dataesr/scientific-paragraphs-categorization huggingface dataset:
e. in the skiing conditions as offered to skiers. Three observations were carried out (Table  1 ). A single air/water gun was used for our observations. The professional snowmakers of Les 2 Alpes kindly provided all available data regarding the production of MM snow on the study site. This covers 15 min time step records of the water flow of the snowgun (m 3 h -1 ), the wet-bulb temperature ( • C), the wind speed (m s -1 ) and direction ( • from North) measured in the vicinity of the study area. These data were used both as inputs to force the snowpack model (water flow, amount of MM snow) and as references for the analysis of the outputs of the model (same variables, wet-bulb temperature). The data also helped the characterization of the production conditions (wet-bulb temperature and wind conditions).
75, depending on snow conditions. The ski slope was wider in January (6632 m 2 ) and April (7067 m 2 ) than on 4 December (4063 m 2 ) since there was very little natural snow at this time. This also made the edge easier to identify (Fig. 1 ). On 20 January and 6 April we collected data across the total of the marked out ski slopes even though the study area for SD and SWE calculations was consistently limited to the area defined by the edge on 4 December 2015 in order to provide comparable data. A sensitivity test of the SD and SWE concerning the study surface was conducted by considering an offset of ±2 m from the edge. The impact on SD and SWE was computed and discussed. -The surface considered to calculate the MM snow production rate in the model was defined as the total marked out ski slope area: the "useful" area (Fig.
Evaluation of uncertainties on snow depth First of all we compared the interpolated snow surface elevations with data from a Terrestrial Laser Scan (see Appendix B for more details). An average elevation difference of -0. 012 m was measured between the interpolated GPS and the TLS snow surfaces (2018 m 2 ). The Root Mean Square of the differences (RMSD) was 0. 055 m (Table  2 ). A significant variability (standard deviation) was measured within each 0. 5 × 0. 5 m 2 pixel thanks to the TLS measurements: 0. 031 m on average across the 8072 pixels. Secondly, we compared the interpolated snow-free surface elevations with the existing Digital Elevation Model of the ground. An average 0. 003 m average elevation difference was measured between the GPS interpolated ground surface and the DEM data (4044 m 2 ).
We specifically analysed the natural snow conditions provided by SAFRAN-Crocus Resort with in situ observations on a local scale from ski patrollers and Automatic Weather Stations (wind, snow/rain elevation limit, precipitation amount). If relevant we adjusted the SAFRAN meteorological forcing data (amount and snow/rain phase of precipitations) to local conditions for this site. The deposition rate of dry impurities on the snowpack surface was also adapted to match the natural melting rate at the end of the season  (Brun et al. , 1992; Dumont et al. , 2012). We also took into account the surrounding slopes of each site and the consequent shading effects  (Morin et al. , 2012; Spandre et al. , 2016b). Lastly, the wet-bulb temperature was computed from SAFRAN dry-air temperature and specific humidity using the formulation from  Jensen et al.
Acknowledgements. The authors thank  Fabian Wolsfperger  and two additional anonymous reviewers for their helpful comments and suggestions. The authors also wish to thank  A. Guerrand  ( Les Deux Alpes Loisirs ) for sharing all details about the management of snow in Les Deux Alpes ski resort, the  IGE (Institut des Géosciences de l'Environnement, Grenoble, France)  for provision of the PICO coring auger ( D. Six ). We also acknowledge the assistance of F. Ousset (Irstea),  Y. Deliot  and  G. Guyomarc 'h in the set up and analysis of field observations,  M. Dumont  in the parameterization of impurities in Crocus as well as  A. Dufour ,  L. Queno ,  L. Charrois  and  J. Revuelto  in the fulfillment of observations (all CNRM/CEN). The  Région Rhônes-Alpes funded Pierre Spandre 's PhD through  ARC Environment.
This work has been supported by a grant from " Eau, Neige et Glace " foundation, from the  LabEx OSUG@2020  (Investissements d'avenir - ANR10LABX56 ), and fundings from SO/SOERE GLACIOCLIM, IGE, IRSTEA, and  CNRM/CEN. Edited by:  R. Brown  Reviewed by:  F. Wolfsperger  and two anonymous referees.
71073 Å). Phi-and omega-scans were used. The data were integrated with SAINT  [36]  and an empirical absorption correction with SADABS  [36]  was applied. The structures were solved by direct methods with SHELXS-97  [37]  or by intrinsic phasing method (SHELXT)  [38]  and refined using a least-squares method on F 2. [37]  All non-H atoms were refined with anisotropic displacement parameters. CCDC 1587350 (2), CCDC 1587350 (4) contain the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif.
ACKNOWLEDGEMENTS This work was supported by the  Babeș-Bolyai University of Cluj-Napoca , by a grant of  Ministery of Research and Innovation ,  CNCS -UEFISCDI , project number  PN-III-P4-ID-PCE-2016-0351 , within  PNCDI III , by the  Centre National de la Recherche Scientifique (CNRS)  and the  Université de Toulouse (UPS).
Social networks (SNs) have become a powerful tool for the jihadism as they serve as recruitment assets, live forums, psychological warfare, as well as sharing platforms. SNs enable vulnerable individuals to reach radicalized people, hence triggering their own radicalization process. There are many vulnerability factors linked to socio-economic and demographic conditions that make jihadist militants suitable targets for their radicalization. We focus on these vulnerability factors, studying, understanding, and identifying them on the Internet. Here, we present a set of radicalization indicators and a model to assess them using a data set of tweets published by several Islamic State of Iraq and Sham sympathizers. Results show that there is a strong correlation between the values assigned by the model to the indicators.
Speaking about feelings and emotion, guilt, indignation, anger, humiliation, frustration and hatred are the most related ones to jihadist radicalisation  [7] ,  [8]. We will focus on these radicalisation factors, studying them on the Internet. Radicalised individuals publish a lot of information in public social media without any security measure, even though there are encryption tools and anonymity software  [4]  that can be used to ''hide'' the content of the information. This means that every user of the Social Media can read the majority of the information published by the radicalised individuals. Hence, Social Media is a perfect data source for tracing radicalisation factors as we can access to this plain information. Social networks such as Tumblr, Instagram, Twitter, Facebook and Youtube have become a powerful propaganda tool for the jihadist cause as they serve as recruitment assets, live forums, psychological warfare and sharing platforms.
This work corresponds to the first step in the development of a risk assessment tool in Social Networks, and its goal is to define (and validate) the different indicators that later can be used to identify those members of a Social Network with high risk of being radicalised. The main contributions of this paper are the following: it proposes five indicators and their corresponding metrics that can be used to measure the online radicalisation assessment of a given individual using public data from his social networks, later an experimental evaluation of these indicators, using a public dataset of tweets from several Twitter accounts of pro-ISIS are carried out. Finally, a detailed analysis of the relationships between these metrics are discussed. The rest of this paper is structured as follows: Section II contains an overview of Social Network Analysis; Section III provides a description of the different radicalisation factors taken into account in this work; A complete description of the proposed model can be found in Section IV and the description of the dataset used is provided in V; Finally, the experimental results obtained in this work are analysed in Section VI and the concluding remarks are explained in Section VII.
II. BACKGROUNDS ON SOCIAL NETWORK ANALYSIS Social Network Analysis (SNA) is the process of extracting knowledge from Social Network Data. The general process of any SNA platform is composed of the following stages of a pipeline: 1) Data Extraction from the Social Networks (SNs). 2) Data Preprocessing. 3) Data Representation. 4) Execution of one, or several, specific algorithm. 5) Results analysis. Any SNA process starts with the extraction of Social Media Data from the specific SN. This first step is required in order to validate the SNA algorithm designed. This process can be skipped if researchers acquire these data from previous own data, or from an existing dataset available on the Internet. Once the data have been gathered, it is needed to perform some preprocessing. This process is required because typically, these data is not ready for being analysed by the SNA algorithm.
The selection process should guarantee that the sample is enough representative of the distribution that governs the data, thereby ensuring that results obtained on the sample are close to ones obtained on the whole dataset. Once the data has been extracted and preprocessed, researchers have to adapt the data to the best representation for the specific algorithm that will perform the SNA task. When working with Social Media, the most classical way to represent the data is using a graph. A graph is a structure that can be defined as G = {V , E} where V is the set of nodes and E represents the edges of the graph. In the case of social networks, the nodes of the graph represent the different users of the SN, whereas the edges represent the different connections between the users. Nevertheless, due to the extremely large number of users in Social Networks (SN)  [16] , researchers try to find reduced representations of the SN to test their algorithms.
e. those that start considering that each element of the dataset belongs to a single community, and then, iteratively, the different communities are merged according to a specific measure). Examples of bottom-up algorithms are Clauset-Newman-Moore  [23] ,  [24]  (based on the Edge Betweenness algorithm  [25] ,  [26] ), or the Walktrap algorithm  [27] ,  [28]  that it is based on the random walks that connect the different communities. A different approach within hierarchical clustering are the top-down algorithms. In this case, the algorithms start with only one community that contains all the elements and then, this community is divided taking into account the existing edges or links  [29] -  [31]. Finally, the third family is composed of probabilistic, or heuristic, approaches, like  [32]  and  [33].
IV. DESCRIPTION OF THE PROPOSED MODEL In order to validate the radicalisation indicators presented in Section III, we propose a knowledge extraction model capable of performing a quantitative analysis about written text in social networks. Our proposal uses features from diverse research field such as Natural Language Processing, Data Mining and Statistics. As was shown in Section II, the model consists of various stages through which the texts obtained from Social Networks are processed and analysed. In the data acquisition stage, the data gathered from the social network (i. e. the posts, tweets and/or status updates) is grouped by the user that posted them. This is a straightforward step that relies solely on the data structure, or storage technology, used when the social network was mined.
To measure this indicator the model counts the average sentence length (in number of words) for every user as well as the number of ellipses in his/her tweets, searching sequences of at least three points in the tweet. The value is normalized dividing it between the maximum value obtained to get an indicator within the range [0. 0, 1. 0]. I3, I4, I5: These indicators are related to a set of keywords that express the perception of being discriminated for being Muslim, negative ideas about Western Society and positive ideas about jihadism, respectively. In order to give a numerical value, the model counts how many times there are at least two of the keywords in a sentence (see Table  1  to check the keywords). As with I2, values are normalized. As stated before, the dataset has about 17000 observations and 8 features, namely: name, user name, description of the account, user's location where one can put on their Twitter profile, number of followers, number of tweets, date and time when the tweet was posted, and the text itself.
Regarding accounts, there are 112 unique user names. There is an interesting observation: there are only 78 unique descriptions among these 112 users, which may suggests that some of the accounts belong to the same person. Using langid, a pretrained language identification tool written in Python  [35] , we found that most of the tweets (14556 out of 17410) were written in English, followed by 742 and 610 tweets written in Arabic and French, respectively. shown in Figure  3 , most of the users in the dataset have posted less than 300 tweets. In fact, there are some users with less than 10 published tweets, which is a surprisingly small quantity of information for a user to be tagged as pro-ISIS as the description of the dataset stated. On the other hand, there are some accounts with a high number of tweets that defines themselves as 'war reporters'.
The same thing happens with the number of followers that each user has, whose distribution is heavily left-skewed, with most users having less than 1000 followers and a unique user (@RamiAlLolah) with over 30000. According to the timestamps of the tweets in the dataset, they were published between 6th January, 2015 and 13 May, 2016, that is roughly a half and a year. Although the day of the month when the tweets were published are rather chaotic and without a clear tendency (see Figure  4 ), there is an interesting trend in the hour of publication: most of the tweets were published in the afternoon and evening. This observation might be a result of the work timetable, it is easier to publish a tweet when you are not at work. In order to identify the main ideas expressed in the written text of the tweets, we computed the word cloud that is shown in Figure  5.
VI. EXPERIMENTAL RESULTS AND DISCUSSION We implemented the model described in Section IV, using Python and R, and then processed the dataset in order to obtain the indicators and analyse common characteristics of users, similarities in the values of indicators, as well as correlation between metrics. As seen in Figure  6 , the values of the variables are distributed on the lower zone of the range of possible values [0. 0, 0. 1], with some outliers in the midrange as well as high values in the case of Swearing and Negative Words. This highlights the fact that there is a high number of users with similar values of the indicators, and a few users whose indicators are far away from the former, as it happens with other metrics of the dataset, such as the number of published tweets and followers (see Section V).
Moreover, this result permits us identify common behaviour patterns that would support radicalised user identification in other datasets. It is worthy to emphasize the higher variability in the values of swearing and negative words compared to this variability in the other variables. In order to find out possible relationships between the metrics, we analysed the pairwise correlation between them (see Figure  7 ). A noteworthy result obtained from this analysis is the lack of correlation between the average sentence length and the rest of the variables, even when this correlation is generally high between the latter. In fact, we found strong correlations between expressing positive ideas about Jihadism and both the perception of discrimination (ρ = 0. 831, p-value < 2. 2e-16) and the use of swear words (ρ = 0.
Therefore, Social Media let vulnerable individuals to reach radicalised people and even people with the same inquietudes, who may be able to encourage each other's radical ideas, supporting the radicalisation process. This paper defines different indicators that can be used to measure the online radicalisation assessment of a given individual. In this sense, 5 different indicators have been defined related to the attitudes and beliefs towards Muslim religion and Western society and personality and interpersonal relationships. It is to take into account that this list of indicators can be extended with any other indicator that can be measured using the information extracted from the social networks. For the experimental phase, we have measured these indicators using a dataset found in Kaggle 3 with over 17000 tweets from several Twitter accounts of pro-ISIS since the Paris attacks in November 2015.
Data were gathered and processed by a digital agency called Fifth Tribe, and it is released under the CC0: Public Domain License. The first analysis of the indicators reveals that values are distributed on the lower zone of the range of possible values [0. 0, 0. 1], with some outliers in the mid-range as well as high values in the case of Swearing and Negative Words. This highlights the fact that there is a high number of users with similar values of the indicators and a few users whose indicators are far away from the former. Also, we have analysed the pairwise correlation between the different indicators. In general, there are strong correlations between the majority of indicators defined in this work: expressing positive ideas about Jihadism, the perception of discrimination, the use of swear words, and the use of negative words.
Nevertheless, we have observed a lack of correlation between the average sentence length and the rest of indicators. This situation may happen due to the limit of characters that the social network Twitter sets to the length of the messages, which are limited to 140 characters without counting URL's. The conclusion that can be drawn from the analysis of these correlations is that it makes sense to measure the indicators with the selected metrics as they share a similar behaviour so people in risk of radicalisation should score high on these metrics; and also, that the dataset used is coherent. This paper supposes an important step in the fight against radicalisation because it defines several indicators that can 3 https://www. kaggle. com/kzaman/how-isis-uses-twitter be used to measure the risk of radicalisation of a given individual.
DJAMAL BENSLIMANE is currently a Full Professor of computer sciences with Lyon 1 University and a member of the Service Oriented Computing Research Team, Lyon Research Center for Images and Information Systems, Lyon, France. He has authored papers in well-known journals including IEEE TKDE, ACM TOIT, SIGMOD Record, IEEE TSC, IEEE INTERNET COMPUTING, WWW Journal, and DPDB Journal. His research interests include distributed information systems, Web services, ontologies, and databases. DAVID CAMACHO received the B. S. in physics from the Universidad Complutense de Madrid in 1994 and the Ph. D. degree in computer science from the Universidad Carlos III de Madrid in 2001. He is currently an Associate Professor with the Computer Science Department, Universidad Autonoma de Madrid, Spain, and the Head of the Applied Intelligence and Data Analysis Group.
This work was supported in part by  EphemeCH ,  Spanish Ministry of Economy and Competitivity , under the  European Regional Development Fund FEDER , under Grant  TIN2014-56494-C4-4-P  and in part by the  Justice Programme of the European Union  ( 2014-2020 )  723180 ,  RiskTrack , under Grant  JUST-2015-JCOO-AG  and Grant  JUST-2015-JCOO-AG-1.
Through this comparative analysis of the data, corrected for multi-testing with a FDR of 5%, we found 645, 786 and 449 genes differentially expressed at least in one group (SH, DH or control) at 30 minutes, 3 hours or 6 hours, respectively (see Supplementary Table  S1 ). For each time point, differentially expressed genes were clustered into similar expression profiles using Kmeans clustering, identifying 4 clusters at 30 minutes, 3 clusters at 3 hours and 3 clusters at 6 hours (see Supplementary Table  S2  and Fig. 2 ). To validate the RNAseq expression data by real time quantitative PCR (RT-qPCR), several genes with diverse expression profiles were selected. In all cases the expression profile observed in the RNAseq were confirmed (see Supplementary Figure  S2 ). To analyze the functional characteristics of genes with similar expression pattern, we searched for biased representation of gene function annotations within individual expression clusters using Flymine (http://www.
RT-qPCR confirmed the expression profile of CrebA and several of its targets (CG5885, Sec. 61beta, Spase25 and TRAM, see   1. Functional annotation enrichments for gene clusters. Gene clusters described in Fig. 2  were analysed for Gene Ontology term enrichment (GO terms) using flymine online database (http://www. flymine. org/). Significant P values (cutoff 0. 05) are indicated (see Supplementary Table  S3  for details) and the number of genes associated to corresponding GO terms is displayed (Nb genes/GO Term). TF-binding motifs enriched in non-coding regions of gene clusters were identified using i-cisTarget (https://gbiomed. kuleuven. be/apps/lcb/i-cisTarget/). The predicted corresponding TF and the NES score of the most representative motifs (cut off 5) are indicated (see Supplementary Table  S4  for details).
To determine the bacterial load in manipulated flies, a sample of 3 flies per group were collected at 0 h, 6 h and 24 h after manipulation and were crushed in 1 ml of LB broth. 10-fold serial dilutions of samples suspension were made and 100 µl plated on LB agar. Colonies were counted after 24 h at 30 °C. Statistical analysis and fly survival. Statistical analysis of survival rates between different batches of manipulated flies was performed using R project software (http://www. r-project. org/). For comparing fly groups, the survival curves were analyzed by the Log Rank test method  39  in order to test the null hypothesis that the survival kinetics of the different populations of flies (the probability of the "death" event at any time point between the different batches) are equivalent. In each group and for each time point, the observed number of death and the expected number of death predicted by the null hypothesis were calculated.
Preset '-sensitive' option ensures good sensitivity and fast processing. End-to-end argument is required in order to align the entire read length. No-unal argument allows discarding reads that failed to align and -k 1 argument allows read to align once. We used the Subread package (R statistical tool; http://www. r-project. org/) to count aligned reads. For each time point (30 min, 3 h and 6 h), differentially expressed genes were identified by the use of the R packages edgeR  14  and DESeq2  15. We used no fold change filtering and results were corrected for multi-testing by the method of the False Discovery Rate at the 5% level. Clustering were done using TIGR Multiexperiment Viewer tool (MeV)  41. Differentially expressed genes were clustered using the unsupervised classification method of the Kmeans  42.