dataesr/scientific-paragraphs-categorization huggingface dataset:
Both experiments have performed dedicated searches for new signatures in various possible final states at the LHC: besides the fermionic final states t t, b b, τ + τ -, t b and τ + ν they include gauge bosons (γγ, Zγ, ZZ, W + W -) and Higgs particles (hh, hZ, HZ, AZ) as the decay products of a heavy resonance. So far, these searches for heavy resonances have remained elusive in the ATLAS and CMS data, and thus the measurements put modelindependent 95% C. L. upper limits on the production cross section times branching ratios for different production processes and decay modes. In the present work, we assess the status of all four types of softly broken Z 2 symmetric 2HDM when all the experimental constraints coming from the latest LHC data are taken into account. We confront these with the theoretical constraints on these models (positivity, stability and next-to-leading order unitarity).
The correlation between µ W W tth and µ τ τ tth was extracted from Figure  17  in  [41]. (We assume that the V V final state therein is dominated by W W. ) Also the CMS correlations in Table  4  were reconstructed from the signal strength contours (or cross section times branching ratio contours) in the plane of VBF vs. ggF production. In Section    1  and 2 , the ones in the fifth column to the amount of underlying data like in Table  3. impact of the signal strengths on the 2HDM parameters, ordered by the decay products. Concerning the direct searches for the heavy CP-even, the CP-odd and the charged Higgs, we have updated the number of used LHC analyses from 16 in  [31]  to 50 Run 1 and 2 measurements in the present article. We calculate the product of the production cross section  [104] [105] [106] [107] [108] [109] [110] [111] [112] [113]  and the branching ratio  [114, 115]  of a specific decay, σ • B.
While the implementation into HEPfit was already explained in  [31] , we updated the experimental values  [34, 116, 117]. Also for the treatment of theoretical constraints we refer to  [31] , with two exceptions: We do not apply any constraints arising from the renormalization group evolution and define our model at the electroweak scale. And for the next-to-leading order unitarity bounds we chose the most conservative approach that appeared reasonable to us, namely requiring that the real and imaginary parts of the S-matrix eigenvalues should be between -0. 5 and 0. 5 and between 0 and 1, respectively. Moreover we impose perturbativity by discarding scenarios for which the one-loop contribution to these eigenvalues exceeds the tree-level term in magnitude. As numerical set-up we use the open-source package HEPfit  [118] , interfaced with the release candidate of the Bayesian Analysis Toolkit (BAT)  [119].
Finally, we have confronted the LHC h signal strengths and heavy Higgs searches with all other relevant indirect constraints from theory and experiment. In detail, we have showed how stability and unitarity constraints and B physics observables set mass dependent limits on the 2HDM angles and on the differences between the heavy Higgs masses, while electroweak precision data only affect the latter. We have compared all different sets of constraints and have showed the results in the mass vs. angle planes as well as in the m A -m H + vs. m H (-m H + ) planes for all four types of Z 2 symmetric 2HDM's together with the simultaneous fit to all constraints. In this global fit we find the following 95% probability limits on the 2HDM parameters marginalizing over all other parameters: |β -α -π/2| cannot be larger than 0.
Acknowledgments We thank  Enrico Franco ,  Ayan Paul ,  Maurizio Pierini  and  Luca Silvestrini  for useful discussions. The fits were run on the  Roma Tre Cluster. We are grateful for the availability of these resources and especially want to thank  Antonio Budano  for the support. We also thank  Jose Miguel  No and  Ken Mimasu  for giving us a hand with the implementation of the H → AZ and A → HZ search limits. This work was supported by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement n. 279972, by the  Indo-French Center for Promotion of Advanced Research/CEFIPRA  (Project no. 5404-2 ) and by the  Spanish Government and ERDF  funds from the  European Commission  (Grants No. FPA2014-53631-C2-1-P  and  SEV-2014-0398 ).
The 2016 presidential campaign in the USA confirmed this trend, with an estimated total budget of around 500M € devoted to the use of social networking sites  [1]. France followed the same trend. Nowadays, the largest political parties call on the services of specialized companies, in order to gain a better understanding of their potential voters, through the analysis of very large volumes of data provided by social networking sites  [2]. Among these, Twitter is very well ranked among accessible sources of information. Moreover, it is the social networking site used by the majority of political figures, in response to current affairs or to provide visibility for their opinions and public statements. This social networking site has thus become an unavoidable medium for the political world, when it comes to delivering messages or establishing legitimacy.
With the objective of evaluating the potential of platforms like Twitter for research on social dynamics, and particularly in political sciences, we propose a method in the framework of complex networks analysis allowing the political communities and their evolution in a multipolar political environment to be reconstructed and analyzed. As a case study, we implement and evaluate this method on data we have collected during the 2017 French presidential elections that feature almost 60 million Twitter exchanges between more than 2. 4 million users (Politoscope project-https://politoscope. org). The unusual hectic nature of these elections (see Text A in S1 File for a summary) represents a worse case scenario for this kind of reconstruction and guaranties the robustness of our approach. We then present some perspectives this type of reconstruction brings to social sciences and discuss its relevance for political sciences.
Related work A considerable volume of academic literature has been devoted to Twitter, as this platform benefits from two different advantages. Its content is massively duplicated by the traditional press or television and it is accessible to the general public, even though it may not be representative of the global or even France's population  [3]. Furthermore, data collection is relatively straightforward, thanks to various APIs proposed by Twitter (https://dev. twitter. com/ overview/api). Numerous methodologies can be used to access Twitter productions, in the context of the study of political activities. Some specific methods  [4] [5] [6]  are designed to take advantage of the content of tweets to infer the political orientation of a tweet or user. However, the constraints associated with tweet formats appear to limit the efficiency of these methods.
Boutet et al. [11]  (resp. Larsson and Moe  [12] ) applied the same method to study the structure of the political space in England (resp. Sweden), and to analyze differences in practice, in terms of the manner in which political groups use Twitter mentions, hashtags and Internet links. Garimella et al. [10]  and Morales et al. [13]  used the notion of retweets to measure the existence of polarization and controversies on Twitter. Amor et al. [14]  used this to characterize the debate on care. data of the English National Health System and Guerrero et al. [15]  also used this approach to study the Catalan Process Toward Independence. These techniques have made it possible to retrieve bipolar political structures, although the notion of retweets also makes it possible to retrieve multi-polar structures.
Data We used the CNRS Multivac platform (http://multivac. iscpif. fr), a platform dedicated to stream data capture and analysis, to capture in real-time the political twittosphere activity in France. For a period of 11 months prior to the presidential election, we continuously collected almost 60 million tweets concerning more than three thousands of the key French political figures and few dozens political keywords, that were used by more than 2. 4 million individual accounts (see Text B in S1 File). We refer to this set of data as D. All data was collected according to Twitter's terms of service and privacy conditions and are made available according to the terms of use of Twitter  [24]. All anonymized data derived from the analysis of this database in the context of this paper are available for download  [25].
We estimated the proportion of tweets in D sent by bots to be less than 5% (see Text C in S1 File), thus ensuring that our results remain representative of online political activism. Retrospectively, given the unusual hectic nature of these elections (see Text A of S1 File for a summary) that took place in a multi-polar political environment, this dataset was found to be both of great interest to understand the French political landscape and of high value to challenge community reconstruction methods.
That being said, we considered the following definition: [Definition] Social groups in the political twittosphere: dense networks of accounts that recurrently relay content, without modification (retweet), between each other. The retweet communities. In order to define user groups based on retweets, we selected the retweets from our dataset D and defined a graph G T = (V, E) in which each node, u 2 V, is a Twitter account and each (u, v, w) 2 E is a weighted and undirected link between nodes u and nodes v, w being proportional to the number of retweets having occurred between accounts u and v during a period of time T. To detect political groups in G T , we adopt an approach similar to those proposed by  [29] [30] [31]  by applying a non-overlapping community detection algorithm to G T. From the set of existing and regularly cited state-of-the-art algorithms  [32] [33] [34] , we apply the Louvain algorithm developed by Blondel et al.
), it may be relevant to analyze the data using several different time scales. For example, the analysis of the 5-communities over the 5 months period August 1st to December 31 2016 (cf. Fig  1 ) depicts a quite accurate picture of the pre-electoral political landscape. Its relevance was confirmed by the subsequent alliances that took place in 2017 during the campaign while respecting the personalities' proximities within this map: Hamon with Jadot, Macron with Bayrou, Sarkozy with Fillon, Le Pen with Dupont-Aignan. For the present study, we chose a time scale matching timing of Twitter and the media in the context of a political campaign. By analyzing the temporal distribution of retweets within our corpus, we observed that both current political events and Twitter activity have a weekly periodicity, and that on average the retweet period of an original tweet is of the order of one week, with 98.
This graph has been spatialized with the Gephi software  [36]  using the ForceAtlas2 algorithm  [37]. See File A1 at DOI: 10. 7910/DVN/AOGUIA for the anonymized gexf version of this graph. https://doi. org/10. 1371/journal. pone. 0201879. g001 We thus considered seven-days intervals to be a characteristic period for political activity on Twitter, and used two-week intervals T i in order to obtain overlapping periods in the temporal reconstruction (see below). In the following analyses, we consider the communities computed every day over a 14 days period to assign a community to each tweet of that day. For the analysis of the dynamics of the communities, we took the subset of the communities computed every Monday, with G ¼ fG T g T2T where T is a set of temporal intervals, such that 8k 2 Each tweet in our dataset is thus taken into account in exactly two retweet graphs for the analysis of community reconfigurations.
We invited them to provide us with their program elements and key expressions that they would consider characteristic of their program. Unsurprisingly, we had almost no response, only the team of a "small" candidate indicated to us that given the size of the campaign team, they were not sure they could participate. We used then the programs of the eleven candidates downloaded from their websites (between February 14 and March 24, 2017) from which we manually extracted 980 policy measures (see File A2 at DOI: 10. 7910/DVN/AOGUIA for an editable version of these policy measures). To complement this corpus, we extracted from the Politoscope database 25, 883 tweets published by the eleven candidates and few other key political figures between August 2016 and January 2017 (see Text B in S1 File).
Consequently, we analyzed these two corpora using the CNRS text-mining software Gargantext (https://gargantext. org; open source at https://github. com/ISCPIF/gargantext), that implements advanced NLP methods and co-word topic detection; as well as visual analytics methods for the representation and interaction with the results. In the first few steps, Gargantext uses a combination of lemmatization, post-tagging and statistical analysis like tf-idf  [47]  and genericity/specificity analysis  [48]  to identify from the text-mining few thousand groups of keywords that are specific to the political discourse. These keywords were further screened by the authors in order to select the most meaningful ones (i. e. stop words or poorly formed expressions that would have passed the text-mining steps have been removed, important hashtags or neologisms from Twitter like frexit have been added).
This manual analysis resulted in two distinct lists of 885 followed accounts and 414 random accounts respectively that were publicly supporting one of the 11 running candidates at the The map has been built from policy measures extracted from the candidates' programs. The nodes of the map are labels for groups of terms deemed equivalent in political discourse. The link between a label A and a label B indicates that the probability that A and B are jointly mobilized in the same political measure is high. Gargantext applies the Louvain algorithm to identify clusters of labels with strong interaction between them and displays them in the same color. To improve readability, the map was edited in the Gephi software (https://gephi. org) to set the size of nodes and labels according to a monotonous function of their PageRank  [50].
e. number of tweets captured by the Politoscope). The activity of a community C T c is then defined as the average of weighted activity of accounts participating to C T c. • the politicization of an account a over a set of periods T is the percentage of political tweets emitted by an account (i. e. number of tweets captured by the Politoscope during T over the total number of estimated tweets during the same period). Since we don't follow most of the accounts that interact with the Politoscope, we estimate for every account the total number of tweets emitted during T as the difference between the total number of tweets for that account mentioned in the meta-data of the last observed tweet and the same quantity mentioned in first observed. This estimate can be biased in two ways. First we might not capture all political tweets.
This is in line with the analysis of  [42]  that demonstrated from the analysis of the leaders mentions in the Politoscope data that the Hamon community was the one that expressed its political ideas in the most detached way from any reference to a candidate or a party. The low average number of days spent in the community reflects the useful voting phenomenon (vote utile): at some point (around March), many people from the Hamon community joined either the Melenchon or the Macron communities when the collective belief ended on the idea that Hamon would not qualify for the second round. And indeed, he ended up far behind all the other candidates. The decomposition of the online engagement of political communities members into activity, politicization, political integration and commitment allows to discriminate different types of political groups.
The question of how comparatively true and false information spreads on a social networking site like Twitter and which contexts influence this spread is an important issue for the "science of fake news" that should help to solve the post-true crisis  [70, 71]. To assess the importance of the reconstruction of political communities in the understanding of this phenomenon, we used the Decodex database, an initiative from Le Monde, one of the major French newspaper (center-left oriented), that has collected, throughout the year 2017, URLs of web pages and Facebook posts that include fake news  [72]. URLs referring to the same fake news (e. g. "Jean-Luc Mélenchon has a Rolex" or "More than 30% of Emmanuel Macron's campaign was financed by Saudi Arabia") have been grouped together in stories and associated to a debunk, a blog post in which Le Monde journalists unveil and explain why this news is a fake.
There are 179 stories and 179 debunks in total (see Files A6 and A7 at DOI: 10. 7910/DVN/ AOGUIA for a json version). The Decodex database is a good entry point to identify the stakes of the fake news phenomenon through the lens of our political landscape reconstruction. Differential echo chamber effet for fake news. After cleaning and normalizing the Decodex URLs; and having double checked that the tweets mentioning a link to a fake news or a debunk have really for purpose to broadcast them (see Text H in S1 File), we have studied their diffusion. We observed only 4 888 shares of fake news links inside our dataset of 60 million tweets (0. 081%). This indicates that either fake news are not heavily shared in France by people interested in politics on Twitter or that the Decodex failed to identify a significant proportion of fake news references on Twitter.
Likewise, the debunks were shared only 1 275 times. Table  8. Entropy of the themes addressed by each candidate, and its 3-community. The two dominant themes as well as their associated percentages are also shown. The maximum entropy is 3. 46, if the 11 themes are discussed in a uniform manner. With respect to the selected themes, Benoı ˆt Hamon is the candidate who uniformly addressed the greatest number of themes, whereas Francois Asselineau is the candidate who was the most focused on a subset of themes (including foreign policy and democracy). The variations in attention paid by the candidates to these themes are plotted in Figs I to N in S1 File. There is an average number of 27. 3 sharing per fake news story whereas the average number of share for debunks is 7. 12. Consequently, for this fake news/debunks dataset, fake news stories were shared almost four times more than debunk news.
Entropy and average number of communities [in brackets] reached on average by fake news, debunks and tweets with or without URL mention over the period. We also present the result when considering all communities as a single one except for the 6 main political online communities identified during the French presidential election. Tweets without communities (43. 44%) were considered as a single community for the processing of entropy. For comparison with the debunks and fakes news dataset, identified by the URLs they spread, we also provide the global statistics for the subset of tweets that mention URLs.
Conclusions We have demonstrated that methods involving the analysis of exchanges of political content on social networking sites such as Twitter allow automatic reconstruction of political communities which, by their semantic profile and patterns of development, provide deep insights into the dynamics at play inside the space of political activists. By applying these methods to data from the 2017 French presidential election, we reconstructed with a high accuracy the multi-polar French political space, retrieved the main events which occurred during the campaign, and revealed the multiple reconfigurations of political communities, some of which took place under relatively confidential circumstances. By using interaction data only, our approach has the advantage of being independent from the language used during these exchanges, provided the studied social activity involves a certain level of commitment from the participants, as it is the case in politics.
It provides access to an intermediate level of resolution between sociological surveys in the field and global statistical studies (such as those conducted by national census agencies), from which we expect that, in the future, it will represent a major source of political and sociological knowledge supplementing the more traditional approaches. We conclude this paper with a comment concerning the political and ethical implications of the development of these methods. In recent years, the boundary between private and public life has become increasingly thin, as the digital trails left by citizens have been systematically analyzed, by combining an ever increasing number of databases (purchase history, web navigation history, votes, store loyalty cards, public transport, GPS location, etc. ), and by considering an increasingly significant history based on population sample sizes that tend to be of the order of magnitude of the population size itself.
The Socialist Party was also made up of a "left-wing" (Hamon community, Montebourg and Pinel community) and a "right-wing" (Holland, Valls). This graph has been spatialized with the Gephi software [36]  using the ForceAtlas2 algorithm [37]. See File A1 at DOI: 10. 7910/DVN/AOGUIA for the anonymized gexf version of this graph.
Acknowledgments This research was supported by the  Complex Systems Institute of Paris Ile-de-France  (https:// iscpif. fr), the  Algodiv  project ( ANR-15-CE38-0001 ), the project  Science en Poche (Emergence(s) program ,  City of Paris)  and the Problématiques transversales aux systèmes complexes DIM ( Field of major interest ) from the  I ˆle-de-France Region. We warmly thanks the reviewers and all our colleagues for their fruitful comments and suggestions about our work.
This work has been supported by the  CNRS  (http://cnrs. fr),as hosting institution and financer of the PI David Chavalarias and  Maziyar Panahi ; The  Complex Systems Institute of Paris Ilede-France  (https://iscpif. fr),that hosted the team and provided computing infrastructure; the  ANR  project  Algodiv  ( ANR-15-CE38-0001 , http://www. agence-nationale-recherche. fr),workpackage leader for CAMS David Chavalarias, that financed NoeGaumont's Post-Doc; The  Region I ˆle-de-France  through the  Domaine d'Interêt Majeur "Problematique Transversales aux Systèmes Complexes" DIM (Field of major interest ) from the  I ˆle -de-France Region  that co-funded the IT infrastructure; and The Science en  Poche  project funded by the  City of Paris  ( Programme Emergence (s), PI David Chavalarias, https://www.
A statistical portrait of sectoral minimum wages in Italy In Italy there are currently 819 collective agreements which set thousands of wage floors for different sectors, occupations and levels. They go from the traditional ones such as those covering metal or chemical workers to more 'exotic' ones such as collective agreements for emotional coaches and sacristans. In what follows we will focus on a sample of the most representative agreements (in terms of workers covered) that ISTAT collects for its database on collective agreements and contractual wages. ISTAT collects data on negotiated gross wages, therefore including tax and social security contributions paid by employees, in around 90 collective agreements (the most representative ones) and the data used in this analysis represent a specific extraction of the minimum value in each agreement (therefore the lowest occupational level excluding seniority or other pay elements defined in collective agreements such as wage supplements for night shifts or particular activities, or bonuses).