dataesr/scientific-paragraphs-categorization huggingface dataset:
The magnetic phase tilt series was aligned using parameters as determined from the electric phase tilt series, which has a higher signal-to-noise ratio than the magnetic one and therefore yields more accurate parameters. Before computation of the derivatives the magnetic phase images were smoothed slightly by a Median filter with a 3 × 3 Pixel kernel. Micromagnetic Simulations. A static micromagnetic simulation of the remanent state of the Co FEBID NW was performed by the GPMagnet software package,  40  which solves the Landau-Lifschitz-Gilbert equation. The computation ran parallelized on an efficient graphics processing unit (GPU). The 3D shape of the NW was adapted from the experimental tomogram and constructed with a maximum length of 1000 nm by assembling cubic cells of 3. 5 × 3. 5 × 3.
In a dataset from Scots pine (Pinus sylvestris L. ) progeny trial conducted in northern Sweden, they demonstrated that estimates from the predictive process approach provided a reliable reduction in the residual variance and an increment in the heritability even when the underlying spatial process was misaligned with the grid"s rows and columns. However, where both environmental heterogeneity and competition effects are important, a full model approach is needed  (Resende et al. 2005). In a simulation study of an empirical sib-trial data set from Pinus banksiana Lamb. ,  Magnussen (1994)  suggested modeling both effects by means of an iterative nearest neighbor adjustment and a standard linear model (two-way ANOVA). In a series of 70 sugar beet trials,  Durban et al. (2001)  simultaneously modeled the fertility trend and the genetic competition by means of a mixed model.
We extend the individual-tree mixed model with additive direct, genetic and environmental competition effects of  Cappa and Cantet (2008)  with a two-dimensional smoothing surface  (Cappa and Cantet 2007)  that captures the complex patterns of environmental heterogeneity. A Bayesian approach via Gibbs sampling was employed to make inference in all dispersion parameters of the model. Developments are illustrated with simulated datasets covering a wide range of competition and environmental parameters, and with real data for diameter at breast height of Pinus taeda L. used by  Cappa and Cantet (2008). The resulting estimates of all dispersion parameters are compared with the corresponding estimates from three reduced individual-tree mixed models: the standard model with block and direct genetic effects only (no competition and spatial continuous effects), the competition model  (Cappa and Cantet 2008) , and the spatial model  (Cappa and Cantet 2007).
Models of analysis Four individual-tree mixed models were evaluated in the loblolly pine data set. All models included a random direct additive genetic effect and a random effect of commercial seed lots. The latter was to avoid R C D R C D ij ij ij n f + n f + n f. It is important to examine the impact of competition effects and environmental heterogeneity on selection decisions (in addition to their impact on the variance component), when both types of effects exist. In that sense, Spearman rank correlations were calculated to assess the extent by which the ranking of predicted tree breeding values for all individuals (offspring) differed among the standard and the competition + spatial models. Additionally, the proportion of common individuals (offspring) in the top 5% (47 trees) from the two models was also compared.
Identification of spatial and competition effects As in  Gilmour et al. (1997) , we examined the spatial distribution of residual to identify spatial patterns in the data, using an exploratory model with fixed overall mean and random commercial seed lot ). The magnitude of this correlation was moderate to large and negative (-0. 85, see below). Finally, a two-dimensional autoregressive (AR(1) × AR(1)) residual structure was fitted to the standard model using the ASREML program  (Gilmour et al. 2006 ). The sign and magnitude of the autocorrelation parameters for row and column suggest that competition between neighbors may be present, and it is dominant over the environmental heterogeneity at the small scale (ρ row = -0. 34 and ρ column = -0. 10). Comment citer ce document : Munoz, F. , Cappa, E.
Computational details and posterior inference The values of the hypervariances in the priors of A ˆd and ˆe were estimated from the same data set using an empirical Bayes approach via Gibbs sampling, with an individual-tree model including fixed effects of blocks, and random additive genetic effects (standard model). As there was no prior information on the The hypervariance 2 ˆp was chosen to be equal to the prior value for A ˆc. The deviance information criterion (DIC) was computed for each model using the output from the Gibbs sampling. A single Gibbs chain of 1,010,000 samples were drawn as discussed above. The first 10,000 iterations were discarded as burn-in. Convergence was monitored by plotting the iterations against the parameter value (trace plots) and against the mean of the draws up to each iteration (running mean plots) for each parameter, and using the Z criterion of  Geweke (1992)  for each parameter.
Altogether, the three environmental scales of variations, three levels of genetic competition variance, and three values of correlation between direct and competition genetic effects resulted in 3 3 = 27 scenarios. We produced 6 replicates for each scenario, giving rise to 162 simulated data sets. All data sets were simulated through the function breedR. sample. phenotype() of the R-package breedR  (Muñoz and Sanchez, 2014). Finally, the simulated data were analyzed using the CSM (model [3]), and the simulated values were compared with the true values using the mean and the average relative deviation from the true value (bias) averaged over the 6 replicates. The average root mean squared relative error was used to measure the performance of the proposed joint CSM [3] in the different scenarios studied, according to the following formula: The hypervariances of all parameters were set equal to the true values used in the simulated data, and degrees of belief were set to 5 to reflect a relatively high degree of uncertainty.
Results from the real data analysis of loblolly pines Lack of convergence of the Gibbs sampler was not detected by inspection of trace plots of all unknowns parameters from the four models evaluated in the real data set (Supplementary Figure  S2 ). When comparing the four models, it is clear that adding competition and/or environmental heterogeneity effects improved the fit compared to TM. This can be seen in Table  2  via the DIC, for which CSM resulted in the lowest value, followed by CM and SM. The CM showed a greater reduction in DIC than SM, most likely due to the fact that competition had a higher effect than the small-scale environmental heterogeneity in this loblolly pine data set.
An application to loblolly pinesDataData came from a field trial of 20 open-pollinated families of loblolly pine (Pinus taeda L. ), originating from Marion County, Florida, USA, and belonging to CIEF (Forestry Research and Experimentation Centre). Five lots of commercial seeds were used as control populations (about 7% of the total of phenotypic data). These trees were included in the analysis due to their contribution to the estimation of environmental variation (Dutkowski et al. 2006 ). The trait analyzed was diameter at breast height (1. 3 m, DBH) measured at age 13 on the 932 surviving trees (93. 2 % of survival). The trial site was located in Villa Olivari (lat. 27º 36' S long. 56º 55' W), northern Corrientes province, Argentina. Families were arranged in randomized complete blocks, with eight replicates of five-trees row-plots, and the spacing was 3.
Acknowledgments This research was supported by grants of  Agencia Nacional de Ciencia y Tecnología  ( FONCyT PICT 00321 ) of Argentina, under the  Programa de Modernización Tecnológica III ,  Contrato de Préstamo BID  1728/OC-AR. The authors would like to thank to  Forestry Research and Experimentation Centre (CIEF, Buenos Aires, Argentina)  for kindly providing the Pinus taeda L. data set used in this study. FM and LS received funding from the  European Union "s  Seventh Framework Programme  for research, technological development and demonstration under grant agreement n°  284181  (" Trees4Future ").
Data Archiving Statement We followed standard  Tree Genetics  and  Genomes policy. Simulated data set used in this manuscript will be made available in the Zenodo repository, http://dx. doi. org/10. 5281/zenodo. 32036. Supplementary information of the Pinus taeda L. trial and family numbers will be also made available in the Zenodo repository, http://dx. doi. org/10. 5281/zenodo. 32040. In addition, diameter at breast height of the Pinus taeda L. data set will be available upon request.
Individual agents take financial investment risk and may occasionally lose all their capital wealth. 6 ur computational economic analysis disentangles two featuring dimensions to be analysed: wealth inequality across individuals, and social mobility relative to wealth dimension. Wealth inequality is captured through the Gini Index G t which summarises the relative concentration of wealth across individual at a certain period of time t, defined as follows: G t = (N + 1) -2 N k=1 (N + 1 -k)w k,t N k=1 w k,t 1 N -1 with 0 ≤ G t ≤ 1 (4) where N is the number of individuals and w k,t ≤ w k+1,t denotes the ranked vector of W i,t at time t. Accordingly, G t → 0 when individual wealths become 5 The Matlab code of the simulations can be found at: https://github. com/simonerighi/BiondiRighi2018_JEIC more equal, while G t → 1 when richer individuals tend to acquire a larger share of aggregate wealth.
acknowledgements We dedicate this article to the memory of  Prof. Pierpaolo Giannoccolo , coauthor and dear friend of us. Pierpaolo had been a committed team member and was working with us on furthering the understanding of the financial economic process. We thank  Alan Kirman ,  Thomas Piketty ,  Stefano Olla ,  Shyam Sunder  and  Marco Valente  for their insightful comments and suggestions. Previous versions of this article were presented at the 'International Economic Law and the  Challenge of Global Inequality' Conference , the 20th Annual Workshop on the Economic Science with Heterogeneous Interacting Agents (WEHIA 2015), and at the  Applied Economics Lunch Seminar, Paris School of Economics  (2 June 2015).
One of the aims of this document is thus to review the constraints on PDFs that measurements at the Large Hadron Collider during the Run I data-taking have provided. With this motivation, we summarize the relevant measurements from ATLAS, CMS and LHCb, and discuss the available phenomenological studies that quantify the information on PDFs provided by these datasets. Then we move on to discuss the prospects for PDF-sensitive analysis at Run II. We explore how the increase in center-of-mass energy and luminosity can provide new opportunities for PDF studies beyond those available at Run I. We also quantify some of the constraints on PDFs that Run II can provide by means of a profiling analysis using W , Z and t t simulated pseudo-data as input, as well as study the impact of Run II inclusive jet data in the CT framework.
A total of 4276 data points are fitted at NLO and 4078 at NNLO. The complete list of LHC measurements that have been included in NNPDF3. 0 is summarized in Sect. 4. Concerning theory calculations, all collider processes have been computed using fast NLO interfaces  [61, 62, 92] , supplemented by NNLO and electroweak K-factors when required. Inclusive jets are treated at NNLO using the approximate threshold calculation  [93, 94] , validated on the exact calculation in the gg channel  [95]. Heavy quark mass effects are computed in the FONLL General-Mass variableflavor number scheme  [96] , with the main difference being that at NLO it is the FONLL-B scheme that is used, rather than FONLL-A as previously, since this provides a better description of the low-Q 2 charm production data. All the fitting code has been rewritten from FORTRAN to C++ and PYTHON, making it robust and modular, so that the modification of the theoretical calculations, the addition of new datasets, or the generation of entirely new sets of pseudo-data for use in closure testing, can be done easily and quickly with no need to modify the rest of the code.
Improved positivity constraints and dynamical preprocessing exponents have also been implemented. As both data and theory improve, it becomes even more necessary to ensure that the fitting methodology is consistent and unbiased. To this end the NNPDF methodology has now been subjected to a closure test  [22]. This is performed by generating pseudodata based on an assumed prior PDF (for example MSTW08) and a particular theory (for example NLO perturbative QCD with given α S , heavy quark scheme, etc). To make the test as realistic as possible, the pseudodata are generated using the experimental uncertainties of the current global dataset. In the context of the closure test, the pseudodata are 'perfect': in particular they are fully consistent both with each other and with the assumed theory.
A full fit to the pseudodata is thus a rigorous test of the fitting methodology: fitted PDFs should have manifestly unbiased central values, and statistically meaningful uncertainties and correlations (so that for example the fitted PDF agrees with the assumed prior at one sigma 68% of the time). The success of the NNPDF3. 0 closure test proves that the NNPDF3. 0 PDF sets fitted to real data are unbiased and have statistically meaningful uncertainties and correlations. This in turn confirms that most modern datasets are consistent (both internally and in the context of the global dataset), in the sense that their systematic errors have been sensibly estimated, and furthermore that NNLO QCD is sufficient to describe the global dataset within a common universal framework. In particular the NNPDF3.
0 fits to real data show no sign of tension between deep inelastic and hadronic data. The NNPDF3. 0 PDFs are available on LHAPDF6 as sets of 100 replicas. Baseline fits are available for α S (m Z ) = 0. 118 at LO, NLO and NNLO, and also at α S (m Z ) = 0. 115, 0. 117, 0. 119, 0. 121 at NLO and NNLO. A LO fit with α S (m Z ) = 0. 130 is also provided. The baseline fits are provided with 5 active flavours: alternative fits with N f = 3, 4, 6 active flavours are also available. NNPDF also provide fits to reduced datasets, for studies by the LHC experimental collaborations: HERA-only, HERA+ATLAS, HERA+CMS, no-LHC, and no-jets. All these are available at NLO and NNLO, for α S (m Z ) = 0. 117, 0. 118, 0. 119. The fits with α S (m Z ) = 0. 118 are also provided with 1000 replicas, for use in reweighting studies.
Central exclusive production of heavy-flavours As mentioned above, the production of charm and bottom quarks at the LHC provides useful constraints on the low-x gluon. Related processes, which can potentially give similar information on the gluon, are the central exclusive production of J/ψ and related mesons, such as the Υ or ψ(2S). These processes have the advantage of providing very clean experimentally final states. Calculations for these processes have been presented in Refs. [140, 141]  within the diffractive photo-production formalism and have been compared to the LHCb data  [142, 143]. While this theoretical approach is distinct from the standard collinear factorization picture, the predictions may be extended so as to be related to the collinear gluon PDF, allowing such data to be incorporated consistently into PDF fits.
Constraining PDFs with LHC data at Run I In this section we present an overview of LHC Run I measurements with potential sensitivity to PDFs, along the lines discussed in Sect. 3. We report here the specific analyses that have been performed and, whenever applicable, the studies used to document their PDF constraining power. This section is split by experiment: we begin with the summary of the results from ATLAS, then we move to CMS and finally to LHCb. A summary table, including only measurements that are published or submitted to a journal, is provided for each experiment. Measurements which are still in preliminary form, or have been superseded, are not included in this report.
Constraints from ATLAS The measurement from the ATLAS collaboration are summarized in Table  2. The available ATLAS jet production measurements, relevant for PDF studies, are the inclusive and dijet differential cross sections from the 2010 dataset  [157] , the ratio of 2. 76 to 7 TeV inclusive jet cross sections  [24]  and, more recently, the inclusive  [158] , dijet  [159]  and trijet  [160]  differential cross sections from the 2011 dataset. The jets are reconstructed using the anti-k T clustering algorithm  [174] , and the cross section measurements are available separately for two radius parameters: R = 0. 4 and R = 0. 6. The different radii have different sensitivity to final state radiation and underlying event effects. Nonperturbative corrections are supplied in the relevant publications, as well as electroweak corrections for the 2011 inclusive and dijet measurements.
The cross sections for either R = 0. 4 or R = 0. 6 jets can be included in a PDF fit, though not both at the same time since the measurements are correlated. A PDF fit to demonstrate the sensitivity to the large-x gluon has been performed, adding the ratio of 2. 76 TeV over 7 TeV inclusive jet data to the HERA I data, and comparing to a baseline fit determined using only the HERA data  [24]. While the measurement has limited statistics, it provides a powerful proof-of-principle of the enhanced PDF sensitivity of such ratio measurements, due to the partial cancellation of the dominant theoretical and experimental systematic uncertainties, as discussed in the previous section and in Ref. [44]. The data were shown to be sensitive to the large-x gluon, both reducing the uncertainty and favoring a larger gluon at high x compared to the fit including only HERA I data.
The jet measurements performed using 2011 data have substantially higher precision compared to previous measurements, and span a wider kinematic range, extending up to 2 TeV in inclusive jet p T , and up to 5 TeV in dijet or trijet invariant mass. They are particularly interesting since, for the first time, the full statistical and systematic correlations between various measurements are provided, allowing their simultaneous inclusion in a PDF fit, thus enhancing the constraining information. Jet measurements using 8 TeV data are also ongoing. Measurements of top quark pair production provide complementary information on the gluon PDF at high x, and at the same time are sensitive to the strong coupling and the top mass. The ATLAS collaboration has released many measurements of the top quark pair production cross section  [164] [165] [166] [167] [168] [169] [170]  with increasing precision and using a variety of final states.
Several of these measurements have been used in recent global fits  [21, 22]. Differential cross sections, using the 2011  [171, 172]  dataset, have also been measured. In the most recent of these measurements, using the full 2011 dataset, the normalized differential cross sections are measured as a function of the top quark transverse momentum, and of the top quark pair mass, transverse momentum and rapidity. Normalizing the cross sections reduces the dependence on higher order QCD corrections, though it also slightly degrades the sensitivity to the gluon PDF overall normalization. When compared to a range of PDFs, the measured data distributions tend to be softer than the predictions, indicating either their power to constrain the gluon, and/or the importance of higher order QCD and/or electroweak corrections.
Further measurements at 8 TeV are also ongoing. These, as well as future precision measurements at higher energy, could place more significant constraints on the gluon. Additional constraints on the gluon PDF at medium and large-x could be provided by isolated prompt photon data. Compared to jets, prompt photons feature a cleaner experimental environment, though current measurements are of limited precision. ATLAS has measured isolated prompt photon production cross sections in the 2010  [161]  and 2011  [162]  datasets. The latter provide differential cross sections as a function of photon transverse energy and pseudo-rapidity, in central and forward pseudorapidity regions. The ATLAS collaboration has studied the sensitivity of these data to PDFs  [122] , providing a quantitative χ 2 comparison of the agreement with NLO QCD predictions for a range of PDFs.
The results show some tension between data and theory for several current PDFs, indicating potential to constrain the shape and uncertainty of the gluon, though theoretical scale uncertainties are also large. Measurements of prompt photons in association with jets  [163]  are also available. Inclusive electroweak boson production data provide constraints on quarks and quark flavor separation. ATLAS has published W and Z rapidity distributions using the 2010 dataset, including full experimental correlations  [123]. This has become a standard dataset, now widely used in the global PDF fits. In a separate ATLAS analysis, these data were used in a full PDF fit using the HERAfitter framework, in order to quantify the sensitivity to the strange quark content of the proton  [123]. The result favors a non-suppressed strangeness.
The data also provide constraints on the valence quarks. The ATLAS inclusive W and Z analysis of 2011 data is also close to publication. The larger dataset will allow for more differential measurements, such as the double differential Z/γ * cross sections binned in dilepton mass and rapidity, and W ± cross sections in bins of lepton pseudo-rapidity, as well as double differentially in lepton pseudo-rapidity and lepton transverse momentum. Importantly, these data have the potential to provide constraints on valence quark PDFs, and will shed further light on the strangeness content of the proton. ATLAS has also published low  [145]  and high  [37]  mass Drell-Yan measurements, providing additional and complementary information to the measurements around the Z mass peak. The ATLAS low mass Drell-Yan cross sections are measured as a function of dilepton invariant mass with coverage 12 < M ll < 26 GeV for the 2010 dataset, and 26 < M ll < 66 GeV on a subset of the 2011 dataset.
High-mass Drell-Yan measurements can provide important constraints on large x quarks and anti-quarks. The ATLAS analysis, based on the 2011 dataset, reaches dilepton invariant masses up to 1. 5 TeV and has been included in recent PDF fits  [21, 22]. In addition, in the presence of QED corrections, this measurement can also be used to constrain the photon content of the proton, and has been included in the NNPDF2. 3QED fit  [130]. A higher precision measurement at 8 TeV is in preparation. ATLAS has also studied the forward-backward asymmetry in Drell-Yan events  [14] , using 2011 data, which may provide sensitivity to PDFs. In the same paper the effective weak mixing angle was extracted. ATLAS has explored the production of vector bosons in association with heavy flavours. The production of W in association with charm quarks has been measured differentially in charged lepton pseudo-rapidity, using the 2011 dataset  [30].