dataesr/scientific-paragraphs-categorization huggingface dataset:
(This modification does not affect the completeness. ) We note that this modification is independent of the fact that DSC probabilities are already posterior probabilities that take into account this class prior (i. e. both modifications must be done). This should also serve as a warning when assessing any classifier: if the validation data set does not have a representative fraction of contamination, or if this is not adjusted, the predicted purities will be erroneous. Table  3  shows the completenesses and purities for the DSC classes and classifiers. This is the performance we expect for a sample selected at random from the entire Gaia dataset that has complete input data for both Specmod and Allosmod. It accommodates the rareness of all these classes, as specified by the global class prior (Table  2 ), both in the probabilities and the application data set.
Objectives The Outlier Analysis (OA) module aims to complement the overall classification performed by the DSC module, by processing those objects with lower classification probability from DSC (see Section 3). OA is intended to analyse abnormal or infrequent objects, or artefacts, and was applied to all sources that received DSC Combmod probabilities below 0. 999 in all of its five classes. This threshold was chosen so as to process a limited number of 134 million sources, corresponding to about 10% of the total number of sources for which DSC produced probabilities. Subsequently, a selection of the sources to be processed is carried out based on several quality criteria, the most restrictive being that the mean spectra correspond to at least five transits (see details in the online documentation).
Training and test sets The sources in the training and test sets were selected from the SDSS DR16 archive  (Ahumada et al. 2020) , which provide position, redshift, magnitudes in the u-, g-, r-, i-, z-bands, photometric sizes (we used here the Petrosian radius), and interstellar extinction for each spectroscopically confirmed galaxy. There are 2 787 883 objects in SDSS DR16 that are spectroscopically classified as galaxies, but we rejected sources with poor or missing photometry, size, or redshift, thus reducing the number of galaxies to 2 714 637. Despite the known lack of uniformity of the SDSS DR16 redshift distribution due to the BOSS target selection 14  , this survey still provides the largest existing database of accurate spectroscopic redshifts of galaxies that can be used as target values in the SVM training and test sets.
Beyond Gaia DR3 We present the non-stellar and classification modules from CU8 in their present status, as for Gaia DR3. However, they are in constant evolution and changes are already planned for Gaia DR4 and later, which we summarise for each module in this section. Although the intrinsic performance of DSC is very good, once we take into account class prior -as we do for all results shown in this paper-the purities of the classified samples are modest. In preparation for Gaia DR4, we will aim to improve this, for example by optimising the feature set in Allosmod and how this is used. We will also reconsider the class definitions and the training data, in particular for white dwarfs and physical binaries. As Specmod uses the entire BP/RP spectrum, we expected better performance (compared to Allosmod), and so we will investigate improving the classifier.
We may also introduce filters to remove the classifications of the lowest quality data (which are the main determinant of the low purities). OA will be upgraded by implementing its own outlier detector, which will be mostly based on unsupervised clustering algorithms. Additionally, we will improve the statistical description and the templates that were used for Gaia DR3. The functionality offered by the GUASOM visualisation tool will be extended in order to allow the user to perform and explore their own clustering analysis. QSOC will use epoch BP/RP spectra re-sampled into logarithmic wavelength bins in order to overcome the issues we encountered while using the Hermite spline polynomials associated with the internal representation of the BP/RP spectra. This internal representation effectively tends to produce wiggles whose strength can be comparable to those of quasar emission lines in faint G ≥ 19 mag spectra  (Creevey et al.
2022). This solution will concurrently allow us to use sampled BP/RP spectra with uncorrelated noise on their flux, as the algorithm described in  Delchambre (2016)  is not optimised to deal with full covariance matrices. The performance of the UGC redshift estimator strongly depends on the training set used. As more epochs are incorporated in the BP/RP spectra, we expect to have more (and generally fainter) sources with redshifts above 0. 4 available for inclusion in the training set, thus improving the performance especially for higher redshifts. We will also investigate optimisation of the SVM model parameters in order to reduce the large variability in the performance with redshift and to minimise the positive bias for bright, low-redshift objects. In future data releases, we can expect the TGE maps to improve with future improvements of GSP-Phot  (Andrae et al.
2022). In particular, we expect that the number of sources with stellar parameters will increase, which will improve the reliability of the TGE maps, and possibly allow for maps at a resolution higher than HEALPix level 9. particular the institutions participating in the Gaia MultiLateral Agreement (MLA). The Gaia mission website is https: //www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. The Gaia mission and data processing have financially been supported by, in alphabetical order by country: - the software products TOPCAT, STIL, and STILTS  (Taylor 2005 (Taylor , 2006)) ; -Matplotlib  (Hunter 2007   (Chambers et al. 2016; Magnier et al. 2020a; Waters et al. 2020; Magnier et al. 2020c,b; Flewelling et al. 2020)  where a is a data-dependent but class-independent normalisation factor.
2000), the construction of which was supported by the Velux Foundation of 1981 and the Danish Space Board; -The Tycho double star catalogue (TDSC, Fabricius et al. 2002), based on observations made with the ESA Hipparcos astrometry satellite, as supported by the Danish Space Board and the United States Naval Observatory through their double-star programme; data products from the Two Micron All Sky Survey (2MASS, Skrutskie et al. 2006), which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center (IPAC) / California Institute of Technology, funded by the National Aeronautics and Space Administration (NASA) and the National Science Foundation (NSF) of the USA; the ninth data release of the AAVSO Photometric All-Sky Survey (APASS, Henden et al. 2016), funded by the Robert Martin Ayers Sciences Fund; the first data release of the Pan-STARRS survey.
(Bonnarel et al. 2000e Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory; the French Centre National d'Études Spatiales (CNES), the L. Delchambre et al. : Gaia DR3: Apsis III -Non-stellar content and source classification the Hungarian Academy of Sciences through the LendületProgramme grants LP2014-17 and LP2018-7 and the Hungarian National Research, Development, and Innovation Office (NKFIH) through grant KKP-137523 ('SeismoLab'); the Science Foundation Ireland (SFI) through a Royal Society -SFI University Research Fellowship (M. Fraser); the Israel Ministry of Science and Technology through grant 3-18143 and the Tel Aviv University Center for Artificial In-Unidad de Excelencia 'María de Maeztu') through grant CEX2019-000918-M, the University of Barcelona's official doctoral programme for the development of an R+D+i project through an Ajuts de Personal Investigador en Formació (APIF) grant, the Spanish Virtual Observatory through project AyA2017-84089, the Galician Regional Government, Xunta de Galicia, through grants ED431B-2021/36, ED481A-2019/155, and ED481A-2021/296, the Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), funded by the Xunta de Galicia and the European Union (European Regional Development Fund -Galicia 2014-2020 Programme), through grant ED431G-2019/01, the Red Española de Supercomputación (RES) computer resources at MareNostrum, the Barcelona Supercomputing Centre -Centro Nacional de Supercomputación (BSC-CNS) through activities AECT-2017-2-0002, AECT-2017-3-0006, AECT-2018-1-0017, AECT-2018-2-0013, AECT-2018-3-0011, AECT-2019-1-0010, AECT-2019-2-0014, AECT-2019-3-0003, AECT-2020-1-0004, and DATA-2020-1-0010, the Departament d'Innovació, Universitats i Empresa de la Generalitat de Catalunya through grant 2014-SGR-1051 for project 'Models de Programació i Entorns d'Execució Parallels' (MPEXPAR), and Ramon y Cajal Fellowship RYC2018-025968-I funded by MICIN/AEI/10.
Acknowledgements This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https: //www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. Acknowledgements are given in Appendix A. Agence Nationale de la Recherche (ANR)  through grant  ANR-10-IDEX-0001-02  for the ' Investissements d'avenir' programme , through grant  ANR-15-CE31-0007  for project ' Modelling the Milky Way in the Gaia era ' (MOD4Gaia), through grant  ANR-14-CE33-0014-01  for project 'The  Milky Way disc formation in the Gaia era ' (ARCHEOGAL), through grant  ANR-15-CE31-0012-01  for project ' Unlocking the potential of Cepheids as primary distance calibrators' (UnlockCepheids ), through grant  ANR-19-CE31-0017  for project ' Secular evolution of galaxies ' (SEGAL), and through grant  ANR-18-CE31-0006  for project ' Galactic Dark Matter' (GaDaMa ), the  Centre National de la Recherche Scientifique (CNRS)  and its  SNO Gaia of the Institut des Sciences de l'Univers (INSU) , its  Programmes Nationaux: Cosmologie et Galaxies (PNCG) ,  Gravitation Références Astronomie Métrologie (PNGRAM) ,  Planétologie (PNP) ,  Physique et Chimie du Milieu Interstellaire (PCMI) , and  Physique Stellaire (PNPS) , the ' Action Fédératrice Gaia' of the Observatoire de Paris , the  Région de Franche-Comté , the  Institut National Polytechnique (INP)  and the  Institut National de Physique nucléaire et de Physique des Particules  ( IN2P3 ) co-funded by  CNES ; the  German Aerospace Agency (Deutsches Zentrum für Luft-und Raumfahrt e.
Chemin); -the National Natural Science Foundation of China (NSFC) through grants 11573054, 11703065, and 12173069, the China Scholarship Council through grant 201806040200, and the Natural Science Foundation of Shanghai through grant 21ZR1474100; -the Tenure Track Pilot Programme of the Croatian Science Foundation and the École Polytechnique Fédérale de Lau-sanne and the project TTP-2018-07-1171 'Mining the Vari-able Sky', with the funds of the Croatian-Swiss Research Programme; -the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010 and INTER-EXCELLENCE grant LTAUSA18093, and the Czech Space Office through ESA PECS contract 98058; -the Danish Ministry of Science; -the Estonian Ministry of Education and Research through grant IUT40-1; -the European Commission's Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission's Seventh Framework Pro-gramme through grant FP7-606740 (FP7-SPACE-2013-1) nology for Frontier Astrophysics and Cosmology' (MITiC); -the Netherlands Organisation for Scientific Research (NWO) through grant NWO-M-614.
Introduction Since the meta-analysis of paclitaxel-eluting device use was published by Katsanos et al. in 2018  [1] , the landscape on the use of drug-eluting devices for peripheral arterial disease (PAD) has undergone significant change. Although several studies have called into question the mortality signal raised in the paper since then, reactions to the metaanalysis by national authorities such as the US Food and Drug Administration (FDA) impacted daily IR practice in many regions. To better understand the real impact and current use of drug-eluting devices for PAD within the IR community more than 4 years after the controversy started, a CIRSE member survey was designed to better assess the effect of the article on PAD practice by IRs. In parallel, the society's Endovascular Subcommittee (EVSC) worked on an Expert Opinion paper, concluding that the re-analysis of the current available data does not support a link between paclitaxel-eluting devices and mortality  [2].
Discussion The survey was answered predominantly by IRs who are active in the field (95. 8%), and in 15. 3% of cases by EBIR-ES holders. The sample was thus highly specialised and can be assumed to be knowledgeable about the Paclitaxel situation. While the CIRSE membership can be considered to be representative of interventional radiology practice in Europe, a potential selection bias must be acknowledged for the present survey, as IRs who work in the endovascular field were more likely to open and respond to this survey, as were those with strong opinions on the topic. While the benefits of paclitaxel-device use in the femoropopliteal segment are supported by medium term outcomes data  [2] , more evidence on the benefits of drugeluting stents in treatments below the knee, such as the long-term data on the PADI trial [4], are needed.
Introduction Sequence-driven modeling and prediction techniques for proteins have recently seen great advances, thanks to the combination of the rapidly growing amount of available proteinsequence data, with powerful statistical and machine learning techniques. Recently, AlphaFold brought a major advance in protein-structure prediction for monomeric proteins, provided that a sufficiently large number of homologous proteins can be found  [1]. Indeed, AlphaFold starts from multiple-sequence alignments (MSAs) of homologs. Extensions to multimers and protein-protein interactions have been proposed, and they also start from MSAs of homologs of the proteins involved  [2] [3] [4]. While these advances are impressive, many protein complexes remain unsolved by current computational means. A possible direction to improve them is to produce better co-alignments of interacting proteins (co-MSAs), where each row contains the concatenation of two interacting proteins.
Indeed, co-MSAs allow to exploit correlations between interaction partners, which convey important information about binding specificity  [5]. Beyond the perspective of improving quaternary protein-structure prediction, being able to accurately pair interacting paralogs is important to unveiling signaling networks and to understanding interaction specificity. Indeed, homologous signaling pathways in a given organism employ homologous mechanisms, but crosstalk between pathways may be unwanted. This is the case for instance in two-component systems in bacteria  [6, 7] , or calcium signaling in plants  [8, 9]. Pairing interacting paralogs and obtaining co-MSAs is difficult because many protein families contain several paralogous proteins encoded within the same genome. Out of the almost 20,000 protein domain families in the Pfam database  [10] , now integrated into InterPro  [11] , more than 1300 have, on average, at least 10 paralogs per species, and about 400 more than 30 paralogs.
More recently, some of us showed that iterative algorithms based on DCA allow paralog pairing  [34, 35]. However, while these methods can function even without an initial seed co-MSA, their performance remains limited for high paralog numbers as well as for small datasets  [34]. Interestingly, these coevolution-based methods already benefit from phylogenetic correlations  [36, 37] , and mutual information, which uses all forms of statistical dependences, performs slightly better than DCA for the pairing task  [38]. These results show the potential of combining phylogenetic and coevolutionary signals. Coevolution-based methods are unlikely to exploit sequence similarity in an optimal way, because they are not designed for that. Here, we show that explicitly combining these two signals substantially improves the pairing of paralogs between interacting protein families.
To solve this difficult problem, we introduce a heuristic stochastic local search algorithm based on simulated annealing (cf. Materials and methods). Fig  2  shows results for a benchmark set of M = 5064 bacterial two-component systems from N = 459 species, describing the interaction between histidine kinases (HK) and response regulators (RR), cf. Materials and methods for details of the dataset and the corresponding similarity networks. Similar results are obtained for other protein-family pairs, as we report in the Supporting information. For the orthology network, we find on average about 2/3 of correct pairings (true positives, TP), and 1/3 of incorrect pairings (false positives, FP), with higher TP fractions corresponding in general to lower GA cost, as is shown in the Supplementary information.
This is much better than a random within-species pairing, which would have an average TP fraction of only N/ M ' 9%. For the kNN network, we find a strong dependence on k. Indeed, for small k, only few edges can be aligned, since they connect different species, and the similarity networks contain little information about the correct paralog pairing. For larger values of k, the results slightly outperform those of the orthology network, with an average TP fraction of about 70% (see  Fig 2). However, the increase of accuracy with increasing k comes with the higher computational cost of aligning denser similarity networks. Fig  2  shows that our GA results reach, on this HK-RR dataset, a lower performance than the IPA starting from random matchings, i. e. without any training set of known paired sequences.
Note that, in terms of the size and the quality of the robust subset, kNN networks outperform the orthology network even at moderate k for which the overall accuracy of the orthology networks for GA is still superior. We also compare the results of our GA method to those of the related MirrorTree family of approaches  [16] [17] [18] , which aim to predict protein-protein interactions by exploiting sequence similarity (and thus phylogeny), see Materials and methods. Indeed, some MirrorTree-based methods have tackled the paralog pairing problem  [19] [20] [21] [22] [23] [24] [25] [26] [27] [28]. To make a comparison, we implemented such a method (see Materials and methods for details). Table A in S1 Text shows that the performance of GA and of MirrorTree is similar for several datasets, but we find a substantially better TP fraction using GA than MirrorTree for the standard HK-RR dataset with hm p i = 11.
03. While GA and MirrorTree share conceptual similarities, one key difference is that GA mainly focuses on close neighbor sequences, by restricting to the k nearest neighbors, or to reciprocal closest matches, and by giving exponentially decreasing weights to distant pairs (see Materials and methods). Conversely, MirrorTree relies on all pairs, including many informatively distant ones, which may become problematic for large datasets and with large numbers of paralogs per species. This may explain the difference observed for the HK-RR dataset. In the MirrorTree approach, one can exploit the stochasticity of the optimization to define robust pairs, exactly as for GA. Table B in S1 Text shows that MirrorTree yields similar numbers of robust pairs as GA but with a lower TP fraction. To summarize, GA of the sequence-similarity networks of the two MSAs of interest allows to identify a robust subset of paired sequences and to construct a robust partial co-MSA with high accuracy.
The IPA can also be run without a seed co-MSA  [34] , as is done e. g. in Fig 2. This serves as a baseline comparison for our combined GA-IPA approach. In this case, a random pairing within each species is used to infer the first DCA model. Since this random matching has on average one correct pair per species, we expect some coevolutionary signal to emerge if the average number of paralogs per species hm p i = M/N is not too large, but the total dataset depth M is large  [40, 41]. The resulting DCA model is used to find the N increment highest-scoring pairs, which in the second step are used to replace the randomly matched MSA. Iterations are then continued as described in the previous paragraph. The fact that signal adds more constructively than noise, and the fact that two interacting pairs from different species tend to be more similar to one another than to non-interacting pairs, both help to bootstrap IPA toward high performance starting from random pairings (see  Fig 2).
84% in IPA). Second, results are very robust across different similarity networks. Even for the small value k = 3, when GA alone has a low TP rate and produces a small robust partial co-MSA, we obtain very good results: IPA is able to benefit even from pretty small seed co-MSAs. We applied GA-IPA to other datasets corresponding to two other pairs of interacting protein families with different biological functions (ABC transporters and enzymes), as well as to two pairs of protein families with no known interaction but encoded in close proximity in prokaryotic genomes (see Materials and methods). Results are shown in Fig C in S1 Text. We find a very good performance of GA-IPA in all cases, showing its robustness, but the gain of performance compared to IPA alone is very limited. Indeed, the limited numbers of paralogs per species in these datasets make the IPA already very efficient and robust without any seed co-MSA.
Note that the successful performance of IPA on pairs of protein families with no known interaction shows that it is already able to exploit phylogenetic signal  [36, 37]. With GA, this signal is exploited more explicitly. As mentioned above, another way of taking into account sequence similarity (and thus phylogeny) is to use MirrorTree approaches. We compare the results of our methods to variants using MirrorTree scores in Table C in S1 Text. We consider two types of such variants (see Materials and methods). First, we use MirrorTree to build the starting robust partial co-MSA (see above and Table B in S1 Text), and we run the DCA-based IPA starting from it. Second, we use MirrorTree scores instead of DCA scores within the IPA, either without a training set, or starting from robust partial co-MSAs from either GA or MirrorTree.
GA-IPA yields accurate co-MSAs for data sets with few sequences or high paralog multiplicities In  Fig 4 and   The first difficult case we consider involves high average multiplicities hm p i = M/N � 1 of paralogs per species, making the pairing task very hard. To investigate this case, we constructed a data set by selecting species with large paralog numbers in our data set of two-component system protein sequences, cf. Materials and methods. The results for hm p i = 29. 2, i. e. for almost three times more paralogs per species than in the previous dataset, are shown in Fig 5A. In this case, a random matching has only 3. 4% TPs. The IPA without seed alignment reaches 16% TP rate, which is better than random, but not sufficient for practical applications. GA alone already performs better in this case: kNN similarity networks with large enough k reach about 40% TP rate.