dataesr/scientific-paragraphs-categorization huggingface dataset:
Other secondary phenomena that can be simultaneous with the precipitation event are also included: TO (tornado), HL (hail), SN (snow), and SW (strong wind). Thunderstorms were not included because it is difficult to have systematic information about lightning production over the entire period. -Main hydrometeorological features: some hydrological (maximum peak flow and/or daily flow) and meteorological data (maximum precipitation recorded in 24 h and/or during the entire event), and river and place in which they were recorded. This information is not available for all the cases, because for most of them no gauge station exists. The objective of this information is to obtain a preliminary approach to the magnitude of the event. -Information about casualties: -number of casualties; -circumstances related with the deaths -people dragged away by water while they were crossing the fluvial course, dead in a campsite, dead in a building or due to an accident or unknown causes; -number of casualties estimated in EM-DAT and NATHAN databases; -number of injured people, when such information is available.
, 2003; Barrera et al. , 2006). -Documentation sources. The main problems found when the common database had been built were the following: -to find river flow data; this happens because Mediterranean rivers typically show an ephemeral regimen, which hampers systematic discharge measurements. -To find rainfall data and select data sufficiently representative (in space and time) of the hydrometeorological severity of the event. Some indicators on the duration of the rain and the extent of the area to be analysed (in term of rainfall) should be defined. -To find an objective criterion to separate one event from another, during prolonged rainy periods. -To have accurate information about damage (public and private) and casualties. In some countries data from insurance companies are not public.
The most usual situation in bibliography is working with factors involved in losses of life  (Jonkman and Vrijling, 2008; Di Mauro et al. , 2012; Ruin et al. , 2008). -To find data sources that can be considered reliable, we often use newspapers, but a large part of the scientific community considers this kind of source unreliable. -The accuracy of damage data included in a data-series can vary greatly, and in such cases data should be labelled according to their level of detail. -To find a criterion to geographically reference damage data, because floods affected areas that can be delimited only by means of on-site surveys (which cannot be performed for past events). -Some minor flood events are not reported in the press and go unnoticed; on the other hand, "major" databases only contain catastrophic events and can introduce an overestimation of the effects of these events.
In the case of the French regions only catastrophic floods have been included. A comparison with the EM-DAT and NATHAN databases shows that these global databases do not include all the catastrophic events that had arisen in a region; only 23 cases from 385 flood events have been recorded in the EM-DAT database and this figure decreases to 8 cases when we refer to the NATHAN. The case of Catalonia is relevant, with only one flood event included in the NATHAN database, and only 6 in the EM-DAT database, when more than 210 flood events producing damage have been identified. It is interesting to note that NATHAN does not report any event for Calabria (or for the Balearic Islands), even though the so-called Soverato event (SE Calabria), which killed 13 people, and after which the Italian government issued a decree imposing the mapping of hydrogeological risk in urbanised sectors, occurred in 2000, then in the period covered by the Munich Re database.
In the same line, southeast France has recorded 29 catastrophic events on the period 1981-2010, of which only 12 are included in the NATHAN database. Another interesting observation is that non-catastrophic events can also cause deaths. In the case of Catalonia, 38 % of the deaths have been as a consequence of extraordinary or ordinary floods, similar in this case to the Balearic Islands, with 36. 56 %. They were usually people dragged along by flash floods when crossing torrential streams, and could usually have been avoided if more precautions had been taken. Table  7  shows that this is the most usual cause of their death. Events that seriously affect a campsite are not frequent, but in the cases that do occur, they are very catastrophic. This is the case of the flash flood that occurred on 7 September 2000 (Calabria, Soverato), with 13 casualties, and of another on 22 September 1992 in France (Vaison-La-Romaine, Paca, Vaucluse Dept.
) that led to 47 deaths (15 were in a campsite). People who died as an indirect consequence of the floods have been also considered in the database (i. e. the case of a woman who died of a heart attack when her home was inundated). The Appendix Table  A1  contains all the catastrophic floods recorded between 1981 and 2010 in the selected regions. The objective of this table is to give the reader information for future studies, and it has not been analysed in depth in this study. This is the reason why only some general comments will be made. A total number of 95 events have been identified, but in light of present knowledge of the evolution and tracking of Mediterranean storms, it is possible to identify some potential common catastrophic events that affected two or more regions. This is the case, for instance, of the event recorded between 5 and 9 September 2005 that produced flash floods in the Catalonia (Spain) and PACA (France) regions, and that also recorded a tornado and some waterspouts near Barcelona, and more than 36 400 lightning flashes in Catalonia.
Conclusions A common database that covers a period of 30 yr, 1981-2010, is being built within the framework of the HYMEX programme. The main objectives of this database are to obtain a complete list of floods and flash flood events that have affected the region of study in order to provide useful information for future studies; to evaluate the importance of minor but frequent events that usually are not considered; to  identify those events that have affected large regions in order to conduct future studies; and to improve the knowledge of fatalities due to floods and the circumstances of the deaths, the economic impact and its evolution, and the societal perception and factors involved. They also have to answer the need for an accurate and standardised monitoring of damage (human and material) and the possibility of testing the connection between the hazard data (rainfall threshold, specific discharge) and the consequences of floods.
Eventually, this database might prove to be a great help in assessing the efficiency of the mitigation measures and the possibility of improving them. In this preliminary study, 385 flood events (including flash-flood events) that affected one or more of the selected regions (Catalonia and the Balearic Islands in Spain, Languedoc-Roussillon Midi-Pyrénées and Provence-Alpes Côte d'Azur in France, and Calabria in Italy) were identified. 55. 3 % of these events affected Catalonia, 27. 7 % Calabria, 9. 3 % Balearic Islands and 7. 5 %, the south of France. Most of them produced extraordinary floods (47. 2 %), while only 24. 6 % were catastrophic events. As a consequence of these 385 flood events, 333 people have died, mainly due to Table  A1. Catastrophic floods recorded between 1981 and 2010 in the selected regions of the study (Keywords: FL: flood, FF: flash flood, HL: hail, LS: landslide, SN: snow, SW: strong wind, TO: tornado).
Regions: CAL: Calabria (Italy), BAL: Balearic Islands (Spain), CAT: Catalonia (Spain), PACA: Provence-Alpes-Côte d'Azur (France), LR: Languedoc-Roussillon (France), MP: Midi-Pyrénées (France). catastrophic events (46. 3 % of catastrophic events produced casualties), but also due to extraordinary floods (10. 4 % of these flood events produced casualties). Considering the total number of casualties, 5 % of them have been as a consequence of extraordinary floods associated with minor damage. 55 % of the victims were swept away by the water when crossing torrential streams, which points to the possibility of reducing the number of casualties through better risk awareness. Comparison with the EM-DAT and NATHAN databases has shown that these global databases do not include all the catastrophic events that occurred in the regions of study for the period analysed, nor all the casualties or the extraordinary floods.
Indeed, only 5. 9 % of the identified events in the FLOODHYMEX database are included in the EM-DAT databases, and this percentage decreases to 2 % when we refer to the NATHAN database. Catastrophic floods usually produce traffic disruption and, in some occasions destruction of the roads and railway network and bridges. The services sector is also usually affected, and more than 80 % of the events caused major damage to buildings or monuments and to historical sites. The worst affected (by direct damage) productive sector is agriculture (62 % of the cases), followed by commerce and handicrafts (54 %), tourism (45 %) and industry (29 %). However, it is difficult to obtain detailed information from each event about damage or specifying the number of roads, buildings, and so on, affected, and consequently they can be considered only as qualitative indicators.
Acknowledgements. This paper has been undertaken within the framework of the  HYMEX  project and  DRHIM  Project (G. A. no. 283568 EC 7th Framework Programme ). Our acknowledgement to  Raül Marcos  for his collaboration in the trend analysis, and to  Joan Gilabert  for his collaboration in database construction. Edited by:  G. Boni Reviewed  by:  D. Miozzo  and one anonymous referee.
ESWD- European Severe No common criteria. Information is 2006 European Severe Yes European Se- convective reported by the sources Storms Laboratory vere Weather storms Germany Database Global Active Global Floods At least one of the following criteria: (a) 1985 DartmouthColorado: Yes Archive of significant damage to structures or agri- Darmouth Flood Large Flood culture, (b) long intervals since the last Observatory Colorado Events similar event or fatalities Swiss Re Global Natural and Losses of US $86. 6 M and/or insured 2003 Swiss Re Partially (Sigma) man-made losses of US $43. 3 M (both in 2010 val- disasters ues) and/or 20 fatalities/people missing.
Introducing the coupling of the back-stress with the aging stress could be the subject of a future development in order to improve the capabiliti es of the model. With these various ingredients, under uniaxial loading condition s, the current stress may be decomposed as follows: r ¼ X k X ðkÞ þ ð1 À nÞðR þ R a Þ þ K 0 þ n R þ R a ð Þ ð ÞAsinh _ p _ e 0 1=n " # ð44Þ in which the sign before the bracket indicates tensile (þ1) or compressive (À1) plastic flow. Considering the thermodynam ic aspects, the fact that isotropic hardenin g variables (R and R a ) appear in the yield stress and in the drag stress (at the denomin ator of the viscoplastic flow equation (4) needs some specific treatment, as detailed in Appendix A. The model has been implemented within the Finite Element software Z-SET  Zebulon (2011).
less arbitrarily, as described above in the successive steps. After these arbitrary choices, the other parameters are identified by automatic error minimizatio n techniques. In this work we used the very friendly software Z-SimOp t (graphical simulation and optimization system), available in our Z-SET platform  Zebulon (2011). At this step, uniqueness can be controlled, but it is submitted to the choice made of the test data that enter into this minimization process (and their corresponding weights). This kind of difficulty is common to any such complex constitutive model.
Acknowled gements Cooperation with Snecma, Turboméca and Centre d'Etudes Aéronautiques de Toulouse (CEAT) during the Program ''PRC Structures Chaudes'' is gratefully acknowledged, especiall y for the availability of the experimental data bases on the Haynes 188. Financial support by the French Ministry of Transportation (Direction des Programme s de l'Aviation Civile) and of the French Ministry of Defence (Délégation Générale à l'Armement) is also gratefully acknowled ged. A special mention should be made concerning the fruitful discussions and suggestions by S. Forest from the Centre des Matériaux of Mines-ParisTech.
To overcome these limitations, Bouveyron and Brunet  [6]  recently proposed a new statistical framework which aims to simultaneously cluster the data and produce a low-dimensional and discriminative representation of the clustered data. The resulting clustering method, named the Fisher-EM algorithm, clusters the data into a common latent subspace of low dimensionality which best discriminates the groups according to the current fuzzy partition of the data. It is based on an EM procedure from which an additional step, named F-step, is introduced to estimate the projection matrix whose columns span the discriminative latent space. This projection matrix is estimated at each iteration by maximizing a constrained Fisher's criterion conditionally to the current soft partition of the data. As reported in  [6] , the Fisher-EM algorithm turned out to outperform most of the existing clustering methods while providing a useful visualization of the clustered data.
The DLM model Let {y 1 ,. , y n } ∈ R p denote a dataset of n observations that one wants to cluster into K homogeneous groups, i. e. adjoin to each observation y i a value z i ∈ {1,. , K} where z i = k indicates that the observation y i belongs to the kth group. On the one hand, let us assume that {y 1 ,. , y n } are independent observed realizations of a random vector Y ∈ R p and that {z 1 ,. , z n } are also independent realizations of a random variable Z ∈ {1,. , K}. On the other hand, let E ⊂ R p denote a latent space assumed to be the most discriminative subspace of dimension d ≤ K -1 such that 0 ∈ E and K < p. Moreover, let {x 1 ,. , x n } ∈ E denote the actual data, described in the latent space E of dimension d, which are in addition X Y Z π µ k Σ k W = [U, V ] ǫ Ψ Figure 1: Graphical summary of the DLM [Σ k β] model.
r. t. U t U = I d , ( 6 ) where S stands for the covariance matrix of the whole dataset and S (q) B , defined as follows: S (q) B = 1 n K k=1 n (q) k (m (q) k -ȳ)(m (q) k -ȳ) t , (7) denotes the soft between covariance matrix with n (q) k = n i=1 t (q) ik , m (q) k = 1/n (q) k n i=1 t (q) ik y i and ȳ = 1/n n i=1 y i. This optimization problem is solved in  [6]  using the concept of orthonormal discriminant vector developed by  [14]  through a Gram-Schmidt procedure. Such a process enables to fit a discriminative and low-dimensional subspace conditionally to the current soft partition of the data while providing orthonormal discriminative axes. In addition, according to the rank of the matrix S (q) B , the dimensionality of the discriminative space d is strictly bounded by the number of clusters K.
Comparison on the usps358 data set We focus now on the usps358 dataset to stress the role of variable selection in the interpretation of clustering results. divided in 10 classes corresponding to the digits from 0 to 9. Each digit is a 16 × 16 gray level image represented as a 256-dimensional vector. For this experiment, we extracted a subset of the data (n = 1 756) corresponding to the digits 3, 5 and 8 which are the three most difficult digits to discriminate. This smaller dataset is hereafter called usps358. Figure  2  depicts the group mean images obtained from the true labels in the usps358 dataset. For this experiment, we used the three sparse-FEM algorithms with the model and the level of sparsity selected in the previous experiment for this data set. For Clustvarsel, Selvarclust and sparse-kmeans, the level of sparsity was again selected with their own selection procedure.
First of all, it appears that Clustvarsel and Selvarclust select significantly fewer variables than both sparse-kmeans or the sparseFEM procedures. Furthermore, most of the selected variables by Clustvarsel and Selvarclust turn out to be irrelevant to discriminate the digit 3 from the digits 5 and 8. For instance, in Figures 3. b and 3. c, we can observe that the black pixels located in right bottom corner, do not correspond to any discriminative variable. This certainly explain the poor clustering performances (48. 3% for Clustvarsel and 36. 7% for Selvarclust) observed on this data set for these methods. On the contrary, sparse-kmeans turns out to perform well in term of clustering performance (74. 7% of clustering accuracy). Nevertheless, the number of selected variables remains higher (213 selected variables amongst 256 original ones) than we would expect to ease the interpretation of results.
Finally, sparseFEM-1 and sparseFEM-2 seem to answer quite well to both the clustering task and the task of feature selection. Indeed, on the one hand, the subset of selected pixels remains small for both algorithms: 6 and 15 pixels are selected amongst 256 for sparseFEM-1 and sparseFEM-2 respectively. Furthermore, the selected pixels appear to be relevant to discriminate the classes associated with the three digits. For instance, the darker pixel on the bottom right corner of Figure  4. b discriminates the digit 8 from the digits 3 and 5. On the other hand, and certainly due to this relevant selection of variables, both algorithms perform particularly well on this high-dimensional data set (84. 7% for sparseFEM-1 and 82. 8% for sparseFEM-2). However, on this data set, the sparseFEM-3 procedure shows a disappointing behavior regarding the variable selection even though its clustering performance remains satisfying.
The fact that sparseFEM-3 succeeds in clustering the data set even with a bad selection of variables is certainly due to the nature of the DLM model which models also the non discriminative information through the parameter β k. Table  4  presents the computing time of the studied clustering methods (for a given model and with λ and K fixed) for clustering the usps358 data set. As we can remark, our procedures are much faster than the sparse-kmeans, Clustvarsel and Selvarclust algorithms. Consequently, the sparseFEM algorithms appear once again to be good compromises, in practice, to cluster high-dimensional data and select a set of discriminative variables in a reasonable time.
Conclusion This article has focused on variable selection for clustering with the Fisher-EM algorithm which has been recently proposed in  [6]. The aim of this work was to introduce sparsity in the Fisher-EM algorithm and thus select the discriminative variables among the set of original variables. We have proposed three different procedures based on a ℓ 1 -penalty term. Experiments on simulations and real data sets have shown that the three sparse versions of the Fisher-EM algorithm are highly competitive with existing approaches of the literature. In particular, the sparseFEM procedures present several assets regarding existing approaches. On the one hand, they tend to select an intermediate number of discriminative variables whereas existing approaches tend to select either too few (Clustvarsel and Selvarclust) or too much variables (sparse-kmeans).
T H E N U M E R I C A L S I M U L AT I O N The simulation used in this paper was done with the GADGET3 code and it is very similar to simulation 116 described and analysed in  Athanassoula, Machado & Rodionov (2013) , except that here we describe the old disc component (disc in the standard GADGET notation) by 200 000 particles and that the softening of the halo and disc is 100 pc (instead of 50 pc in simulation 116). The adopted simulation has four components (HALO, DISC, GAS, STARS), having the following initial settings. (i) A disc made of two initial components that are a gaseous disc (component GAS), and an old stellar disc (component DISC) having the same initial azimuthally averaged density distribution. Therefore, the density distribution of the initial total disc is ρ d (R, z) = M d 4πh 2 z 0 exp - R h sech 2 z z 0 , ( 1 ) where M d = 5 × 10 10 M is the total mass of the disc (gas and old stars), the initial gas fraction is 0.
6)  However, they still have some limitations for actual applications. For  xample, the Kobayashi model can be applied only to dilute multi-component alloys. 4, 7)  The Zhang et al. model, based on the Tourret-Gandin model  [8] [9] [10]  for binary alloys, is limited to dendritic  5)  and peritectic structures  6)  but can not be applied to eutectic structures. Considering the different diffusivities of interstitial and substitutional elements in a multi-component alloy,  Hillert 11)  firstly analyzed the back-diffusion of carbon and chromium in liquid and primary FCC in Fe-C-Cr alloy with a one-dimensional numerical solution. Afterwards, Chen and Sundman 3) developed a Partial Equilibrium (PE) approximation to predict the partial equilibrium solidification behaviour in Fe-C-Cr alloy, which is implemented in the software package "Thermo-Calc".
The whole computational process is schematically drawn in Fig. 1. The program is implemented using the TQ program interface for coupling with thermodynamic equilibrium calculations made by Thermo-Calc  16)  and access to the thermodynamic properties of steels from database TCFE6. 17) he temperature step is -1 K. The calculation finishes once the liquid mass fraction is less than 10 -3. For the present Fe-C-V-W-Cr-Mo alloy, only carbon behaves as interstitial element, others are assumed as substitutional elements. Considering the prediction of solidification behaviour with carbide precipitation, PE approximation is prior to GS approximation.
Background One of the most important objectives of systems biology is developing a common language for a formal representation of the rapidly growing molecular biology knowledge  [1, 2]. Currently, most of the information about molecular mechanisms is dispersed in thousands of scientific publications. This knowledge exists in a humanreadable form, which limits its formal analysis by bioinformatics and systems biology tools. One of the approaches to formalize biological knowledge is to collect the information on molecular interactions in the form of pathway databases  [3]. Examples of them are Reactome  [4] , KEGG  [5] , Panther  [6] , SPIKE  [7] , WikiPathways  [8] , TransPath  [9]  and others that are created using various frameworks and formalisms  [10]. Most of the pathway databases provide ways for exploring the molecular pathways visually, some include analytical tools for analyzing their structure and some have a possibility to collect users' feedback (see Table  1 ).
A parallel approach for formalizing the biological knowledge consists in creating graphical representations of the biochemical mechanisms in the form of maps of molecular interactions  [11] [12] [13] [14] [15] [16] [17] [18] [19]. The idea of mapping some aspects of molecular processes onto a two-dimensional image appeared at the dawn of molecular biology. The first large maps of metabolism, cell cycle, DNA repair have been created manually starting from the '60s and were not supported by any database structure  [20, 21]. Such maps can be considered as a collection of biological diagrams, each depicting a particular cellular mechanism, assembled into the seamless whole, where the molecular players and their groups occupy particular "territories". Molecules put close on the map are assumed to have similar functional properties (though it is not always possible to achieve in practice).