dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Stephane Le Crom  and  Laurent Jourdren  for helping to set up EOULSAN. CB acknowledges funding from the  ERC Advanced Award "Diatomite" , the  Louis D Foundation of the Institut de France , the  Gordon and Betty Moore Foundation , and the  French Government "Investissements d' Avenir" programmes MEMO LIFE  ( ANR-10-LABX-54 ),  PSL* Research University  ( ANR-1253 11-IDEX-0001-02 ), and  OCEANOMICS  ( ANR-11-BTBR-0008 ). CB also thanks the  Radcliffe Institute of Advanced Study at Harvard University  for a scholars fellowship during the 2016-2017 academic year. Funded in part by grants to (A. E. A. ) from  National Science Foundation  ( NSF-OCE-0727997 ), the  United States Department of Energy  ( DE-SC00006719  and  DE-SC0008593 ), and the  Gordon and Betty Moore Foundation  grant  GBMF3828.
DATA SETS The imaging data sets on which measurements are performed in this paper are obtained from various surveys: MIPS 24 µm data is the GO3 image from the COSMOS-Spitzer program (PI: D. Sanders; Le  Floc'h et al. 2009) , Herschel/PACS 100 µm & 160 µm data are from PEP (PI: D. Lutz; Lutz et al. 2011 ) and CANDELS-Herschel (PI: M. Dickinson) programs, while SPIRE 250 µm, 350 µm and 500 µm data are nested maps from the Herschel Multi-tiered Extragalactic Survey (HerMES, PI: S. Oliver). The SCUBA2 850 µm images are from the S2CLS program  (Cowie et al. 2017; Geach et al. 2017). The AzTEC 1. 1 mm data are nested maps from  Aretxaga et al. (2011)  and MAMBO 1. 2 mm images are from  Bertoldi et al. (2007). The deep radio data are VLA 3 GHz images from  Smolčić et al. (2017)  and 1. 4 GHz images from  Schinnerer et al.
2011) , and the dust continuum emission is shown in green  (Magdis et al. 2012). The z p is the NIR photometric redshift from the COSMOS2015 catalog; Umean=101 is our code to mark that the source was fitted by a starburst-like template (GN20 template from  Magdis et al. 2012). Downward arrow shows the 2-sigma upper limit at given wavelength. cant depth variations over the COSMOS field, we adapted the flux threshold for accepting priors to the actual local depth, removing from the fitting pool all priors with predicted flux (plus twice the flux uncertainty, as in L18) below the 1σ depth (i. e. , we exclude sources with S SED + 2 σ SED < σ rms noise from the fitting, where σ rms noise varies locally). (5) Because the 24µm, radio and, most importantly, PACS imaging is shallower in COSMOS with respect to GOODS-N, the flux prediction process at each wavelength step displays considerably larger uncertainties.
Finally, we notice that the COSMOS field contains a subarea observed by the ESA CANDELS-Herschel key program (PI: M. Dickinson) with much deeper PACS imaging data  (Schreiber et al. 2015). We have fitted the deep PACS 100 µm & 160 µm images in the CANDELS field, and used the results based on these deeper images for the CANDELS area. Their fluxes and flux uncertainties have been calibrated by dedicated Monte Carlo simulations executed in the deep images. Note that PACS fluxes measured from images need to be scaled up by a factor of 1. 12× because of the flux losses from the high-pass filtering processing of PACS images (e. g. Mag-nelli et al. 2013; Popesso et al. 2012). We have applied this factor in the SED fitting and also the released catalog, while this factor is not applied for the flux comparison in Fig 15.
As shown in the right column of Fig. 10 , the four-step correction significantly improves the performance of the deblended photometry with lowering the overall rms noise of the photometry and reducing (often removing) the prominent non-Gaussian tails in the flux uncertainties distributions. Also, we have to account in COSMOS for more substantial effects of depth variations of the data across the field. This is important often towards the edges of each dataset, but also in some cases across the datasets as for SCUBA2. We find that we can rescale simulation results for calibration of flux uncertainties to areas with different depth in the same dataset by calibrating performances based on normalized observables (flux bias terms are not affected). Notably, we have normalized the parameters used in the simulations by the local rms noise, i.
e. , we consider quantities as σ galfit /σ rms noise , S residual /σ rms noise and S subtracted /σ rms noise (this is not necessary for crowdedness that does not depend on the noise in the dataset). We tested this scaling by directly performing additional simulation in data portions with different depths, and comparing the direct simulation results from those rescaled from a region with different depth. For example, for the SCUBA2 image we have executed our primary simulations in the deep area and we also carried out our independent simulations in a 3. 6 × shallower area, then applied the scaled deep-area simulation results on the galfit outputs to the shallow area. We find that the flux uncertainty scaled from the deep area simulation are slightly larger than those directly performed on the shallower data, but overall consistent (or at least conservative).
Liu et al. , in prep. )   2. The A 3 COSMOS team has processed all public ALMA archive in the COSMOS field and obtained accurate (sub)mm continuum photometry with both blind source extraction (mainly with the code PyBDSM) and prior source fitting. The catalogs are already available and verified with extensive Monte-Carlo simulations, and will be publicly released after the publication of their first paper (D. Liu et al. , in prep. ). Here we use their catalogs in advance (priv. comm. ), benefiting from the exquisite ≈ 1 ALMA resolution which resolves all blending issues, to verify our super-deblended flux measurements. Fig. 18  shows the flux comparison for common sources between this work and the A 3 COSMOS prior source fitting catalog, including all prior sources within the ALMA primary beam.
SUMMARY In this work we obtain (and publicly release) detailed "super-deblended" photometry for far-infrared to (sub-)millimeter imaging datasets in the COSMOS full 2 deg 2 field, with the most accurate photometry information lying where complete prior information is available, i. e. , in the 1. 7 deg 2 UltraVISTA area. In order to overcome the heavy blending problems introduced by the large beam size at Herschel SPIRE and (sub)mm bands, we adopted the "Super-deblending" technique which has been pioneered in the GOODS-North field by L18, and critically adapted it to the COSMOS field where data at PACS, Spitzer/MIPS and radio are shallower than in the GOODS-N field. We selected a highly complete set of 194,428 priors for deblending the FIR to mm images. This prior catalog contains 88,008 detections from MIPS 24 µm and radio fitting, and ∼ 1 × 10 5 mass-selected priors from UltraVISTA catalogs.
Finally, we conservatively selected 85 robust high redshift candidates with solid detections at FIR/(sub)mm wavelengths and requiring z > 4 (we use zz error > 4). These candidates have often well-determined counterparts in IRAC and/or radio images, weak or no detection at K s band. Their SEDs suggest redshifts over z ∼4-7, including possibly some of the most distant galaxies known. Confirmation of redshifts by future observations is needed. This unique sample will allow us to statistically investigate the first generation of vigorous star formation in the early Universe. We are grateful to the full COSMOS team for their contributions in the build-up of such a rich multi-wavelength dataset. We thank the referee for useful comments and suggestions that helped improving the paper. We thank B.
SJ acknowledges funding from the China Scholarship Council. SJ and QG acknowledge supports from the National Key Research and Development Program of China (No. 2017YFA0402703) the National Natural Science Foundation of China (No. 11733002). VS, JD and ID acknowledge support from the European Union's Seventh Frame-work program under grant agreement 337595 (ERC Starting Grant, "CoSMass"). ES and DL acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 694343).
SJ acknowledges funding from the China Scholarship Council. SJ and QG acknowledge supports from the National Key Research and Development Program of China (No. 2017YFA0402703) the National Natural Science Foundation of China (No. 11733002). VS, JD and ID acknowledge support from the European Union's Seventh Frame-work program under grant agreement 337595 (ERC Starting Grant, "CoSMass"). ES and DL acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 694343). The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. ALMA is a part- nership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooper- ation with the Republic of Chile.
Taking this feedback into account, the new CALMOS-2 calorimeter has been designed both for extending the heating range up to 13W. g -1 and for improving the "zero method" measurement thanks to the implementation of a 4-wires technique. In addition, the new calorimeter has been designed as a real operational measurement system, well suited to characterize and to follow the radiation field evolution throughout the reactor cycle. To meet this requirement, a programmable system associated with a specific software allows automatic complete cell mobility in the core, the data acquisition and the measurements processing. This paper presents the analysis of results collected during the 2015 comprehensive measurement campaign. The 4-wires technique was tested up to around a 4 W. g -1 heating level and allowed to quantify discrepancies between "zero" and calibration methods.
The main goal of the CALMOS R&D program was to transpose the current in-core static measurement system to a mobile one, in order to plot the nuclear heating distribution as finely as required. This innovative calorimetric system was studied and developed at the Saclay research center. The so-called CALMOS-1 device, equipped with a displacement system suited to in-core measurements was tested in 2014. Results obtained during the cell development, modelling, mock-ups irradiation, and first results obtained with the complete system up to 10 W. g -1 heating level have been already detailed and presented  [3, 4]. However, major problems came along while operating CALMOS-1. Indeed, because of safety rules, a thermal limitation does not allow to perform measurements at nominal power in any core locations.
IX. COMPARISON OF HEATING AND THERMAL FLUX PROFILES For each scan, CALMOS-2 allows measuring separately the nuclear heating, the thermal flux or both at the same time. One notes that, the rhodium has a response both in thermal and epithermal ranges. As it moves inside in-core and ex-core areas, the neutron spectrum changes and has to be calculated along the total stroke to be able to transpose the current to a neutron flux. Therefore, the epithermal flux/thermal neutron flux ratio has been calculated by the TRIPOLI-4® Monte Carlo code for all accessible altitudes accounting the cell geometry, rods position and the actual core loading  [8]. The associated uncertainty to the conventional thermal flux by this rhodium SPND is 8% (1   The only significant difference appears in the mid-plane region in which the CALMOS-2 signal (red curve) is around 3% higher, -Thermal neutron flux distributions in Fig.
A complete study, including the model adjustment of the cell and all the data processing during the measurement campaign, is ongoing. The intensive use of CALMOS-2 in the core, cumulating a total 70 hours automatic scanning time, has demonstrated its reliability for in-core conditions for the calorimeter itself and the enhanced displacement system. In addition, it has been demonstrated the ability of such device to measure the evolution of the residual power after shutdown whatever the thermohydraulic conditions, forced or natural convection regimes. The device meets better safety requirements and becomes an equipment well suited to the surveillance and qualification of an MTR, preparing the transfer of this technique to the incoming JHR reactor in France.
submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment. For example, "This work was supported in part by the  U. S. Department of Commerce  under Grant  BS123456. " Hubert. Carcreff  is with the CEA/DEN/DANS/DM2S/SERMA, 91191, /DANS/DM2S/STMF, 91191,.
, 2016; De-Silva et al. , 2016 , 2017; Elias et al. , 2009; Mallarino et al. , 2005)  and we additionally sequenced COI-COII, EF1a, tektin, CAD (849 bp), GAPDH (690 bp), MDH (732 bp), and RPS2 (408 bp)  (Wahlberg & Wheat, 2008)  for Dircennina specimens (Appendix S1). Our final dataset comprised 170 individuals representing 86 Dircennina and 45 outgroup species. Fourteen Dircennina species were not included in our analyses (Appendix S1), because no DNA sequences could be obtained. As the taxonomy of the genus Pteronymia has already been revised by  De-Silva et al. (2017) , here we only included one representative per species from that genus, corresponding to the consensus sequences of all individuals available for each species (see  De-Silva et al. , 2017  for more details). PCR conditions followed  Elias et al.
| Dircennina individual-level phylogeny We aligned sequences with CodonCode Aligner v6. 0. 2. The molecular dataset was partitioned by gene and codon positions. We performed a maximum-likelihood analysis including all individuals using IQ-TREE software as implemented in the W-ID-TREE server  (Nguyen, Schmidt, von Haeseler, & Minh, 2015; Trifinopoulos, Nguyen, von Haeseler, & Minh, 2016). IQ-TREE automatically selected the best partition scheme, and we performed 1000 ultrafast bootstrap analyses  (Minh, Nguyen, & von Haeseler, 2013). The tree can be found in Appendix S2.
| Dircennina species-level phylogeny and dating analyses We computed a consensus sequence for each species and gene region, resulting in a dataset containing 86 species and 44 outgroups. We used the "greedy" algorithm and linked rates implemented in PartitionFinder 1. 1. 1 (Lanfear, Calcott, Ho, & Guindon, 2012)  to select the best models of substitution for optimized sets of nucleotides over all models implemented in BEAST. A time-calibrated phylogeny was generated using BEAST 1. 8. 2  (Drummond, Suchard, Xie, & Rambaut, 2012) , using the best partition scheme and uncorrelated lognormal relaxed clock for each partition. We applied eight secondary calibration points based on Nymphalidae node ages obtained from  Wahlberg et al. (2009)  and Solanaceae host-plant ages following De-Silva et al.
ACKNOWLEDGMENTS This project was funded by an  ATIP (CNRS, France)  grant awarded to ME and LDS's postdoc was funded by this grant. ME also acknowledges support from the  French National Research Agency  grant  SPECREP  ( ANR-14-CE02-0011-01 ). NC was funded by a doctoral fellowship from  Ecole Doctorale 227 (France)  and  Lund University (Sweden). We thank authorities and counterparts at institutions in Peru, Ecuador, and  Brazil (ICMBio)  for delivering research and collection permits, as well as many assistants for their help in the field. Molecular work was performed at the  GenePool (University of Edinburgh, UK ),  CBMEG-Unicamp (Brazil) , and the  Service de Systématique Moléculaire UMS2700  of the  MNHN (France). AVLF.
Programme;  French National Research Agency  grant  SPECREP, Grant/Award Number :  ANR-14-CE02-0011-01 ;  Ecole Doctorale 227 (France) ;  Lund University (Sweden) ;  FAPESP , Grant/Award Number:  BIOTA-FAPESP 2011/50225-3, 2012/50260-6  and  2013/50297-0 ;  Brazilian Research Council-CNPq , Grant/Award Number:  302585/2011-7 ,  303834/2015-3  and  563332/2010-7 ;  SISBIOTA-Brasil/CNPq ;  Dimensions US-Biota-São Paulo ;  US NSF ;  NASA ;  National Science Foundation (NSF) , Grant/Award Number:  DEB-1256742  and  DEB-0639861 ;  Florida Natural History Museum Associates ;  National Geographic Society, NSF ;  Leverhulme Trust ;  Darwin Initiative.
Mutational signatures contribute to the total mutational burden of a cancer genome, commonly referred to as mutational "catalogue" or "spectrum" in the recent computational biology literature. Such original methodological framework, applied to tens of thousands of genomes and exomes from 40 different cancers types from large data repositories such as TCGA (The Cancer Genome Atlas), has led to the identification of 30 mutational signatures characterized by a unique probability profile across the 96 mutation types. These validated mutational signatures are listed in a repertory on the COSMIC (Catalogue Of Somatic Mutations In Cancer) website  [3]  and have been widely used as references. More recently, Alexandrov et al. have introduced an updated set of signatures identified from an even larger collection of both exome and whole-genome sequences (including the sequences from the PCAWG project) using two different methods (a new version of the original framework and a Bayesian alternative)  [4].
de novo approaches Most tools that have been developed to identify mutational signatures were based on decomposition algorithms including NMF or a Bayesian version of NMF  [14, 17]. The original method developed by Alexandrov et al. was based on NMF and was implemented in MATLAB  [2]  and is available also as an R package developed independently  [20]. An updated and elaborated version named SigProfiler, was proposed recently for extracting a minimal set of signatures and estimating their contribution to individual samples  [4]. The latter article also discusses an alternative method based on Bayesian NMF, called SignatureAnalyzer, that led to the identification of 49 reference signatures. Another tool that utilises NMF is maftools that is one of the few de novo tools that allows systematic comparison with the 30 validated signatures in COSMIC by computing cosine similarity and assigning the identified signatures to the COSMIC one with the highest cosine similarity  [5].
Other tools such as SomaticSignatures  [15]  or the recent Helmsman  [21]  allows the identification of mutational signatures through Principal Component Analysis (PCA) in addition to NMF. For the sake of our formal comparison of the tools performance, we have tested Soma-ticSignatures only with the NMF implementation because with PCA the factors are orthogonal and the values inside the matrix can potentially be null or negatives, which is a deviation from the paradigm postulating that catalogues are the superposition of positively weighted signatures. We note, however, that PCA could be a promising way to explore complex situations in which mutational processes interfere with each other (e. g. relatively error free repair processes competing with error prone repair processes). Developed in the Python language, Helmsman allows the rapid and efficient analysis of mutational signatures directly from large sequencing datasets with thousands of samples and millions of variants.
All the other tools minimise the distance through equivalent approaches based on quadratic programming  [24, 25, 28] , non-negative least square  [26]  linear combination decomposition  [27]  and simulated annealing  [25]. A faster implementation. We propose an alternative implementation of the decomposition performed in  [26, 27]  based on a simple geometric framework. Finding the linear decomposition of the input catalogue M on a set of given signatures minimizing the distance can be seen as the problem of projecting M on the geometric cone whose edges are the reference signatures. We propose to solve this problem by applying the very efficient R package called coneproj  [34]. More details about our algorithm, which we called MutationalCone, together with the R code implementing it, can be found in the S1 File.
Combining de novo and refitting procedures Sigfit  [29]  is a recently introduced R package for Bayesian inference based on two alternative probabilistic models. The first of such models is a statistical formulation of classic NMF where signatures are the parameters of independent multinomial distributions and catalogues are sampled according to a mixture of such distributions with weights given by the exposures, while the second model is a Bayesian version of the EMu model. An interesting innovation of Sigfit is that it allows the fitting of given signatures and the extraction of undefined signatures in the same Bayesian process. As argued by the authors, this unique feature might be helpful in cases where the small sample of catalogues makes it difficult to try to identify new signatures or when the aim is to study the heterogeneity between the primary tumour and metastasis in terms of the signatures they show.
Real cancer data from TCGA In order to evaluate the performance of the available algorithms on real data, we used the exome sequences in The Cancer Genome Atlas (TCGA) repository (https://cancergenome. nih. gov/) for four cancer types: breast cancer, lung adenocarcinoma, B-cell lymphoma, and melanoma. Mutation Annotation Format (MAF) files with the whole-exome somatic mutation datasets from these cohorts were downloaded from the portal gdc. cancer. gov on 6 March 2018. Data were annotated with MuSE  [35]  and the latest human reference genome (GRCh38). Mutational catalogues from these cohorts were obtained by counting the number of different mutation types using maftools  [5]. The distribution of the number of mutations for each sample and separately for each cancer type is depicted in Fig  1.
We therefore decided to consider a unique r for each set of simulations. This formulation allowed us to study empirically the performance of a given signature detection method as a function of the average number of mutations r while fixing the average proportion of mutations due to each mutational process q n , according to different profiles that mimic real cancer catalogues. Interestingly, the ZIP model appeared to be more appropriate to represent mutational catalogues than the pure Poisson model used in previous publications  [9, 14]   We adopted the following simulation protocol: 1. We chose N signatures from the COSMIC database, thus obtaining the matrix P. 2. For each sample g and process n we sampled e n g from a ZIP distribution with parameters λ n = q n r/(1-π) and π and obtained E.
Here q n ,r and π are fixed parameters to set. 3. We computed the product P×E. In order to obtain the final simulated catalogue M, we added some noise to the latter matrix by taking m k g � PððP � EÞ k g Þ. We generated four alternative sets of simulated catalogues, referred to as Profiles 1, 2, 3 and 4, each set mimicking a particular cancer: breast cancer, lung adenocarcinoma, B-cell lymphoma and melanoma. In order to do so, for each tumour type, we applied MutationalCone to the corresponding TCGA datasets and we calculated the mean contribution across all samples of each signature known to contribute to the specific cancer type q n. Signatures with q n = 0 do not contribute to the final catalogue and were not in the matrix P. S4 Fig depicts the resulting four sets of configurations (q 1 ,.
This can happen for two reasons: the estimated signature is assigned to a COSMIC signature not used to build the catalogue, or it is not sufficiently similar to any COSMIC signature. This last situation usually takes place when setting a very high cosine similarity threshold h. In this case, signatures that have maximal cosine similarity lower than the cutoff, will be termed as "new". Finally, true negatives are all COSMIC signatures not used for the simulation, nor estimated. From these four measures, we compute specificity (number of true negatives divided by the total number of negatives) and sensitivity (number of true positives divided by the total number of positives). In our empirical study, for each simulation setting described in the Simulated data section (that is for each profile given by a choice of proportions (q 1 ,.