dataesr/scientific-paragraphs-categorization huggingface dataset:
2011; Jolleys et al. 2015). These observations have major implications for understanding the climatic effects of biomass burning aerosols, since the aging processes take place over large spatial and temporal scales. Future research specifically targeting aerosol microphysical and chemistry processes is therefore essential to understand the cause of this variability and provide reliable parameterizations of BB processes. In addition to providing a wealth of observations to validate and improve atmospheric chemistry models, the data from these flights will be used to assess the impact of long-range transport of BB aerosols on air quality in the DACCIWA region compared to city emissions. They will also serve to improve our understanding of the radiative impact of BB aerosols on the formation and life cycle of midlevel clouds, which was the primary objective of the MICWA operation conducted with the ATR on 13 July 2016.
CONCLUSIONS AND OUTLOOK. The DACCIWA project organized an ambitious field campaign over SWA (Ivory Coast, Ghana, Togo, Beand Nigeria) during June and July 2016, bringing together over 200 people from European and African institutions. The campaign included aircraft, groundbased, urban, and radiosounding components to improve our knowledge of the physical processes involved in the low-level cloud life cycle in the complex chemical environment of SWA and the implications for weather, climate, and air pollution. During this detachment, DACCIWA scientists conducted a total of 155 flight hours using three aircraft, acquired over 1,100 h of data from three ground supersites, and launched over 770 radiosondes. An unprecedented wealth of in situ and remote sensing observations was obtained over SWA using state-of-the-art instruments, which were implemented in a timely manner thanks to the genuine cooperation between European and African partners.
Finally, observations acquired during the DACCIWA field campaign will undoubtedly contribute to improve climate, weather, and air-quality models as well as satellite retrievals, which are needed to support policies toward a more sustainable development for the region. This ambitious goal can hardly be achieved by researchers directly involved in the DACCIWA campaign alone. Therefore, all qualitychecked data, along with necessary meta information, will be made available in the DACCIWA database after a two-year embargo period (May 2019, at the latest) but are available for immediate use for associated partners. The DACCIWA database is hosted by the Base de l'Afrique: Beyond AMMA Base (BAOBAB) website (http://baobab. sedoo. fr), which allows accessing data from AMMA and several other West African field activities.
ACKNOWLEDGMENTS. The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) under Grant Agreement  603502. The European Facility for Airborne Research (EUFAR; www. eufar. net/)also supported the project through the funding of three Transnational Activity projects: MICWA, APSOWA, and OLACTA (for additional details, see www. eufar. net/media/uploads/documents/filer_public/8a/93/8a93ecc5-c9b4-4536-8b9c-daa541ada8fc /eufar_newsletter_sept2016_issue17. pdf). The  Centre National d'Etudes Spatiales (CNES)  also provided financial support to the project. The authors are grateful to the numerous colleagues from European and African institutions that have contributed to the DACCIWA field campaign. We have listed them in the supplement to this overview paper.
The analysis is based on the full pp collision dataset collected by the ATLAS detector in 2015 and 2016 at the centre-of-mass energy of √ s = 13 TeV, corresponding to an integrated luminosity of 36. 1 fb -1. The results are interpreted in terms of different benchmark models. For the case of a scalar resonance produced by gluon-gluon fusion (ggF) or vector-boson fusion (VBF), two scenarios with different intrinsic widths are considered. Constraints on the heavy neutral scalar in two-Higgs-doublet models (2HDM) are also obtained. The neutral member of the fiveplet in the Georgi-Machacek (GM) model  [15, 16]  also serves as a reference model in the VBF production mode. The parameterisation of heavy vector triplet (HVT) Lagrangians  [17, 18]  permits the interpretation of searches for spin-1 resonances in a generic way.
The bulk Randall-Sundrum (RS) model  [11, 19]  features a spin-2 Kaluza-Klein (KK) graviton excitation (G KK ) decaying into WW, while a tensor resonance signal in the VBF production mode is based on an effective Lagrangian model (ELM)  [20]. A previous search for a heavy Higgs boson in the eν µν final state was performed by ATLAS  [21]  based on a data sample with an integrated luminosity of 20. 3 fb -1 at √ s = 8 TeV. The CMS Collaboration also published a search for a high-mass scalar decaying into two W bosons in the fully leptonic final state  [22] , using datasets at √ s = 7 and 8 TeV with integrated luminosities of 5. 1 fb -1 and 19. 5 fb -1 , respectively. A search for heavy resonances in the RS models in the leptonic decays of the WW channel, using a dataset of 4. 7 fb -1 at 7 TeV  [23] , was reported by the ATLAS Collaboration.
The ATLAS and CMS collaborations have obtained constraints on the HVT and bulk RS models, based on other decay modes of the VV channels, with V being either a W or a Z boson  [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36]. The search in the eν µν decay mode is complementary to searches performed in other decay modes. In particular, the sensitivity to low mass resonances is higher in the fully leptonic final state than in final states that include jets due to background from jet production. The article is organised as follows. Section 2 presents the various models used in this analysis. Section 3 describes the ATLAS detector. The data and simulated event samples are discussed in Section 4. The event reconstruction and selection are described in Sections 5 and 6, respectively, followed by the background estimation techniques in Section 7.
Data and simulation samples The data used in this analysis were collected with a single-electron or single-muon trigger. These triggers have a transverse energy or momentum threshold, E T or p T , that depends on the data-taking period, with the lowest threshold varying between 20 GeV and 26 GeV. The trigger efficiency for WW events passing the offline event selection (Section 6) is greater than 99%. Data quality criteria are applied to ensure that events are recorded with stable beam conditions and with all relevant subdetector systems operational. Samples of simulated signal and background events are used to optimise the event selection and to estimate the signal acceptance and the background yields from various SM processes. The sample for the NWA heavy Higgs boson signal was produced with P -B 2.
Forecast dataset The ensemble prediction system used for this study is AROME-EPS, which covers the geographical area depicted in figure  1. AROME-EPS has been in operational production at Météo-France since November 2016. It uses the non-hydrostatic convection-permitting AROME model at an horizontal resolution of 2. 5km  (Seity et al. , 2011) , stochastic physics perturbations  (Bouttier et al. , 2012) , initial and surface perturbations  (Bouttier et al. , 2016) , and large scale boundary conditions provided by the global 35member PEARP ensemble system  (Descamps et al. , 2015). In its 2017 operational configuration, AROME-EPS had 12 members driven by a subsampling of the PEARP ensemble, using the algorithm described in  Nuissier et al. (2012) , which is based on the proposal of  Molteni et al.
Each AROME-EPS member is driven by a perturbed PEARP member. This setup allows a clean comparison of various subsampling methods, without rerunning a full 12-member AROME-EPS ensemble for each, which would be computationally expensive. In doing so, the centering of the initial condition perturbations uses a fixed 34-member ensemble mean, which is an approximation to the genuine 12-member AROME-EPS system. The centering procedure is explained in  Bouttier et al. (2016) , who showed that details of the initial perturbations do not matter beyond a few hours of forecast. We restrict our present study to forecast ranges beyond 12 hours, so the centering approximation is not expected to affect the conclusions of this paper. The forecast dataset comprises 88 ensemble forecasts from PEARP and AROME-EPS.
A simple illustration of selection algorithms In this section, we illustrate the impact of changing the selection methods using a simple two-dimensional framework. We randomly draw 20 points at random and subsample this dataset as two members using the  '  This example has shown the typical impact expected from changing the subsampling algorithm. In other datasets, the impact may be less obvious than in the example shown, because the Molteni 2001 algorithm does not always produce very unequal clusters or extreme RMs. The behaviour of the subsampling algorithms may depend on the dataset dimensionality and number of RMs. In the sequel, we shall demonstrate that these results remain statistically valid in meteorological ensembles.
• the CRPS (Continuous Ranked Probability Score): this is an integral measure of the consistency between forecast probabilities and observed values. A lower value indicates a better ensemble. • the ROCA (or area under the Relative Operating Characteristic curve): this is a summary measure of success rates of binary forecasts based on the ensemble output. The ROCA is normalized so that zero corresponds to an unskillful (random) forecast, one corresponds to a perfect forecast. Here, the binary forecast events used to compute the ROCA are defined as the exceedance of fixed thresholds of (8K, 2. 7ms -1 , 10mm) for (T2m, ff10m, rr6), respectively. These thresholds are chosen to provide large amounts of events and non-events from our dataset; the results are not very sensitive to them. Observations errors are taken into account in all scores (except ROCA) by assuming standard measurements errors of (1K, 0.
33ms -1 , 0. 1 + 0. 1rr) for (T2m, ff10m, rr6), respectively, where rr is the observed precipitation. Score differences are deemed significant if they have the same sign as their average, according to a bootstrap test at the 95% level. The observations are taken from real-time meteorological networks. The observing stations conform to WMO (World Meteorological Organization) standards for ground-based meteorological measurements. Most are located in mainland France, with a typical spacing of 30km between stations, plus lower density stations in neighbouring countries, and ship or buoy data over sea. At each verification time, there are typically about 1600 temperature observations and 1000 observations of wind and precipitation. The data is screened for gross errors. About 3% of T2m and wind stations are permanently excluded because their departures from AROME forecasts are consistently much higher than the others over the experiment period, in terms of bias or rms.
Impact of member selection methods in the PEARP ensemble This section investigates the consequences of subsampling in the global PEARP ensemble before looking at them in the AROME-EPS the regional ensemble. Note that no data from the latter is used in the subsampling process. The first step is the examination of the clustering behaviour. Clustering is not relevant for the 'random' algorithm, where representative members are directly picked from the PEARP ensemble. The  'Molteni 2001 ' and 'Molteni central' methods have identical clusters, since both use the complete linkage agglomerative algorithm. Assessing the performance of a clustering algorithm is difficult for two reasons. First, a clustering contains complex information that involves multiple subsets of a 34-member high-dimensional ensemble.
Large clusters (5 members and above) occur in about 10% of ensemble forecasts, where one single large cluster usually coexists with very small ones. These cases were independently flagged by human forecasters during a subjective evaluation of the AROME-EPS ensemble system at Météo-France. From summer 2016 to end 2017 (which includes the dataset used in this paper), forecasters were asked to record if they obtained added value from the ensemble forecasts as a daily guidance tool, beside the inspection of deterministic models from various centres. A survey of their remarks showed that AROME-EPS was often deemed uninformative because the cluster sizes were unbalanced: members associated to small clusters were felt to be too unlikely to represent credible scenarios, and the member associated to the large, main cluster did not account for the intracluster variability.
The spread of each method is normalized by the spread of the 'random' subensemble, which is statistically undistinguishable from the spread of the full 34-member PEARP ensemble (taking into account spread biases linked to the varying ensemble size). The  'Molteni 2001 ' subsampling increases the ensemble spread by 2 to 6%, depending on the parameter, and a bootstrap test shows that these differences are significant at the 95% level. The 'penalized Ward' subsampling exhibits much smaller impacts that depend on the considered parameter; bootstrap testing did not show them to be significant at the 95% level for any of the shown parameter. A bigger dataset might show that there is indeed some significant spread change, but it seems to be of no practical significance.
ROCA score The ROCA score is another measure of ensemble quality. Figure  6  shows its sensitivity to different subsampling methods, using the same conventions as above. None of the displayed variations between clustering-based subsamplings appear to be significant, but all are better than a random subsampling for temperature and wind. This improvement is significant at forecast ranges close to the one used for clustering (between 24 and 36 hours). There seems to be a similar impact for precipitation, but it was not deemed statistically significant, probably because our dataset is too short: the frequency of the binary event used to compute the ROCA is much smaller for precipitation than for temperature and wind. If the precipitation threshold is lowered, its exceedance frequency increases, but then the ROCA differences become too small to achieve statistical significance.
(2001)  and the 'penalized Ward' procedures. They confirmed that the latter does indeed produce a better subsampling, in the subjective sense that it seems more representative of the distribution of synoptic-scale scenarios in the PEARP ensemble. We propose the following interpretation of our results: clustering techniques are designed to summarize datasets by grouping similar data points (here, weather scenarios) while maximizing intercluster distances. By using clusters to guide representative member selection, one obtains a subensemble where the distance between members has been maximized. It explains why the clustering-based procedures tend to produce more dispersive ensembles than random subsamples. This effect is probably smaller with the penalized Ward method, because the penalization of large clusters is a constraint that reduces the ability of the clustering to reach its goal (of grouping similar members while maximizing intercluster distance).
This interpretation, though, is not convincing for precipitation, which is not underdispersive. It also fails to explain why the clear reduction of spread between the Molteni et al. (  2001 ) and the 'penalized Ward' algorithms does not translate into visible degradations of CRPS and ROCA. We argue that clustering-based subsampling methods improve ensemble-based forecasts for reasons that are deeper than an enhancement of spread: the conversion of ensembles into point probability forecasts is, essentially, a numerical integration of the probability distribution functions (PDF) implied by the members. Thus, subsampling can be regarded as an approximate numerical integration of the PDF of the complete forcing ensemble: for instance, in the dataset used in this paper, we are trying to approximate the 34-member PEARP ensemble PDF by the 12 members used to force the AROME-EPS ensemble.
In the CRPS and ROCA scores, point forecast probabilities are evaluated by counting the number of ensemble members below any given threshold, which is equivalent to a Riemann sum of the PDF. This computation is usually more accurate if it is performed on equally spaced points; intuitively, clustering methods attempt to find equally-sized partitions of the full ensemble, whereas the random subsampling selects integration points at random. If this interpretation is correct, one would expect point ensemble forecasts from our clustering-based subsamples to be more evenly distributed than random-based subsamples, among the members of the 34-member ensemble. Preliminary tests (not shown) with our dataset show that it is indeed the case, but a larger dataset is needed to substantiate it with good statistical significance.
Molteni 2001', 'Molteni central', and 'penalized Ward' methods. The results are shown in figure2. The complete linkage clustering creates uneven classes of sizes 5 and 15, whereas the 'penalized Ward' clustering produces nearly equal classes of sizes 9 and 11. In the Molteni 2001 selection method, the RM is often 'pushed' towards the extremes of the dataset, which occurs less often with the centroid selection, particularly in smaller clusters. As a result, the dispersion of the RMs is smaller with the 'penalized Ward' than the'Molteni 2001' method.
As a consequence of the accelerated globalization process, today major cities all over the world are characterized by an increasing multiculturalism. The integration of immigrant communities may be affected by social polarization and spatial segregation. How are these dynamics evolving over time? To what extent the different policies launched to tackle these problems are working? These are critical questions traditionally addressed by studies based on surveys and census data. Such sources are safe to avoid spurious biases, but the data collection becomes an intensive and rather expensive work. Here, we conduct a comprehensive study on immigrant integration in 53 world cities by introducing an innovative approach: an analysis of the spatio-temporal communication patterns of immigrant and local communities based on language detection in Twitter and on novel metrics of spatial integration.
In parallel, the last few years have brought a paradigm shift in the context of socio-technical data. Human interactions are being digitally traced, recorded and analyzed in large scale. Sources as varied as mobile phone records  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] , credit card transactions  [35] , or Twitter data  [36] [37] [38]  have been used to study mobility and land use in urban areas. Most of these works have been carried out in the zones where data was available, mostly inside cities or single countries. Twitter data has, however, the particularity of extending beyond national borders and, therefore, it allows researchers to analyze mobility and city hierarchies at an international level  [36, 39]. Besides activity and mobility, the content of the tweets bears also a wealth of information starting by the language in which the text is written.
The spatial distribution of languages has been investigated in Refs. [40] [41] [42] , exploring as well the relations between languages trough multilingual individuals, and in Refs. [43, 44] , where the spatial extension of Spanish and English dialects was examined. Of course, one of the weak points of Twitter as data source is its representativeness. This question has been boarded in Refs. [37, [45] [46] [47] , find-ing acceptable coverage for the American, British and Spanish populations in terms of geographic allocation, race, religion and mobility, although the data shows a bias towards younger individuals. In this context, it is of special interest the mix of location and language detection. This issue opens the door to characterize foreign users in short visits, temporal or permanent stays.
MATERIALS AND METHODS We selected 53 of the most populated cities in the world (see Figure  1a ) and analyzed the geo-localized tweets originating in each city between October 2010 and December 2015 as captured from the Twitter API. The data was collected respecting Twitter's terms of service and privacy conditions. Several items are extracted from each tweet: user ID, geographical coordinates (latitude and longitude), date and time and the text of the tweet. In order to get a coherent picture in the different time zones, we convert the Twitter UTC time into the local timezone for each city. Be-fore starting with the analysis, it is necessary to filter out non-human users from the dataset. This is fundamental in order to prevent result pollution by signals coming from automatic tweet generators (bots), which are not rare in social networks  [52].
In Figure  S1  in Appendix, we plot the evolution of the number of users left in the dataset as a function of ∆ for different values of N =  [5, 10, 15, 20]  in each of the 53 cities. As the shape of the curves is similar for different values of N , it does not seem to be a natural features that would allow us to define a clear cutoff. We fix ∆ ≥ 0. 2 and N ≥ 5, as a trade-off between being relatively sure about the users' residence area and keeping enough number of users to have proper statistics. Table  S3  in Appendix lists the final number of residents per city after this data cleaning procedure. Note that there are at least 1000 reliable users per city.
Bipartite spatial integration network To quantify the spatial segregation of each immigrant community in every city, we build a bipartite spatial integration network H (see Figure  2 ). Every language is connected to the cities where the corresponding immigrant communities has been detected. The weight of an edge between language l and city c, h l,c , corresponds to the level of spatial integration measured with a new metric inspired by the Shannon entropy, but modified to take into account the finite character of the sampling of communities in our Twitter database. Shannon entropy-like descriptors have been used before in this context especially when considering the spatial segregation of ethnic minorities in the US cities  [53]. Recalling that the cities have been divided in equal area grid cells and focusing first only on one generic city c, we can directly calculate from the data the fraction of users of a certain community l having their residence at cell i, p l,i.
Power of Integration Once the limits of the data and the method to assess the spatial segregation levels of foreign communities have been checked, it is the moment to advance and study what can be said about the way that the cities integrate the foreign groups detected in Twitter. To this end and starting from the bipartite spatial integration network, we perform a clustering analysis based on the distribution of edge weights h l,c. For each city c, the weights of the edges are sorted in descending order and stored into a vector ⃗ E c. This vector ⃗ E c contains thus the information on how many foreign linguistic communities have been found in the city c and it quantifies how they are integrated. We can compare next the vectors ⃗ E c of pairs of cities to assess whether they behave in a similar way respect to the integration of external communities.
Figure1. Dataset and framework description. The cities passed through the lens of our analysis are mostly distributed over four continents (a). Africa has been not considered due to the lack of data. We cover each city with a square grid in order to keep a homogeneous spatial division over the whole urban area where the users are going to be distributed (b), selecting resident users and their most frequent location thanks to their activity over space and time (c). In addition, we assign the users' most probable native language (d) and perform a spatial analysis over the cities (e) to get information about the population distribution in function of the language spoken by the users.