2011CB403405 ). H. Wang ,  R. C. Easter ,  P. -L. Ma , and  P. J. Rasch  acknowledge support from the  US Department of Energy (DOE), Office of Science, Biological and Environmental Research  as part of the  Earth System Modeling Program. T. Bergman  and  H. Kokkola  were supported by the  Academy of Finland Centre of Excellence  (project no. 272041 ). The ECHAM-HAMMOZ model is developed by a consortium composed of  ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford , the  Finnish Meteorological Institute , and the  Leibniz Institute for Tropospheric Research , and managed by the  Center for Climate Systems Modeling  ( C2SM ) at  ETH Zurich.
This raises questions if hydrodynamics and/or recombination can also be applied to describe these small systems  [39] [40] [41]. The centrality evolution studies for Pb-Pb collisions can therefore also be seen as a possible experimental interconnection between the smallest and the largest QCD bulk systems. The outline of this paper is as follows. In Sec. 2, the data analysis is described. The method using the energy loss in the TPC for particle identification is laid out first and then the procedure using the Cherenkov angle measured by the HMPID is presented. In Sec. 3, the final spectra are presented, and the particle ratios and nuclear modification factors are discussed and compared with theoretical calculations ALICE Collaboration and results from previous experiments at lower center-of-mass energies.
It uses 1+ β γ to ensure that the logarithmic term is always positive. The relative resolution, σ / dE/dx , as a function of dE/dx is parameterized with a second-degree polynomial, which was found to describe the data well: σ / dE/dx = a 0 + a 1 dE/dx + a 2 dE/dx 2. ( The TPC response (Bethe-Bloch and resolution curves) is determined for each η region. Due to the deterioration of the TPC dE/dx performance with increasing multiplicity, the curves differ significantly and have to be extracted separately for pp and each Pb-Pb centrality class. The parameters a, b, d, and e are well determined using external PID information. Secondary pion (proton) tracks identified via the reconstruction of the weak decay topology of K 0 S (Λ) and data samples with TOF enhanced (β TOF > 1) primary pions are used.
〉 MIP x /d E d 〈 ) / x /d E (d Figure  8  shows the final parameterizations of the Bethe-Bloch and resolution curves for pp and the most central Pb-Pb collisions. The values obtained for the external PID data are also shown. Table  1  shows the values of the parameters of Eq. 2 for different centrality classes and pp collisions. All parameters except c are close for the four |η| intervals and similar across systems. As previously mentioned, the parameter c is related to the transition in the logarithmic rise to the Plateau and the large difference mainly reflects γ β (arb. unit)  that the parameter is statistically not well constrained for some of the datasets. For the pp dataset, where the largest variation is observed, we obtain similar results within statistical uncertainties if c = 2 is used for all |η|-slices.
1•σ i , σ i + 0. 1•σ i ] for the widths. After this first step, the p T dependence of each parameter is fitted with a continuous function. In the second step, the fitting is repeated with only the yields as free parameters and constraining the mean and sigma values to the continuous functions. The means and widths constrained in this way are all found to be independent of centrality as shown in Fig. 18  for 0-5% and 40-50% centrality classes. In Fig. 19 , a comparison is shown between the mean values of the Cherenkov angle obtained from the fitting procedure with those obtained using a clean sample of protons and pions identified from Λ and K 0 S decays. To correct for the incorrectly assigned Cherenkov rings, a PID efficiency is used. This efficiency has to be derived from a dataset containing identified particles of a single species, so one can use MC or V 0 daughters.
Left panel: the kaon-to-pion ratio. Right panel: the proton-to-pion ratio scaled by a factor of 3 for clarity. Statistical and PID systematic uncertainties are plotted as vertical error bars and boxes around the points, respectively. Note that this kaon-to-pion (proton-to-pion) "high-p T " ratio is ≈4 (≈2) times larger than the bulk ratio  [44]. in the high-p T dE/dx analysis discussed in Sec. 2. 1 are done independently for each centrality class (using disjunct datasets) so one does not expect any direct correlations of the results. We conclude that all kaon-to-pion (proton-to-pion) ratios as a function of N part are consistent within the systematic uncertainty of ≈10 % (≈20%). Measurements with improved precision using Run 2 and Run 3 LHC data could reveal possible subtle particle-species differences.
EPOS  [70]  is a full MC generator which contains both soft and hard physics. It incorporates a hydrodynamical phase and additional hadronization processes at intermediate p T where the interaction between bulk matter and quenched jets is considered  [71]. This interaction introduces a baryon-meson effect, where fully quenched jets are allowed to hadronize with flowing medium quarks. When we study the full set of ratios at all centralities (Fig. 25  and Fig. 26 ) EPOS generally reproduces the centrality depen-dence well, even for very peripheral events, where it is known that pure hydrodynamical calculations fail to describe the data  [44]. However, EPOS overpredicts the magnitude of both the proton-to-pion and the kaon-to-pion peak; it is therefore critical to understand how important the additional hadronization processes are, relative to the hydrodynamic flow, when all parameters have been tuned.
Conclusion We have reported the centrality dependent measurement of charged pions, kaons and (anti)protons at large transverse momenta in Pb-Pb collisions at the LHC. When combined with previously published data at lower p T , the new results provide a comprehensive dataset of pion, kaon, and (anti)proton p T spectra with unprecedented systematic precision and p T reach. The spectra are sensitive to physics mechanisms that differentiate between baryons and mesons, strange and non-strange, or heavy and light hadrons. 7 Values taken from https://drupal. star. bnl. gov/STAR/files/starpublications/65/data. html for protons and a similar feed-down correction has been assumed for anti-protons. 8 We note that in a recent preprint it is shown that soft recombination together with pQCD+quenching can give a good description of pion, kaon, and (anti)proton spectra in central heavy-ion collisions both at the RHIC and the LHC for 1.
We use Fermi g-ray and IceCube neutrino data to demonstrate that the IceCube astrophysical neutrino signal has significant contribution from the Milky Way galaxy. The astrophysical neutrino spectrum above 10 TeV is found to lie at the high-energy extrapolation of the g-ray spectrum below several TeV. The combined spectrum is well described by a pion decay emission from a parent Galactic cosmic ray population with a powerlaw spectrum with the slope p ' 2. 45. In the energy band above 100 TeV, where the astrophysical neutrino signal is background-free, the Galactic latitude distribution of the neutrino signal reveals an evidence for significant Galactic component of the flux. The average Galactic cosmic ray spectrum derived from the combined g-ray and neutrino data is consistent with a theoretical model of cosmic ray injection by diffusive shock acceleration followed by escape through the Galactic magnetic field with Kolmogorov turbulence.
PoS(ICRC2015)1121 PoS(ICRC2015)1121 Galactic contribution to the IceCube astrophysical neutrino signal Andrii Neronov a simple model in which the injection of cosmic rays with the spectrum with the slope suggested by the DSA, p in j ' 2. 2. 2 is followed by the softening produced by the escape through the galactic magnetic fields with Kolmogorov turbulence spectrum. 2. g-ray and neutrino all sky spectra and anisotropies Our analysis uses all publicly available Fermi / Large Area Telescope (LAT) data collected over the period from August 2008 till June 2014. We have processed the data using the Fermi Science Tools v9r32p5 1  -a standard software package, provided by the Fermi collaboration to reduce the data, obtained by the Fermi/LAT. We have used the Pass 7 "reprocessed" event selection. We have filtered the event lists using the gtselect tool with parameter evclass=3, which leaves the g-ray events and rejects most of the cosmic ray background events.
Presence of significant Galactic component of the neutrino flux (at least 50% of the flux) is also consistent with the anisotropy properties of the neutrino signal. We find that the isotropic-only model of neutrino flux above 100 TeV is inconsistent with the IceCube data at & 3s level. Combination of the g-ray and neutrino data suggests that the Galactic cosmic ray spectrum is harder than previously thought, with the slope p ' 2. 5 in the TeV -PeV energy range. The discrepancy between the slopes of the average Galactic cosmic ray spectrum and the locally measured cosmic ray spectrum is readily explained by the discreteness of the cosmic ray source distribution and time variability of the local star formation rate.
Dataset We collect weekly returns from CRSP to estimate stock crash risk measures, the corporate governance attributes are constructed using information from the Corporate Library and firm-specific financial information are from Compustat. Information for CEO duality and managerial incentives are obtained from ExecuComp. To construct the institutional ownership variables we use data from the Thompson-Reuters Institutional Holdings Database. 3 Our sample covers the period 2002-2013. Similar to prior literature, we exclude financial services firms (SIC 6000-6999), utilities (SIC 4900-4999), firm-years with price at the fiscal year-end less than $2. 5, and firm-years with less than 26 weeks of stock returns during a fiscal year. Further, we exclude observations with missing information and we winsorize continuous variables at the 1 st and 99 th percentiles to mitigate possible data errors and influential extreme observations.
This 4 % uncertainty is propagated to all values related to the carbon mass. Measured 14 C / 12 C ratios are corrected for fractionation based on the analysis of the 13 C ion beam on an AixMI-CADAS Faraday cup. 14 C data are then expressed as a normalized activity F 14 C ratio equivalent to fraction modern  (Reimer et al. , 2004). Blank measurements are performed using CO 2 derived from fossil sources (without 14 C). Measurements of CO 2 produced from oxalic acid 2 standard (OxA2; National Institute of Standards and Technology, SRM 4990C) are used to normalize all 14 C / 12 C ratios of the measured samples. Both blank and standard CO 2 are contained in bottles directly coupled to the GIS and its injection syringe. During 2015, 85 blank gas samples were measured, giving an average F 14 C of 0.
Combining Eqs. (2) and (3) leads to Eq. (  4 ) in which the measured values M M and F 14 C M and the known F 14 Cs values are used to derive M C and F 14 C C of the contaminating carbon. F 14 C M = (M M -M C ) × F 14 C S + M C × F 14 C C M M (4) OxA2 and PA samples with different carbon mass (M M ) were measured and a nonlinear weighted least squares method (weights corresponding to the measured uncertainties on F 14 C M values) was applied to determine F 14 C C and M C. The results of the contamination model for the blank and the standard are represented in Fig. 1a ; the estimated parameters from the fit are F 14 C C = 0. 73 ± 0. 11 and M C = 1. 45 ± 0. 26 µgC (95 % confidence interval). Figure  1b  and c depicts the same dataset corrected for the contamination parameters. and OxA2 are in agreement with the expected values, confirming the constant contamination assumption.
Radiocarbon analyses All samples were analyzed twice to increase the precision of 14 C / 12 C and carbon mass data and to check for possible heterogeneity of individual filters. This represents a total of 124 measurements including the sampling blanks (4 field blanks for Chamonix and 12 for Passy). Blank sampling filters are treated as real samples (in the lab and in the field) with the exception that no actual sampling is carried out: they are used to ensure that no significant contamination occurs during the different steps of the sampling campaign (e. g. , during storage or transport). L. Bonvalot et al. : PM sources in the valley of Chamonix Table  3. Results of analysis of Passy samples. PM 10 is determined by TEOM-FDMS; days with a PM 10 concentration higher than 50 µg m -3 (winter smog) are reported in bold italics (19 February 2014 is sampled with two filters and the sum is greater than 50 µg m -3 ).
These results indicate that the main non-fossil sources differ between seasons. For the winter season, TC NF is directly related to biomass burning, whereas during summer these sources are most probably biogenic emissions. C ratio measurements, with NO x. TC F corresponds to the carbon concentration multiplied by f F. For the winter samples, f F is determined for p bio = 0 and for summer samples p bio = 1. It has to be underlined that a variation in p bio does not affect the significance of the relationship between levoglucosan and TC NF (see Table  5 ). Purple squares denote Chamonix winter samples, red dots denote Passy winter samples, and blue dots designate Passy summer samples. Each dataset exhibits a good correlation between NO x and TC F concentrations. The fit parameters have been calculated by taking into account both error bars on the x and y axes and are given with their 95 % confidence interval.
A higher slope value is obtained for the summer datasets than for the winter ones, which suggests either different fossil carbon sources or NO x degradation rate depending on the season. To attribute the fossil fraction of the carbonaceous particles, the concentrations of fossil carbon (TC F ) are plotted against NO x concentration (Fig. 8 ), which is considered as a vehicle emission proxy. Linear correlations are highly statistically significant for all three different datasets. All origin intercepts are equivalent or close to zero. For winter datasets, the slope obtained for the French datasets are roughly equivalent to those given by  Zotter et al. (2014)  for the EC F vs. NO x correlations in many Swiss sites (no correlations were observed with OC F in this last study). However, in our study, the slopes for Passy and Chamonix are clearly different, with that in Passy being 50 % higher.
Figure8. Comparison of TC F , based on 14 C / 12 C ratio measurements, with NO x. TC F corresponds to the carbon concentration multiplied by f F. For the winter samples, f F is determined for p bio = 0 and for summer samples p bio = 1. It has to be underlined that a variation in p bio does not affect the significance of the relationship between levoglucosan and TC NF (see Table5). Purple squares denote Chamonix winter samples, red dots denote Passy winter samples, and blue dots designate Passy summer samples. Each dataset exhibits a good correlation between NO x and TC F concentrations. The fit parameters have been calculated by taking into account both error bars on the x and y axes and are given with their 95 % confidence interval. A higher slope value is obtained for the summer datasets than for the winter ones, which suggests either different fossil carbon sources or NO x degradation rate depending on the season.