dataesr/scientific-paragraphs-categorization huggingface dataset:
(2017) , our addition of noise-derived group velocities act to render crustal and lithospheric velocity anomalies more pronounced, both in the fast and the slow directions. We find localised crustal and lithospheric thickening beneath the two hotspot-generated islands of La Réunion and Mauritius, which had not been picked up by  Mazzullo et al. (2017) , but is a priori expected, and independently confirmed by receiver function studies  (Fontaine et al. 2015; Singh et al. 2016). In future work, group velocities of the 628 station pairs identified as potentially usable could be used to investigate whether they can further refine the model of Fig. 11. There is not much prior imaging work to compare to. Ma & Dalton (2017)  investigated the lithosphere of Africa and the Indian Ocean with combined earthquake and ambient noise tomography, but limited to the sparse, permanent land and island stations and without the benefit of data from RHUM-RUM stations.
C O N C L U S I O N S Group velocities between 3 and 50 s period are calculated from OBS-to-OBS, land-to-OBS, and land-to-land noise crosscorrelations. The 100 very best measurements are inverted for 3-D S-wave velocity structure of crust and lithosphere, jointly with the earthquake-generated data set (periods >16 s) of  Mazzullo et al. (2017). Our relatively shorter-period data are ideal for adding constraints on the the shallowest (i. e. crustal and lithospheric) depths of the S V -wave velocity model of  Mazzullo et al. (2017) , who used phase and group velocities with periods of 30-300 and 16-250 s, respectively. The joint inversion provides the first tomographic S Vwave velocity image of the crust and the uppermost mantle of the western Indian Ocean between Madagascar and the three spreading ridges, centred on the hotspot island of La Réunion.
A C K N O W L E D G E M E N T S We thank Céline Davy, Eric Delcher, Fabrice Fontaine and John-Robert Scholz for their contributions to installing and maintaining the temporary RHUM-RUM island seismometers; and Valérie Ferrazzini for providing seismological data from the monitoring network of Observatoire Volcanologique du Piton de la Fournaise on La Réunion island. We acknowledge the GEOSCOPE network (https://doi. org/10. 18715/GEOSCOPE. G) and Mauritius Meteorological Services for installing and maintaining permanent stations RER and MRIV, respectively. We thank the participants and crew of cruises R/V Marion Dufresne MD192 and R/V Meteor M101. This study arose as part of the RHUM-RUM project (www. rh um-rum. net), funded by Deutsche Forschungsgemeinschaft in Germany (grants SI1538/2-1 and SI1538/4-1) and by Agence Nationale de la Recherche in France (project ANR-11-BS56-0013).
Intermediate scale façade fire propagation tests according to the ISO 13785-1 standard,  2  with additional heat release rate (HRR) and gas analysis, were previously performed by Guillaume et al  3  on nine different combinations of aluminium composite materials (ACM) and insulants. Numerical simulations, detailed in Dréan et al,  4  were performed with the code Fire Dynamics Simulator (FDS)  5  to reproduce three of these fire tests. Thermal characteristics of the system components were integrated into the model. The justification of the numerical model used for thermal degradation analysis was addressed. The first aim of that study was to validate the numerical model by comparing its output with the aforementioned experimental results, including details in terms of flows and thermal conditions at each location in the tested system.
34  In this paper, the development of the initial internal kitchen fire at Grenfell Tower, its propagation through/around the kitchen window opening, over the façade and re-entry into subsequent compartments are investigated. Three-dimensional (3D) numerical simulations were performed with the computational fluid dynamics (CFD) code FDS. The Grenfell Tower façade is modelled to investigate fire behaviour at window frames and its spread through the façade. Fire propagation from the façade into additional apartments through window frames is also assessed. It includes validation by comparison with video and photographic observations of the real fire. The thermomechanical analysis performed in Koohkan et al  35  is used to validate proposed window failure criteria. The modelling of the Grenfell Tower façade is based on the numerical model previously validated against experimental results from large-scale BS 8414-1 tests.
3D modelling assesses local effects and these can be compared with video and photographic observations. The numerical simulations were performed with the CFD code FDS version 6. 7. 0. FDS is a computational code in fluid dynamics that incorporates a combustion model and a large-scale model (LES) for the description of turbulent flows. This tool allows 3D modelling of the computational domain. It considers heat transfer at walls, ventilation conditions for the removal of hot gases, and air intake. The Navier-Stokes equations are solved in the limit of low Mach number, thermally driven flow with an emphasis on smoke and heat transport from fires. The radiative heat transfer is included in the model through the solution of the radiative transport equation for a grey gas. Detailed information is provided in the FDS reference guide.
Accepted Article This article is protected by copyright. All rights reserved. L'Atalante during the PTOLEMEE survey in 2014 with a hull-mounted ADCP (150 kHz) and were processed using Cascade 7. 1 software. The core was described on board, photographed, and analysed with a GEOTEK multisensor core logging system (MSCL) and an AVAATECH energy dispersive X-ray fluorescence (ED-XRF) core scanner every centimetre. MSCL provided bulk density and reflectance data (light/dark value). Semiquantitative compositional data for various chemical elements, including strontium (Sr), was provided by ED-XRF. In order to corroborate ED-XRF results, samples were taken in the core every 10 cm for bulk mineralogical composition using X-ray diffraction (XRD) analysis, performed in-house using a Bruker AXS diffractometer.
(1998). Once the age model was established, the percentage of aragonite in the core (as determined by XRD) was compared to sea level for a given time interval, rounded to the closest 1 kyr. Sea-level values used were taken from  Spratt and Lisiecki (2016)  until 800 ka, and from  Miller et al. (2011)  for older dates (mean values). Percentage of aragonite was also compared to the estimated available optimal area for carbonate production on the platform, as determined from bathymetric data on the present-day platform.
Accepted Article This article is protected by copyright. All rights reserved. where ρ b is the bulk density, FP is fractional porosity, and ρ w is the density of water. ρ b was directly measured on the core onboard the RV Pourqoui Pas? using a multisensor core logging system, and FP was directly calculated from these bulk density measurements. The potentially productive area of the platform top was calculated for various hypothetical sea levels by generating smoothed bathymetric contours on the high-resolution Lidar grid shown in Fig. 3A. As this dataset merged with the lower-resolution deeper bathymetric data at the platform top margin, the uppermost part of the slope did not have continuous coverage. Calculated areas in the upper slope therefore were estimated using a simple geometric model and projecting the slope gradient out laterally from the areas where data was acquired, leading to similar values for depths below 60 m.
Acknowledgements The oceanographic expeditions PTOLEMEE and PAMELA-MOZ4 were co-funded by TOTAL and  IFREMER  as part of the  PAMELA (Passive Margin Exploration Laboratory ;  13/1210499 ) scientific project. The  REEFCORES  project was supported by  France's "Iles Eparses" program  (2011Eparses" program ( -2014) ) managed by  CNRS-InEE  ( Institut Ecologie et Environnement ). This work was supported by the  "Laboratoire d'Excellence" LabexMER.
Method subjects. A chamber orchestra consisting of 8 violinists (2 sections of four violinists: S1 and S2) and 10 instrumentalists participated in the study along with two professional conductors (C1 and C2). Data were collected from the two violinists' sections and conductors. Each violinists section counted four players. The study was approved by the SIEMPRE Project Management Committee and adhered to the standards laid down in the Declaration of Helsinki. All participants gave written informed consent before participating. The synchronized multimodal recordings of the musicians obtained for this experiment as well as the details of the SIEMPRE platform for multimodal recordings are made available to the research community from the EU ICT FET SIEMPRE web pages (http://www. siempre. infomus.
1  in red) S1 violinists -still lined in a single row -were able to see S2 violinists, but not C (since they were facing backwards with respect to him). This condition altered the standard position of the musicians. We decided to perturb S1 orientation to act on the strongest relationship (S1 -Conductor) and elicit a significant re-adaptation of the whole orchestra dynamics. By doing so, we could highlight the co-regulation of the different modes of communication, across different channels of communication. The two pieces were repeated six times (three times with C1 and three other times with C2) in each experimental condition (normal versus perturbed). In total, 24 trials were recorded. Apparatus and set-up. Movement data were collected (1000 Hz) by using a Qualisys motion capture system equipped with 7 cameras, integrated with the EyesWeb XMI platform: http://www.
infomus. org/eyesweb_ita. php  23  , including audio and physiological signals (not used here). Each violinist was equipped with a cap on which were placed three passive markers of the Qualisys motion capture system. The positions, based upon the 10-20 electroencephalographic system, corresponded to Pz, F3 and F4. Before starting recording sessions, we ensured that the cap was not moving with musicians' movements and facial expressions. An average of these three markers was taken for further analysis on head movement, to minimize loss of data and interpolation. An additional marker was placed on the bows of the players and on the baton of the conductors. After data tracking by using the Qualysis Track Manager software, the data was exported and analyzed in MATLAB. Data pre-processing and analysis.
Intra-section imitative coordination Principal Component Analysis. To evaluate the level of imitative coordination between violinists' movements of each section of violinists (playing the same score), we used a principal component analysis (PCA)  26. PCA is a standard statistical technique generally used to extract a low-dimensional structure from a high-dimensional dataset. Dimensionality reduction method are classically used in the motor synergies field to extract invariant/similar features across time between muscle or kinematic parameters. In particular, PCA has been used to characterize the degree of covariance across time of different body segments in whole-body movements (e. g. locomotion  27  ; reaching)  28. Here, PCA was performed on the acceleration profiles of the four violinists of each section (Fig.
1 , lower panel), windowed and pre-processed in the same way as Granger Causality analysis. Mathematically, the method involves the eigenvalue decomposition of a dataset covariance matrix in order to find the principal directions in the high-dimensional space. For each of the windows, we considered an input matrix composed of 300 rows (temporal frames) and 4 columns (the acceleration profiles of the four violinists in each section) to which we applied the Matlab princomp function, after a zscore normalization of the input matrix. The PCA gives four principal components (PC) each written as a linear combination of the initial waveforms (the four violinists' acceleration profile). The variance accounted for (VAF) by the first principal component (noted PC1%) is defined as the ratio between the first eigenvalue and the sum of all the eigenvalues.
The VAF represents the degree to which the linear combination associated to each PC is able to approximate the initial dataset. A high PC1% value means that the trajectory in the space of angles is close to a straight line (i. e. , all angles were linearly correlated together) while, a low PC1% value indicates that one principal component is not sufficient to describe precisely the trajectories. Conductor behavior predictability: auto-regressive model's fitting. Following sensorimotor communication literature (for a review)  29  , we evaluated conductor behavior predictability to verify whether intrinsic variability of conductor's behavior was altered by our experimental manipulation. In cooperative joint action tasks, leaders tend to make their movements more consistent over time to help their partner build a predictive model of other's action.
More details on Granger computation are reported in Supplementary Material. Gca was evaluated (pairwise), every 500 milliseconds on 3-s sliding windows using the "Granger Causality connectivity analysis" Matlab toolbox  31. Windows containing more than one third (i. e. 166 ms) of absent values were not used in the analysis (less than 5% of the total windows number). The Granger Causality computation is similar to the one used in  16, 18. From this point, we will represent gca of X on Y by the notations G X->Y or X-> Y. We were interested in the causality relations between the conductor and each section of violinists (S1 and S2). This analysis is illustrated in Fig. 1  (upper panel). We performed three different types of Conditional Granger causality computations: (1) Causality between each conductor and violinists of S1 (taken separately): defining as causing variable the conductor, as dependent variable each S1 violinist separately and the other way around [conditional variable: musicians in S2 -taken separately].
Primary application areas of possibilistic keys are found in database design and data cleaning, which have been investigated in related work  [35, 45]. A case study from web data extraction illustrates the use of possibility degrees in data integration as well as their acquisition. In addition, we will also illustrate the applicability of possibilistic keys in constraint maintenance and query processing. Contributions. Our contributions can be described in detail as follows. • First, we define a semantics for possibilistic keys. Here, uncertainty is modeled qualitatively by degrees of possibility. The degrees bring forward a nested chain of possible worlds, with each being a classical relation that has some degree of possibility. Smaller worlds are more possible and satisfy at least as many keys as any of the worlds they are contained in.
This "possible world" semantics is quite different from that used in the context of probabilistic databases. Our possible worlds form a linear chain with the smallest world forming a kernel that contains all tuples that are certain to occur, and adding tuples that become less possible whenever less constraints are imposed. Classical relations form the special case of possibilistic relations with only two degrees of possibility, that is, where k = 1. For example, the possible worlds of the p-relation from Table  1  are shown in Fig. 1. The key {time, rfid} is satisfied by r 3 but not by r 4 , and {zone, time} is satisfied by r 1 but not by r 2. • We have conducted a use case study in web data extraction. The case study shows how p-degrees can naturally quantify our confidence in data that is integrated from different sources, which p-keys are satisfied by such possibilistic data, and how the possibilistic data can be summarized by a sample that consists of only 2% of the tuples contained in the original p-relation but satisfies the same p-keys.
Related work This section will elaborate on the wider context of work on uncertain data. We will start with the general trade-offs between the use of probabilistic and possibilistic approaches, and highlight some recent work on constraints over probabilistic databases. Subsequently, we will identify database design and data cleaning as the main target areas of our possibilistic model in contrast to other possibilistic data models that target database queries. We will then comment on recent related work on other classes of database constraints that are founded on the possibilistic model we use. We will say how our contributions to possibilistic keys generalize previous findings for the special case of keys over certain relations. Finally, we will detail how our current submission extends our conference article on possibilistic keys.
Probabilistic databases Probabilistic databases have received much interest  [61]  due to the need to deal with uncertain data. While the many types of uncertainty information make it challenging for uncertain data to become first-class citizens in commercial database systems, the benefits and applications of uncertain data are broad and deep. For example, the METIS system is "an industrial prototype system for supporting real-time, actionable maritime situational awareness". It uses web harvesting, information extraction, and data integration to collect substantial data on ships, their routes, and actions. The system represents the uncertainty in this data explicitly, and utilizes the information for reasoning purposes with ProbLog. When coast guards notice ships in their waters, they ask queries of the following type to METIS: "What is the probability of smuggling and how reliable is this assessment?".
Research on probabilistic databases has naturally focused on query processing, e. g. [3, 30]. Keys and cardinality constraints on probabilistic databases have been studied in  [7, 6, 29, [56] [57] [58]. Here,  [7, 6, [56] [57] [58]  study similar problems for probabilistic keys and cardinality constraints that we investigate in this article for possibilistic keys. Probabilistic and possibilistic keys complement one another and are incomparable: Probabilistic keys are based on underlying probability distributions, and possibilistic keys are based on underlying possibility distributions. The use of either probabilistic or possibilistic keys therefore depends on what kind of uncertainty information is available and/or required. The paper  [29]  focuses on the use of probabilistic keys for query optimization.
Possibilistic data models and their target use cases Similar to probabilistic databases, possibilistic approaches to uncertain data have mainly dealt with query languages. Queries are not the main subject of this article, so we refer the reader to a survey on fuzzy approaches to database querying  [66]. Nevertheless, we will now comment on various possibilistic data models, their expressiveness, and their main target areas. From the least to the most expressive, we can at least distinguish four possibilistic models for uncertain data  [55] : • databases with layered tuples • tuples involving certainty-qualified attribute values • tuples involving attribute values restricted by possibility distributions, and • possibilistic c-tables. Layered tuples. This is the data model we use here. The idea is just to provide a total ordering of the tuples in the database according to the confidence we have in their truth.
This is encoded by assigning a possibility degree with each tuple. The result is a layered database: all the tuples having the same degree are in the same layer (and only them). The tuples of highest possibility degree are also associated with the highest certainty degree, while tuples with a smaller possibility degree are not certain at all. This means that any possible world contains all the tuples with the highest possibility degree, while the other tuples may or may not be present in a particular possible world, see  [44]  for details. Our model does not provide information about the uncertainty of specific attribute values. Consequently, enforcing the p-degrees on tuples in situations where the degree of uncertainty is known for some attribute values and can be quantified, we would loose some information.
The trade-off can be viewed as a generalization of the situation that is already apparent in databases with null markers: SQL permits only one type of null marker, so we lose information whenever we know more about the kind of missing value (e. g. the value exists but is unknown, or the value does not exist). The possibilistic grounding of this model was first presented in  [44]  where the duality between p-degrees and c-degrees is pointed out and exploited, but the model has its roots in an early proposal dealing with weighted tuple databases  [31]. The possibilistic model has gained recent momentum in a series of articles that highlight its applications in data modeling  [21, 38] , database design  [45] , and data cleaning  [35]. In  [44] , a new class of possibilistic functional dependencies was introduced, and the equivalence of their associated implication problem to that of Horn clauses in possibilistic logic was proven.
The main target area of this new class of possibilistic functional dependencies was database schema design, since different degrees of data redundancy can be introduced and different normal forms guarantee that no redundant data values of these degrees can occur in any instances of the schemata that meet the normal form condition. This normalization framework for uncertain data has been reported in  [45]. Possibilistic keys form the important special case of possibilistic functional dependencies that represent the sub-class of functional dependencies which do not case any form of data redundancy. Possibilistic keys were the first class of database constraints introduced for the underlying possibilistic model  [33]. The current article is an extended version of  [33]. Another important area of application for any form of constraints over this possibilistic model is data cleaning.
A real-world example from web data integration In this section we illustrate the application of our framework on a real-world example from Web data integration. Here, basic information about the top-100 best-selling books was extracted from five online book sellers: 1. eBay book store 1  2. Amazon book store 2  3. book directory 3  4. Barnes & Noble 4  5. Easons. 5 or our collection the tool Web Scraper 6  was used on a Chrome extension plugin. It extracted the content from the same HTML tags from every book web page. The extracted results had many mistakes initially, since not all books have the same details and the same HTML tags may also have different values. Errors were fixed manually. In total, 344 tuples were extracted over the p-schema (BOOKS, {α 1 ,. , α 5 , α 6 }) with BOOKS = {title, author, pages, price, publisher}.
The benefit is that the resulting Armstrong p-relation consists of real-world tuples and is a subset of the original p-relation. Such Armstrong p-relations are known as informative Armstrong databases  [49, 64]. Informative Armstrong databases may be understood as semantic samples of the original database, since they represent the same set of constraints. Their big advantage is that they are easier to understand by humans because of their smaller size. In our example, the resulting informative Armstrong p-relation is shown in Table  2. It consists of seven tuples, which is 2% of the number of tuples in the original p-relation. The sample may serve different purposes. It can be understood as a semantic data profile of the given data set, and thus be used for testing purposes, such as queries or updates.