dataesr/scientific-paragraphs-categorization huggingface dataset:
Differences in the size of the PDF uncertainties In general the sizes of the PDF uncertainties between the three global sets, NNPDF, CT and MMHT, turn out to be in rather reasonable agreement. On the other hand, the sizes of the uncertainties for PDF sets based on a HERA-only dataset, such as HERAPDF, are be expected to be larger, and in some cases much larger, than those of the global fits. This is indeed what happens for the HERAPDF2. 0 PDFs, despite including the legacy HERA combination data  [9] , since deep-inelastic scattering cannot constrain a number of quark flavor combinations and the medium and large-x gluon. The much larger PDF uncertainties in HERA-only fits as compared to global fits have also been verified in the NNPDF framework. The NNPDF3. 0 HERA-only fit  [11]  indeed shows substantially larger uncertainties for most PDF combinations (with the exception of the quark singlet and the small-x gluon) than the corresponding global analysis.
Whereas HERAPDF2. 0 includes the legacy HERA combination data  [9] , which are not included in any other fit, it has been shown  [96, 97]  that the impact of these data on a global fit that already included the HERA-I data was moderate. In addition, it has been checked  [97]  that their impact on a global fit like NNPDF3. 0 which already included both the HERA-I combination and the individual HERA-II measurements is very small. Therefore, the availability of the legacy HERA inclusive dataset does not modify the general anticipation that HERA-only fits (or more general, DIS-only fits) will have larger PDF errors than the global fits. However, it is worth emphasizing that the uncertainties of HERAPDF2. 0 are in many cases comparable (for the u quark) or even smaller (for the gluon) to the global fits, as will be illustrated in the comparisons of Sect.
3, The PDF uncertainties in ABM12 are systematically smaller, and often much smaller, that those of the global fits. Therefore the PDF uncertainty estimates of the three global fits, on one hand, and those of ABM12, on the other, do not appear to be consistent. The most likely explanation for the disparate sizes of the uncertainties is their varied statistical interpretations adopted by the PDF analysis groups. In the global sets, they are determined according to complex procedures, and include more sources of uncertainties than the purely experimental uncertainty obtained from the ∆χ 2 = 1 criterion, for example, arising from partial inconsistencies of the input datasets, using a variety of methods  [10, 11, [98] [99] [100] [101].
Parton distributions We begin by comparing the gluon and the up quark PDFs, with the corresponding one-sigma uncertainty bands. These are shown in Fig. 3  for Q = 100 GeV, normalized to the central value of the CT14 distribution. Similar plots are shown for the d and ū quarks in Fig. 4. For each flavor, the left plots compare the three global sets among them, while the right plots compare CT14 with the two sets based on reduced datasets, HERAPDF2. 0 and ABM12. In general, the gluon distributions for CT14, MMHT2014 and NNPDF3. 0 agree well, but not perfectly, both in central value and in uncertainty, except at very low x and at high x. This level of agreement is reassuring, given the importance of the gluon distribution in the dominant Higgs boson production mechanism. Although each group uses similar, but not identical, data sets, the fitting procedures and tolerances are not the same, and for this reason, exact agreement is not expected.
PDF luminosities It is also instructive to examine the parton-parton luminosities  [104] , which are more closely related to the predictions for LHC cross-sections. The gluon-gluon and quark-antiquark luminosities, as a function of the invariant mass of the final state M X , for a center-of-mass energy of 13 TeV are shown in Fig. 5 , where we compare, on the left, the three global fits, NNPDF3. 0, CT14 and MMHT14, and, on the right, CT14 with the fits based on reduced datasets, HERA-PDF2. 0 and ABM12, using for the latter exactly the same settings as in the PDF comparison plots. All results are shown normalized to the central value of CT14, as before. The corresponding comparison for the quark-quark and gluon-quark PDF luminosities is then shown in Fig. 6. The luminosity uncertainty ranges tend to blow-up at low invariant masses (M X ≤ 50 GeV) and high masses (M X ≥ 500 GeV for gg, M X ≥ 1 TeV for q q and M X ≥ 5 TeV for qq), that is, in the regions that are relatively unconstrained in current global PDF fits.
HERAPDF2. 0 best-fit luminosity for q q is above CT14 by about 5% in the region of intermediate M X , with better agreement at smaller and larger masses, but markedly less  certain. ABM12 is consistent with the global fits except for M X ≥ 2 TeV when it becomes rather softer. Turning to the quark-quark and gluon-quark PDF luminosities shown in Fig. 6 , we see that for the global fits we get consistent results within uncertainties, though the agreement is not quite as good as for the gluon-gluon luminosity, especially in the region between 100 GeV and 1 TeV. The luminosities for HERAPDF2. 0 and ABM12 in the qq case are harder than those of the global fits, by around 5% at low masses to up 15% in the TeV region, with important phenomenological implications. For the qg luminosity, there is good agreement for the global fits (similar to the gg luminosity) and slightly worse for the PDF sets based on reduced datasets.
While these requirements may evolve with time, for the current combination we select the individual PDF sets that satisfy the following properties: 1. The PDF sets to be combined should be based on a global dataset, including a large number of datasets of diverse types (deep-inelastic scattering, vector boson and jet production,. ) from fixed-target and colliders experiments (HERA, LHC, Tevatron). 2. Theoretical hard cross sections for DIS and hadron collider processes should be evaluated up to two QCD loops in α s , in a general-mass variable-flavor number scheme with up to n max f = 5 active quark flavors. 1 Evolution of α s and PDFs should be performed up to three loops, using public codes such as HOPPET  [105]  or QCDNUM  [106] , or a code benchmarked to these.
Sets entering the combination must account for these through suitable methods, such as separate estimates for additional model and parametrization components of the PDF uncertainty  [9] , tolerance  [6, 10] , or closure tests  [11]. Following the needs of precision physics at the LHC, future updates of the PDF4LHC recommendations might be based on a different set of conditions. For instance, in addition to the above PDF sets, one might be required to provide fits in a range of m c and m b values, or to provide direct evidence of the ability to describe, reasonably accurately, a wide variety of different data types that constrain PDFs of different flavour and in different kinematic regions. In the future, with the progress in combination techniques, it might be possible to relax the first requirement, and also include in the combination PDF sets based on datasets of a different size by using suitable weighted averaging techniques -note that such a weighted averaging would have to account for the fact that different data affect different PDFs and kinematic regions, and thus the weights chosen cannot be the same for all PDF flavours.
PDF4LHC15 nlo mc PDF4LHC15 nnlo mc In addition, the compressor code use to generate the CMC-PDF sets is also publicly available from https://github. com/scarrazza/compressor together with the corresponding user documentation. For completeness, Sect. 6. 2 reports the formulae that should be used to compute the PDF and PDF+α s uncertainties whenever the PDF4LHC15 mc sets are used.
Hessian reduction methods A Hessian representation of PDF uncertainties has certain advantages and disadvantages in comparison to a Monte Carlo representation. The main disadvantage is that a Hessian representation assumes that the underlying probability distribution is multi-Gaussian (though in general possibly asymmetric, i. e. with different upper and lower uncertainties). The main advantage is that Hessian uncertainties can be treated as nuisance parameters, and thus on the same footing as other nuisance parameters: this can be useful in analysis, for example for the determination of PDF-induced correlations in large datasets, and also when using profiling in order to understand the dominant PDF contributions to a given process. Two techniques have been suggested to turn a Monte Carlo PDF set into a Hessian set: META-PDF and MCH-PDFs.
For instance, as will be shown below, the entries of the PDF correlation matrix can be reproduced at the per-mile level. For these reasons, in the following the Hessian PDF4LHC15 combined sets with N eig = 30 eigenvectors PDF4LHC15 nlo 30, PDF4LHC15 nnlo 30. are constructed from the META30 sets, while those with N eig = 100 eigenvectors PDF4LHC15 nlo 100 PDF4LHC15 nnlo 100 are constructed from the MCH100 sets. The MP4LHC code used to generate the META sets is publicly available from the HepForge website https://metapdf. hepforge. org/. The mc2Hessian code use to generate the MC2H sets is also publicly available from: https://github. com/scarrazza/mc2Hessian. together with the corresponding user documentation. For completeness, Sect. 6. 2 reports the formulae that should be used to compute the PDF and PDF+α s uncertainties whenever any of the two Hessian sets, PDF4LHC15 100 and PDF4LHC15 30, are used.
Acknowledgments We are grateful to  Sergey Alekhin ,  Johannes Blümlein ,  Claire Gwenlan ,  Max Klein ,  Katerina Lipka ,  Kristin Lohwasser ,  Sven Moch ,  Klaus Rabbertz  and  Reisaburo Tanaka  for their feedback on this report. We are also grateful to  Richard Ball ,  André David ,  Lucian Harland-Lang ,  Maxime Gouzevitch ,  Jan Kretzschmar ,  José Ignacio Latorre ,  Alan Martin ,  Patrick Motylinski ,  Ringaile Placakyte ,  Jon Pumplin ,  Alessandro Tricoli ,  Dan Stump ,  Graeme Watt ,  C. P. Yuan , as well as to many other colleagues from the  PDF4LHC Working Group community  for illuminating discussions about the topics presented in this report. S. C. and S. F. are supported in part by an  Italian   PRIN2010  grant and by a  European Investment Bank   EIBURS grant. S. F. and Z. K.
are supported by the  Executive Research Agency (REA)  of the  European Commission  under the Grant Agreement PITN-GA-2012-316704 (Hig-gsTools). S. F. thanks  Matteo Cacciari  for hospitatly at LPTHE,  Université Paris VI , where part of this work was done, supported by a  Lagrange award. S. C. is also supported by the  HICCUP ERC   Consolidator grant  ( 614577 ). The research of J. G. in the  High Energy Physics Division at Argonne National Laboratory  is supported by the  U. S. Department of Energy ,  High Energy Physics, Office of Science , under Contract No. DE-AC02-06CH11357. The work of P. N. is supported by the  U. S. Department of Energy  under grant  DE-SC0013681. J. R. is supported by an  STFC Rutherford Fellowship  and Grant  ST/K005227/1  and  ST/M003787/1 , and by an  European Research Council   Starting Grant  " PDF4BSM ".
Data acquisition Gene locations were downloaded from the Biomart server using the Martview tool  [55]  (www. ensembl. org/biomart/martview/) on the last version of the human genome (GRCh37. p10 = hg19). Over a total of 62,380 genes in the human genome, we filtered for protein coding genes located on the 22 autosomal and the two sexual chromosomes, removing those located on the mitochondrial genome and unidentified chromosomes, and retrieved 19,071 genes. For each gene, Ensembl identification number, strand orientation, and localization (start and end positions on the chromosome) were collected. TE insertions in human genome were previously identified using RepeatMasker  [56] , a program that determines the occurrences of sequences with homology to consensus TE sequences present in the Repbase database  [57]  and were retrieved from the website of the University of California, Santa Cruz (ftp://hgdownload.
cse. ucsc. edu/ goldenPath/hg19/chromosomes/). The RepeatMasker output files were parsed using the program "One code to find them all"  [58]  (with the -strict option) to assemble each TE copy and determine their localization. Locations of histone modifications produced by ChIP-seq experiments were downloaded for the last version of the human genome on the ENCODE Genome Browser (http://hgdownload. cse. ucsc. edu/golden-Path/hg19/encodeDCC/wgEncodeBroadHistone/). They correspond to broader regions of enrichment (broadPeaks)  [59]. These regions were retrieved for 10 histone modifications (H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K9me3, H3K27ac, H3K27me3, H3K36me3, H3K79me2, and H4K20me1) and for two different conditions: a lymphoblastoid cell line originated from normal peripheral blood lymphocyte of a female donor (GM12878 named "normal condition") and a leukemic cell line originated from derived from a female patient with chronic myeloid leukemia (K562 named "cancer condition").
Whereas the density rather estimates the number of insertions, the coverage measures the proportion of nucleotides belonging to an element in the sampled sequence. The relationship between these two statistics was tested by a Spearman correlation test. Genes were clustered according to their level of density and coverage of TEs using the pam() function of the R package  [62]. This algorithm, called "Partitioning Around Medoids", provides a robust clustering method because outliers have a less important impact than in the k-means method often used for clustering  [63]. The main difference between the two methods is that pam() uses a minimization of dissimilarities instead of a sum of Euclidean distances, and that the medoids (center of a cluster) is an actual point within the dataset. The genes with density and coverage equal to 0 were defined as TE-free genes (4,300 genes).
Differential gene expression and functional analyses by GO term enrichments RNAseq reads from both samples GM12878 and K562 were trimmed to ensure sequencing quality using the unsupervised approach of the program UrQt  [64]  and aligned against human genes using Tophat2  [65]. Alignment counts were obtained on sorted bam files using htseq-count  [66] , and differential gene expression was assessed using DESeq2  [67]. We used an adjusted pvalue threshold <0. 1 for significance, which allowed us to identify 7,724 genes differentially expressed over the 19,071 total protein coding genes. We determined the enrichment in particular GO terms in a list of target genes (for example down-regulated genes in cancer condition) by comparing it with the list of all the genes in the genome using GOrilla  [68]  and REVIGO  [69].
We have also compared our datasets of down-and upregulated genes to the set of census cancer genes identified in the COSMIC database (http://cancer. sanger. ac. uk/ cosmic;  [72] ). The results show that among the 596 census genes that have been identified as "cancer genes", meaning genes for which mutations have been causally implicated in cancer, 156 and 120 correspond to genes from our sets of down-and up-regulated genes respectively. The mean histone enrichment of up-and downregulated genes in cancer condition in comparison to the normal one is reported in Table  1 , for both conditions. The histone enrichment is significantly different between the two conditions for all modifications, and for up-and down-regulated genes (Wilcoxon paired tests, p < 0. 0025) with the only exceptions of H3K4me2 for down-regulated genes and H3K27me3 for up-regulated genes.
These differences could potentially explain the divergence of expression of these genes between the two tested conditions. In order to determine if some particular functions could Fig. 3  Heatmap of the mean enrichment for the 10 histone modifications of genes according to the TE category of their neighborhood in the two conditions: normal (GM12878) and cancer (K562). The number of genes of each category is given (n). High enrichments are toward yellow color whereas low enrichments are toward dark blue color be more represented among these genes, we looked at the Gene Ontology terms of the most highly downregulated genes that are TE-rich and enriched in H3K27me3 in cancer condition (Additional file 8). Interestingly, seven out of the 15 genes are implicated in immune system process and response to stress, among which one gene, LCK, is identified as a "cancer gene" in the COSMIC database.
Similarly, we looked at the most highly up-regulated genes that are either TEintermediate or TE-rich, and enriched in H3K79me2 (Additional file 9). In that case, there is less common GO terms but we can notice that among the 43 genes, six are involved in immune system process and response to stress, and four are involved in transcription from RNA polymerase II. Among the genes from this last category, two have been identified as "cancer genes" in the COSMIC database (GATA1 and GATA2). The TE environment appears to be associated with the variation in histone modifications observed between the up-and down-regulated genes (Table  2 ). Among the differentially expressed genes displaying enrichment or depletion in particular histones, we tested whether the number of genes regarding their local TE landscape is different from that observed in the total genome.
Calculation Scheme The 137 Cs total activity is calculated in two steps. The reaction rates are first calculated for all fissile isotopes in the dosimeters. The 137 Cs activity is then calculated using a depletion code with reaction rate values, the initial isotope concentration, and a power history. The 137 Cs activity from neutron induced fission and photofission are calculated separately as shown in Fig. 2. Four different power histories are taken into account: 1 cycle, 4 cycles, 9 cycles, and 14 cycles. A one cycle power history corresponds to experiments in a PWR reactor such as the H. B. Robinson-2 experiment  [3]  (benchmark referenced in the SINBAD data bank  [4] ). The three other power histories are representative of average durations of irradiation for standard surveillance capsules in French PWR's.
137 Cs Activity The 137 Cs activity is calculated using a depletion code, and the following data: reaction rate values from the TRIPOLI-4 ® simulations, initial isotopes concentrations, and a power history related to the studied reactor. For neutron induced fissions, the DARWIN/PEPIN2 depletion code  [7]  is used. This code solves Bateman's generalized differential equations governing the time dependence of isotope concentrations. It may be coupled with the TRIPOLI-4 ® code. Fission yield values used to calculate 137 Cs activity are derived from the JEFF3. 1. 1 nuclear data library. For the photofission, a specific depletion program has been developed to calculate 137 Cs activity. In this program, only the nuclear data useful to calculate the 137 Cs activity from the 238 U, 237 Np, 235 U, and 239 Pu isotopes have been implemented: fission yields, half-lives, and branching ratios.
Results and Discussion We integrated Algorithm 1 to the software provided at  [24]  by the authors of  [9]. Note that the previous version of the program only permitted to check satisfiability and consistency of a constraint graph with respected to a given species tree S. We used the modified software to reanalyze the data set in  [9]. This data set was constructed by randomly choosing 265 gene families of vertebrates with more than 20 genes from Ensembl  [25]. Each gene family was then analysed with ProteinOrtho  [26]  using 9 different parameter settings, yielding 2385 different constraint graphs. Here S is the Ensembl species tree, which can be downloaded at  [27]. For this data set we have that, apart from one case, all satisfiable constraint graphs are also consistent. In 533 out of 2385 cases, the constraint graph was found to be consistent, but not consistent with S.
Participants were asked to translate the titles and abstracts of scientific articles indexed in the Scielo database. Training and test data were provided for two subdomains, biological sciences and health sciences, and three language pairs, Portuguese/English, Spanish/English and French/English. This task therefore provided data for a language not previously covered in WMT, Portuguese. The system outputs for each language pair were evaluated both automatically and manually. The quality estimation task ( §6) this year included three subtasks: sentence-level prediction of post-editing effort scores, word and phrase-level prediction of good/bad labels, and document-level prediction of human post-editing scores. Datasets were released with English→German IT translations for sentence and word/phrase level, and English↔Spanish news translations for document level.
The automatic post-editing task ( §7) examined automatic methods for correcting errors produced by an unknown machine translation system. Participants were provided with training triples containing source, target and human post-edits, and were asked to return automatic post-edits for unseen (source, target) pairs. In this second round, the task focused on correcting English→German translations in the IT domain. The primary objectives of WMT are to evaluate the state of the art in machine translation, to disseminate common test sets and public training data with published performance numbers, and to refine evaluation and estimation methodologies for machine translation. As before, all of the data, translations, and collected human judgments are publicly available. 1 We hope these datasets serve as a valuable resource for research into statistical machine translation and automatic evaluation or prediction of translation quality.
Producing the human ranking The collected pairwise rankings are used to produce the official human ranking of the systems. Since WMT14, we have used the TrueSkill method for producing the official ranking, in the following fashion. We produce 1,000 bootstrapresampled datasets over all of the available data (i. e. , datasets sampled uniformly with replacement from the complete dataset). We run TrueSkill over each dataset. We then compute a rank range for each system by collecting the absolute rank of each system in each fold, throwing out the top and bottom 2. 5%, and then clustering systems into equivalence classes containing systems with overlapping ranges, yielding a partial ordering over systems at the 95% confidence level. The full list of the official human rankings for each task can be found in Table  6 , which also reports all system scores, rank ranges, and clusters for all language pairs and all systems.
Data The test set consisted of 1000 answers from the Batch 3 of the QTLeap Corpus. 10 The in-domain training data contained 2000 answers from the Batches 1 and 2 and also localization files from several open-source projects (LibreOffice, KDE, VLC) and bilingual dictionaries of ITrelated terms extracted from Wikipedia. The outof-domain training data contained all the corpora from the News Task (see Figure  1 ), plus PaCo2-EuEn Basque-English corpus and SETimes with Bulgarian-English parallel sentences. "Constrained" systems were restricted to use only these training data provided by the organizers. Linguistic tools such as morphological analyzers, taggers, parsers, word-sense disambiguation or named entity recognizer were allowed in the constrained condition. The split of Batches 1 and 2 into the training set and development test set was left to the participants.