dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and simulation samples The event sample for the mistag rate measurement was collected using a logical OR of inclusive jet triggers, analogous to what was done in the D analysis (see Section 13. 1). The analysis also makes use of simulated inclusive jet samples, similar to those used in the muon-based b-jet tagging efficiency measurements and the D c-jet tagging efficiency measurement, but without any muon or D + filter. About 2. 8 million events have been simulated per p⊥ bin.
Flavour composition The ratio of the fractions of b and c jets in data can be different than that found in the simulated events used to calculate ε HF. The systematic uncertainty associated to the limited knowledge of the b-to-c composition in data is assessed by doubling the fraction of b jets in the simulated QCD jet sample and re-deriving the heavy-flavour tagging efficiency for the IP3D+JetFitter algorithm. This leads to a relative 3-4% difference in the mistag rate which is taken as a systematic uncertainty.
Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region 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; the Royal Society and Leverhulme Trust, United Kingdom.
Acknowledgements We are grateful to  V. Schomerus  for useful discussions. The work of YH was supported in part by  JSPS KAKENHI  Grant Number  24740170. The work of TC is supported by  NSERC  grant number  RES0019997. The work of PBR was partly funded by  AFR  grant  3971664  from  Fonds National de la Recherche, Luxembourg.
Acknowledgements We would like to thank  G. Luisoni  for his assistance with the code GoSam. The research of GZ is supported by the  ERC  consolidator grant  614577   HICCUP -High Impact Cross Section Calculations for Ultimate Precision. PM and ER have also benefited from the  ERC  grant  HICCUP. TM is supported by  U. S. DOE  grant  DE-AC02-05CH11231  and acknowledges computational resources provided through  ERC  grant number  291377  LHCtheory. PM was partly supported by the  Swiss National Science Foundation (SNF)  under grant  PBZHP2-147297. We all gratefully acknowledge the  Mainz Institute for Theoretical Physics (MITP)  for its hospitality and support while part of this work was carried out. Additionally, GZ gratefully acknowledges KITP and PM and ER acknowledge the  CERN's Theory Department  for hospitality while completing this work.
Data The data used for this study is mainly precipitation, streamflow and the climate index NAO. The duration of these series is variable between 30 and 45 years. Both precipitation and streamflow data were obtained from ABHT (Agency of the Hydraulic Basin of Tensift and Haouz). Rainfall measurements are carried out through the rain gauges located on the basin. The monthly time series of runoff are calculated from the values of water high and calibration curves, both are measured at the same gauging stations by the ABHT. To study climate and hydrological variability in the Tensift basin, we analyzed data from 7 stations: (1) Aghbalou in the Ourika sub-basin; (2) Tahanouat in the Rheraya sub-basin; (3) Iguir N'Kouris in the N'Fis sub-basin, the largest sub-basin in the Tensift; (4) Sidi Rehal in the R'dat sub-basin; (5) Chichaoua in the Chichaoua sub-basin; (6) Abadla in the Tensift River; and (7) Adamna in the Ksob basin.
Located at the base of the High Atlas Mountain, except for the N'fis, the measuring station is located away from the downstream of sub-basin and Abadla located in the Tensift, the principal river (Haouz plain). The data must meet two important criteria: the length of the Chronicles (cover the greatest possible duration) and the quality of data (the fewest missing data). We also used rainfall gridded data to compare with the data from the gauging stations of the ABHT. These gridded data come from the SIEREM (System of environmental information for the water resources and their modeling) dataset, developed at the HydroSciences Montpellier laboratory  [50, 51]. These data are available at the monthly time scale, over the duration 1940-1999, and at the half-square degree scale. The interpolation method used to prepare this dataset is the kriging method  [52]  from observed values.
Comparison between Rainfall and Gridded Rainfall Data The rainfall data used in this study are related to gauging stations, as there were no other available rainfall data from authorities. Thus, we might miss some significant variabilities that might occur outside the study area. To overcome this problem, we also used the continuous wavelet analysis on the rainfall data of the SIEREM database. This also allows comparing the signals of the streamflow with rainfall, which could better take into account regional influences of rainfall over the basin. The Tensift basin is covered by 14 grid cells. The wavelet analysis shows fluctuations generally of very low intensity. The annual cycle is identified over the whole time series showing a discontinuity in the North of the basin. The seasonal alternation is moderately expressed, the modes of variability 8-12 years are identified at the end of time series from mid-1980 to 1999 on the grids located in the South of the basin (Figure  8a, b ).
2010)  or in the low density gas deposited outside of galaxies by ram-pressure stripping  (Boissier et al. 2012; Vollmer et al. 2012; Verdugo et al. 2015). At a distance of 366 Mpc (from the NASA Extragalactic Database, based on its recessional velocity of 24750 km/s), Malin 1 is the prototype of giant LSBGs. It was first discussed by  Bothun et al. (1987)  who showed that it has a very low surface brightness disk (µ 0,V = 25. 7 mag arcsec -2 ) and a prominent bulge. Moore & Parker (2006)  obtained a deep R band image, with detection up to 120 kpc from the center. Recent HST observations of the central kpc suggest that the part initially considered as a "bulge" is actually a normal SB0/a galaxy with a small bulge, a bar, and a high surface brightness disk with a spiral structure  (Barth 2007).
New UV and optical images In the NUV and FUV bands of GALEX, we use images from the GALEX Ultraviolet Virgo Cluster Survey, GUViCS  (Boselli et al. 2011). The typical exposure time of one GALEX orbit (1500s) corresponds to a surface brightness limit of about   2008 ) computed UV integrated magnitudes for Malin 1 using these data, in this paper we combine all GALEX tiles in which the galaxy is imaged to obtain an accumulated exposure time 3 times longer than in this previous work. To do so, we used the Montage software  (Jacob et al. 2010 ) and resampled the data on same grid and pixel scale as the MegaCam images (in a similar manner as in  Boissier et al. 2015) , to facilitate the comparison between the UV and optical data. The GALEX PSF of the images is about 5 arcsec. Few foreground stars are present at these wavelengths, and they were manually masked.
Surface photometry (radial profiles and regions) was measured in these images, after correcting for a foreground Galaxy extinction with A V =0. 101 mag (from Schlafly & Finkbeiner 2011, as found on the NASA Extragalactic Database). The sky level and dispersion was measured in all cases in a large number of small regions surrounding the galaxy. A simple examination of the final images already provides us with information concerning Malin1. First, the extended disk presents a clear spiral structure in its bluer bands. It is apparent in the g band (Fig. 2 ), confirming the very recent result of  Galaz et al. (2015). The spiral arms are similarly striking in the u band. As  Galaz et al. (2015) , we also observe several diffuse areas such as the region marked 15 in Fig. 2. Region 1 has a brighter emission than the other inter-arm areas.
This work is supported in part by the Canadian Advanced Network for Astronomical Research (CANFAR) which has been made possible by funding from CANARIE under the Network-Enabled Platforms program. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. JK acknowledges the support from NASA through grant NNX14AF74G. This work is based in part on data products produced by GAZPAR located at the Laboratoire d'Astrophysique de Marseille. We especially thank Denis Burgarella and Olivier Ilbert for their feedback and help in this process. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
This research made use of Montage. It is funded by the National Science Foundation under Grant Number ACI-1440620, and was previously funded by the National Aeronautics and Space Administration's Earth Science Technology Office, Computation Technologies Project, In each panel, the non-reduced χ 2 is given (from top to bottom: Le Phare, CIGALE, single generation). In the first two cases, the fit allows for the presence of dust, and the reddening of the best model is also given.
The basis for much of the earlier research in SS is the WordNet, a large lexical database of the English language, freely available online. However, the last decade has witnessed an explosion in the number of applications of SS to biomedical ontologies, and specifically in the GO  [ 6 ]. The GO structure provides meaningful links between GO terms, based on the various relationships it establishes. This structure allows us to capture the similarity between GO terms. In general, the closer two terms are in the GO graph, the more similar their meaning is. Moreover, we can also determine the similarity between two GO-annotated gene products by expanding on this notion to compare sets of GO terms. This provides a measure of the functional similarity between two proteins, which has numerous applications in biomedical research.
External methods typically make use of information-theoretic principles. This type of approach has been demonstrated to be less sensitive or not at all to the issue of link density variability  [ 14 ] , i. e. , that the ontology graph may be unbalanced and edges linking nodes may not be evenly distributed, so that the same depth or distance indicate a different level of specifi city or similarity. Information content (IC)-based measures are based on the intuition that the similarity between two concepts can be given by the extent to which they share information. The IC of a concept c is a measure of how likely the concept is to occur, which can be quantifi ed as the negative log likelihood, -log p(c) where p(c) is the probability of occurrence of c in a specifi c corpus, usually estimated by the annotation frequency in the Gene Ontology Annotation database.
Tools There are two main kinds of available tools to compute SS measures in GO: webservers, which typically provide easy to use solutions with fewer parametrizations possible; and software packages, which are more customizable, though more complex to use. Many of the recently proposed SS measures provide specifi c webservers, but some online tools provide a wider array of measures, such as ProteInOn  [ 31 ] , FunSimMat  [ 32 ] , or GOssToWeb  [ 33 ]. These tools rely on their own GO and GOA versions, and though they can output similarity scores with an input of just GO terms or Uniprot accession numbers, these scores are based on the tool's ontology and annotation versions. If a user needs more control over the parametrization of the input data, then the best option is to employ a software package.
Options include R packages (e. g. , GoSemSim  [ 34 ] ) or standalone programs (GOssTo  [ 33 ] ), which give the user more freedom in terms of ontology and annotation versions as well as in programmatic access or the computation of SS for larger datasets. A Java library has been recently developed for ontology-based SS calculations  [ 35 ] , which includes over 50 different SS measures and accepts input ontologies in a number of formats, including OWL, OBO, and RDF. This library is well suited for large input datasets, being able to run over 100 million comparisons in under 1 h. In the case of webtools, we advise readers to check their update frequency to ensure that recent versions of GO and the annotations are in use.
The need to provide more semantically sound measures of SS for biomedical ontologies has been argued  [ 36 ] , and though GO is commonly viewed as a DAG for a controlled vocabulary it is actually well axiomatized in OWL  [ 37 ]. The presence of these axioms should be considered by SS measures, and the exploration of disjointness in SS has been recently proposed in ChEBI  [ 38 ]. In general, the computational complexity of SS measures has not been addressed. Current GO-based SS applications happen in an offl ine context where computational speed is not a relevant factor. However, for applications such as similarity-based search, which so far are based on precomputed similarities  [ 32 ] , performance should be taken into consideration. In addition, the growth in size of biomedical datasets spurred by genomic scale studies in the last few years, also places further computational constraints on SS measures.
The challenge of handling very large datasets is increasingly recognized, and recent implementations of SS measures allow for parallel computation  [ 35 ] , but the development of SS measures is not taking this issue into consideration a priori. The next generation of SS measures should take into account these two aspects, on one hand, the possibility for increased complexity in SS measures to provide more accurate similarity scores, and on the other the need for effi cient SS computation, and strive to achieve a balance between increased accuracy and effi ciency.
Measurements of hadron production in p+C interactions at 31 GeV/c are performed using the NA61/SHINE spectrometer at the CERN SPS. The analysis is based on the full set of data collected in 2009 using a graphite target with a thickness of 4% of a nuclear interaction length. Inelastic and production cross sections as well as spectra of π ± , K ± , p, K 0 S and Λ are measured with high precision. These measurements are essential for improved calculations of the initial neutrino fluxes in the T2K long-baseline neutrino oscillation experiment in Japan. A comparison of the NA61/SHINE measurements with predictions of several hadroproduction models is presented.
Yields of K 0 S and Λ were also published  [14]. All of those measurements were performed using the data collected during the NA61/SHINE pilot run in 2007. A detailed description of the experimental apparatus and analysis techniques can be found in Refs. [5, 25]. This article presents new NA61/SHINE measurements of charged pion, kaon and proton as well as of K 0 S and Λ spectra in p+C interactions at  31  GeV/c, based on an eight times larger dataset collected in 2009, after the detector and readout upgrades. These results are important for achieving the future T2K physics goals  [26]. T2K -a long-baseline neutrino oscillation experiment from J-PARC in Tokai to Kamioka (Japan)  [7]  -aims to precisely measure ν µ → ν e appearance  [15, 18, 20]  and ν µ disappearance  [16, 19, 21]. The neutrino beam is generated by the J-PARC high intensity 30 GeV (kinetic energy) proton beam interacting in a 90 cm long graphite target to produce π and K mesons, which decay emitting neutrinos.
Some of the forward-going hadrons, mostly protons, reinteract in the target and surrounding material. To study and constrain the reinteractions in the long target, a special set of data was taken by NA61/SHINE with a replica of the T2K target: the first study based on pilot data is presented in Ref. [27] , the analysis of the high-statistics 2009 dataset is finalized  [28] , while the analysis of the last 2010 dataset is still on-going. The T2K neutrino beam  [17]  is aimed towards a near detector complex, 280 m from the target, and towards the Super-Kamiokande (SK) far detector located 295 km away at 2. 5 • offaxis from the hadron beam. Neutrino oscillations are probed by comparing the neutrino event rates and spectra measured in SK to predictions of a Monte-Carlo (MC) simulation based on flux calculations and near detector measurements.
Until the NA61/ SHINE data were available, these flux calculations were based on hadron production models tuned to sparse available data, resulting in systematic uncertainties which are large and difficult to evaluate. Direct measurements of particle production rates in p+C interactions allow for more precise and reliable estimates  [17]. Precise predictions of neutrino fluxes are also crucial for neutrino cross section measurements with the T2K near detector, see e. g. Refs. [22] [23] [24]. For the first stage of the experiment, the T2K neutrino beamline was set up to focus positively charged hadrons (the so-called "positive" focusing), to produce a ν µ enhanced beam. While charged pions generate most of the low energy neutrinos, charged kaons generate the high energy tail of the T2K beam, and contribute substantially to the intrinsic ν e component in the T2K beam.
3. ticles in the laboratory frame for "positive" and "negative" focusing, respectively. See Refs. [5, 6, 17]  for additional information. The much higher statistics available in the 2009 data makes it possible to use finer {p, θ } binning (especially for charged kaons, K 0 S and Λ ) compared to previously published results  [5, 6, 14]  from the 2007 data. The improved statistics of the 2009 data also allows for the first measurements of negatively charged kaons within NA61/SHINE. The NA61/SHINE results on hadron production are also extremely important for testing and improving existing hadron production models in an energy region which is not well constrained by measurements at present. The paper is organized as follows: A brief description of the experimental setup, the collected data and their processing is presented in Section 2.
The experimental setup, collected data and their processing The NA61/SHINE apparatus is a wide-acceptance hadron spectrometer at the CERN SPS. A detailed description of the NA61/SHINE setup is presented in Ref. [25]. Only parts relevant for the 2009 running period are briefly described here. The NA61/SHINE experiment has greatly profited from the long development of the CERN proton and ion sources, the accelerator chain, as well as the H2 beamline of the CERN North Area. Numerous components of the NA61/SHINE setup were inherited from its predecessors, in particular, the last one -the NA49 experiment  [29]. The detector is built arround five Time Projection Chambers (TPCs), as shown in Fig. 3. Two Vertex TPCs (VTPC-1 and VTPC-2) are placed in the magnetic field produced by two super-  S and protons contributing to the predicted neutrino flux at SK in the "negative" focusing configuration, and the regions covered by the previously published NA61/SHINE measurements  [5, 6]  and by the new results presented in this article.
[25]  for more details). The same target was used in 2007 and 2009 -an isotropic graphite sample with a thickness along the beam axis of 2 cm, equivalent to about 4% of a nuclear interaction length, λ I. During the data taking the target was placed 80 cm upstream of the VTPC-1. TPC readout and data acquisition (DAQ) system upgrades were performed before the 2009 run. To utilize the new DAQ ca-pability, a much higher intensity beam was used during the 2009 data taking compared to the 2007 running period. To cope with the high beam intensity in 2009 the passing times of individual beam particles before and after the event were recorded. These are later used in the analysis to study and remove possible pileup effects. Reconstruction and calibration algorithms applied to the 2007 data are summarized in Ref.
[5]. Similar calibration procedures were applied to the 2009 data resulting in good data quality suitable for the analysis (see e. g. Ref. [11] ). Measurements of the specific energy loss dE/dx of charged particles by ionisation in the TPC gas are used for their identification. The dE/dx of a particle is calculated as the 50% truncated mean of the charges of the clusters (points) on the track traversing the TPCs. The calibrated dE/dx distributions as a function of particle momentum for positively and negatively charged particles are presented in Fig. 5. The Bethe-Bloch (BB) parametrization of the mean energy loss, scaled to the experimental data (see Section 4. 5), is shown by the curves for positrons (electrons), pions, kaons, (anti)protons, and deuterons. The typical achieved dE/dx resolution is about 4%.
Thus the same selection cuts (see Section 3. 2) could be applied to all triggers. In contrast, during the 2007 run the beam information was recorded by scalers which were not read out by the main DAQ. As a consequence, event-by-event quality selection was not possible  [34]. Instead, special runs were taken with the beam trigger to estimate biases. It had to be assumed that the effect of the selection was stable over the whole data taking period. On the other hand, the statistical uncertainty on σ trig from the 2009 data (see Eq. (  10 )) is slightly larger because of the smaller number of recorded T beam triggers. The fraction of out-of-target interaction background in the sample of the target inserted events is ε = P R Tint P I Tint = (12. 3 ± 0. 4)%. ( 11 ) The uncertainty of ε is larger for the 2009 than the 2007 data because of a larger statistical uncertainty of P R Tint.
Spectra analysis techniques and uncertainties This section presents analysis techniques developed for the measurements of the differential inclusive spectra of hadrons. Details are shown on data selection and binning, on particle identification (PID) methods as well as on the calculation of correction factors and the estimation of systematic uncertainties. The data analysis procedure consists of the following steps: (i) application of event and track selection criteria, (ii) determination of spectra of hadrons using the selected events and tracks, (iii) evaluation of corrections to the spectra based on experimental data and simulations, (iv) calculation of the corrected spectra. Corrections for the following biases were evaluated and applied: (i) geometrical acceptance, (ii) reconstruction efficiency, (iii) contribution of off-target interactions, (iv) contribution of other (misidentified) particles, (v) feed-down from decays of neutral strange particles, (vi) analysis-specific effects (e.
The contribution to the systematic uncertainty associated with this source is up to 12% (7%) for K 0 S (Λ ). (iii) Reconstruction algorithm: Two different primary interaction vertex reconstruction algorithms were used, either fitting all three coordinates or fixing the z-coordinate to the survey position. The uncertainty connected to the algorithm is from 5% to 8% for both analyses. (iv) Quality cuts: All quality cuts were varied independently within a range given in Ref. [42]  and the uncertainty connected to this source was found to reach up to 10% (5%) for K 0 S (Λ ). (v) Kinematic cuts: As for the previous source of uncertainty, all cuts were varied and the resulting uncertainty is up to 10% (7%) for K 0 S (Λ ). The uncertainty estimates of items (iv) and (v) are strongly correlated since the same datasets and analysis techniques were used.