dataesr/scientific-paragraphs-categorization huggingface dataset:
Conflict of interest A. G. is a consultant for Ipsen, Novartis and Pfizer. J. S. B is a consultant and study investigator for Ipsen, and an advisory board member for Novartis and ViroPharma. M. D. B. has served on steering committees for Chiasma, Ipsen and Novartis, has been a clinical trials principal investigator for Ipsen and Novartis and has received speaker fees from Ipsen, Novartis and Pfizer. F. F. C. has received research grants, advisory board or speakers fees from, Pfizer, Novartis and Ipsen. P. C. has received unrestricted research and educational grants from Ipsen, Novartis, Novo-Nordisk, and Pfizer at the Department of Endocrinology and Reproductive Diseases, Hopitaux Universitaires Paris-Sud; he has served as investigator (principal or coordinator) for clinical trials funded by Novartis, Pfizer, Ipsen, Italopharmaco, Antisense and Prolor Biotech, is an advisory board member for Ipsen, Novartis, and Viropharma and has given lectures for Ipsen, Novartis, and Pfizer, all fees and honoraria were paid to his Institution.
Consequently, a marked difference is expected in the lepton charge asymmetry between Pb+Pb and pp collisions. Prior to this analysis, the only published charge asymmetry measurement in heavy-ion collisions was reported by the CMS collaboration  [25]  with an integrated luminosity of 7. 3 µb -1 using the W → µν µ channel in Pb+Pb collisions at √ s NN = 2. 76 TeV. The measurement presented here uses a dataset from 2011, which corresponds to an integrated luminosity of 0. 14 and 0. 15 nb -1 for the muon and electron channels, respectively. In addition, the W → eν e decay mode is employed for the first time in a heavy-ion environment. The paper is organised as follows: a brief overview of the ATLAS detector and trigger is given in Sect. 2. A description of the simulated event samples used in the analysis is provided in Sect.
2 The TRT provides tracking information up to |η| < 2. The ATLAS detector also includes a three-level trigger system  [37]  : level one (L1) and the software-based High Level Trigger (HLT), which is subdivided into the Level 2 (L2) trigger and Event Filter (EF). Muon and electron triggers are used to acquire the data analysed in this paper. The trigger selection for muons is performed in three steps. Information is provided to the L1 trigger system by the fast-response resistive plate chambers (RPCs) in the barrel (|η| < 1. 05) and thin gap chambers (TGCs) in the endcaps (1. 05 < |η| < 2. 4). Both the RPCs and TGCs are part of the MS. Information from L1 is then passed to the HLT, which reconstructs muon tracks in the vicinity of the detector region reported by the L1 trigger. The L2 trigger performs a fast reconstruction of muons using a simple algorithm, which is then further refined at the EF by utilising the full detector information as in the offline muon reconstruction software.
Monte Carlo samples Simulated event samples are produced using the Monte Carlo (MC) method and are used to estimate both the signal and background components. The response of the ATLAS detector is simulated using Geant4  [38, 39]. The samples used throughout this paper are summarised in Table  1. Each signal process and most of the background processes are embedded into minimum-bias (MB) heavy-ion events from data recorded in the same run periods as the data used to analyse W boson production. Events from the Z → µ + µ - channel are embedded into Hijing  [40]  -a widely used heavy-ion simulation that reproduces many features of the underlying event  [17]. The production of W bosons and its decay products are modelled with the Powheg  [41]  event generator, which is interfaced to Pythia8  [42]  in order to model parton showering and fragmentation processes.
Centrality definition Pb+Pb collision events are selected by imposing basic requirements on the beam conditions and the performance of each sub-detector. In order to select MB hadronic Pb+Pb collisions, a hit on each side of the MBTS system with a time coincidence within 3 ns is required for each collision. In addition, each event is required to have a reconstructed vertex with at least three associated high-quality tracks  [47]  compatible with the beam-spot position. These requirements select MB hadronic Pb+Pb collisions in the data with an efficiency of (98 ± 2)% with respect to the total non-Coulombic inelastic cross-section  [5]. After accounting for the selection efficiency and prescale factors imposed by the trigger system during data taking  [48] , approximately 1. 03 × 10 9 Pb+Pb events are sampled (denoted by N events hereafter).
Muon reconstruction Muon reconstruction in ATLAS consists of separate tracking in the ID and MS. In this analysis, tracks reconstructed in each sub-system are combined using the χ 2 -minimisation procedure described in Ref. [52]. These combined muons are required to satisfy selection criteria that closely follow those used in the Z boson analysis in Pb+Pb data  [22]. To summarise, these criteria include a set of ID hit requirements in the pixel and SCT layers of the ID, a selection on the transverse and longitudinal impact parameters (|d 0 | and |z 0 |), and a minimum requirement on the quality of the muon track fit. Additional selection criteria specific to W bosons are discussed below. Decays-in-flight from pions and kaons contribute a small background fraction in this analysis. They are reduced by requiring the difference between the ID and MS muon p T measurements (corrected for the mean energy loss due to interactions with the material between the ID and MS) to be less than 50% of the p T measured in the ID.
The background from electroweak processes with electrons in the final state is estimated from the MC samples listed in Table  1. The nominal selection criteria of this analysis are imposed on each MC sample. The absolute normali-sation is derived from the W and Z Powheg cross-sections in pp collisions. These cross-sections are scaled by N coll in each centrality bin and normalised to the integrated luminosity of the Pb+Pb data sample. This method gives a valid estimate of the electroweak background in this analysis since ATLAS has recently demonstrated that the Z → e + e -yields in Pb+Pb collisions at √ s NN = 2. 76 TeV are consistent with the pp expectation scaled by T AA to within 3%  [22]. The Z → e + e -background is the dominant electroweak background in this analysis and amounts to 6.
Thus, when produced in association with jets, W boson production introduces an additional avenue for exploring in-medium modificationsenergy loss due to multiple scattering and gluon radiationto energetic partons traversing the heavy-ion medium. Nuclear modifications to the PDF are explored in Figs. 10  and 11 , which present the differential W → ν production yields per binary nucleon-nucleon collision and the lepton charge asymmetry, respectively, as a function of |η |. Each figure includes NLO predictions with the CT10 PDF set, both with and without EPS09 nuclear corrections. The EPS09 corrections incorporate modifications to the PDF that account for contributions from shadowing, anti-shadowing, the EMC-effect, and Fermi-motion  [34]. Both the CT10 and CT10+EPS09 predictions in Figs. 10 and 11 describe the data well.
With improved statistical and systematic precision, along with additional data from different colliding systems such as p+Pb, it will be possible to decisively evaluate the extent of nuclear effects on PDFs and to further test theoretical predictions. MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COL-CIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF ,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania;.
Cluster identification procedures are based on those developed for the 400 deg 2  X-ray galaxy cluster survey (400d,  Burenin et al. 2007 ), and for an earlier 160 deg 2 survey  (Vikhlinin et al. 1998; Mullis et al. 2003). As was shown with the last two surveys and will be demonstrated below, with 1. 5-m class telescopes clusters can be identified at redshifts up to z ≈ 1, and redshifts can be measured spectroscopically for clusters at approximately z < 0. 4. Therefore, data taken with such telescopes are sufficient to provide optical identifications and redshift measurements for a large fraction of galaxy clusters detected with Planck. A large fraction of the cluster identifications and redshift measurements presented in this paper were included in the recently published Planck SZ source catalogue, (Planck Collaboration XXIX 2013).
Observations Deep multi-colour observations were obtained for all cluster candidates except those unambiguously detected in SDSS. Images were obtained with the RTT150 telescope and the TFOSC instrument through Sloan g ′ r ′ i ′ filters, typically with 1800 s exposures per filter. Longer exposures were used for cluster candidates with brightest galaxies fainter than m r ′ ≈ 21, i. e. , which may be located at redshifts z > 0. 7 (e. g. , Vikhlinin et al. 1998). Images were obtained in a series of 300 or 600 s exposures with ≈ 10 ′′ -30 ′′ pointing offsets between exposures. Standard CCD calibrations were applied using Iraf 4  software. Individual images in each filter were then aligned and combined. With these data, galaxy clusters can be efficiently identified at redshifts up to z ≈ 1. We identified cluster members from a red sequence.
Complicated cases In a few cases, optical data are not sufficient to reliably identify observed SZ sources. In order to determine the nature of these objects, additional SZ or X-ray data are needed. PSZ1 G103. 50+31. 36: This SZ source may be identified with an irregular group of galaxies (Fig. 16 ). Its redshift can be estimated photometrically as z ≈ 0. 238; however, the very bright star HR 6606 (m V = 5. 8) is located at the edge of the field, about 5. ′ 6 from the Planck position. HR 6606 is detected by Planck with flux density about 1 Jy at 857 GHz (Planck Collaboration XXVIII 2013), and probably affects the photometry of the detected SZ source, artificially increasing its significance. PSZ1 G115. 70+17. 51: There is a group of galaxies with red sequence colour corresponding to a redshift of z ≈ 0.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). The authors thank  TUBITAK ,  IKI ,  KFU , and  AST  for support in using the RTT150 (Russian-Turkish 1. 5-m telescope,  Bakyrlytepe, Turkey ), and in particular thank  KFU  and  IKI  for providing a significant amount of their observing time. We also thank the BTA 6-m telescope  Time Allocation Committee (TAC)  for support of the optical follow-up project. We are grateful to  S.
N. Dodonov ,  A. Galeev ,  E. Irtuganov ,  S. Melnikov ,  A. V. Mescheryakov ,  A. Moiseev ,  A. Yu. Tkachenko ,  R. Uklein ,  R. Zhuchkov  and to other observers at the RTT150 and BTA 6-m telescopes for their help with the observations. This work was supported by  Russian Foundation for Basic Research , grants  13-02-12250 -ofi-m,  13-02-01464 ,  14-22-03111 -ofim, by  Programs of the Russian Academy of Sciences  P-21 and OPhN-17 and by the  subsidy of the Russian Government to Kazan Federal University  (agreement No. 02. A03. 21. 0002 ). This research has made use of the following databases: the NED database, operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with NASA; SIMBAD, operated at  CDS, Strasbourg, France ; and the SZ database operated by Integrated Data and Operation Center (IDOC) operated by  IAS  under contract with  CNES  and  CNRS.
INTRODUCTION In usual supervised learning methods, a classification model is built by performing several passes over a static dataset with sufficiently many labelled data. Firstly, this is not possible in the case of data streams where data is massively and continuously arriving from an infinite-length stream. Secondly, manual labelling is costly and time consuming. Active learning reduces the manual labelling cost, by querying from a human labeller only the class labels of data which are informative (usually uncertain instances). Active learning methods  (Settles, 2012)  are convenient for data stream classification. Several active learning methods  (Settles, 2012; Kremer et al. , 2014; Huang et al. , 2014; Kushnir, 2014)  and stream-based active learning methods  (Zliobaite et al. , 2014; Bouguelia et al.
This represents an additional difficulty for active learning to reduce the label complexity  (Dasgupta, 2005). If the potential labelling errors are not detected and mitigated, the active learner can easily fail to converge to a good model. Therefore, label noise is harmful for active learning and dealing with it is an important issue. Detecting label noise is not trivial in stream-based active learning, mainly for two reasons. Firstly, in a data stream setting, the decision to filter or not a potentially mislabelled instance should be taken immediately. Secondly, because the learning is active, the mislabelled instances are necessarily among those that the classifier is uncertain about their class. Usual methods to deal with label noise like those surveyed in  (Frénay and Verleysen, 2013) , assume that a static dataset is available beforehand and try to clean it before training occurs by repeatedly removing the most likely mislabelled instances among all instances of the dataset.
A method proposed in  (Zhu et al. , 2008)  is designed for cleansing noisy data streams by removing the potentially mislabelled instances from a stream of labelled data. However, the method does not consider an active learning setting where the mislabelling errors concern uncertain instances. Moreover, they divide the data stream into large chunks and try to detect mislabelled instances in each chunk, which makes the method partially online and reduces the importance of its streaming nature. The method in  (Rebbapragada et al. , 2012 ) considers an active label correction, but the learning itself is not active. Rather, the method iteratively selects the top k likely mislabelled instances from a labelled dataset and presents them to an expert for correction rather than discarding them. Some other methods like  (Fang and Zhu, 2013; Tuia and Munoz-Mari, 2013 ) are designed for active learning with label noise and are intended only for label noise whose source is the uncertain labelling knowledge of the labeller.
MITIGATING LABEL NOISE When an instance x is detected as potentially mislabelled, the next step is to decide what to do with this instance. In usual label noise cleansing methods, the dataset is available beforehand and the methods just discard or remove the instances that are likely to be mislabelled or predict their labels. In order to mitigate the impact of label noise on the stream-based active learning, we study three strategies including discarding, weighting and relabelling by an expert labeller, and we show a hybrid approach.
EXPERIMENTS We use for our experiments different public datasets obtained from the UCI machine learning repository 1. We also consider a real administrative documents dataset provided by the ITESOFT 2  company. Each document was processed by an OCR and represented as a bag-of-words which is a sparse feature-vector containing the occurrence counts of words in the document. Without loss of generality, the label noise intensity is set to σ = 0. 3. An SVM is used as a base classifier (we use the python implementation available on scikit-learn  (Pedregosa and et al. , 2011) ). Threshold t D is fixed to 0. 6 for all datasets to allow reproducibility of the experiments, although a better threshold can be found for each dataset.
Mislabelling Likelihood This experiment figures-out the ability of the proposed mislabelling likelihood to correctly characterize mislabelled instances as proposed in Section 3. 1, without considering a stream-based active learning. We corrupt the labels of each dataset such that instances with a low prediction probability are more likely to be mislabelled. Then, instances of each dataset are ranked according to degrees of disagreement defined in terms of D 1 and D 2. Results are compared with an entropy measure E which is commonly used in active learning. Instances with a low entropy implies a confident classification, thus, instances for which the classifier disagrees with their queried label are more likely to be mislabelled when they have a low entropy E = - ∑ y∈Y P h (y|x) × log P h (y|x) We select the top n ranked instances of each dataset and we compute 2 types of errors: e 1 represents the correctly labelled instances that are erroneously selected, and e 2 the mislabelled instances that are not selected: e 1 = correct selected correct e 2 = mislabelled unselected mislabelled where "correct selected" is the number of correctly labelled instances that are selected as being potentially mislabelled.
"mislabelled unselected" is the number of mislabelled instances that are not selected. "correct" and "mislabelled" are the total number of correctly labelled and mislabelled instances respectively. We also compute the percentage of selected instances that are actually mislabelled prec, which represents the noise detection precision: prec = mislabelled selected selected where "mislabelled selected" is the number of mislabelled instances that are selected, and "selected" is the number of selected instances. Table  1  shows the obtained results. We can see that for documents and pendigits datasets, D 2 achieves better results than D 1 and inversely for optdigits and letter-recognition datasets. However combining D 1 and D 2 using D = max(D 1 , D 2 ) may yield better results than both D 1 and D 2 , and always achieves better results than the entropy measure E.
Let c 1 and c 2 respectively be the cost required by the first labeller and the expert labeller to label a single instance. It is assumed that c 2 > c 1 since the expert labeller is supposed to be reliable (or much more reliable than the first labeller). Then, the labelling cost is c 1 × N 1 , the relabelling cost is c 2 × N 2 , and the overall cost is C = c 1 × N 1 + c 2 × N 2. Firstly, the results on Fig. 5  confirm that the "full relabelling" is obviously the most effective strategy in terms of classification accuracy, since all instances that are identified as being mislabelled are relabelled by the expert. Secondly, the results obtained on all datasets show that discarding the possibly mislabelled instances is not better than the weighting strategy. Actually, it has been observed in many works on label noise cleansing  (Frénay and Verleysen, 2013; Gamberger et al.
We can see on Table  2  that the "hybrid weighting and relabelling" strategy achieves a final classification accuracy which is pretty close to that of the "full relabelling" strategy. For example, the final accuracy achieved by the "hybrid weighting and relabelling" strategy for the optdigits dataset is 97. 21%, whereas the one achieved by the "full relabelling" strategy is 97. 49%. As explained in Section 2. 2, mislabelling errors causes the active learner to ask for more labelled instances. This explains why N 1 in Table  2  is smaller in the "full rela-  belling" strategy. However, by taking into consideration the cost induced by relabelling the mislabelled instances, the "full relabelling" strategy will have a higher overall cost than the other strategies, since all the instances that are identified as being mislabelled are relabelled.
CONCLUSION AND FUTURE WORK In this paper we addressed the label noise detection and mitigation problem in stream-based active learning for classification. In order to identify the potentially mislabelled instances, we proposed a mislabelling likelihood based on the disagreement among the probabilities and the quantity of information that the instance carries for the predicted and the queried class labels. Then, we derived an informativeness measure that reflects how much a queried label would be useful if it is corrected. Our experiments on real datasets show that the proposed mislabelling likelihood is more efficient in characterizing label noise compared to the commonly used entropy measure. The experimental evaluation also shows that the potentially mislabelled instances with high conflicting information are worth relabelling.
Introduction We present a new technique for structural analysis in single crystals, extending the capabilities of existing multiplescattering methods. Lines similar to Rutherford, Kikuchi and Kossel lines arising from incoherent X-ray multiple-scattering processes have been observed for numerous materials. We will present data from Cu and Au samples. In multiple-wave diffraction, the interaction between diffracted waves can lead to an increase or decrease in the measured intensity. When the intensity is decreased due to the energy being diverted to other reflections, the intensity reduction is called aufhellung and was first reported by  Wagner (1920)  and then by  Berg (1926). On the other hand, when a secondary reflection acts as the source for a tertiary reflection emerging in the direction of the primary reflection, the intensity can be modified by constructive or destructive interference.
Acknowledgments We thank  Tom Appelquist ,  Aoife Bharucha ,  Cedric Delaunay ,  Shelly Glashow ,  David London ,  Marco Nardecchia ,  Maurizio Piai ,  David Straub  and  Edwige Tournefier  for stimulating and helpful conversations. KL gratefully acknowledges support of this project by a  CERN Scientific Associateship  and by the  Labex ENIGMASS. He thanks the  CERN Theory Group and Laboratoire d'Annecy-le-Vieux de Physique Théorique (LAPTh)  for their gracious hospitality in 2014-15. KL's research is also supported by the  U. S. Department of Energy  under Grant No. DE-SC0010106.