dataesr/scientific-paragraphs-categorization huggingface dataset:
The limits derived from these searches cannot easily be applied to other branching ratio values, due to the potentially large expected signal contamination from mixed decay modes. A more general search strategy should consider simultaneously all three decay modes, providing a more extensive coverage of possible signal contributions. In absence of an excess, quasi-model-independent limits would be set in the plane defined by the branching ratios to two of the decay modes  1  as a function of the heavy-quark mass. The first search that considered all three decay modes in the interpretation of results, performed by the ATLAS Collaboration using pp collisions at √ s = 7 TeV, primarily targeted the T T → W + bW -b process  [24]. Using the full dataset collected at √ s = 8 TeV, the ATLAS Collaboration has recently published searches for heavy quarks decaying to a Z boson and a third-generation quark  [25] , and searches for heavy quarks decaying predominantly to Wt in events with one lepton and jets  [26]  and in events with two leptons of the same charge or three leptons  [27].
In the context of vector-like quarks, these searches are used to probe T T and B B production, and the three decay modes are considered in the interpretation of the results. The CMS Collaboration has published an inclusive search for T T production  [28]  resulting from the combination of several analyses in lepton-plus-jets and multilepton final states at √ s = 8 TeV. This search set 95% confidence level (CL) lower limits on the T quark mass ranging between 690 GeV and 780 GeV for all possible values of the branching ratios into the three decay modes. The results presented in this paper complete the program of searches for pair production of vector-like quarks decaying into third-generation quarks by the ATLAS Collaboration using the pp dataset collected at √ s = 8 TeV. Three separate searches are presented, all of them focused on the pair production of vector-like quarks in final states involving one isolated electron or muon, high missing transverse momentum from the undetected neutrino and multiple jets.
Using multilepton final states, the ATLAS Collaboration has also searched for SM t tt t production at √ s = 8 TeV, setting an observed (expected) 95% CL upper limit of 70 fb (27 fb)  [27]. The observed limit is higher than the expected one owing to an excess of data above the background expectation with a significance of 2. 5 standard deviations. In addition, the ATLAS multilepton search sensitively probes several of the above BSM scenarios giving rise to large enhancements in t tt t production. Given its sensitivity to a wide range of models, the T T → Ht+X search presented in this paper is also used to search for a t tt t signal, within the SM as well as in the same BSM scenarios as the ATLAS multilepton search, with comparable sensitivity.
Data sample and event preselection This search is based on pp collision data at √ s = 8 TeV collected by the ATLAS experiment between April and December 2012. Only events recorded with a single-electron or single-muon trigger under stable beam conditions and for which all detector subsystems were operational are considered. The corresponding integrated luminosity is 20. 3±0. 6 fb -1  [57]. Single-lepton triggers with different p T thresholds are combined in a logical OR in order to increase the overall efficiency. The p T thresholds are 24 or 60 GeV for the electron triggers and 24 or 36 GeV for the muon triggers. The triggers with the lower p T threshold include isolation requirements on the candidate lepton, resulting in inefficiencies at high p T that are recovered by the triggers with higher p T threshold.
In Powheg+Pythia the modelling of t t+HF is via the parton-shower evolution. To study uncertainties related to this simplified description, an alternative t t+jets sample is generated with Madgraph5 1. 5. 11 using the CT10 PDF set. It includes tree-level diagrams with up to three additional partons (including b-and c-quarks) and is interfaced to Pythia 6. 425. Since the best possible modelling of the t t+jets background is a key aspect of these searches, a correction is applied to simulated t t events in Powheg+Pythia based on the ratio of the differential cross sections measured in data and simulation at √ s = 7 TeV as a function of top quark p T and t t system p T  [94]. The stability of the ratio between √ s = 7 TeV and √ s = 8 TeV was studied to support the usage of √ s = 7 TeV data to correct the simulation at √ s = 8 TeV.
This correction significantly improves agreement between simulation and data in distributions such as the jet multiplicity and the p T of decay products of the t t system. This correction is applied only to t t+light-jets and t t + cc events. The modelling of the t t + b b background, particularly important for the Ht/Hb+X searches, is improved by reweighting the Powheg+Pythia prediction to an NLO prediction of t t + b b including parton showering  [95] , based on Sherpa+OpenLoops  [96, 97]  using the CT10 PDF set. This reweighting is performed for different topologies of t t + b b in such a way that the inter-normalisation of each of the categories and the relevant kinematic distributions are at NLO accuracy. More details about the modelling of the t t+jets background can be found in ref.
The WW+jets samples require at least one of the W bosons to decay leptonically, while the WZ/ZZ+jets samples require one Z boson to decay leptonically, with the other boson decaying inclusively. Additionally, WZ+jets samples requiring the W and Z bosons to decay leptonically and hadronically respectively, are generated with up to three additional partons (including massive b-and c-quarks) using Sherpa v1. 4. 1 and the CT10 PDF set. All diboson samples are normalised to their NLO theoretical cross sections  [108]  Samples of t tV events, including t tWW, are generated with up to two additional partons using Madgraph5 1. 3. 28 with the CTEQ6L1 PDF set, and interfaced to Pythia 6. 425 with the AUET2B UE tune. A sample of t tH events is generated with the PowHel framework  [109] , which combines the Powheg-Box generator and NLO matrix elements obtained from the HELAC-Oneloop package  [110].
Both probabilities are measured in data control samples. To measure real , samples enriched in real leptons from W bosons decays are selected by requiring high E miss T or m W T. The average real is ∼0. 75 (∼0. 98)  in the electron (muon) channel. To measure fake , samples enriched in multijet background are selected by requiring either low E miss T (electron channel) or high impact parameter significance for the lepton track (muon channel). The average fake value is ∼0. 35 (∼0. 20)  in the electron (muon) channel. Dependencies of real and fake on quantities such as lepton p T and η, ∆R between the lepton and the closest jet, or number of b-tagged jets, are parameterised in order to obtain a more accurate estimate.
Statistical uncertainties in each bin of the discriminant distributions are also taken into account via dedicated parameters in the fit. The test statistic q µ is implemented in the RooFit package  [126, 127]  and is used to measure the compatibility of the observed data with the background-only hypothesis (i. e. the discovery test) setting µ = 0 in the profile likelihood ratio: q 0 = -2 ln(L(0, θ0 )/L( μ, θ)). The p-value (referred to as p 0 ) representing the compatibility of the data with the background-only hypothesis is estimated by integrating the distribution of q 0 from background-only pseudo-experiments, approximated using the asymptotic formulae given in ref. [128, 129] , above the observed value of q 0. Some model dependence exists in the estimation of the p 0 -value, as a given signal scenario needs to be assumed in the calculation of the denominator of q µ , even if the overall signal normalisation is left floating and fitted to data.
This paper presents a method for human action recognition in videos which learns a dictionary whose atoms are spatiotemporal patches. We use these gray-level spatio-temporal patches to learn motion patterns inside the videos. This method also relies on a part-based human detector in order to segment and narrow down several interesting regions inside the videos without a need for bounding boxes annotations. We show that the utilization of these parts improves the classification performance. We introduce a rejection-based classification method which is based on a Support Vector Machine. This method has been tested on UCF sports action dataset with good results.
As a consequence, we are using small patches compared to the spatiotemporal volumes. Since the patches are small compared to the complexity of the data, there may be no specific or discriminative atoms for each class (different classes may utilize similar atoms) and the recognition is done solely on the proportion of used atoms. That is why we chose to implement K-SVD  [9]  for learning the dictionary. Let p be a spatio-temporal gray-level patch of size (s × s × t), with s being the size of a square patch in the spatial dimensions and t being the number of frames considered in the temporal dimension. The patch p norm is the patch p whose set of pixels are normalized so that it has zero mean and unit norm. Let Y = [p norm,1 , p norm,2 ,. , p norm,m ] ∈ R n×m be a a matrix composed of spatio-temporal patches, with m being the size of the dataset, n the dimension of the patches and D = [d 1 , d 2 ,.
EXPERIMENTATIONS We tested the proposed algorithm of the UCF sports action dataset  [13]. This dataset is normally composed of 10 classes: "Walking", "Swinging", "Skateboarding", "Running", "Riding horse", "Lifting", "Kicking", "Playing golf" and "Diving". The resolution of the videos is (720 × 480) pixels. We decided to remove the class "Lifting" because of the lack of annotations. In total, we used 9 classes and 140 videos. For the classification of the videos, we used libLinear  [14]. We did a leave-one-group-out using all the signatures computed for a single video as a group. The size of the patches considered is (5 × 5 × 3) pixels. With the selected temporal size t = 3, we obtained between 20 and 90 signatures for each video depending on its length. Each human was described with 9 volumes of (24 × 24 × 3) pixels computed with the part-based detector.
Rejection rule We compare the proposed method with different algorithms of the literature in the Table  3. We can see that our method achieves good results. Even if the descriptors obtained with the part-based detector alone only reach 80%, the combination of the proposed descriptors and classification method perform really well. The size of the codebook used in our method is 150 compared to 4000 for  [15]  and 40 for  [16]  even if the final dimension of the signatures is 1350. Speed wise, the experiments were done on MATLAB so a significant gain in speed is possible: the part-based human detector was the limiting factor with about 0. 5 frame per second on a modern computer, once the parts are extracted we can compute about 7 signatures per second. Results from  [17, 18]  are given as a comparison: they use techniques different from dictionary-based approach and show that the proposed method is competitive.
CONCLUSION Given a raw video as input, the proposed dictionary-based action recognition method performs efficiently. Despite the small dimension of the patches, we show that using localized spatio-temporal volumes improves the results. We also introduced a rejection-based classification method to select the most descriptive signatures to select the labels. The method has been tested on UCF sports action dataset with good results. For future work, we are looking for a way to take into account the evolution of the signatures extracted from successive temporal blocks instead of treating them separately. We are also working on more recent databases like UCF50.
Reproducibility of the Axes Measurement We further investigate the directional information shown by the thrust-major axes of the individual ROIs in Figure  5. From the simplified simulations in Section 3 we saw that thrustmajor directions are reproducible in repeated experiments for scenarios where coherent deflections contribute, and turbulent deflections are not too large. In additional simulation studies it was shown that evidence for anisotropy could sometimes be found in reproducibility of axis directions even when the thrust scalar values were consistent with isotropy  [22]. Hence, analysis of the directions of the thrust-major axes could potentially reveal further information. As we have obtained a single set of measured UHECR data at this point in time, we perform here a stability test on subsets of the data in the following sense.
If the measured thrust-major direction obtained in a single ROI is related to a deflection pattern reasonably constant in time then the analysis of subsets of the measured data should also reflect this pattern. As only a fraction of the ROIs may contain such a deflection pattern we perform tests of reproducibility on each ROI individually. We first define the ROIs as before using all available data. We then split the dataset into n independent subsamples and compare the directions n 2, j=1. n 2, j=n obtained in each subsample for every individual region of interest. A low variability of directions in the subsets of the data provides evidence for a non-triviality of the thrust-major axis and consequently for an anisotropic distribution of UHECRs. The optimal choice for the number of subsamples to split the data into is not known a priori.
Limits on Propagation Parameters A prime value of the measurements lies in their ability to constrain UHECR propagation scenarios. We outline the procedure to derive limits on scenario parameters using a simple model for extragalactic propagation of protons based on parameterizations as implemented in version 1. 2 of the PARSEC software  [20]. Although this model is likely too coarse to allow definite conclusions on the sources of UHE-CRs, it includes at least qualitatively the effects influencing patterns in the UHECR distributions. Its fast computability allows a scan of a large range of parameter combinations in the source density and the strength of the deflection in the extragalactic magnetic field, thus limiting these important parameters within this model. The procedure to obtain limits from the measurements reported in this paper as outlined here can be applied to any other model.
The PARSEC software simulates ultra-high energy protons by calculating the probability-density function (pdf) to observe a cosmic ray for discrete directions and ener- -75 • -60 • -45 • -30 • -15 • 0 • +15 • +30 • +45 • +60 • +75 • -180°-120°-60°0°60°120°180°F ig. 5 Hammer projection of the map of principal axes of the directional energy distribution in galactic coordinates. The red shaded areas represent the regions of interest. Black lines denote the second principal axes (thrust-major axes) n 2 , black dots mark the positions of the thrust axes n 1. The blue shading indicates the exposure of the Pierre Auger Observatory; the dashed line marks the extent of its field of view. gies using parameterizations for energy losses and energydependent deflections. In the calculations, energy losses of the UHECRs from interaction with extragalactic-photon backgrounds, effects from the expansion of the universe and deflection in extragalactic magnetic fields are accounted for using parameterizations.
In the Thrust Minor (a) (b) (c) (d) C E [deg Mpc -1 2 EeV] Source Density [Mpc -3 ] Fig. 6  95% CL S limits on the strength of the deflection of cosmic-ray protons C E (cf. Equation (  13 ) and (  6 ) ff. ) and density of point sources ρ in simulations using the PARSEC software  [20]  from the analysis of the (a) energy-energy correlations, (b) thrust, (c) thrust-major and (d) thrust-minor distributions. The gray areas are excluded by the measurements. CL S method, not Q directly, but the modified likelihood ratio CL S = P a (Q ≥ Q obs ) 1 -P 0 (Q ≤ Q obs ) (15) is used as test statistic. Here P a (Q ≥ Q obs ) is the frequency with which likelihood ratios Q larger than the observed value are obtained in simulations of the alternative hypothesis and 1 -P 0 (Q ≤ Q obs ) the corresponding frequency in simulations of the null hypothesis.
1. 05/2. 1. 00/03. 0058and the  Czech Science Foundation  grant  14-17501S ,  Czech Republic ;  Centre de Calcul IN2P3/CNRS ,  Centre National de la Recherche Scientifique (CNRS) ,  Conseil Régional Ile-de-France ,  Département Physique Nucléaire et Corpusculaire  ( PNC-IN2P3/CNRS ),  Département Sciences de l'Univers (SDU-INSU/CNRS) ,  Institut Lagrange de Paris ,  ILP LABEX ANR-10-LABX-63 , within the  Investissements d'Avenir Programme   ANR-11-IDEX-0004-02 , France;  Bundesministerium für Bildung und Forschung (BMBF) ,  Deutsche Forschungsgemeinschaft (DFG) ,  Finanzministerium Baden-Württemberg, Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ,  Ministerium für Wissenschaft und Forschung ,  Nordrhein Westfalen, Ministerium für Wissenschaft, Forschung und Kunst, Baden-Württemberg, Germany ;  Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR) ,  Gran Sasso Center for Astroparticle Physics (CFA) ,  CETEMPS Center of Excellence, Italy ;  Consejo Nacional de Ciencia y Tecnología (CONACYT) ,  Mexico ;  Ministerie van Onderwijs, Cultuur en Wetenschap ,  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) ,  Stichting voor Fundamenteel Onderzoek der Materie (FOM) ,  Netherlands ;  National Centre for Research and Development , Grant Nos.
7] [8] [9]  Another study conducted between 2003 and 2006 among suspected dengue cases of all ages in Jamaica found a seroprevalence rate of 51%. 10  A recent study among pregnant women in 10 Caribbean islands (not including Guadeloupe or Martinique) found dengue seroprevalence rates of 80-100% in all but one of the islands. 11 No such data exist for Guadeloupe or Martinique. We estimated the seroprevalence of antibodies against dengue in samples collected between May 2011 and June 2011 from adults at blood donor centers. On each Island blood donations are collected either at the central French Blood Establishment center, or in mobile donor units. Participants were therefore recruited from throughout the islands. Each volunteer orally answered a short series of demographic ques-tions to document age, gender, country of birth, and place of residence, and their dengue history: recollection of having had dengue, number of infections, and year of last infection.
Data were analyzed using Stata v10. 0 software (Stata, College Station, TX). Sera were tested using a dengue IgG indirect enzymelinked immunosorbent assay (ELISA) (Panbio, Queensland, Australia) at the Etablissement Francais du Sang, Martinique. Overall, 732 of 783 (93. 5%) samples (95% confidence interval [CI]: 91. 5; 95. 1) (Table  1 ) were positive for dengue antibodies, 350 of 386 (90. 7%) in Martinique, 382 of 397 (96. 2%) in Guadeloupe. To our knowledge, there is no evidence to suggest the current or past circulation of other flaviviruses among humans on these two islands, where there is no yellow fever vaccination program. Our results are therefore likely to closely reflect the prevalence of dengue. In comparison, only 30% of the enrolled participants recalled having had dengue disease.
Acknowledgments: We thank  Grenville Marsh  at  Sanofi Pasteur  for providing editorial assistance. Financial support: This study was sponsored by  Sanofi-Pasteur. Disclosure: M. L. is employed by  Sanofi Pasteur. All other authors declare no other conflicts of interest. Authors' addresses:  Maı ¨na L'Azou ,  Sanofi Pasteur Global Epidemiology Department, Lyon, France , E-mail: maina. lazou@sanofipasteur. com. Janick Jean-Marie ,  Centre Hospitalier Universitaire de Martinique -Centre d'Investigation Clinique et d'Epidemiologie Clinique des Antilles et de la Guyane  ( CIC1424 ), Fort-de-France cedex, E-mail: janick. jean-marie@chu-fortdefrance. fr. Mae ¨l Bessaud and  Xavier de Lamballerie, UMR_D 190 "Emergence des Pathologies Virales", Marseille, France  ( Aix Marseille University, IRD French Institute of Research for Development, EHESP French School of Public Health ), E-mails: bessaud@laposte.
IV. DATA AND MONTE CARLO SAMPLES The data used in this analysis correspond to 20. 3 ± 0. 6 fb -1  [41, 42]  of integrated luminosity taken during periods in which the data satisfied baseline quality criteria. Further details of the event selections applied, including the ATLAS data quality criteria and trigger strategy, are 1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the center of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the center of the LHC ring, and the y-axis points upward. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the beam pipe. The pseudorapidity is defined in terms of the polar angle θ as η = -ln tan(θ/2). given in Sec. V. The primary systems of interest in these studies are the electromagnetic and hadronic calorimeters and the inner tracking detector.
The data were collected with triggers based on either single-jet or multi-jet signatures. The single-jet trigger selection has a transverse momentum threshold of 360 GeV using a large-R anti-k t jet definition  [43]  with a nominal radius of R = 1. 0 within the high-level jet trigger. The multi-jet trigger selection requires at least six anti-k t R = 0. 4 jets with a nominal p T threshold of 45 GeV in the high-level trigger. Data collected using several additional multi-jet requirements (from three to five jets) are also used for background estimation studies. Multiple simultaneous proton-proton (pp) interactions, or pile-up, occur in each bunch crossing at the LHC. The additional collisions occurring in the same and neighboring bunch crossings with respect to the event of interest are referred to as in-time and out-of-time pileup, respectively, and are uncorrelated with the hardscattering process.
This can be expressed as: N n-jet = N data m-jet -N MC m-jet, Other BGs × N MC n-jet N MC m-jet + N MC n-jet, Other BGs (3) where the number of predicted background events with n jets (N n-jet ) is determined starting from the number of events in the data with m jets (N data m-jet ). The extrapolation factor, N MC n-jet N MC m-jet , is determined from multi-jet simulation and validated in the data. This procedure is performed in exclusive bins of jet multiplicity. Since the simulation is not guaranteed to predict this scaling perfectly, cross-checks in the data and a data-driven determination of systematic uncertainties are performed as described in Sec. VI C. It is assumed here that the simulation used for this extrapolation given by PYTHIA 6. 426 predicts the relative rate of events with one additional order in the strong coupling constant in a consistent way across jetmultiplicity regions.