dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). The PET time series were calculated using the  Oudin et al. (2005)  formula. The streamflow data were retrieved from the "Banque Hydro" database  (Leleu et al. , 2014). For TOPMODEL and dynamic TOPMODEL, a   The results of Fig. 5  show that the packages based on models coded with a compiled programming language have lower CPU times than the packages integrating models coded in R. The CPU times associated with the packages integrating models coded with a compiled language have the same order of magnitude. The HBV. IANIGLA package has lower CPU times than the other selected packages. The CPU times associated with sacsmaR and the models coded in R of hydromad are lower than the CPU times of dynatopmodel and WALRUS (CPU times can also depend on the model implementation). WALRUS has the highest CPU times, which is probably caused by the flexible time step approach: the computational time step is decreased automatically to improve numerical stability.
Aspects of practical implementation and maintenance of packages The adaptability and efficiency of packages can also be assessed in terms of R structure. Among the choices that developers have made for their R packages, programming languages used for the core functions of the models and dependence on external packages for some functions resulted in very different R structures. Using a compiled programming language for the core model functions (airGR, HBV. IANIGLA, hydromad, topmodel and TUWmodel) tends to lower CPU times and ease parameter estimation procedures, though computation times can also depend on algorithm efficiency, choice of parameter estimation technique, number of parameters to be estimated and operations not related to the core model runs. As a result, applications on studies relying on a large database of catchments and uncertainty analysis procedures such as Monte Carlo realisations are easier with these packages.
Furthermore, one might ask whether the choice of package should exclusively rely on the performance of the models. We could argue that in conceptual hydrology modelling, models are mainly assessed by their ability to reproduce river discharges at the gauging station of a catchment  (Singh et al. , 2017). Therefore, a hydrologist who would need to use an R package for modelling might favour the efficiency of a given model in their selection procedure. Even though we have run the models contained in the selected packages several times on different datasets, we have chosen to not show any comparison of model performance, which would be dependent on specific catchment characteristics and more representative of the model than of the package itself. We provide with this comparison R scripts to test one parameter set for each model on a simple hydrology example, whereas it is becoming increasingly important to be able to use sensitivity analysis to explore location-specific behaviour of a model  (Haghnegahdar et al.
Assessment of package documentation. Description: information on the general purpose of the function and the associated possibilities. Details: precise explanations of the function. Arguments: description of the function arguments that requires details about the unit, the R object class and how to obtain it. Value: description of the function outputs. References: related documentation where users can find more information on the function. Examples: R commands to use the function. Examples are considered as "comprehensive" if they cover mostof the functionalities and if there is an example for each function. Dataset: an example dataset is provided and can be used with the package functions. Steps between functions: explanations of the required stages to run the main functions. The last two fields are not explicit parts of the package manuals but can help to understand and use a package.
run the semi-distributed version of Sacramento because the example found in one of the vignettes does not illustrate the required discretisation. Coherence between dynatopmodel functions is not made explicit by the provided function, especially for the catchment spatial discretisation. All the packages except HBV. IANIGLA include datasets that can be used with the examples provided in the documentation. HBV. IANIGLA provides an example on how to generate random time series of inputs.
Code availability. R scripts to use the packages on simple hydrology examples are provided under the terms of the GNU General Public License 2. 0 and available at: https://doi. org/10. 15454/3PPKCL. They enable the application of each package on a simple hydrology example. These scripts consist of basic input data or spatial data preparation and data check procedures, R formatting functions, run of a single parameter set for each model, calculation of the KGE criterion and plot of results. For the packages that include a snow-accounting function, the R scripts include the related steps. We do not provide the hydrometeorological data that were used in these scripts. However, all the packages except HBV. IANIGLA include one or several example datasets. These scripts do not provide R commands to run parameter estimation procedures or to perform uncertainty analyses.
run the semi-distributed version of Sacramento because the example found in one of the vignettes does not illustrate the required discretisation. Coherence between dynatopmodel functions is not made explicit by the provided function, especially for the catchment spatial discretisation. All the packages except HBV. IANIGLA include datasets that can be used with the examples provided in the documentation. HBV. IANIGLA provides an example on how to generate random time series of inputs. Package Description Details Arguments Value References Examples Dataset Steps between fun. airGR dynatopmodel HBV. IANIGLA hydromad sacsmaR topmodel TUWmodel - airGR includes several vignettes, for example on how to estimate parameters within a Bayesian Markov Chain Monte Carlo (MCMC) framework. The WALRUS package is stored on the GitHub platform where a complete set of documents, tutorials and data can be found (e.
g. an R script to run a Monte Carlo parameter estimation procedure). A comprehensive user manual, WALRUSThe topmodel's main example does not inform on the preprocessing steps to discretise a catchment. It may be complicated to use sacsmaR to whose structure is different from the usual R documentation, can also be found on GitHub. sacsmaR is stored on GitHub with a vignette on how to use the different functions. hydromad offers a vignette and nine demos are available and deal with subjects such as how to estimate the model parameters or how to conduct a sensitivity analysis. Examples of sensitiv-.
A full Bayesian statistical treatment of complex pharmacokinetic or pharmacodynamic models, in particular in a population context, gives access to powerful inference, including on model structure. We present here the results of our implementation of the simulated tempering Markov Chain Monte Carlo (MCMC) algorithm in the GNU MCSim software. Simulated tempering MCMC has a number of advantages over usual MCMC algorithms: it can sample from sharp multi-modal posteriors; it provides insight into identifiability issues useful for model simplification; it rapidly converges. The main challenge to implementing this approach as been setting an adequate scale of auxiliary temperatures, which we solved by stochastically optimizing a grid of inverse-temperatures, or "perks," through a Robbins-Munro process.
Introduction In modeling pharmacokinetics and pharmacodynamics, a Bayesian statistical framework gives access to a range of flexible inference options, including hierarchical non-conjugate models for population modeling  (Damien et al. 1999; Gelman 2006)  or seamless integration of prior and sequential knowledge. The incorporation of prior knowledge has been particularly important for physiologically-based pharmacokinetic (PBPK) models  (Gelman et al. 1996; Langdon et al. 2007; Tsamandouras et al. 2013; Zhang et al. 2011 ) and systems biology models  (Bois 2009; Hamon et al. 2014). The development of powerful numerical Markov chain Monte Carlo (MCMC) algorithms  (Girolami and Calderhead 2011; Robert and Casella 2011)  has fueled the increasing use of Bayesian methods. However, those algorithms are still very computationally intensive and for complex models or large datasets can take hours or days to reach convergence on current computers  (Hsieh et al.
This algorithm has excellent mixing properties and can deal with difficult or even multimodal posteriors, because the set of powered distributions provides a smooth landscape that a Markov chain can efficiently explore. This translates into rapid convergence, and, if perk zero is reached, the chain renews and convergence is guaranteed with only a single chain being run  (Geyer and Thompson 1995; Robert and Casella 2011). Reaching power zero also provides, at no extra computation cost, an estimate of the integrated data likelihood (the posterior normalization constant), which in turn can be used to compute Bayes factors enabling statistical testing of model structure  (Calderhead and Girolami 2009; Friel and Wyse 2012; Geyer and Thompson 1995). To date, validation of this method has been confined to limited datasets and simple models, because setting adequate perk values in simulated tempering MCMC for complex or differential models has proved to be very difficult  (Behrens et al.
A pathological linear regression test case This test case is a simple Bayesian linear regression applied to an artificially pathological dataset (see Figure  3 ). The data, y, are in fact a mixture of two linear components, for which we want to calibrate only one slope parameter a. There are clearly two solutions to this inference problem: a positive slope with one very small SD, σ 1 , for the part of the data that is increasing with the independent variable x, the other SD, σ 2 (for the data decreasing with x), being other very large; or a negative slope, with σ 1 small and σ 2 large. The intercept was assumed to be known and equal to zero. The data likelihood was assumed to be Normal, with mean a•x and SDs σ 1 or σ 2. Parameter a was assigned a uniform prior ranging from -10 to 10; σ 1 and σ 2 were assigned the same log-uniform prior ranging from 0.
Theophylline population PK test case To demonstrate population PK modeling with a compartmental model, we used plasma theophylline concentration data from the first six subjects (labeled 1 to 6) of a study by  Trembath et al. (1980). Briefly, six healthy subjects aged 21-36 years were administered an immediate-release dose of 190 mg theophylline; plasma theophylline concentration was followed up to 12 hours after dosing. At another occasion the same subjects received five doses of a sustained release formulation containing 190 mg theophylline, twice a day; plasma concentration was measured up to 12 hours following the first and the fifth dose. In addition, subjects 4 and 6 received five doses of a sustained release formulation containing 380 mg of theophylline, twice a day, and plasma concentrations were measured up to 12 hours following fifth dose.
Acetaminophen population PBPK test case Pharmacokinetic data from published clinical studies on acetaminophen and its metabolites, acetaminophen-sulfate and acetaminophen-glucuronide, were used for model calibration. The study references, their dose levels and number of subjects used are given in Supplementary Material Table  S1. We focused on studies using single oral doses, ranging from 325 mg to about 1,400 mg (20 mg/kg). Within each study, the individual subjects' data were averaged. The same data were used in  Hsieh et al. (2018). For acetaminophen and its conjugated metabolites, we used a PBPK model described in  Zurlinden and Reisfeld (2016, 2017). Model details such as structure, parameters, and state variables are detailed in those publications. Briefly, the model assumes physiological bloodflow limited transport.
Software and Hardware GNU MCSim version 6. 1. 0 (www. gnu. org/software/mcsim) (Bois 2009 ) was used to run the models and perform MCMC sampling. R version 3. 2. 3 (www. R-project. org) (R Development Core Team 2013) was used for some processing calculations and graphics. The Stan software version 2. 18. 2 was used for Hamiltonian MCMC simulations for the linear regression model. All simulated tempering (TI) MCMC computations were performed on a 6-core 12-threads workstation equipped with Intel Xeon CPUs E5-1650 V2 clocked at 3. 5 GHz.
Inference about a Gaussian mean test case This problem has a known solution: the expected value of the posterior mean μ is -0. 063 (close to the data average), and its posterior SD should be very close to 0. 1  (Bernardo and Smith 1994). The probability density functions of the prior and exact posterior distributions of μ, shown on Supplementary Material Figure  S4 , were used to benchmark the results. Thermodynamic integration was performed with 500,000 iterations (one in five of them was recorded). Only one chain was needed, since perk zero was reached and convergence guaranteed. Supplementary Material Figure  S5  shows the evolution of the perks' schedule, progressively adding or removing perks during the automatic adjustment phase. The eight perks proposed (0, 10 -6 , 10 -5 , 10 -4 , 10 -3 , 10 -2 , 0.
We used Bayes factors to show that the theophylline dataset is much better described by a population model including both intra-and inter-individual variability than by a model with inter-individual variability only. In addition, for that dataset, a one-compartment model is preferable to a two-compartment model (for the sake of parsimony). However, as discussed above, other data may justify the use of a two-compartment model. The ability of TI variants to yield estimates of normalization constants justifies the interest it is currently receiving in the statistical literature  (Behrens et al. 2012; Bhattacharya et al. 2019; Friel et al. 2014; Miasojedow et al. 2013; Zhou et al. 2016). The two population PK case-studies also show that the same or slightly better results are obtained with simulated tempering compared to those of standard Metropolis-Hasting MCMC sampling, but with a lower computational burden.
Plagioclase is the most abundant mineral in the early tephra and phenocryst compositions range from An = 60 to 78 [An = 100 × Ca/(Ca + Na + K) mol%] in the earliest tephra to An = 56 to 74 in the latest tephra. Pyroxenes are rare in all samples. Orthopyroxene crystals are resorbed and some are zoned (Fig. S2 ) with cores ranging between Mg# = 0. 7 and 0. 74, and rims of more variable compositions, from Mg# 0. 65 to 0. 77. Clinopyroxene microphenocrysts are sector zoned and with Mg# = 0. 70 to 0. 77. Diffusion, thermodynamic modelling approaches, and timescales of ascent rates from olivine zoning. We modelled the Fo  38  and Ni 37 concentration profiles using the software DIPRA  42  including the effects of diffusion anisotropy (Fig. 4  and Table  S1 ). Olivine crystals display Fo patterns that are broadly a mirror image of the NiO concentrations, but the NiO profiles are sharper than those of Fo.
Rim-to-core traverses were performed on olivine, orthopyroxene, and feldspar macrocrysts with a spacing of 1-5 µm between analyses. Individual analyses were performed on microcrysts. Electron backscattered diffraction (EBSD) patterns of olivine to determine the orientation of its crystallographic axes were obtained at the Nanyang Technological University using a JEOL JSM-7800F field emission scanning electron microscope (SEM) equipped with an Oxford NordlysNano detector and Oxford's AZtec microanalysis software. The modal abundance  55  of each mineral and the vesicles were estimated from backscattered electron images (BSE) acquired with low magnification (Fig. S1 ). Analysed crystals. In total we analysed 42 olivine (≈ 1,800 analyses, including 17 rim-to-core profiles), six orthopyroxene (≈ 270 analyses, three rim-to-core profiles), three clinopyroxene (≈ 150 analyses, two rim-to-core profiles), 14 plagioclase (≈ 280 analyses including 11 profiles), and 28 spinel (≈ 41 analyses) crystals.
Acknowledgements We thank  J. Herrin ,  L. Cheng  and  J. Oalmann  for assistance during EPMA and SEM work. We thank  T. Girona  for discussion about cyclic zoning in olivine crystals and its possible relation with unrest processes. Constructive reviews by  M. Rowe  and  I. A. Petrinovic , and editor  C. Beier  are appreciated and helped to clarify various aspects of the manuscript. H. Albert and F. Costa were supported by a  National Research Foundation   Investigatorship Award  (Grant Number  NRF-NRFI2017-06 ) from  Singapore. P. Larrea  and  E. Widom  were supported by the  National Science Foundation  grant  EAR 1019798. C. Siebe was supported by  Dirección General de Asuntos del Personal Académico  through project  UNAM-DGAPA IN-103618.
Numerical results Having established the accuracy through the comparison study illustrated in Figures  1  and 2 , further numerical results are given in this section. The material properties used by Galstyan et al. [26]  were taken to construct this numerical example. Thus, effect of different parameters, including sphere radius, fluid compressibility, viscosity and viscoelas- ticity on the vibrations characteristics of the sphere are investigated. In this work, the generalized eigenfrequency equation (  18 ) is solved employing Mathematica software. After finding the real part and the imaginary part of the angular frequency, the frequency and quality factor can be calculated. First ten vibration frequency and quality factor of the spheroidal modes are given in Table  1  for Newtonian and Maxwell fluids with compressional relaxation process.
They concluded that the measurement of the HVP at MUonE is not vulnerable to these NP scenarios. Therefore, the analyses of  [7]  and  [8]  reach similar conclusions where they overlap. These results confirm and reinforce the physics case of the MUonE proposal. In what follows we will discuss all issues related to obtaining a theoretical prediction for µ-e scattering at 10 ppm. We start in Section 2 by briefly revisiting the kinematics of µ-e scattering. Next, we discuss in Section 3 the fixed-order perturbative calculations in QED. This is followed by a discussion on how to include the HVP in Section 4. Possible strategies on how to deal with and estimate the importance of contributions beyond those included in the fixed-order calculations are considered in Section 5. Finally, in Section 6 we give an outlook on how the various pieces can be combined into a general purpose Monte Carlo code that provides a sufficiently accurate theoretical prediction, before we present our summary in Section 7.
Then, the integration over the angular variables is performed analytically thanks to the orthogonality properties of these polynomials, so that each diagram is eventually cast in the form of a residual radial integration, ∫ ∞ 0 dQ 2 Q 2 Π h (-Q 2 ) f (Q 2 , s, t), (39) which is computed numerically once provided with the HVP in the space-like region. The expressions of the kernels f (Q 2 , s, t) were presented in  [91]. Their implementation into a numerically stable code is necessary for future use in the Monte Carlo. The fact that the hadronic NNLO corrections can be obtained from Π h (q 2 ), with just q 2 < 0, suggests the possibility for MUonE to determine the HVP in an iterative way without making use of the R ratio. As a first step the hadronic NNLO corrections can be switched off in the Monte Carlo and a first approximation for Π h (q 2 ) extracted.
The presently available MC at NLO accuracy represents just the first step towards the realisation of a high-precision theoretical tool necessary for the data analysis of µ-e scattering by MUonE. The ultimate goal is the realisation of a MC code including NNLO corrections and resummation of QED contributions due to multiple photon radiation. However, over a relatively short term, a number of intermediate results could be obtained about some important contributions beyond NLO. A first example is given by the matching of NLO corrections to a QED parton shower, following the formulation already applied to Bhabha scattering and e + e -annihilation processes in QED  [101, 102] , Drell-Yan processes  [103, 104]  and Higgs decay into four leptons  [105]. This would allow to estimate the most relevant QED corrections beyond NLO under realistic event selection criteria.
Summary With this report we want to document that within the theory community there is sufficient interest, manpower, and expertise to provide the necessary theory support for the MUonE experiment. The minimal goal that will be achieved in a first step is a fully differential parton-level Monte Carlo program containing the following contributions: (i) the fully massive NLO QED (and electroweak) contributions; (ii) the fully massive (dominant) electronic contributions at NNLO; (iii) the fully massive NNLO hadronic contributions; (iv) the remaining contributions at NNLO in a massified approach, i. e. neglecting finite electron mass terms. In addition, this fixed-order calculation will be matched to a parton shower taking into account multiple photon emission at leading logarithmic accuracy. Given the current status described in this report, there are no further conceptual challenges that need to be overcome to achieve this.
Acknowledgements We would like to thank the  Physik-Insitut of UZH  for its hospitality in hosting the 2 nd WorkStop/ThinkStart. A special thanks to  Monika Röllin  for her invaluable help in organising this event. We would also like to thank the  University of Padova  and  INFN Padova  and the  MITP  for their hospitality and support of the 2017 Theory Kickoff Workshop and 2018 Topical Workshop, respectively. P. M. and W. J. T. wish to thank  Giulio Dondi  for testing the software AIDA on the real-virtual NNLO calculations. P. B. acknowledges support by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  701647. The work of M. C. is supported by the  Investissements d'avenir, Labex ENIGMASS. T. E. and Y. U. are supported by the  Swiss National Science Foundation  under contract  200021_178967.
Y. U. is partially supported by a  Forschungskredit of the University of Zurich  under contract number  FK-19-087. M. F. is supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under grant  396021762 -TRR 257  " Particle Physics Phenomenology after the Higgs Discovery ". The work of P. M. is supported by the grant Supporting TAlent in ReSearch at  Padova University (UniPD STARS  Grant  2017  "Diagrammalgebra"). W. J. T. is partially supported by Grants No. FPA2017-84445-P  and No. SEV-2014-0398  ( AEI/ERDF, EU ), the  COST Action   CA16201 PARTICLEFACE , and the " Juan de la Cierva Formación" program  ( FJCI-2017-32128 ). G. O. was supported in part by  PSC-CUNY  Award  61155-00 49. M. P. acknowledges partial support by  FP10 ITN Elusives  ( H2020-MSCA-ITN-2015-674896 ) as well as  Invisibles-Plus  ( H2020-MSCA-RISE-2015-690575 ).
Patients The Ethics Committee of the University Hospital of Saint Etienne (IRBN142018/CHUSTE) approved this study. All patients with a metastatic lung adenocarcinoma diagnosed on surgically resected samples in our institution from 2010 to 2019 were included in this study. For lymph-node metastases, N1, N2, and N3 lymph nodes were not included; only distant lymph nodes were considered. The clinical data of 253 patients were collected from medical and pathological records. The clinical variables were date of birth, sex, date of diagnosis, date of death or last follow-up, smoking status, tumor location, treatment received, and performance status (PS).