dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, if either of the photons converts into an e + e -pair and the tracks from the conversion are reconstructed and identified, the direction of the converted photon, determined by combining the conversion vertex position and the position of the ECAL supercluster, can be extrapolated to identify the diphoton interaction vertex. For each reconstructed interaction vertex the following set of variables are calculated: the sum of the squared transverse momenta of all tracks associated with the vertex and JHEP06(2013)081 Comparison of the vertex-identification efficiency between data (circles) and MC simulated Z → µµ events (squares), as a function of the Z boson p T. two variables that quantify the p T balance with respect to the diphoton system. In the case of a reconstructed photon conversion, an additional "pull" variable is used, defined as the distance between the vertex z position and the beam-line extrapolated z position coming from the conversion reconstruction, divided by the uncertainty in this extrapolated z position.
This procedure results in four event classes for both the 7 and 8 TeV data sets. The systematic uncertainties in the diphoton identification classifier and photon energy resolution discussed above can cause events to migrate between classes. In the 8 TeV analysis, these class migrations are up to 4. 3% and 8. 1%, respectively. They are defined as the relative change of expected signal yield in each category under the variation of the photon ID BDT classifier and the per-photon energy resolution estimate, within their uncertainties as explained above. The sensitivity of the analysis is enhanced by using the special kinematics of Higgs bosons produced by the VBF process  [110]. Dedicated classes of events are selected using dijet-tagging criteria. The 7 TeV data set has one class of dijet-tagged events, while the 8 TeV data set has two.
They range from several percent for associated production with W/Z to almost 20% for the gluon-gluon fusion process. The detailed values for the 8 TeV analysis, together with all the other systematic uncertainties discussed above, are summarized in table  4. The corresponding uncertainties in the 7 TeV analysis are very similar, with the exception of the already mentioned uncertainty on the photon ID classifier, which was significantly larger in the 7 TeV analysis. The reason for this is a worse agreement between data and MC simulation.
25% between electron and photon is assigned, amounting to half of the absolute energy scale difference between electrons and photons for non-showering electrons/photons in the barrel. Adding these two numbers in quadrature results in the additional energy scale uncertainty of 0. 47%, that is treated as fully correlated among all event classes. The modelling of the background relies entirely on the data. The observed diphoton invariant-mass distributions for the eleven event classes (five in the 7 and eight in the   4. Largest sources of systematic uncertainty in the analysis of the 8 TeV data set. Eight photon categories are defined, depending on their η and R9, where R9 is the ratio of the energy of the most energetic 3 × 3 crystal cluster and the total cluster energy. The four pseudorapidity regions are: |η| < 1 (low η), 1 ≤ |η| < 1.
JHEP06(2013)081 The result is extracted by counting events in the signal region, in classes that are defined by the output distribution of a BDT. This mass-window BDT takes two dimensionless inputs: the diphoton BDT output (as described in section 5. 4), and the mass, in the form ∆m/m H , where ∆m = m γγ -m H and m H is the Higgs boson mass hypothesis. The output of the BDT is binned to define the event classes. The bin boundaries are optimized to give the maximum expected significance in the presence of a Standard Model Higgs boson signal, and the number of bins is chosen such that any additional increase in the number of bins results in an improvement in the expected significance of less than 0. 1%. The same bin boundaries are used for the signal region and for the six sidebands. The dijet-tagged events constitute an additional bin (two bins for the 8 TeV data set) appended to the bins of the mass-window BDT output value.
Examples of tag-and-probe results for the lepton identification efficiencies obtained with data and MC simulation are shown for electrons (top) and muons (bottom) in figure  16. The efficiencies measured with data are in agreement with those obtained using MC simulation. The mean differences (at the percent level) are used to correct the MC simulation predictions, and the uncertainty in the difference is propagated as a systematic uncertainty per lepton. The overall lepton selection efficiencies are obtained as the product of the reconstruction, identification, and isolation efficiencies. The overall efficiency for selecting electrons in the ECAL barrel (endcaps) varies from about 71% (65%) for 7 < p The photon selection criteria are optimized as a function of the angular distance between the photon and the closest lepton in (η, φ) space.
The reducible Z + bb, tt, Z + jets, Z + γ + jets, and WZ + jets backgrounds contain at least one nonprompt lepton in the four-lepton final state. The main sources of nonprompt leptons are electrons and muons coming from decays of heavy-flavour quark, misidentified jets (usually originating from light-flavour quarks), and electrons from photon conversions. The lepton misidentification probabilities are measured in data samples of Z + jet events with one additional reconstructed lepton, which are dominated by final states that include a Z boson and a fake lepton. The contamination from WZ production in these events is suppressed by requiring E miss T < 25 GeV. The lepton misidentification probabilities measured from these events are consistent with those derived from MC simulation. These misidentification probabilities are applied to dedicated Z 1 + X control samples, where X contains two reconstructed leptons with relaxed isolation and identification criteria.
Here, the dominant source of E miss T is the mismeasurement of the hadronic jet energy, and the optimal performance is obtained by requiring E miss T > 45 GeV. Finally, the momenta of the dilepton system and the most energetic jet must have an angle in the transverse plane smaller than 165 •. These selections reduce the Drell-Yan background by three orders of magnitude, while rejecting less than 50% of the signal, as determined from simulation. After applying the full set of selection criteria, referred to as the WW selection, the observed yields in the combined 7 and 8 TeV data set are 1594, 1186, and 1295 events in the 0-jet, 1-jet, and 2-jet categories, respectively. This sample is dominated by nonresonant WW events in the 0-jet category and by a similar fraction of WW and top events in the other two categories.
The systematic uncertainty in the determination of loose dominates the overall uncertainty of this method, which is estimated to be about 36%. The uncertainty is obtained by varying the requirements to obtain loose , and from a closure test, where the tight-loose rate derived from QCD simulated events is applied to a W + jets simulated sample to predict the rate of events with one real and one misidentified lepton. The normalization of the top-quark background is estimated from data by counting the number (N tagged ) of top-quark-tagged events and applying a corresponding top-quarktagging efficiency top. The top-quark-tagging efficiency ( top ) is measured with a control sample dominated by tt and Wt events, which is selected by requiring a b-tagged jet in the event. The number of top-quark background events in the signal region is then given by: N tagged × (1top )/ top.
The τ -isolation discriminator R τ Iso defined in section 3 is used to select loosely isolated τ h so that the overall τ h identification efficiency is 60-65%, for a jet misidentification probability of 2-3%. Finally, electrons and muons misidentified as τ h are suppressed using dedicated criteria based on the consistency between the tracker, calorimeter, and muon-chamber measurements. In the eτ h and µτ h channels, we select events containing either an electron with p T > 20 GeV or a muon with p T > 17 GeV, and |η| < 2. 1, accompanied by an oppositely charged τ h with p T > 20 GeV and |η| < 2. 3. In the 8 TeV data set analysis, the electron and muon p T thresholds are increased to 24 and 20 GeV, respectively, to account for the higher trigger thresholds. In these channels, events with more than one loosely identified electron JHEP06(2013)081 or muon with p T > 15 GeV are rejected to reduce the Drell-Yan background.
Combination methodology Combining the Higgs boson search results requires a simultaneous analysis of the data selected by the individual decay modes, accounting for their correlations and for all the statistical and systematic uncertainties. The statistical methodology used in this combination was developed by the ATLAS and CMS Collaborations in the context of the LHC Higgs Combination Group. A description of the general methodology can be found in refs. [20, 107]. Results presented in this paper are obtained using asymptotic formulae from ref. [154]  and recent updates available in the RooStats package  [155]. The Higgs boson mass is tested in steps accordant with the expected Higgs boson width and the experimental mass resolution  [107].
JHEP06(2013)081 The current data set is insufficient to measure all eight independent parameters. Therefore, we measure different subsets, with the remaining unmeasured parameters either constrained to equal the SM Higgs boson expectations or included in the likelihood fit as unconstrained nuisance parameters. A. Test of custodial symmetry. In the SM, the Higgs boson sector possesses a global SU(2) L × SU(2) R symmetry, which is broken by the Higgs boson vacuum expectation value down to the diagonal subgroup SU(2) L+R. As a result, the tree-level relations between the ratios of the W and Z boson masses, m W /m Z , and their couplings to the Higgs boson, g W /g Z , are protected against large radiative corrections, a phenomenon known as "custodial symmetry"  [161, 162]. However, large violations of custodial symmetry are possible in BSM theories.
Liquid Chromatography Electrospray Ionization Mass Spectrometry (LC/ESI-MS) LC/ESI-MS was performed on a 6210 LC-TOF spectrometer coupled to a HPLC system (Agilent Technologies). Samples were desalted on a protein macroTrap (reverse phase-C4, Michrom Bioresources) for 3 min at 100 µl/min with 100% solvent A (0. 03% trifluoroacetic acid in H 2 0). The reverse phase-C4 column (Jupiter, 5 µm, 300 Å, 1 mm ID×50 mm, Phenomenex) was eluted at 50 µl/min with a linear gradient from 5% to 95% of solvent B (95% acetonitrile, 5% H 2 0, 0. 03% trifluoroacetic acid) in 15 min. MS acquisition was carried out in the positive ion mode in the 300-3000 m/z range and the data processed with MassHunter software (v. B. 02. 00, Agilent Technologies).
The labeled proteins were visualized after SDS-PAGE with a Geldoc XR fluorescent imager (Biorad) using a 530 nm excitation light. The fluorescence intensities were quantified with Image Lab Software (Biorad). Standard curve was established with soluble-PBP2x (PBP2x*), purified as previously described  [23]. The activity of PBP2x was assayed by measuring its ability to hydrolyze the S2d thioester analog of cell wall stem peptides as previously described  [44, 45]. The assay was performed in a 96-well plate format at 37°C in the presence of 50 mM potassium phosphate (pH 7. 0), 2. 0 mM of S2d, 3. 2 mM dithiodipyridine, and 4 to 7 10 -12 mole of PBP2x (0. 7 to 1. 2 10 -7 M). The increase of absorbance at 330 nm was monitored with a FLUOstar Optima microplate reader (BMG labtech).
Acknowledgements We thank  L. Bayle ,  F. Gerin  and  B. Duchene  for the purification of PBP2x*, DivIB* and  H-PatAB ,  C. Durmort  for the pETDuet-HisPatAB plasmid,  J-P. Andrieu  for the N-terminal sequencing and  B. Joris  for the supply of S2d. We are grateful to  A-M. Di Guilmi ,  A. Dessen  and  A. Royant  for useful comments and discussion.
This work was partly funded by the " Coopol innovation France-Chine" program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study. This work used the platforms of the Grenoble Instruct centre (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
One major challenge in the emerging field of movement ecology is the inference of behavioural modes from movement patterns. This has been mainly addressed through Hidden Markov models (HMMs). We propose here to evaluate two sets of alternative and state-of-the-art modelling approaches. First, we consider hidden semi-Markov models (HSMMs). They may better represent the behavioural dynamics of foragers since they explicitly model the duration of the behavioural modes. Second, we consider discriminative models which state the inference of behavioural modes as a classification issue, and may take better advantage of multivariate and non linear combinations of movement pattern descriptors. For this work, we use a dataset of. 200 trips from human foragers, Peruvian fishermen targeting anchovy. Their movements were recorded through a Vessel Monitoring System (,1 record per hour), while their behavioural modes (fishing, searching and cruising) were reported by on-board observers.
While HMMs characterize behaviour at the step scale, HSMMs characterize behaviour at the segment scale; a segment is composed of consecutive steps associated with a same state. HSMMs do account for transitions between consecutive but distinct states and for durations of state segments corresponding to one behavioural mode. For most living organisms studied in ecology, groundtruthed datasets -samples of tracks or positions for which behavioural modes are known -are hardly available. Therefore, inference issues are generally stated within a non-supervised framework. Furthermore, rigorous model validation (e. g. by cross-validation as in  [25, 26] ) cannot be performed. Model validation mainly relies on some expert-driven evaluation of the ecological or behavioural plausibility of the behavioural modes inferred.
Nowadays, tracking data on non-human foragers can be enriched by concurrent deployment of additional devices that explicitly record activities, at least for a sample of individuals (e. g. time-depth recorders for diving as in  [9, [30] [31] [32] , video-cameras as in  [33, 34]  and tri-axial accelerometers as in  [35] ). Given such partially groundtruthed datasets, we are no more in a fully non-supervised context, but rather in supervised or semi-supervised ones  [36, 37]. Markovian models may apply in such supervised setting. Nevertheless, alternative models may also be considered, particularly because Markovian models are limited for handling multiple observed variables. Choosing and fitting the most appropriate multivariate distribution may be delicate. A simplifying hypothesis is commonly adopted to solve this issue: observed variables conditioned on states are assumed mutually independent, so the multivariate distribution becomes the product of the univariate conditional distributions of each variable.
By contrast, discriminative models, such as random forests (RFs), artificial neural networks (ANNs) and support vector machines (SVMs), provide robust solutions for non-linear discrimination in high-dimensional spaces. They have been shown to be highly efficient for a wide range of applications  [38] [39] [40] [41] [42]. Their availability in several software without the need of strong computational skills makes them attractive for applications to ecological datasets  [43, 44]. This includes a few studies dedicated to behavioural modes  [45] [46] [47]. This context of technological advances for data collection enables a wide range of supervised models. Hence, evaluating and comparing models accuracy for inferring behavioural modes becomes necessary. Here, we consider as study case the foraging movement of 50 Peruvian purse-seiners targeting anchovy.
More than 200 of their fishing trips were documented by a Vessel Monitoring System and their behavioural modes simultaneously registered by on-board observers. This unique and large groundtruthed dataset allows performing, via cross-validation, a comprehensive evaluation and comparison of Markovian (HMMs and HSMMs) and discriminative models (random forests, artificial neural networks and support vector machines) for inferring the behavioural modes of a moving forager. We show that HSMMs provide the most accurate inference of the behavioural modes with 80% of global accuracy. We also show via simulation that this result could be greatly reinforced with position records of higher frequency.
Materials and Methods The purse-seine Peruvian anchovy fishery is the world largest mono specific fishery  [48]. Satellite tracking by Vessel Monitoring System (VMS) is mandatory for the whole Peruvian industrial fishing fleet (. 1000 vessels) since 2000. Vessel positions (6100m of accuracy; ,1 record per hour) for tens of thousands of fishing trips are thus available for scientific purposes since (e. g. ,  [28] [29] [30] 46, 47, 49] ). Although most records are given according to one-hour intervals, some irregularities (e. g. 0. 17, 0. 99, 12) seldom occur. Since there is no straightforward optimal interpolation method for these cases  [8] , we work with the records as they are. Therefore, the considered VMS data consist in tracks (i. e. , series of positions) with non-regular steps. For each VMS track, several observed variables are computed at each step: speed (sp), heading (h), changes of speed and turning angles between the previous and the current step (Dsp {1 and Dh {1 ) and between the current and the next step (Dsp z1 and Dh z1 ).
In addition, IMARPE (Peruvian Marine Research Institute) runs a program of observers on-board for a ,1% sample of the fishing trips. They record the location and time of the different behavioural modes occurring during the trips: fishing, searching, cruising (i. e. travelling following a predetermined course), drifting, helping other vessels, and receiving or giving fish to other vessel. For the remaining 99% of the fishing trips, behavioural modes are unknown. Based on the criteria described in  [28, 29, 47] , a groundtruthed dataset gathering tracking data and their corresponding behavioural modes is built. Overall we consider three behavioural modes, fishing, searching and cruising. Fishing trips involving 'helping', 'receiving/giving' and 'drifting' modes are discarded, due to the low number of occurrences of these modes.
Together they represent less than 6% of the groundtruthed dataset. We work with a dataset corresponding to 2008, consisting of 242 fishing trips (,36000 fishing trips were performed in total that year). Fig. 1  shows an example of a trip with each VMS record associated with a behavioural mode. For hidden state inference, two different approaches are investigated and evaluated. Markovian models, which take into account the sequential nature of data; and discriminative models, remarkably popular in the pattern recognition and machine learning domain  [50] [51] [52]. Henceforth, we will denote by S t the state variable at time t taking a discrete value s t , which encodes a behavioural mode (fishing, searching or cruising). A state sequence starting at time 0 and ending at T is then denoted by S T 0 :S 0 ,S 1 ,.
,S T , taking discrete values s T 0. Likewise, X T 0 represents the sequence of continuous observed variables taking values x T 0. Under the two approaches, the goal is to infer s T 0. We perform a quantitative evaluation of the models performance using a classic cross-validation procedure. It proceeds as follows. The groundtruthed dataset is split into two sub-samples. The first partition is used for training the models, i. e. , learning from the data and estimating the parameters. The second partition is used for validating the models, i. e. , evaluating model performance. Training and validation partitions gather each 50% of the original sample of trips and are built by repeated random subsampling (20 repetitions). This parameter setting provides us with a trade-off between the performance evaluation and computational efficiency.
Goodness-of-fit (GOF) is tested using the robust Cramer-von Mises statistic  [54]. In cases where two or more distributions provided significant fits, the AIC criterion  [55]  is used for selection among them. All fishing trips start in cruising mode, so initial probabilities are set to one for cruising and zero for the other states. Given the training partition, the ML estimation of the transition probabilities resorts to computing the relative frequencies of the transitions between successive states  [36]. Using all these elements, the inference of the sequence of hidden states s T 0 is done by global decoding via the Viterbi algorithm  [23]. Hidden Markov Model toolbox for Matlab  [56]  is used. HSMM. A first-order Markov state process may not be, however, the most natural choice for the interpretation of movement patterns.
Although state durations are inherently discrete, continuous distributions provide flexibility under certain irregularities on the frequencies of positioning of satellite records. They enable the incorporation of those data directly into the model. Extensive literature on the use of continuous distributions for modelling duration is available (e. g. ,  [58] [59] [60] [61] ). Here we examine seven continuous probability distributions for modelling the duration of each of the three behavioural modes. Their parameters are estimated by maximum likelihood using the training dataset. Then, GOF is tested using Cramer-von Mises statistic and AIC criterion is used for selection among distributions not rejected by the test. Using all these elements, the inference of the sequence of hidden states s T 0 is done by global decoding via the forwardbackward Viterbi algorithm  [62].
For SVMs, the goal is to maximize the margin around the hyperplane that separates classes. For ANNs, the objective is to minimize the classification error. And for RFs, discrimination is achieved by the simultaneous minimization of the within-group variances and maximization of the between-group variances. The relative performances between these methods are application-dependent and vary according to the structure of the observation space  [66]. A key feature of discriminative models is that they do not require any assumption on the nature of the observed variables, their distributions or covariances. To prevent over-fitting during the learning stage, a cross-validation procedure can be applied. Still, it requires sufficiently large and representative groundtruthed datasets. As for HMMs and HSMMs, the subset of observed variables giving the highest inference accuracy is selected.
The Matlab implementation of the random forest library  [68]  is used. SVMs. Support vector machines are based on linear discrimination. A Gaussian kernel is used here for mapping the originally observed vectors into a new space in which classes (i. e. , behavioural modes) may be linearly separated. Tested values for the scale parameter of the Gaussian kernel are f10 {4 ,10 {3 ,0:1,0:5g. SVMs also involve a regularization parameter C. Increasing the value of C increases the cost of misclassifying points and decreases generalization power of the model. We test C~f0:1,1,10,100g. The Matlab TM implementation of the Libsvm library  [68]  is used. ANNs. Multilayer perceptrons (MLPs) are the most widely used architectures of ANNs. Neurons are organized in layers. The first layer is composed of the observed variables and the last layer is composed of the model classification output.