dataesr/scientific-paragraphs-categorization huggingface dataset:
The merging of multi-jet production processes at leading order  [26, 27, 28]  and next-to-leading order  [29, 30]  is based on truncated parton showers. Multiple parton interactions are implemented via the Sjöstrand-van-Zijl model  [31]. The hadronisation of partons into hadrons is modelled by a cluster fragmentation model  [32]. Alternatively, in particular for uncertainty estimations, an interface to the Lund fragmentation model  [33]  of PYTHIA  [34]  is available. SHERPA provides a large library for the simulation of τ -lepton and hadron decays, including many form-factor models. Furthermore, a module for the simulation of QED final-state radiation in particle decays  [35] , which is accurate to first order in α for many channels is built-in. To account for spin correlations in production and subsequent decay processes the algorithm described in  [36]  is implemented.
Events generated with SHERPA can be cast into various output formats for further processing, with the HEPMC  [37]  format being the most commonly used. In the specific case of parton-level events, at the leading and next-to-leading order in QCD, additional output formats are supported. They include Les Houches Event Files  [38] , NTUPLE files for NLO QCD events  [39]  and cross-section interpolation grids produced via MCGRID  [40, 41]  in the APPLGRID  [42]  and FASTNLO  [43, 44]  formats. To analyse events on-the-fly a runtime interface to the RIVET package  [45]  can be used conveniently. The SHERPA Monte Carlo is publicly available from its HEPFORGE project page sherpa. hepforge. org. The actual code development and bug-tracking facilities are hosted on gitlab. com/sherpa-team/sherpa. The current release version is SHERPA 2.
Highlighting SHERPA Components In the following we will briefly describe the central components of the SHERPA framework. We focus on the physics models and features available, providing references to the original literature for more detailed theoretical derivations and discussions. The SHERPA framework is written in C++ in a highly modular structure, reflecting the factorised ansatz to calculate the evolution of scattering events. The SHERPA core module is responsible for steering the event generation process. It initialises the required physics modules and iterates the steps of the simulation. The setup of each generator run, including the specification of model parameters and all switches, is read from a simple ASCII file, called Run. dat per default. Parameters of a specific simulation aspect are collated in blocks following a simple bracket syntax: Examples of blocks are (run), where general settings are kept, while the specification of the hard scattering process to be considered is compiled in (processes).
BSM Simulations An interface to FEYNRULES  [58, 59, 60] , i. e. the UFO model definition files  [61] , allows the user to consider a wide range of models. In AMEGIC, however, only vertices with up to four external particles are supported, imposing some limits on its abilities, while in COMIX this number is limited only by computing power, allowing calculations in more complicated theories. With the physics-model information encoded in the standard UFO format  [61] , SHERPA creates and links complete C++ source code necessary to compute arbitrary scattering processes, employing an automatic generator for Lorentz  [8]  and colour  [62]  structures which represent the elementary vertices of the theory. The generators AMEGIC and COMIX have been extensively benchmarked, internally and against other codes, for example in the SM  [54]  and the MSSM  [63]   1.
Processes that have already been evaluated at full EW one-loop order with a development version of SHERPA include: • three-jet production at the LHC  [90] , • four-lepton production  [91, 16, 92] , • t th production  [16] , • W (Z) production with up to three (two) jets  [93, 16] , • γγW and γγZ production  [94] , • and γγj production  [95]. NNLO QCD Calculations For a few phenomenologically highly relevant processes SHERPA allows for the evaluation at NNLO QCD precision, using the q T -slicing method based on the ideas of  [96, 97]. QCD NNLO cross sections can be computed in SHERPA for neutral and charged current Drell-Yan processes  [25, 98]  and for Higgs-boson production  [24]. 2  Note, the NNLO facilities are not distributed with the public code releases, but can be obtained in the form of plugins from http://www.
Acknowledgements We would like to thank all our former collaborators on the  SHERPA  project, in particular the various students that contributed with their theses works to further improve our physics models and the corresponding computer codes. We are indebted to our users that have helped us to find and fix short-comings in the program. This work has received funding from the  European Unions Horizon 2020  research and innovation programme as part of the  Marie Sk lodowska-Curie Innovative Training Network MCnetITN3  (grant agreement no. 722104 ). It was supported by the  U. S. Department of Energy  under contract  DE-AC02-76SF00515. It used resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No.
DE-AC02-07CH11359. SS acknowledges support through the  Fulbright-Cottrell Award  and from  BMBF  (contracts  05H15MGCAA  and  05H18MGCA1 ). FS's research was supported by the  German Research Foundation (DFG)  under grant No. SI 2009/1-1. MS acknowledges the support of the  Royal Society  through the award of a  University Research Fellowship. The work of DN is supported by the  French Agence Nationale de la Recherche , under grant  ANR-15-CE31-0016.
Influence of the choice of the maximum change of velocity allowed by the breakup model A set of ∆V , with norm and orientation randomly distributed in space, is applied to the velocity vector of the parent body. Based on the positions and velocities of the real observed fragments in April 2008 and on the velocity of the parent body, derived from the initial conditions, the velocity increments distribution of the generated fragments is determined. The datafile containing the positions and velocities of the fragments at the reference epoch 04/23/2008 has been provided by Carmen Pardini and Luciano Anselmo and is described in  [3]. From the study of this ∆V distribution, and in particular by the examination of the data serie quantiles, it can be established that approximately 99% of the objects have a ∆V of about 435.
Data collection Data collection was performed retrospectively using a standardized data collection by two dermatologists (A. B. and M. P. M) and one medical oncologist (P. C. ). Data collected included the following patient characteristics: age at treatment initiation, gender, history of autoimmune disease, Eastern Cooperative Oncology Group performance status, body mass index (BMI) and smoking habits (active smoker, never smoker, or former smoker since at least one year) prior to treatment with ICIs. Disease characteristics included: tumour type and number of metastatic sites at treatment initiation. Treatment characteristics included the number of prior systemic treatments; type of ICI and concomitant use of systemic corticosteroids. The concomitant use of systemic corticosteroids was defined by a prescription of ≥ 20mg of oral prednisone or equivalent for more than 10 consecutive days within the period of ICI treatment.
05. In order to assess the adjusted effect of age on OS and PFS, we used multivariate Cox proportional hazard models. All variables with a statistically significant impact on OS or PFS in univariate analysis plus age were included in the multivariate models. All analyses were performed using R statistical software (R Foundation for Statistical Computing). Database follow-up was closed in July 2018. Data were rarely missing, and no data imputation was performed through the analyses.
We chose a cut-off age of 70 years for its better relevance considering the higher prevalence of age-related changes over 70 years  [18]. Between 1980 and 2012 in France, 45% of newly diagnosed cancers occurred in patients older than 70 years  [19]. However, only 10% of patients enrolled in ICIs clinical trials were ≥ 75 years  [5]. In our study including unselected patients receiving an ICI monotherapy, patients ≥ 70 years represented 34% of the total population, a clearly increased percentage compared to what is usually seen in immune-oncology clinical trials. Therefore, older patients included in this series appear to be representative of a real-life older population. Several retrospective series have explored the relative efficacy of ICIs according to age. For patients with melanoma, Chiarion and colleagues first suggested in an Italian multicentric study that efficacy and safety data from 188 patients aged > 70 years treated with ipilimumab were consistent with the outcomes observed in a younger population, with a safety profile also consistent with that observed in the general population  [10].
Neither the timing between the start of the use of corticosteroid and the ICI initiation nor the reason for the corticosteroid administration were collected in this study. A specific impact on patient outcomes according to the timing, dose, and reason of administration of the corticosteroid can then not be ruled out. However, this study does not support a strong prognosis or predictive value of these clinico-biologic markers. Preclinical and clinical ex vivo data suggest that older patients undergo age-related immune changes, grouped under the term "Age Related Immune Dysfunction" (ARID), as part of the immunosenescence process  [28]. ARID leads to a decline in the production of naïve CD8+ T cells  [29] , reducing the antigenic diversity of immune cells which could make the immune response to newly encountered antigens less effective  [30].
First, we query the digital libraries with the keyword "Interoperability Assessment". A total of 135 papers are identified. From these papers, we extract the most used keywords (i. e. repeated more than five times) and the most repeated terms (i. e. repeated more than fifteen times) in their titles and abstract. To do so, we download the papers metadata (i. e. title, year of publication, authors, abstract and keywords) in. RIS format. Next, we perform a data mining on the extracted metadata in order to identify the relevant keywords using VOSviewer software  [38]. VOSviewer is used to construct and visualise cooccurrence networks of important terms extracted from the metadata. After the co-occurrence analysis, two more keywords are defined: Interoperability Maturity Model and Interoperability Evaluation.
In the next step, we query the four digital libraries again with these two new keywords. We identify 88 and 81 papers related to Interoperability Maturity Model and Interoperability Evaluation, respectively. Further, we extract the metadata from these new 169 papers. Before performing another co-occurrence analysis, we exclude the metadata from the redundant papers. Consequently, the new analysis considers 263 papers, i. e. the 135 from the previous analysis more 128 papers specifically related to the two new keywords. We define four more keywords and terms based on the analysis, which are Interoperability Measurement, Interoperability Analysis, Interoperability Methodology and Interoperability Performance Evaluation. Further, we identify 56 papers related to Interoperability Measurement, 134 papers associated to Interoperability Analysis, 57 papers to Interoperability Methodology and finally, 8 papers related to Interoperability Performance Evaluation.
Finally, we perform a last co-occurrence analysis considering the total of non-redundant papers, which is equal to 418. Figure  1  and Figure  2  present the most occurred keywords and key terms from the identified papers. As additional search strategy, we also include 29 papers using the "snowball sampling" technique [39] whereby we consider the referrals of assessment approaches made by experts, as well as the most cited papers in the existing INAS surveys and reviews. A total of 447 publications are identified at the end of this sampling phase. Furthermore, the selection of the papers to be analysed and compared is done in two steps. In the first one, we apply for each one of the papers, the inclusion and exclusion criteria corresponding to the step St1 as described in Table  2. In this step, we only consider the metadata of the papers.
The twenty-six interoperability requirements defined for the performance assessment are related to three main factors: the time, quality and cost of interoperations. In order to verify the interoperability requirements (independent of the type of assessment) a computer-mediated tool is proposed. The requirement verification is mainly based on model checkers. For evaluating a-temporal requirements (i. e. requirements that are independent of time), they first transform the requirements into conceptual graphs. Next, they use the COGITANT (Conceptual Graphs Integrated Tools Allowing Nested Typed graphs) tool  1  for performing the requirement verification. For evaluating the temporal requirements (i. e. verifiable only at certain stages of the collaboration), they first model the requirements using the Networks of Timed Automata (a behavioural modelling language).
, 2012; Yates et al. , 2017) , the generic process of carcinogenesis underlying each tumour type remains elusive. The availability of public data sets describing the genetic characteristics of multiple specimen of various tumour types however represents an opportunity to decipher oncogenesis as a stochastic process. All tumours characterised so far are outcomes of their type-specific evolutionary process. This is akin to Gould's contingency definition  (Gould, 1990) , as if "replaying the tape" of somatic evolution: recording all malignant developments starting from the mostly identical genetic backgrounds of different human individuals  (Rosenberg et al. , 2002). Here, we investigate these recorded outcomes to infer the evolutionary landscape of each tumour type, mapping the evolutionary trajectories that can lead normal cells to cancerous transformation.
| TCGA data We downloaded data for 2,742 samples from The Cancer Genome Atlas (TCGA), for which we could obtain both allelic frequencies for mutations and copy number alterations (CNAs). This represented 133 bladder cancers (BLCA), 914 breast cancers (BRCA), 195 colorectal cancers (COAD), 256 glioblastomas (GBM), 296 head and neck squamous cell cancers (HNSC), 306 kidney clear cell renal cell carcinomas (KIRC), 262 lung adenocarcinomas (LUAD), 132 lung squamous cell cancers (LUSC) and 248 skin melanomas (SKCM). Raw SNP array data were normalised using the aroma R package  (Ortiz-Estevez, Aramburu, Bengtsson, Neuvial, & Rubio, 2012)  with paired normals, and copy number profiles were called using ASCAT  (Van Loo et al. , 2010).
| Colorectal adenoma and carcinoma data We retrieved whole-genome sequencing data from 31 samples from 9 adenomas and 72 samples from 11 carcinomas  (Cross et al. 2018). Mutations were called with platypus  (Rimmer et al. , 2014) , copy number data and ploidy were obtained using the cloneHD software  (Fischer, Vázquez-García, Illingworth, & Mustonen, 2014). Mutation clonality was estimated using the EstimateClonality package  (McGranahan et al. , 2015)  on each of the 31 samples as for the TCGA data. Mutations were considered clonal in an entire adenoma when they were predicted as clonal in ≥75% of the related samples.
| MET500 data Genomic data were retrieved from  Robinson et al. (2017)  and via the MET500 website (https://met500. path. med. umich. edu/). Sample annotation was manually curated to attribute a tumour type to the primary site and metastatic site. All curated annotations are reported in Supporting Information Tables  2  and 3. Sample purity was collected from the Supp. Information of the original publication; sample ploidy was calculated as the mean copy number weighed by number of targeted exons. Mutation clonality was estimated using the EstimateClonality package.
| Clonality and driver alterations Mutation clonality was estimated thanks to the EstimateClonality package  (McGranahan et al. , 2015) ; clonal mutations were defined as those for which the 95% confidence interval of the cancer cell fraction (CCF) included 1. Gain and losses of each gene in each sample were defined relatively, if the copy number deviated by 0. 6 or more from the ploidy given by ASCAT in the segment that included the gene of interest. Segments with <10 probes were filtered out. Driver alterations for each tumour type were retrieved from the IntOGen website  (Gonzalez-Perez et al. , 2013; Rubio-Perez et al. , 2015). Due to the lack of an appropriate clonality estimation method, all copy number alterations were considered clonal. Final matrices of clonal mutations per sample are available via github.
| Genetic parameters of local malignant adaptation The selective advantage of each driver alteration was defined as the ratio between expectations and observations in a given tumour type, considering mutations and CNAs separately. The expected number of mutations occurring in a gene was calculated by dividing the total number of mutations by the total number of genes in which at least 1 mutation was observed, weighted by each gene's length in base pairs, as given by the median transcript length for the gene in Ensembl  (Zerbino et al. , 2018). Only clonal mutations were considered. CNA selective advantage was calculated separately for gains and losses, by the ratio of expected occurrences given all events to the actual occurrences of each CNA, giving equal probability to all genes without weighing for length.
Acknowledgments This work was funded by  NSERC  ( Discovery grant program ), the  French Polar Institute  (IPEV, program  1042 ), and the  BNP Paribas foundation. Logistical support for the research at Bylot Island was provided by the  Polar Continental Shelf Program (PCSP). Bylot Island is located within  Sirmilik National Park , and we thank  Parks Canada  and the  Pond Inlet community (Mittimatalik)  for permission to work there. We are grateful to  Gilles Gauthier  and  Marie Christine Cadieux  for the use of the  Bylot Research station, which is part of the station network of the Centre d'Études Nordiques. Denis Sarrazin provided invaluable assistance in the deployment and maintenance of instrumentation at Bylot Island. FD thanks  Carl Benson  for providing a copy of the seminal 1972 Trabant and Benson paper.
The Schur complement system is solved iteratively using the Portable, Extensible Toolkit for Scientific Computing (PETSc) computational environment  [20]  and more specifically the Krylov subspace package (KSP), using hierarchical GMRES options and Block Jocobi preconditioning  [20]. In all the computations, 384 subdomains have been considered, with ndx = 12, ndy = 8, ndz = 4 and in each subdomain Ω k a 30 × 30 × 30 mesh has been used. For a plate with length L, the inflow x in of the computational domain has been set at a distance L/2 from the plate's leading edge and the domain's length in the streamwise direction is L x = 3L (that is the distance from the plate's trailing edge to the domain's outflow boundary is 3L/2). The numerical approach has been used in  [16] , for similar flow conditions and equivalent plate motions, in however a quasi two-dimensional setting.
Plug-and-play pTX here is done by pre-calculating RF pulses that were optimized on a series of B þ 1 and B 0 offset maps obtained from a representative sample of the adult population. The RF pulse optimization thus takes place separately from the scan session, and consists in minimizing an objective function that maximizes the average performance of the pTX excitation across the database of field maps  (Gras et al. , 2017a). The result of this procedure is universally valid pulses, hence the term Universal Pulses (UP). To better cope with inter-subject variability of the B þ 1 profile, UPs are often designed by considering a broader class of pTX excitation modes than RF shimming, namely dynamic RF shimming  (Padormo et al. , 2016). Here, the capabilities of pTX are more fully exploited in that dynamic RF shimming pulses take advantage of the transmit-sense  (Katscher et al.
nmr. mgh. harvard. edu/software/c2p/sms), which uses sequential application of Slice-GRAPPA  (Setsompop et al. , 2012)  with leak-block  (Cauley et al. , 2014)  and GRAPPA  (Griswold et al. , 2002). The T 1 -and T 2 -weighted acquisitions were in sagittal orientation, isotropic resolution of 0. 8 mm, FOV ¼ 256 Â 224 Â 208 mm (read, phase and partition axes) and a GRAPPA acceleration factor of 2 in the phase encode direction. Other parameters were: TR ¼ 2. 6/3. 0 s, MPRAGE inversion time ¼ 1. 1 s, echo spacing (ES) ¼ 10. 2/8. 6 ms, readout bandwidth ¼ 240/370 Hz/pixel, flip angle ¼ 4 /Muglerfls approach  (Mugler, 2014; Mugler et al. , 2000)  respectively for the MPRAGE and SPACE acquisitions.
Design of parallel transmission Universal Pulses Dynamic RF shimming with spoke (slice-selective) and k T -point (nonselective) pulses use a simple and low-dimensional parameterization of the RF and gradient waveforms. It consists of i) the different RF shimming weights, defined by the time integral of the RF shim sub-pulses, and ii) the transmit k-space  (Cloos et al. , 2012b)  displacement vectors, i. e. the time integral of the interleaved magnetic field gradient blips along the three axes. In this framework, efficient non-linear constrained optimization algorithms can be applied to attempt finding the best possible dynamic RF-shimming solution  (Hoyos-Idrobo et al. , 2014). The UPs used for the MB-EPI, the MPRAGE and the SPACE acquisitions in pTX were designed offline on a database B of measured subjectbased B þ 1 and ΔB 0 maps of size N B ¼ 10 (5 female), acquired in a separate study  (Gras et al.
, 2017a). The UP design consisted in evaluating the subject-specific objective ε tailored (typically a measure of the mean deviation of the flip angle distribution across the region of interest) on every subject of the database and computing the mean objective across this database. The UP-objective ε UP may thus be written as: ε UP ðpÞ ¼ 1 N B X j2B ε tailored À S j ; p Á ; (1) where p denotes the dynamic RF shimming parameterization to be optimized, and ε tailored ðS j ; pÞ, the evaluation of the subject-based objective on subject S j of the database for the parameterization p. Hence, without any additional difficulty, hardware (RF power and gradient slew rate limits) and safety (SAR) constraints can be enforced explicitly for the UP design following exactly the same methodology used for a subject-tailored pulse design.
This was implemented using the active-set non-linear constrained optimization algorithm  (Hoyos-Idrobo et al. , 2014) , available in the optimization toolbox of MATLAB (R2016b, the Mathworks, Natick, MA). The hardware constraints were i) the peak RF amplitude limit (170 V per TX channel at the coil plug), ii) the average RF power limit per TX channel (3 W), iii) the total average RF power limit (16 W) and iv) the maximum slew rate of the magnetic field gradient coils (200 mT/m/ms). The SAR thresholds were expressed in terms of global and local SAR limits of 3. 2 W/kg and 10 W/kg respectively  (International Electrotechnical Commission, 2015). For the design of the non-selective UPs (SPACE and MPRAGE sequences), the subject-tailored objective was defined as the normalized root mean square (NRMS) deviation of the flip angle distribution αðrÞ from the target flip angle α T , calculated across the brain (region R ), i.