dataesr/scientific-paragraphs-categorization huggingface dataset:
At the end, there is one data set mapping, called FoodOntoMap, where for each concept that appears at least in two datasets, the mapping between them is provided by listing the artificial ID of the concepts from each of the datasets in which it is encountered. The datasets consist of 13 205, 1069, 111 and 582 unique food concepts, obtained using Hansard corpus, FoodOn, OntoFood and SNOMED CT, respectively. The FoodOntoMap mapping consists of 1459 food concepts that are found in at least two of the food semantic resources. The motivation for building such a resource in the food domain comes from the existence of the UMLS, which is extensively used in the biomedical domain. For example, the MRCONSO. RRF table that is a part of the UMLS is used in a lot of semantic web applications since it can map the medical concepts to a variety of different biomedical standards and vocabularies.
Conclusions Our motivation to start building the FoodBase corpus has been to provide the scientific community with a fundamental resource required for learning corpus-based methods that can be used for food-named entity recognition. FoodBase is presented in two versions: curated and uncurated. The curated version is manually evaluated, consisting of 1000 recipes, while the un-curated version consists of 21 790 recipes. For this reason, we can consider FoodBase as a whole as a 'silver standard'. It can be also used as a benchmark dataset for several ML tasks, such as multiclass classification  (29) , multi-label classification  (30)  and hierarchical multi-label classification  (31). Multi-class classification is applied when a food entity may be annotated with several semantic tags (e. g. 'Food' (AG:01); 'Production of food, farming' (AG:02); and 'Acquisition of animals for food, hunting' (AG:03)).
Multi-label classification is performed when an output is a more complex structure such as a vector of tags with some dependencies among them (i. e. the food entity can belong to multiple semantic tags simultaneously). Hierarchical multi-label classification is needed when the classes are hierarchically structured and food entities can be assigned to multiple paths of the class hierarchy at the same time (e. g. the food hierarchy from the Hansard corpus). As part of future work, we are working on presenting benchmarking results obtained from corpus-based foodnamed entity recognition using three datasets of different scale and quality (e. g. 200 recipes-manually annotated, 1000 recipes-manually annotated and 21 790 recipesautomatically annotated), in order to explore the utility of having such a corpus by applying sensitivity analysis.
Availability FoodBase http://cs. ijs. si/repository/FoodBase/foodbase. zip FoodIE https://github. com/GorjanP/FOM_mapper_client FoodOn https://foodontology. github. io/foodon/ OntoFood (OF) https://bioportal. bioontology. org/ontologies/OF/?p=summary SNOMED CT https://confluence. ihtsdotools. org/display/DOC/Technical+Resources Hansard corpus https://www. hansard-corpus. org/ NCBO annotator http://bioportal. bioontology. org/annotator NCBO annotator REST API http://data. bioontology. org/documentation FoodOntoMap https://doi. org/10. 5281/zenodo. 2635437.
ACKNOWLEDGMENTS The authors would like to express their appreciation to  Professors C. L. Morfey  and  M. Petyt , formerly of the  Institute of Sound and Vibration Research, Southampton, UK , for their support in the initial theoretical development of the work presented here. Professors R. Wilson  and  A. Cummings ,  formerly of the University of Hull, UK , are acknowledged for providing the impedance tube measurements. The second author gratefully acknowledges the funding from the  Swedish Research Council , Grant No. 2015-04258 , which has partially contributed to this work.
From now on, let us consider the collision of two proton beams. The primary goal of the LHC is to explore physics at high energies -or, equivalently, short distances -typically around the TeV scale (i. e. around 10 -19 m or 10 -4 fm). The vast majority of the analyses at the LHC involve either measurements of standard-model processes or searches for new phenomena involving physics beyond the standard model. Given the constraints already obtained from earlier colliders such as the Tevatron or LEP, the LHC analyses explore either energy (kinematic) domains previously unreachable, or rare phenomena in extreme corners of the phase-space, or both. With collisions at a centre-of-mass energy of 13 TeV, the LHC is able to boldly probe energies that no collider had probed before. This is however not sufficient to explore rare corners of the phase space for which we also need to accumulate as much data as possible.
The rescaling approach is available for both grid and jet-based methods. To encode the background shape, the user defines a rescaling function derived from FunctionOfPseudoJet<double> (see the FastJet manual for details). FastJet provides a 4th order polynomial in y as a simple illustrative example: // gives rescaling(y) = 1. 16 + 0 • y -0. 023 • y 2 + 0 • y 3 + 0. 000041 • y 4 fastjet::BackgroundRescalingYPolynomial rescaling(1. 16, 0, -0. 023, 0, 0. 000041); Then one tells the background estimator (whether jet or grid based) about the rescaling with the call bge. set_rescaling_class(&rescaling); Subsequent calls to rho() will return the median of the distribution p t /A/rescaling(y) (rather than p t /A). Any calls to rho(jet) and sigma(jet) will include an additional factor of rescaling(y jet ).
Subtraction for jet shapes The area-median subtraction procedure for jet shapes introduced in Section 2. 5 is not directly included in FastJet but is made available as GenericSubtractor in fjcontrib. This introduces a class named GenericSubtractor. In practice, the code varies the ghost transverse momentum in order to estimate the derivatives  (2. 31). Note that to do that, it brings the ghost p t scale from 10 -100 to a value where its effect on the shape is tractable within machine precision. The basic usage of this class is as follows: GenericSubtractor gensub(&some_background_estimator); FunctionOfPseudoJet<double> shape; // whatever shape you are using double subtracted_shape_value = gensub(shape, jet); By default, this only subtracts the transverse momentum density ρ (obtained from the background estimator provided to the class).
Recommendations for practical usage We have introduced all the concepts and ingredients needed to apply the area-median pileup subtraction method. However, at the end of the day, several details have to be specified for a practical 34 This requires at least version 3. 1 of FastJet. For the FastJet 3. 0 series, additional arguments have to be provided. With improved subtraction switched on, the computation of FF moments automatically includes the correction  (2. 55). Note also that the actual FastJet code uses mu to denote the argument. Here we have used kappa simply for internal coherence with the rest of this document. use. We give below our recommendations for applications at the LHC and we will provide extensive studies and tests of these settings in the next chapters. For simplicity, we assume that we have events where particles are kept up to a rapidity |y| = y max.
In that context, it is important to check that our conclusions are not affected neither by the details of the HI-UE, nor by the details of the embedded hard jets, including the fact that they are quenched of not. In that context, the subtraction procedure can be seen as directly belonging to our definition of jets. In practice, if the Monte Carlo program used to simulate heavy-ion events explicitly provides a separation between hard events and a soft part -e. g. as does Hydjet  [81, 82]  -one can extract one of the former as the single hard event and take the complete event as the full one. Alternatively, one can generate a hard event independently and embed it in a heavy-ion event (obtained from a Monte Carlo or from real collisions) to obtain the full event. This second approach, which we have adopted for the bulk of results presented here because of its greater computational efficiency, is sensible as long as the embedded hard event is much harder than any of the semi-hard events that tend to be present in the background.
In this section, we examine the robustness of our HI background subtraction in the presence of (simulated) jet quenching. 17 For this purpose we have used two available models which allow one to simulate quenched hard jets, Pyquen, which is used by Hydjet v1. 6, and QPythia. Pyquen has been run with the parameters listed in footnote 5 for the LHC, and with T0 = 0. 5 GeV, tau0 = 0. 4 fm and nf = 2 for RHIC. 18 For QPythia we have tested two options for the values of the transport coefficient and the medium length (q = 3 GeV 2 /fm, L = 5 fm and q = 1 GeV 2 /fm, L = 6 fm), with similar results. No serious attempt has been made to tune the two codes with each other or with the experimental data, beyond what is already suggested by the code defaults: in the absence of strong experimental constraints on the details of the quenching effects, this allows us to verify the robustness of our results for a range of conditions.
We close this section with a summary of our findings. Based on its description in Ref. [11]  and our findings about NpC v. area subtraction, cleansing with f cut = 0 would be expected to have a performance very similar to that of CHS+area subtraction with f cut = 0 trimming. 15 The differences in behaviour between (linear) cleansing and trimmed CHS+area-subtraction therefore call for an explanation, and appear to be due to a step in the cleansing code that was undocumented in Ref. [11]  and that we dubbed "zeroing": if a subjet contains no charged tracks from the leading vertex it is discarded. Zeroing is an extreme form of a procedure described in Ref. [148]. In can be used also with area or NpC subtraction, and we find that it brings a benefit for the peak of the ∆p t and ∆m distributions, but appears to introduce long tails in ∆p t.
One sees that SK gives slightly improved mass peaks relative to the area-median method and also avoids area-median's spurious peak at m = 0, which is due to events in which the squared jet mass came out negative after four-vector area-subtraction and so was reset to zero. The plot also shows results from the recently proposed Constituent Subtractor method  [28] , using v. 1. 0. 0 of the corresponding code from FastJet Contrib  [159]. It too performs better than area-median subtraction for the jet mass, though the improvement is not quite as large as for SK. 12 11 using a "safe" subtraction procedure that replaces negative-mass jets with zero-mass jets, as described in Section 2. 4. 4. 12 A further option is to use an "intrajet killer" that removes soft particles inside a given jet until a total p t of ρA jet has been subtracted.
Improved R dependence Motivation and description. One piece of physical information that is not encoded in the Soft-Killer technique is the fact that the QCD radiation from a hard jet is collinear to the jet, peaking at small angles, while pileup tends to be mostly uniform in rapidity and azimuthal angle. In practice, this means that if one goes further away from the jet axis, particles (of a given p t ) are more likely to come from pileup. In the context of the SoftKiller, the consequence is that if we want to reconstruct jets with a larger radius, one has to increase the p t cut on the particles which is done in practice by increasing the size parameter a of the SoftKiller. In that context, it is natural to wonder if one can encode some form of angular dependence directly in the SoftKiller recipe to automatically allow for jet clustering with any radius.
The remaining average bias is around 1-2 GeV, larger than what we obtain from the area-median method but of the order of, or smaller than, the jet-energy-scale corrections applied in an experimental context. To summarise our main findings and the many pileup mitigation techniques introduced throughout this document, we have selected a few representative methods (see below) and studied them with the framework developed for the "PileUp WorkShop" held at CERN in May 2014  [40]. Since the goal of the workshop was precisely to discuss and compare different pileup mitigation techniques using a common open-source software framework, this is perfectly suited for our summary. The code used to obtain the results presented hereafter is publicly available in the comparisons/review folder of the https://github.
Acknowledgements The body of work presented here is the result of many scientific collaborations and human interactions. Elements of this document date back from the early days of 2007 when I started to be involved in jet physics. Trying to look back, it seems to me like the path between then and now has been paved with a pile of positive encounters which have left a trace on who I am today. First and above all, a very special thanks goes to  Matteo Cacciari  and  Gavin Salam  for a decade worth of fruitful team work. I hardly see how any of this could have existed without them. It has been, and still is, a great adventure during which I have learned a lot about physics and human aspects of a longstanding collaboration. I can only look forwards to more of this. There are many other scientists who contributed to various degrees in some of the results presented here.
DE-AC02-98CH10886  with the  U. S. Department of Energy , the  French ANR  grant  Jets4LHC  under number  ANR-10-CEXC-009-01 , the  ERC  advanced grant Higgs@LHC and the  French ANR  grant  OptimalJets  under number  ANR-15-CE31-0016 ). I thank them all for their support.
However, in the classical samples, small molecular transporters, such as the glutamine SLC1A1 transporter, and cholesterol transporters, like NPC1L1, are overexpressed as a consequence of DNA hypomethylation. Our most recent work implemented an experimental design to provide a comprehensive view of genomic regulatory elements, which included the integration of ChIP-seq from five histone marks along with RNA-seq and DNA methylation datasets  33. Histone modifications associated with distinct transcriptional regulatory outcomes were selected, such as H3K4me3 for active promoters, H3K27ac for active enhancers and promoters, H3K4me1 for active and poised enhancers, H3K9me3 for heterochromatin, and H3K27me3 for Polycombrepressed regions. In concordance with Nicolle et al. 39  , Lomberk et al. described the classical subtype to be associated with pancreatic morphogenesis (e.
g. , PDX1, BMP2, GATA6, and SHH) and metabolic processes (e. g. , HKDC1 and FBP1)  33. Functional analysis of the basal subtype found genes related to oncogenic signaling pathways (e. g. , ErbB/EGFR, PI3K-AKT, Hippo, and Wnt), EMT (TGFβ pathway), and deregulation of cell differentiation, proliferation, and apoptosis (e. g. , YAP1, HEY1, MYC, and E2F7), reflecting the more aggressive nature of the basal tumor subtype. Importantly, our multi-parametric integrative analyses of the datasets revealed that super-enhancers better reflect the heterogeneity of the different PDAC subtypes (Fig. 2 ). Specific superenhancers were identified in the classical tumors, which seemed to be influenced by at least nine different transcription factors, including GATA6, FOS, FOXP1, FOXP4, KLF4, ELF3, NFIX, CUX1, and SSBP3.
For instance, "dedifferentiation", which has been associated with malignancy, has been promoted for decades to be a hallmark of cancer  44. However, similar to normal tissue, basal and classical pancreatic cancer subtypes follow distinct molecular differentiation patterns in a reproducible and inheritable pattern, through distinct epigenomic landscapes. It is provocative to consider whether we are born with both, a predictable normal and a similarly predictable cancer phenotype. Analyses of our RNA-Seq datasets  33  , indicates a significant correlation with the two subtypes described by Moffitt et al. 41  , suggesting that these subtypes are stable as to at least maintain their pattern of gene expression, even when passage through mice. The second conclusion is that these subtypes cannot be distinguished on the basis of pathogenic somatic cancer-associated variants.
2 ). The amount of information, however, that these datasets  33  provides is far richer than what can be described here, but offers a foundation for future diagnoses, prognoses, and treatments, as well as a plan for additional mechanistic experiments. The underlying genetic alterations of PDAC are unable to distinguish subtypes as mentioned above, but rather epigenetic landscapes are able to identify subtypes. Similarly, within a single untreated PDAC patient, the genetics of metastatic lesions is not discernable from the primary tumor  45. Utilizing samples of matched primary and metastatic PDAC lesions collected by rapid autopsy, McDonald et al. 34  performed ChIP-seq on a series of histone marks for heterochromatin (H3K9me2, H3K9me3, and H3K27me3) and euchromatin (H3K27ac and H3K36me3) to demonstrate that global epigenetic reprogramming is also responsible for the heterogeneity of tumors in each individual patient  34.
On the other hand, the bioinformatics-based modeling of data derived from many epigenomic methodologies makes significant assumptions (e. g. , cutoffs), often uses bioinformatics-based definitions with uncertain biological equivalence and is not easy to integrate  72. The results acquired by state-of-the-art methodologies, such as those used in our recent study  33  , are developed to visualize their output on a two-dimensional map of the human genome (e. g. , UCSD Genome Browser). Through the rational, combined use of these methodologies, significant information can be obtained on gene regulatory regions that are responsible of a particular cancerassociated trait. In fact, numerous large-scale epigenome mapping projects (e. g. , ENCODE  73, 74  and AHEAD  75, 76  ) of DNA methylation, histone modifications, chromosome interaction analyses, transcription factor binding sites, among others have markedly increased the datasets available from various normal and diseased cell types  72.
Acknowledgements This work has been supported by  La Ligue Contre le Cancer  ( Programme Cartes d'Identité des Tumeurs® (CIT) ),  INCa ,  Canceropole PACA ,  DGOS (labellisation SIRIC ), INSERM to JI, the  National Institutes of Health  (grants  R01 CA178627  to GL and  R01 DK52913  to RU), and in part by the  Linda T. and John A. Mellowes Endowed Innovation and Discovery Fund (RU)  and  Theodore W. Batterman Family Foundation, Inc  (GL and RU).
AERONET sunphotometer observations In Fig. 1  and Table  1 , we show the location of the AERONET sunphotometers selected for the year 2010 over Europe and North America. We select only those stations with a minimum of 10% of valid data in 2010. Since our focus is on aerosol absorption properties, we use version 2 inversion products  (Dubovik and King, 2000)  which, in addition to the spectral (at nominal wavelengths λ = 440, 675, 870, and 1020 nm) aerosol optical depth (τ(λ)), provide estimates of the single scattering albedo (ω 0 (λ)) and the asymmetry parameter (#(λ)), among other quantities. The cloud-screened and quality-assured data are those labelled Level 2. 0  (Dubovik et al. , 2002) , and we start from this dataset. Absorption retrievals for scenes with τ(λ = 440 nm) < 0. 4 are automatically discarded in Level 2.
0, because they are considered too uncertain  (Dubovik et al. , 2002). The uncertainty associated with the single scattering albedo is estimated to increase from ±0. 03 for τ(λ = 440 nm) ≥ 0. 5 to ±0. 05-0. 07 for τ(λ = 440 nm) ≤ 0. 2  (Dubovik et al. , 2000). The result is that more than 90 % of absorption-related observations in Level 2. 0 data are discarded over regions with relatively low values of τ: for year 2010, the median τ(λ = 440 nm) is 0. 15 (0. 08-0. 24 interquartile range) over Europe and 0. 08 (0. 05-0. 13) over North America. Similar to what was done by  Wang et al. (2016) , we thus add Level 1. 5 absorption data to the dataset, so as to reinforce the model to observation comparison statistic. In Figs. S1 and S2 we show the time series of τ and ω 0 at 440 nm for each site. Aerosol absorption in the visible-near-infrared part of the solar spectrum is primarily determined by black carbon (BC), brown carbon (BrC), and mineral dust  (Bergstrom et al.
As explained in the next section, optical calculations are carried out assuming the same size distributions (and other physical and chemical quantities) for all models. The native grid spacing and domain projections were specific to each model, but outputs were remapped onto a single grid for each continent at a horizontal resolution of 0. 25° × 0. 25°. Aerosol profiles on 18 layers (up to 9km) were extracted for each hour of the year over AERONET locations and data delivered to a common database (ENSEMBLE, http:// ensemble. jrc. ec. europa. eu/; last access: 3 January 2019) hosted by the Joint Research Centre (JRC) (e. g. Galmarini et al. , 2012). Most model simulations analysed in this study have been evaluated in terms of their skills in reproducing seasonal patterns of ground-level pollutants  (Im et al.
, 2018) , temporal and spatial patterns of ground-and upper-level concentrations  (Solazzo et al. , 2017) , and wet and dry deposition processes  (Vivanco et al. , 2018). A general underestimation of surface total PM was found over both continents, particularly in winter. The underestimation is confirmed by Table  S1 , which shows the observed and modelled PM2. 5 average values in 2010 at the available surface monitoring stations over Europe and North America in the ENSEMBLE database (~ 1000 stations for each continent). In Europe, the ES1 model is the only one with average values slightly above the observations (due to a known overestimation of desert dust); all the others underestimate PM 2. 5 by 10 to 60%. In North America, the US3 model has almost no bias, while DK1 underestimates PM 2.
These two models are also those with the smallest difference against observed PM 2. 5 over Europe. Black carbon concentrations near the surface are mostly in the 0. 2-0. 6 μg m -3 range, except for FI1 and TR1, which have values above 1 μg m -3. Primary organic carbon concentrations are mostly around 1 μg m -3 , with models DE1 and UK3 below 0. 5 μg m -3 and models NL1 and TR1 above 1. 5 μg m -3. The secondary organic fraction displays the highest degree of model diversity, with most models simulating values below 0. 2 μg m -3 , and IT2 and FI1 having average concentrations near the surface of about 1 and 5 μg m -3 , respectively. FI1 also has a relatively small bias of about 25 % with respect to PM 2. 5 surface observations. Some models (DE1, DK1, NL1, and TR1) did not simulate secondary organic aerosol or did not provide results for this component to the common database.
Again, this bias could potentially be addressed by refining the FlexAOD log-normal parameters, but this is not the intent of this study. Instead, our focus is on the simulated absorption properties, which vary little with changing log-normal size parameters (e. g. Obiso and Jorba, 2018). However, in order to avoid confusion in the interpretation of results, we restrict the subsequent analysis to scenes where the mass and the size of the particles are reasonably simulated by the models. To this end, we discard all scenes where the difference of volume concentration and effective radius between AERONET retrievals and FlexAOD simulations is larger than a factor of 2. This reduces the size of the dataset to about 10 % of the original. In Fig. 5  and Table  5  we present the comparison of the single scattering albedo at 440 nm ω 0,440 between AERONET retrievals and AQMEII-3 simulations, for the different sensitivity tests on aerosol mixing state.