dataesr/scientific-paragraphs-categorization huggingface dataset:
All samples of simulated events were passed through the ATLAS detector simulation  [66]  based on GEANT 4  [67]  and were reconstructed with the standard ATLAS reconstruction software. The effects of multiple interactions in the same and nearby bunch crossings (pile-up) were modelled by overlaying minimum-bias events, simulated using the soft QCD processes of P 8. 186  [68]  with the A2  [69]  set of tuned parameters (tune) and MSTW2008LO  [70]  parton distribution functions (PDF). For all samples of simulated events, except for those generated using S  [71] , the E G 1. 2. 0 program  [72]  was used to describe the decays of bottom and charm hadrons. A summary of all the generators used for the simulation of the signal and background processes is shown in Table  4. Samples produced with alternative generators are used to estimate systematic uncertainties in the event modelling, as described in Section 5.
Multivariate analysis As discussed in Section 3. 3, the global likelihood fit comprises eight signal regions, defined as the 2-and 3-jet categories in the high-p V T region for the three channels, and in the medium-p V T region for the 2-lepton channel. The BDT V H multivariate discriminant output distributions in these regions are input to the fit. The event yields are used in the two W+HF control regions of the 1-lepton channel. In the four eµ control regions of the 2-lepton channel, the m bb distributions are input to the fit, except for the 2-jet category of the high-p V T region, where the event yield is used. The post-fit normalisation factors of the unconstrained backgrounds in the global likelihood fit to the 13 TeV data are shown in Table  8. The effects of systematic uncertainties on the measurement of the signal strength are displayed in Table  9.
), consistent with the value of the Yukawa coupling to bottom quarks in the SM. In addition, the Run 2 V H, H → b b result is further combined with the results of other Run 2 searches for the Higgs boson decaying into either four leptons (via Z Z * ) or diphotons in the V H production mode, assuming the relative branching fractions of the three decay modes to be as predicted by the SM. The result is an observed significance of 5. 3 standard deviations, to be compared with an expectation of 4. 8 standard deviations. Assuming the SM branching fractions, the measured signal strength relative to the SM expectation is µ V H = 1. 13 ± 0. 15(stat. ) +0. 18  -0. 17 (syst. ). This provides a direct observation of the Higgs boson being produced in association with a vector boson. Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région 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; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom.
ACKNOWLEDGMENTS The authors acknowledge the  Centre National de la Recheche Scientifique (CNRS)  and the  Conseil Reǵional d'Aquitaine  ( Project Nano Trans  no. #  20111101010 ) for funding. The support of the  "Agence Nationale pour la Recherche"  for the  DIRAN  project (contract #  ANR 11 NANB 0001 ) is gratefully acknowledged. The authors acknowledge the LOMA for the use of  NSI  platform.
Acknowledgements The authors would like to thank  Andru Gheorghiu  and  Petros Wallden  for enlightening discussions and feedback. This work was supported by grant  EP/L01503X/1  for the  University of Edinburgh School of Informatics Centre for Doctoral Training in Pervasive Parallelism , from the  UK Engineering and Physical Sciences Research Council (EPSRC)  and by grants  EP/K04057X/2 ,  EP/N003829/1  and  EP/ M013243/1 , as well as by the  European Union 's  Horizon 2020 Research and Innovation program  under  Marie Sklodowska-Curie Grant  Agreement No. 705194.
Acknowledgement The EXILL&FATIMA campaign would not have been possible without the support of several services at the ILL and the LPSC. We are grateful to the  EXOGAM collaboration  for the loan of the detectors, to GANIL for assistance during installation and dismantling, and to the FATIMA collaboration for the provision of LaBr 3 (Ce) detectors and analogue electronics. This work was supported by  German BMBF  under grant  05P15PKFNA  and  05P12PKNUF  and by the  UK Science and Technology facilities council  and the  UK National Measurement Office. Theoretical studies including the MCSM calculations were supported in part by  JSPS   Grants-in-Aid for Scientific Research  ( 23244049 ), in part by  HPCI Strategic Program  (hp150224), in part by  MEXT  and  JICFuS  and Priority Issue on Post-K computer (Elucidation of the fundamental laws and evolution of the universe) (hp160211), and by the  CNS-RIKEN  joint project for large-scale nuclear structure calculations.
Gift from Naoyuki Fuse. Embryo preparation, RNAi, and drug injections. Embryos were prepared as described before  34, 62, 63. Briefly, embryos were dechorionated using bleach, for about 40 s and then washed thoroughly with distilled water. The embryos were then aligned on a flat piece of agar and then glued to a glass coverslip. These embryos can be submerged in water and can be imaged directly. Alternatively, glued embryos were kept in an airtight box containing Drierite for about 7 min, then covered in halocarbon oil, and then injected with RNase-free water containing either dsRNA or drugs. α-Catenin RNAi (Fig. 1c-e ) was achieved by injecting dsRNA in embryos, as previously described  9. Briefly, dsRNA probes against α-Catenin were made using PCR products containing the sequence of the T7 promoter targeting nucleotides 101-828 of α-Catenin sequence (GenBank accession D13964).
Imaging. Time-lapse images were acquired to encompass stage 7 to 8 of the embryonic development  64  , which needs ~15 min at room temperature (~22 °C). Embryos were imaged for 20-30 min depending on the experiment, on a Nikon spinning-disk Eclipse Ti inverted microscope using a ×100 1. 45 NA oil immersion objective. MyoII-mCherry and E-cadherin-GFP signals were captured every 30 s or higher, on 11 Z-planes, separated by 0. 5 μm. Vinculin-mCherry and E-cadherin-GFP signals were captured every 30 s or higher, on 7 Z-planes, separated by 0. 5 μm. A Nikon spinning-disc Eclipse Ti inverted microscope using a ×100 1. 4 NA oil immersion objective was used for imaging α-Catenin-YFP. Both systems acquire images using the MetaMorph software. Laser power and exposure settings had to be optimized separately for each experiment, as the fraction of fluorescently tagged vs untagged protein pool changes, in accordance with changes in the maternal and zygotic genotype (see Methods: Fly lines and Genetics).
Pearson correlation, in contrast, looks for linear relationships. In our analyses, the differences between Spearman correlation coefficients and Pearson correlation coefficients are minimal, if any, indicating that most relationships are linear. Though, we have used Spearman correlation when the number of data points is small (<100), where an assumption of linear relationship might not be justified due to fewer data points. All measurements were performed on 4-25 embryos spread over at least three rounds of embryo collection and preparation. The sample sizes were not predetermined using any statistical methods. The experiments were not randomized, and the investigators were not blinded to allocation during experiments and outcome assessment.
Indeed, the time scales of junction shrinkage/ extension (in the order of 100 s) are at least an order of magnitude longer than the time scales corresponding to the on/off kinetics of cytoskeletal components through activation/inactivation, as well as the turnover of adhesion complexes (in order of 1-10 s). Such separation of time scales would further justify the assumption that the changes in junction length occur in a quasistatic manner. We implement mechanical inference on segmented images of cell network based on the E-cadherin channel. We collect 30 images at a time interval of 30 s for each embryo. The E-cadherin channel images are processed using the freeware ilastik for pixel classification. The resulting probability maps of pixels are processed using a customized MATLAB script for cell segmentation using a watershed algorithm.
Acknowledgements We thank members of  Lecuit and Lenne group  for discussions throughout the course of this project and for providing a stimulating environment,  Claire Chardes  ( Lenne group ) and  Claudio Collinet  ( Lecuit group ) for assistance with laser ablation experiments, IBDM imaging facility for assistance with maintenance of the Microscopes, and FlyBase for maintaining curated database and  Bloomington fly facility  for providing transgenenic flies. G. R. K. was supported by  Ph. D. fellowship from the  LabEx INFORM  ( ANR-11-LABX-0054 ) and of the A* MIDEX  project ( ANR-11-IDEX-0001-02 ), funded by the " Investissements d'Avenir French Government program ". We also thank  NCBS-TIFR  for a bridging post-doctoral fellowship for G. R. K. , hosted in the laboratory of Satyajit Mayor during the completion of this manuscript.
2 cm (red circles) and ∆z = 3. 6 cm (blue triangles). In each case, the measured phase shift agrees with the expected phase estimated from Eq. 2. We estimated the Allan deviation of the measured phase shift to measure the short-term sensitivity of our gradiometer. Figure  6  shows the Allan deviation of three independent sets of 3740 measurements each for T = 20 ms and gradiometer baseline ∆z = 3. 2 cm. The relative phase shifts were obtained by inducing an artificial gradient of up to Γ artif = 2. 8 s -2 with a frequency jump respectively of ∆ π = -159 MHz (black squares), ∆ π = -179 MHz (red circles) and ∆ π = -229 MHz (blue triangles). The cycle time was set to 2. 4 s for an overall measurement time of about 2. 5 h. For all the datasets, the Allan deviation scales as τ -1/2 (where τ is the averaging time) showing that the main noise contribution comes from white phase noise.
ACKNOWLEDGMENTS We would like to thank  G. Rosi  for helpful discussions and  C. W. Oates  for a critical review of the manuscript. We would also like to thank  W. Bowden  for his assistance on improving our data fitting techniques while on secondment at Florence. We acknowledge financial support from  INFN  and the  Italian Ministry of Education, University and Research (MIUR)  under the  Progetto Premiale "Interferometro Atomico"  and  PRIN-2015. We also acknowledge support from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 grant agreement 250072 -"iSense" project and grant agreement 607493 -ITN  " FACT " project).
Controlling the AFP process In order to ensure the quality of the produced composite parts, the thermoplastic has to be melted at an optimal temperature denoted u opt. To reach it, the heat source must be drastically controlled. The numerical simulation is a great  tool to avoid experiments: instead of the expensive loop of test/error, we can take advantage of a numerical model. However, this expensive loop can also be implemented with a time-consuming model: the realistic try is simply replaced by its numerical equivalent. We perform a plethora of standard finite element computations depending on the set of parameters under consideration. These results are then stored in a kind of database. To circumvent this issue, we benefit from the ability of the PGD to deal with extra-coordinates. That way, we are able to build a multi-parametric chart in a single computation, providing the solution for any value of the parameters in given domains, instead of running a computation for each possible combination.
Data and simulated event samples The data used in this analysis were collected by the ATLAS detector in pp collisions at the LHC with √ s = 13 TeV and 25 ns proton bunch crossing interval over the 2015 and 2016 data-taking periods. The full dataset corresponds to an integrated luminosity of 36. 1 fb -1 after the application of beam, detector, and data-quality requirements. The uncertainty in the combined 2015+2016 integrated luminosity is 2. 1%. It is derived, following a methodology similar to that detailed in Ref. [27] , from a calibration of the luminosity scale using x-y beam-separation scans performed in August 2015 and May 2016. On average, upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θ as η =ln tan(θ/2).
Rapidity is defined as y = 0. 5 ln[(E + p z )/(Ep z )], where E denotes the energy and p z is the component of the momentum along the beam direction. Angular distance is measured in units of ∆R ≡ (∆y) 2 + (∆φ) 2. there are about 14 or 25 inelastic pp collisions ("pile-up") in each bunch crossing in the 2015 and 2016 datasets, respectively. In all search regions, events are required to pass a missing transverse momentum (E miss T ) trigger with an online threshold of 70 GeV or 110 GeV for the 2015 and 2016 data periods, respectively. This trigger is fully efficient for events passing the preselection defined in Section 5. 1. Events in control regions are selected using triggers requiring the presence of one electron or muon. Monte Carlo (MC) simulated event samples are used to model the SUSY signal and to help in the estimation of SM background processes.
Additional samples from different event generators and settings are used to estimate systematic uncertainties in the backgrounds, as described in Section 7. All simulated events were overlaid with multiple pp collisions simulated with the soft QCD processes of P 8. 186 with the A2 tune settings and the MSTW2008LO PDF set  [49] , and reweighted to match the pile-up distribution observed in the data. The MC samples were processed through the ATLAS full detector simulation  [50]  based on G 4  [51]  or a fast simulation  [52]  that uses a parameterisation for the calorimeter response and G 4 for the rest of the detector. All simulated events are reconstructed with the same algorithms as used for the data. In order to improve agreement between data and simulation, corrections are applied to the lepton trigger and identification efficiencies, momentum scale, energy resolution, and the efficiency of identifying jets containing charm hadrons  [54] [55] [56].
The discriminants are based on the MV2 algorithm presented in Refs. [25, 61]. MV2c100 is trained with b-jets as signal and with background consisting exclusively of c-jets. MV2cl100 is trained with c-jets as signal and with background consisting exclusively of light-flavour jets. A "tight" working point was adopted in the analysis, resulting in a charm-tagging efficiency of about 18%, a b-jet rejection factor of 20, a light-flavour jet rejection factor of 200, and a hadronic τ jet rejection factor of 6, as evaluated in a sample of simulated t t events. The c-jet tagging rate is measured using a data sample rich in t t events, identifying c-jets from W boson decays. Correction factors dependent on the jet p T are derived to match the tagging rate in the simulation to that measured in data. They are found to range from 0.
Background-only fit For each signal region, the normalisations of the W+jets, Z+jets and top-quark backgrounds are determined through a binned likelihood fit performed simultaneously to the observed number of events in the associated control regions. The likelihood is constructed as a product of Poissonian probability density functions describing the observed data counts in the control regions and Gaussian constraint terms for the systematic uncertainties described in Section 7. Poisson distributions are also included in the likelihood to describe the MC simulation's statistical uncertainties. The fit is performed using the HistFitter package  [65]  with the assumption that no signal is present and it is referred to as the "background-only" fit. Correlations in background yield predictions across different regions due to common systematic uncertainties are accounted for in the fit.
Conclusion This paper reports a search for direct top-squark and charm-squark pair production in final states with charm-tagged jets and missing transverse momentum, based on a 36. 1 fb -1 dataset of √ s = 13 TeV proton-proton collisions recorded by the ATLAS experiment at the LHC in 2015 and 2016. No significant excess above the expected Standard Model background is found and model-independent exclusion limits at 95% CL are set on the visible cross-section. Assuming a 100% branching ratio to c χ0 1 , top and charm squarks with masses up to 850 GeV are excluded at 95% CL for a massless lightest neutralino. For m t1 , c1m χ0 1 < 100 GeV, top and charm squarks with masses up to 500 GeV are excluded.
GAMBIT defines a series of theoretical models available for analysis, and various rules that relate the different module functions to each other, to models, and to functions that are available for modules to call from external physics codes, known as backends. At runtime, the GAMBIT dependency resolver identifies the module and backend functions required to compute the observables and likelihoods requested by a user, and arranges them into a dependency tree. It then uses methods from graph theory to 'solve' the tree, and determine the order in which the functions must be called so that all dependencies and model consistency requirements are guaranteed to be satisfied. GAMBIT's sampling module ScannerBit  [12]  runs the user's choice of sampling algorithm on the solved dependency tree, and saves the resulting parameter samples, derived observables and likelihoods in an output database for subsequent statistical analysis and plotting.
It is also possible to set the values of spectrum parameters, but not via the base-level Spectrum object interface. For this more advanced usage, see Sec. 2. 3. 3. Most users will not need to do this. This form of string getter allows the spectrum information to be accessed in a simple, uniform way across all models and all GAMBIT modules. The user simply needs to know the specific tags and string names used for each piece of information. Particle names, for example, can be found in the GAMBIT particle database (see Sec. 10. 1 of Ref. [11] ). This split mirrors a common requirement of spectrum generators that Standard Model input parameters be first supplied at a common scale, then run by the "low energy" SubSpectrum object to a matching scale, after which the main spectrum generation for the BSM model is performed and wrapped into the "high energy" SubSpectrum object.
Extra overloads for get/set/has functions In the discussions of the interface for Spectrum and SubSpectrum objects, we have mentioned only one set of function signatures for the get, set and has functions used to access spectrum data, however several overloads of these functions are available. Most of these are designed to streamline the interaction between Spectrum objects and the GAMBIT particle database (see Sec 10. 1 of Ref. [11] ), and they allow particle data to be retrieved according to PDG codes and, where appropriate, antiparticle string names (see Models/src/particle_database. cpp for the full set of definitions known to GAMBIT). For Spectrum objects, the extra function signatures are as follows: When PDG codes + context integers are used with these functions to specify a particle, the GAMBIT particle database will be used to translate the code into a string identifier, which will then be checked against the string keys defined in the underlying SubSpectrum wrapper to do the data lookup.
When writing new wrappers it is therefore important to ensure that particle string names match the strings registered in the GAMBIT particle database. // PDG Similarly, whenever any of these interface functions are called with a string name (potentially also with one index), the underlying wrapper will first be searched for a match to that string. If a match cannot be found, then the GAMBIT particle database will be used to try to translate the string name into a short name plus an index (e. g. "∼chi_0" will be translated to the pair ("∼chi",0)) or vice versa. If there is still no match found with any spectrum contents, the GAMBIT particle database will again be used to try to translate the string name into the string for a matching antiparticle (e. g. "∼chi+_0" will be translated to "∼chi-_0") and the search will be tried again (including translating from string name to short name plus index and vice versa).
DecayBit All particle decay widths used in GAMBIT are provided by the dedicated module DecayBit. These range from known decays of SM particles, to modifications of SM decay widths and branching fractions due to new physics (e. g. top and Higgs decays), to decay widths of new particles present in BSM theories. These data are needed by ColliderBit  [14]  for predicting Higgs decay rates and calculating corresponding LHC Higgs likelihoods. They also allow ColliderBit to connect predictions for LHC production of BSM particles with observable final states, by simulating particle collisions and subsequent decays of the particles produced in the initial interaction. The same data are required by DarkBit  [13]  to simulate cascade decays of final states produced in dark matter annihilation and decay, allowing it to make predictions for cosmic ray fluxes in dark matter indirect detection experiments.
Here we begin by describing the decays currently implemented in DecayBit (Sec. 3. 1), including the details of the calculations, input data and external codes it employs. We then go through the specific module functions it offers (Sec. 3. 2), and the details of the code (Sec. 3. 3), covering the associated data structures and utility functions, how to add support for new channels and models, and an explicit worked example of the computation of Higgs boson decays.
We take these data directly from the Particle Data Group compilation of experimental results  [61]. These data are used in GAM-BIT in two scenarios. The first is whenever a decay has no relevant BSM contribution, e. g. if BSM contributions are heavily loop-suppressed, or if the particle sector of the scan is simply the SM, as when scanning e. g. the DM halo model only. The second is in cases where the only significant BSM modification to the decay of the SM particle is to open a new decay channel, meaning that the new decay can simply be added to the existing SM decays. Where neither of these scenarios apply, i. e. where BSM corrections are relevant for SM → SM decays, DecayBit accounts for the corrections via an appropriately model-dependent calculation. We group together hadronic channels for W and Z decays, assigning them a final state consisting of two generic hadrons.
Note that 'hadron' is recognised as a generic particle by the GAMBIT particle database; see Sec 10. 1 of Ref. [11]. DecayBit also features various calculations of Higgs boson partial and total decay widths. For a pure SM Higgs, the user can choose to either calculate decays at the predicted value of the Higgs mass with FeynHiggs, or extract them from precomputed tables contained in DecayBit. The latter are the predicted SM Higgs partial and total widths given as a function of Higgs mass in Ref. [111]. Using these tables wherever possible is preferable to simply calling HDECAY, as they include additional higher-order corrections for 4-body final states obtained using PROPHECY4F  [112, 113].