dataesr/scientific-paragraphs-categorization huggingface dataset:
10 ). First, each processor core takes a sub-image horizontal strip and applies the first pass of either algorithm. Except for the modified loop indexes, there is no difference between the sequential and parallel code. Then, the next step consists in applying a pyramidal merge. As we divide the image by the number of cores available p, the total number of merges required is equal to p -1. For each merge step, we divide the number of active cores doing a two way merge until we have only one core left. On the border line between two subimages, we apply a merging pass on each column. For each SIMD vector of pixels, we have at most two calls to VecUnion. Algorithm 12 describes the body of the border merge loop. Finally, we apply the second pass which does not require any code modification compared to the sequential version.
II. DESIGN AND IMPLEMENTATION OVERVIEW BlockZoom is designed to gives insight to blockchain developers to validate alternative options. For instance by experimenting with different configurations they can collect performance metrics and compare them with the needed requirements. However, re-configuring a private blockchain network requires significant manual work due to the many components that need to be readjusted. Thus, a tool is required to automate time-consuming and low level details, such that the developers and researchers can focus on the critical questions to answer. In  [7]  we detailed an orchestration framework for large-scale private blockchain networks. In this work, we extend the orchestration framework with an experiment configuration module as illustrated in Figure  1. Experiment configuration module.
III. ACKNOWLEDGEMENTS Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including  CNRS ,  RENATER  and several Universities as well as other organizations. The present project is supported by the  National Research Fund, Luxembourg  under grant agreement number  11785982. Also, this work has received funding from  CONCORDIA , the  Cybersecurity Competence Network  supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  830927.
SPH-FE coupling strategy The same SPH-FE coupling strategy as Fourey et al. [25]  is considered here, which is based on classical partitioned approaches. Here the deformable body behaviour is modelled using the commercial software: Abaqus (developed by Simulia). The fluid part is modelled using SPH-flow, jointly developed by Ecole Centrale Nantes and NEXTFLOW Software  [55, 56].
It corresponds to a summation of the areas where the tire cells intersect the ground cells, which explains the differences between rough and smooth grounds. Note that both force and surface increase in a non-physical manner. These differences could be explained by some difficulties in correctly representing the tire model using Abaqus software. Unfortunately, these uncertainties on the solid part prevent the validation of the FSI strategy with experimental data from Michelin's facilities. Indeed, the contact surface between the tread and the ground could be experimentally captured using imaging techniques, as shown in Fig. 34. For that purpose, the tire should be correctly modelled. Nevertheless, these uncertainties on the solid modelling do not prevent us from studying the performances of the present FSI coupling and the physic of aquaplaning problems in relative terms.
Simulation Results of HR Collision In the ensuing analysis, a comparison between V2SOM and a constant stiffness (CS) VSM was performed through simulation of the HR collision model under Matlab/Simulink. An identical elastic deflection value was considered for CS and V2SOM deflection at 80% of. This torque value defines the deflection range of the normal working mode for the V2SOM after which the shock absorbing mode is triggered. The shock absorbing mode is triggered when the torque reaches  value (see Figure  15 ). In this case, the springs are compressed by the followers' displacement as a result of the cam rotation (as shown in Figures  5  and 6 ). The simulation aims to emphasize the decoupling capability of V2SOM along inertia and torque in comparison to an equivalent CS-based variable stiffness mechanism.
Simulation Results of HR Collision In the ensuing analysis, a comparison between V2SOM and a constant stiffness (CS) VSM was performed through simulation of the HR collision model under Matlab/Simulink. An identical elastic deflection value was considered for CS and V2SOM deflection at 80% of. This torque value defines the deflection range of the normal working mode for the V2SOM after which the shock absorbing mode is triggered. The shock absorbing mode is triggered when the torque reaches  1 value (see Figure  15 ). In this case, the springs are compressed by the followers' displacement as a result of the cam rotation (as shown in Figures  5  and 6 ). The simulation aims to emphasize the decoupling capability of V2SOM along inertia and torque in comparison to an equivalent CS-based variable stiffness mechanism.
However, it is a centralized localization approach. Motivated by the above reasons, based on RSSI measurements, we solve the minimization problem using advanced algorithms to reduce the running time and the computational complexity while ensuring a good localization accuracy. After completing the EDM, the second step is to estimate the location of sensors. For this, the completed pairwise distance information can be transformed into the estimated coordinates of sensor nodes by applying a factorization process  [14]. This factorization requires that the matrix is Semi Definite Positive (SDP). The SDP problem can be efficiently and accurately solved by CVX toolbox on Matlab  [25]. However, if the matrix is not SDP, the problem can also be solved by introducing Semidefinite Relaxations (SDR)  [26, 27].
• EDM reconstruction error using the mean square error MSE = || X -X true || 2 F ||X true || 2 F , (39) • Localization error error loc = || Ĉ -C|| 2 n -m , (40) As mentioned before, Ĉ is the matrix of estimated coordinates of unknown nodes and C are their real coordinates. We recall that parameters α, λ, , µ, ρ, β 1 and β 2 are adjusted by simulations, selected to ensure the best result in terms of localization mean error on a validation dataset. To find the best set of these parameters, an empirical process is conducted. Thus, several simulations are required to identify the optimal value of each parameter. The parameters used in the rest of the paper are presented in Table  2. The rest of algorithms includes GD, NAG, Adagrad, RMSProp and Adadelta. β 1 0. 001 - β 2 0. 001 - To verify that trilateration guarantees better localization accuracy when more distance information is provided, we firstly apply the trilateration with observed distances only.
Method Verification Using Real Measurements For method verification, we consider a classroom of our university as an indoor area of 88 m 2 illustrated in Figure  7. The used technology is LoRa which uses 868 MHz as frequency band. We placed eight sensor nodes which can transmit and receive messages. We collect RSSI data during an afternoon. Then, we construct the RSSI matrix, in which there are some missing values which correspond to unknown detected sensors. This matrix is used to obtain the partially known EDM matrix. We mention that the used technology and the number of sensor nodes explored for experimental verification relates to the availability of such equipment in our laboratory. Thus, we do not consider the optimal conditions determined before. The aim is to validate the effectiveness of our method.
| Studied species and geographic range We focused on three (West)-Palaearctic bumblebee species: Bombus lapidarius, B. pascuorum and B. terrestris. These species are some of the most abundant bumblebees where they occur  (Rasmont et al. , 2015). They are sympatric in most of their range and not threatened (i. e. , IUCN Red List status: Least Concern with stable populations)  (Rasmont et al. , 2015). The occurrence data came from the database "Base de données fauniques de Gembloux-Mons"  (Rasmont et al. , 2015). We extracted all species occurrences between 1950 and 2014 in the West-Palaearctic region but because of the rather low sampling effort and unavailability of lineage/ESU statuses, we had to exclude Russia, Belorussia, Kazakhstan and Ukraine. We obtained 35,252 observations for B. lapidarius, 63,565 observations for B.
pascuorum and 34,970 observations for B. terrestris and called the resulting datasets "raw observation datasets. " We aggregated these data to presence/absence data at 10 arc-minutes resolution grid (i. e. , = 0. 17° or ~14. 5 km at 40°N) to account for differences in local sampling effort and to obtain reliable absence data. Additionally, we also used a lower resolution (i. e. , 30 arc-minutes) to further taking into account the sampling effort differences. To further increase the reliability of absence data, we considered only grid cells as valid for absence data when at least one bumblebee individual from one of the 69 Bombus species had been observed between 1950 and 2014 (based on 575,421 observations in the database). As (a) most of observations from the database "Base de données fauniques de Gembloux-Mons" were based on studies investigating the whole bumblebee fauna from a particular geographic area and (b) the three studied species were very abundant, we considered that such grid cells without B.
For the data points corresponding to the individuals used in the baseline phylogeographic and ESU delimitation studies (about 5% of the dataset), we used the status underlined by these studies  (Lecocq, Brasero, Martinet et al. , 2015; Lecocq, Coppée et al. , 2016; Lecocq, Dellicour et al. , 2015). When the observation was identified to subspecies level (i. e. , data were available from previous studies that identified the subspecies of the observations; about 25% of the remaining dataset) and this subspecies status corresponded to a particular lineage/ESU exclusively, we attributed the corresponding lineage/ESU to the observation (see matches between lineages/ESUs and subspecies in Table  1 ). For the remaining data points, we assigned lineage/ESU status based on the geographic region of the observation when it was in an area where only one lineage/ESU occurs.
Doubtful observations (i. e. , occurrences in areas where several lineages/ESUs overlap and none of the abovementioned criteria were applicable) were removed from the datasets (about 10% of the dataset). To avoid a potential bias by considering grid cells with doubtful observations as absences, we excluded these grid cells entirely for model development. The resulting dataset was called "used observation datasets" and was used for niche determinations and for SDMs (Table  1 ).
| SDM development and evaluation We developed SDMs for each species and classification level separately using boosted regression trees (BRTs)  (Elith, Leathwick, & Hastie, 2008)  in r (R-package gbm version 2. 1. ; Ridgeway, 2013)  assuming a binomial error structure and using a learning rate of 0. 001, a tree complexity of three (lower complexities resulted in worse models while higher complexities performed similarly), and a bag fraction of 0. 75. To ensure an overall prevalence of 0. 5 and thus comparable resulting occurrence probabilities, we weighted the absences by the ratio of the number of presences on the number of absences  (Maggini, Lehmann, Zimmermann, & Guisan, 2006). The BRT analyses converged to the best fitting model for all classification levels for all test case species except two ESUs with a very small geographic ranges at 30 arc-minute resolution: ESU Siciliensis We merged individual lineage/ESU models (so-called summed models) by keeping the highest probability obtained among models for a particular grid cell.
To transform the resulting occurrence probabilities into presence/absence maps, we identified a threshold by maximizing the true skill statistic (TSS)  (Allouche, Tsoar, & Kadmon, 2006). Spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors in SDM  (Crase, Liedloff, Vesk, Fukuda, & Wintle, 2014). Therefore, we used correlograms plot Moran's I (i. e. , a measure of spatial autocorrelation;  Moran, 1950)  between grid cells as a function of the distance between them  (Kissling & Carl, 2007)   into presence/absence data by maximizing TSS, and performance of the summed models was evaluated using sensitivity, specificity and TSS. We based these assessments on the "raw observation dataset" to take into account all observations for each species (including observations for which we were not able to determine the lineage/ESU status).
| Future projections We aimed at examining the impact of potential differences among the three SDM approaches on future projections of climatically suitable areas. We used the respective climate values obtained for the five global circulation models (GCMs; i. e. , CCSM4, HadGEM2-AO, MIROC-ESM, MPI-ESM-LR and NorESM1-M) and according to the representative concentration pathways 8. 5 scenario  (Moss et al. , 2008). We used only one climate change scenario as we aimed only at comparing the general consequences when using the three different SDM approaches and not at performing for a comprehensive assessment of potential future changes in the three species. We obtained these data from the WorldClim database  (Hijmans et al. , 2005)   were obtained using the same procedure as used for the current conditions.
| D ISCUSS I ON Our analyses were based on occurrence data from several previous field surveys. We argue that these datasets can be considered reliable for SDM analyses (see Methods). However, we cannot completely rule out that field surveys failed to detect or disregarded (i. e. , some surveys may have only targeted a particular species and therefore did not record others) our species of concern. Therefore, climatic niche and modelled current and future distribution of the three bumblebee species should be considered with caution.
Lost means area projected as turning unsuitable. Gained means novel area projected to become suitable. Total gives the extent of suitable area in 2070. The percentage reflects the future surface of suitable climatic areas relative to the current prediction less observations) can lead to an overestimation of species range because smaller observation datasets can decrease the SDM precision resulting in broader climatic parameters inference  (Stockwell & Peterson, 2002). In our study, the quite similar performances and the larger species range predictions of ESU and lineage-based SDMs do not confirm the first concern but could reflect the second potential issue. Another potential issue is the relevance of the a priori classification of differentiated populations. Indeed, intraspecific units defined on the basis of genetic markers or ESU delimitation may not display physiologically different responses to climate change.
obtained 19 bioclimatic variables from the WorldClim database version 1. 4 (downloaded 01 October 2017) (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005)  for current climatic conditions at a 10 arc-minutes resolution grid. For the analyses based on 30 arc-minutes resolution, we aggregated the bioclimatic layers to the same resolution as the bumblebee observation dataset (a) by merging the 10 arc-minutes and the 30 arc-minutes resolution grid in ArcGis 10. 2 (www. esri. com) and (b) by calculating the mean of each bioclimatic variable within each of the 30 arc-minutes grid cell in r 3. 2. 5. (R Core Team, 2017). To avoid statistical problems due to high levels of collinearity among bioclimatic variables, we selected the following relevant and least correlated variables (r < 0. 6) using a complete linkage cluster analysis based on a Spearman's correlation matrix calculated for the 19 bioclimatic variables: annual mean temperature, temperature seasonality, maximum temperature of the warmest month, precipitation of the wettest month and precipitation seasonality.
We evaluated the models using (a) 10-fold cross-validated area under the receiver characteristic curve (AUC) scores (i. e. , receiver operating characteristic; Hanley & McNeil, 1982) , (b) sensitivity (the proportion of observed presences that are predicted as such), (c) specificity (the proportion of observed absences that are predicted as such) and (iv) TSS (sensitivity + specificity -1). We first ran the models with a 10-fold cross-validation to get performance metrics and then fitted the model again using the whole set of points to build the final models. We assessed significance for differences in all indices (sensitivity, specificity and TSS) among the summed models for each classification level by (a) randomly resampling 90% of occurrences, (b) developing the SDM based on these resampled datasets and (c) calculating sensitivity/specificity/TSS based on the "raw observation datasets.
OMI) analyses of each classification level (species, lineage and evolutionary significant units) for three bumblebee species. These analyses are based on the datasets at the resolution of 10 arc-minutes. Spaces occupied by studied species along the first and second axes of the OMI analysis are given on the left with the projection of mean position (centre of red crosses) and its variance (branches of the red crosses) occupied by each group of each classification level. Canonical weights of the five bioclimatic variables are given on the right (Bio1 = annual mean temperature, Bio4 = temperature seasonality, Bio5 = maximum temperature of the warmest month, Bio13 = precipitation of the wettest month and Bio15 = precipitation seasonality).
Results of species distribution models for three bumblebee species. Potential climatically suitable area according to speciesbased models, summed lineage-based models and summed ESU-based models for three bumblebee species. Green areas indicate model predictions. Analyses are based on datasets at 10 arc-minutes resolution. The thresholds used to transform the occurrence probabilities into presence/absence map are those maximizing the true skill statistic: 0. 56/0. 7/0. 72 for Bombus lapidarius, 0. 5/0. 61/0. 65 for Bombus pascuorum and 0. 6/0. 6/0. 67 for Bombus terrestris (for species-level model/for summed lineage-based model/for summed ESU-based model) autocorrelated and the intraspecific units have allopatric distributions. Therefore, the results could be explained by geographic range differences shaped by biogeographic history or limited dispersal ability rather than by actual niche divergence.
I G U R E 3 Predictions of suitable climatic areas in 2070 for three bumblebee species according to alternative species distribution models based on the consensus of five general circulation models. Changes in suitable climatic conditions projected for 2070 according to the representative concentration pathways 8. 5 scenario for species-based models, summed lineage-based models and summed ESU-based models of three bumblebee species: orange, conserved area; red, lost area; green, gained area. Analyses are based on datasets at 10 arcminutes resolution. The thresholds used to transform the occurrence probabilities into presence/absence map are those maximizing the true skill statistic: 0. 56/0. 7/0. 72 for Bombus lapidarius, 0. 5/0. 61/0. 65 for Bombus pascuorum and 0. 6/0. 6/0. 67 for Bombus terrestris (for specieslevel model/for summed lineage-based model/for summed ESU-based model) suitability in Eastern Europe.
As SDM-based climatic risk predictions are increasingly used to produce guidelines to (non-)governmental agencies and to assess conservation plans (Carroll, 2010) , divergences and limitations of each modelling approach should be taken into account for developing efficient biodiversity management strategies. Lineage and ESU-based SDMs offer the advantage to drag attention to species in which allopatric populations could display physiologically different responses to climate change when they lead to different results than species-based models. Therefore, they pave the way to further assessments of ecoclimatic tolerance of particular species where intraspecific differentiation could lead to an unexpected response to climate change. ACK N OWLED G EM ENTSThe research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 244090, STEP Project (Status and Trends of European Pollinators, www.
F I G U R E 3 Predictions of suitable climatic areas in 2070 for three bumblebee species according to alternative species distribution models based on the consensus of five general circulation models. Changes in suitable climatic conditions projected for 2070 according to the representative concentration pathways 8. 5 scenario for species-based models, summed lineage-based models and summed ESU-based models of three bumblebee species: orange, conserved area; red, lost area; green, gained area. Analyses are based on datasets at 10 arcminutes resolution. The thresholds used to transform the occurrence probabilities into presence/absence map are those maximizing the true skill statistic: 0. 56/0. 7/0. 72 for Bombus lapidarius, 0. 5/0. 61/0. 65 for Bombus pascuorum and 0. 6/0. 6/0. 67 for Bombus terrestris (for specieslevel model/for summed lineage-based model/for summed ESU-based model) suitability in Eastern Europe.
Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include nonexpert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https:// github. com/ygorg/KPTimes.
In this study, we address this need by providing KP-Times, a dataset made of 279 923 news articles that comes with editor-assigned keyphrases. Online news are particularly relevant to keyphrase generation since they are a natural fit for faceted navigation  (Tunkelang, 2009)  or topic detection and tracking  (Allan, 2012). Also, and not less importantly, they are available in large quantities and are sometimes accompanied by metadata containing human-assigned keyphrases initially intended for search engines. Here, we divert these annotations from their primary purpose, and use them as gold-standard labels to automatically build our dataset. More precisely, we collect data by crawling selected news websites and use heuristics to draw texts paired with gold keyphrases. We then explore the resulting dataset to better understand how editors tag documents, and how these expert annotations differ from author-assigned keyphrases found in scholarly documents.
Existing datasets Frequently used datasets for keyphrase generation have a common characteristic that they are, by and large, made from scholarly documents (abstracts or full texts) paired with non-expert (mostly from authors) annotations. Notable examples of such datasets are  SemEval-2010 (Kim et al. , 2010)  and KP20k  (Meng et al. , 2017) , which respectively comprises scientific articles and paper abstracts, both about computer science and information technology. Detailed statistics are listed in Table  1. Only two publicly available datasets, that we are aware of, contain news documents: DUC-2001  (Wan and Xiao, 2008)  and KPCrowd  (Marujo et al. , 2012). Originally created for the DUC evaluation campaign on text summarization  (Over, 2001) , the former is composed of 308 news annotated by graduate students.