dataesr/scientific-paragraphs-categorization huggingface dataset:
This section reports the evaluation of Legalo based on the validation of Hypothesis 1 and Hypothesis 2. Evaluation sample. As evaluation data, a corpus C rel-extraction for relation extraction developed at google research 29 was used. There are five datasets available in this corpus, and each dataset is dedicated to a specific relation: place of birth, attending or graduating from an institution, place of death, date of death, degree of education. Each dataset includes a snippet from Wikipedia, a pair (subject, object) of freebase entities, and at least five user judgments that indicate if the snippet contains a sentence providing evidence of a referenced relation (e. g. , place of death) between the given pair of entities. It is important to remark that Wikipedia snippets included in the corpus contain more than one sentence, which can be evidence of other relations than the ones for which they were evaluated.
Based on this observation, the corpus has been used also for evaluating Legalo on its ability to assess the existence of open-domain relations. It has to be noticed that Legalo addresses all the capabilities of an OKE system (cf. Section 2), however by using C rel-extraction for its evaluation and considering the homogeneous writing style of Wikipedia authors, additional experiments are needed to properly assess Legalo scalability performance on Web diversity (e. g. blogs, twitter, etc. ). In other words, Legalo can be used with any input text, but the different styles of the diverse Web sources could affect its performance. We leave the investigation of possible bias caused by different writing styles to future development. The evaluation was performed using a subset of C rel-extraction. More specifically, three evaluation datasets were derived from C rel-extraction and used for performing different experimental tasks.
The resulting triples, predicate formalisations, and scope annotations are accessible via a Virtuoso SPARQL endpoint  30. There are several works demonstrating that crowdsourcing can be successfully used for building and evaluating semantic resources  [17, 47, 34]. Following these experiences, Legalo was evaluated with the help of crowdsourcing. Five different crowdsourced tasks were defined: It is important to remark that Task 1 duplicates the information already available in C rel-extraction : this choice was driven by the need for using smaller datasets (C rel-extraction samples) as Legalo evaluation experiments needed to address different evaluation tasks. From an analysis of C rel-extraction it has been noticed that some judgements were incorrect, which can be irrelevant on big numbers while it can bias the results on smaller sets.
The results for "institution" relation and for "any" relation are computed both on the overall set of results, as well as on a subset that ensured a higher confidence rate (i. e. , only results with conf idence(j i , u) > 0. 65 are included). As far as the evaluation of the "institution" relation is concerned, the subset of results with high confidence is 68% of the whole evaluation dataset, while for "any" relation it is 76%.
Evaluation of Hypothesis 2 Table For these tasks, positive values (i. e. when Legalo generates a relation, i. e. "true") can be judged by the crowd with "agree", "partly agree" and "disagree". Let A be the number of "agree", PA the number of "partly agree" and D the number of "disagree". As for the negative values, the definition is the same as for Task 1-2, and we reuse their results, as they are on the same datasets. Hence, we compute: True positive = (A + 0. 5 * P A), False positive = D, True negative = the number of (false, no) pairs, False negative = the number of (false, yes) pairs. Finally, Hypothesis 2 was evaluated also by computing a similarity score between human created predicates and Legalo generated ones. Task 5 was performed for collecting at least three labels λ i for each triple (s, e subj , e obj ).
The type of an entity playing a passive role, i. e. Y in this case, is a relevant information as far as the relation between an entity playing an agentive role, and another playing an oblique role in an event, is concerned. This pattern can be generalised to other relations than institution, which explains a similar behaviour of Legalo in the two tasks focusing on assessing usability of predicates for "institution" and "any" relations. Another example that shows this pattern is given by the sentence, "Hassan Husseini became an organizer for the Communist Party. " taken from the dataset C general. In this case, the representation is the following: :become_1 vn. role:Agent Hassan_Husseini ; :become_1 vn. role:Patient :Organizer ; :become_1 :for :Communist_Party. and Legalo would produce the predicate "become for".
It performs graph-based summarisation, generating concise abstractive summaries of highly redundant opinions. It uses a word graph data structure to represent the text, whereas Legalo uses a semantic graph. As the authors say: "Opinosis is a shallow abstractive summariser as it uses the original text itself to generate summaries. This is unlike a true abstractive summariser that would need a deeper level of natural language understanding". Legalo is indeed based on FRED  [39] , which provides such deeper level of understanding. In order to be considered an abstractive summariser, Legalo will need to be complemented with more capabilities to rank discovered relations across an entire or even multiple texts, to associate them in a way that final users can make sense of, and to evaluate summaries appropriately.
e. is a good summarisation of) any relation expressed by the content of s, between two given entities e subj and e obj mentioned in s -based on data from C general ; 5. creating a phrase λ that summarises the relation expressed by the content of s, between two given entities e subj and e obj mentioned in s -based on data from C general. Task 1 and 2 were used for validating Hypothesis 1. The results of these two tasks were then combined with those from Tasks 3 and 4, for validating Hypothesis 2. cnr. it:8894/sparql. The reader can submit a pre-defined default query for retrieving an overview of the dataset. 3Finally, task 5 was used for comparing the similarity between λ values generated by humans and λ values generated by Legalo, for validating Hypothesis 2 from a different perspective.
Nowadays, forest fires are a serious threat to the environment and human life. The monitoring system for forest fires should be able to make a real-time monitoring of the target region and the early detection of fire threats. In this paper, we propose a new approach based on the integration of Data Mining techniques into sensor nodes for forest fire detection. This approach is based on the clustered WSN where each sensor node will individually decide on detecting fire using a classifier of Data Mining techniques. When a fire is detected, the correspondent node will send an alert through its cluster-head which will pass through gateways and other cluster-heads until it will reach the sink in order to inform the firefighters. We use the CupCarbon simulator to validate and evaluate our proposed approach.
According to  [1] , in summer, between June 21, 2015 and August 21, 2015, there have been 956 forest fires with 743 fires between 7 am and 9 pm and 213 fires between 9 pm and 7 am. In this case, the rate for sensing data pi to the next summer is computed as follows : pi = r h i /t (1) where t is the number of hours in a day when the fire is detected. In our example, 743 fires are detected in the 92 days of summer in the period of 14 hours in one day. The rate for the sensing of node i in this period is : pi = (743/92)/14 ' 0. 58 fire/h or 1 fire in 1 hour and 45 minutes. Therefore, the sensor nodes declared as member turns periodically on and o↵ its radio and its multiple sensors according to pi. On the other hand, the node can sense in the determinate period of 20 minutes every hour and 25 minutes.
Networks can be designed and prototyped by an ergonomic and easy to use interface using the OpenStreetMap (OSM) framework to deploy sensors directly on the map. It includes a script called SenScript  [12]  which allows to program and to configure each sensor node individually. CupCarbon o↵ers the possibility to simulate algorithms and scenarios in several steps. The energy consumption can be calculated and displayed as a function of the simulated time. This allows to clarify the structure, feasibility and realistic implementation of a network before its real deployment. Figure  3  shows an example of detecting fire with our approach using the CupCarbon simulator. When the member node s1 detect fire, it sends the alert to its cluster-head s32 , which itself sends the alert to the gateway s7, which corresponds to the first node recorded in its routing table.
Results In order to evaluate the performance of our approach for forest fire detection, the nodes are deployed in the plane representing forest. The maximum communication range Rc of each node is set to be 100m. Each sensor node is equipped with battery and multi-sensor devices. These sensors are used to collect data such as temperature, humidity, light and smoke data of the corresponding sensor TMP36, 808H5V5, GL5537 IDR and MQ-135, respectively. The MAC protocol used in the simulation is 802. 15. 4 which is implemented in the CupCarbon simulator. To estimate the energy consumption of the proposed approach, we compute the energy consumption in transmission/reception, sensing and computation and we don't take into account the energy consumption in mode of standby, idle and sleep. First, to estimate the transmission/reception energy consumption, we use the energy model of the TelosB sensor node.
The restriction models build conditions in the position of the bodies or in the loads supported by them according to Section 2. 2. The motion of any part can be defined using the Newton-Euler equations. With the aim of creating the model for REBs combining the different parts explained previously, this work uses Modelica ® modelling language. Modelica ® takes advantage of acausal modelling, its multi-domain modelling capability, its object-oriented approach and the ability to create and connect components of models to simulate dynamic systems represented by a set of differential algebraic equations (DAEs)  [8]. As there are many different needs in industry with respect to rotary machines, many kinds of REBs have appeared over the years. This work seeks to model any kind of REB. To achieve this goal, the following issues are covered, based on the scheme of.
Indeed, the need of flexible access to the information in each mesh, balancing load between computing processors, and optimum use of cache memory during a parallel AMR simulation should be taken into account carefully to design "powerful" AMR software. Recently, many new software propose AMR framework as Chombo  [47] , p4est  [37] , etc. In a recent project, Drui et al. [13]  developed a new interface between a two-phase flow finite volume solver and the cell-based library: p4est. The code shows a high performance computing, efficient parallel simulation and strong scaling. These first results were an encouragement to continue the development of a more general interface between p4est and finite volume schemes for several applications: separated phases, spray and astrophysics. This code was named CanoP, and the results presented in this paper, Section 4, are obtained using this code.
CanoP Code CanoP is C/C++ code dedicated to finite volume numerical AMR simulation for various applications (dispersed twophase model, separated two-phase model and astrophysics applications). These applications involve multi-scale phenomena. Therefore, CanoP uses the AMR p4est library to manage the meshes and benefits from its high performance. CanoP can be presented at three stages: the user: implements easily a new application by defining appropriate data model (for example: the density, the pressure, etc. ), finite volume scheme and refinement criterion. CanoP provides a flexible and simple framework to develop new applications; the CanoP core: adapts the user data model to its data structure, manages the time loop, calls p4est function to iterate over the meshes, then apply the numerical scheme and update the data and finally call the p4est functions for mesh adaptation and processor load balancing; the libraries: CanoP integrates different libraries to benefit from their performance: p4est (AMR), HDF5 (Parallel output) and Lua (Input files).
The details are considered as the errors at the level l. Following Harten's idea, we implement a refinement criterion adapted to CanoP for spray applications. The method consists of predicting the moment m 0 for the children quadrants of a given parent quadrant q. The prediction is based on the slope computation in each direction for each face depending on the neighbor quadrants (not necessary of the same size as the quadrant q. But for Harten's predictor operator, he used neighbors of the same level to predict the variable in the next high level): m0 x; y ð Þ ¼ m 0;q þ Dm l 0 x À x q À Á þ Dm u 0 y À y q ; if x; y ð Þ 2 child 1 m 0;q þ Dm r 0 x À x q À Á þ Dm u 0 y À y q ; if x; y ð Þ 2 child 2 m 0;q þ Dm l 0 x À x q À Á þ Dm d 0 y À y q ; if x; y ð Þ 2 child 3 m 0;q þ Dm r 0 x À x q À Á þ Dm d 0 y À y q ; if x; y ð Þ 2 child 4 0 B B B B B B B B @ ð54Þ The subscripts l; r; d; u refer respectively to the left, right, down and up edge.
Homogeneous Isotropic Turbulence in 2D/3D In order to assess the code ability in a more complex gaseous flow field representative of mode complex applications and in a challenging case, where spray is to be found everywhere in the domain, we consider a non evaporating spray in a two-and three-dimensional configurations with a frozen HIT gas. The HIT gas field was generated independently by Sabat et al. [52]  with the ASPHODELE code of CORIA  [53] , which solves the three-dimensional Navier-Stokes equations for the gas phase under the low-Mach assumption. The characteristics of this HIT is given in Table  5 , where Re t is the turbulent Reynolds number, u t is the velocity root-mean-square, e is the mean dissipation rate, g k is the smallest structures scale, l int is the integral scale of the turbulence, s k is the Kolmogorov time scale of the turbulence, and s int is the eddy turnover time.
The results achieved by the CanoP code show that we can reach the same level of accuracy for AMR solution as uniform mesh solution with low computational time and memory. Furthermore, the use of AMR in a unified simulation of the fuel injection (from the separated phases to the evaporating spray) should allow to have an accurate simulation of this multi-scale problem with reasonable computational need. Indeed, for diffuse interface model, we need fine meshes to limit the interface diffusion. Moreover, in the fuel injection, we encounter some vacuum region and some high concentrated droplets region. Therefore, a large spectrum of size is involved in this multi-scale problem and the use of AMR grid should be very beneficial. To reach the ultimate goal of designing adequate model, numerical scheme and HPC application for a fully Eulerian simulation of fuel injection, we still need a deep study to understand this kind of flow, then, to improve the present model for more realistic case.
ACKNOWLEDGMENTS This research was supported by Ph. D. grant for Mohamed Essadki from IFP Energies nouvelles and EM2C. We would like to thank our colleagues from La Maison de Simulation (Samuel Kokh, Florence Drui and Pierre Kestener) for their collaboration in developing CanoP code, Florence Drui for giving helpful comments on writing this paper and Aymeric Vié for several interesting discussions. The help of Macole Sabat in providing the Lagrangian numerical results of HIT is also gratefully acknowledged to validate the numerical results.
Applied solutions represent the sequence of the reads in an assembly graph: the labels along a path of the graph encode a sequence. Currently, most assemblers rely on two types of graphs: either the de Bruijn graph (DBG) for the short reads produced by the second generation of sequencing technologies  [3] , or for long reads the overlap graph (which was introduced in the Celera Assembler  [4] ) and variants thereof, like the string graph  [5]. Then, the assembly algorithm explores the graph using heuristics, selects some paths and outputs their sequences. Due to these heuristics, the set of sequences obtained, called contigs, is biased and fragmented because of complex patterns in the graph that are generated by sequencing errors, and genomic variants and repeats. The set of contigs is rarely satisfactory and is usually post-processed, for instance, by discarding short contigs.
Data sets and CDBG construction For our experiments we used publicly available Illumina read data sets from species of increasing complexity: from the bacterium E. coli, the worm C. elegans, and from Human. Detailed information about the data sets are given in Table  1  of the Additional File (identifiers, read length, read numbers, and coverages -from 70x to 112x-). For each of these three data sets, we generated a CDBG using BCALM. From the C. elegans read set, we additionally generated an artificially complex graph, by using small k and c values (respectively 21 and 2). This particular graph, called C. elegans cpx, contains lot of small unitigs. We used it to assess situations of highly complex and/or low quality sequencing data. The characteristics of the CDBG obtained on each of these data sets are given in Table  1.
Let us emphasize that on the Human dataset, GGMAP maps 22 additional percents of reads on the graph than Bowtie2 does on the assembly. We notice that the more complex the graph, the higher the advantage of mapping on the CDBG. This is due to the inherent difficulty of assembling with huge and highly branching graphs. This is particularly prominent in the results obtained on the artificially complex C. elegans cpx CDBG. We also highlight that our approach is resource efficient compared to most assembly processes. For instance, Velvet used more than 80 gigabytes of memory to compute the contigs for the C. elegans data set with k = 31. On this data set, our workflow used at most 4 GB memory (during k-mer counting). In terms of throughput, using BGREAT and then Bowtie2 on long unitigs is comparable to using Bowtie2 on contigs alone.
GGMAP accuracy To measure the impact of the read alignment heuristics, we forced the tool to explore exhaustively all potential alignment paths once a read is anchored on the graph. Results on the E. coli dataset show that the greedy approach is much faster than the exhaustive one (38× faster), while the mapping capacity is little impacted: the overall number of mapped reads increases by only 0. 03% with the exhaustive approach. We thus claim that the choice of the greedy strategy is a satisfying trade-off. To further evaluate the GGMAP accuracy, we assess the recall and mapping quality in the following experiment. We created a CDBG from Human chromosome 1 (hg19 version). Thus, each k-mer of the chromosome appears in the graph. Furthermore, from the same sequence, we simulated reads with distinct error rates (0%, 0.
Availability of data and materials Our implementations are available at github. com/Malfoy/BGREAT In addition to the following pieces of information, Table  1  in Additional File presents the main characteristics of these datasets. SRR959239 http://www. ncbi. nlm. nih. gov/sra/?term=SRR959239 SRR065390 http://www. ncbi. nlm. nih. gov/sra/?term=SRR065390 SRR1522085 http://www. ncbi. nlm. nih. gov/sra/?term=SRR1522085 SRR345593 and SRR345594 http://www. ncbi. nlm. nih. gov/sra/?term=SRR345593.
Thus, an essential improvement of road infrastructure maintenance is the integration of risk analysis into the asset management processes on different levels. The main objective of the project X-ARA is the development of a comprehensive risk assessment framework including a set of guidelines and a practical software tool (X-ARA risk tool) for the network level assessment of asset risks and impacts. X-ARA considers different asset categories and the cross-asset risk on network level. A working tool fit for use by National Road Administrations around Europe is to be delivered at project end. The output of the tool is a visual representation (map) showing "heat maps" (i. e. a colouring scheme) of the network (as shown as example in Fig. 1 ) that visually represents the overall maintenance risk for each section and therefore allows a visual comparison of sections.
Dose-response (or 'concentration-effect') relationships commonly occur in biological and pharmacological systems and are well characterised by Hill curves. These curves are described by an equation with two parameters: the inhibitory concentration 50% (IC50); and the Hill coefficient. Typically just the 'best fit' parameter values are reported in the literature. Here we introduce a Python-based software tool, , and describe the underlying Bayesian PyHillFit inference methods that it uses, to infer probability distributions for these parameters as well as the level of experimental observation noise. The tool also allows for hierarchical fitting, characterising the effect of inter-experiment variability. We demonstrate the use of the tool on a recently published dataset on multiple ion channel inhibition by multiple drug compounds.
We describe two different types of Bayesian statistical models: one where all data points are treated equally (as though they were obtained from the same experiment); and another where we believe that each repeat of an experiment has distinct properties (through some source of inter-experiment variability) and therefore its own set of parameters to infer. The first case we will refer to as 'single-level', and the second case as 'hierarchical'. The single-level case does not consider extrinsic variability, since we are assuming that all data points are generated by the same behaviour. The hierarchical case does consider extrinsic variability, which we model by assuming that each experimental dataset was generated according to its own IC50 value and Hill coefficient, which may vary across experiments.
Hill coefficient and pIC50 distributions for ion channel screening datasets of up to N e > 12,000 repeats were published in  Elkins et al. (2013) , there they were found to fit independent log-logistic and logistic distributions, respectively. We assume the same type of distributions would occur here (if the experiments were repeated enough): that is, each experiment's Hill i is drawn from a log-logistic distribution with parameters α and β; and each experiment's pIC50 i is drawn from a logistic distribution with parameters μ and s. We have assumed that the observational errors are drawn from the same Normal distribution across all N e repeats, so we infer just a single noise standard deviation parameter σ. A schematic of this hierarchical statistical model is given in Figure  7 , with the 'mid-level' parameters and 'bottom-level' data points being independently distributed according to Hill i ∼log-logistic(α,β ), (10) ( ) 50 logistic , , i pIC s µ ∼ (11) ( ) ( ) ( ) ( ) 2 ; 50 , , , j j i i i i y f x pIC Hill σ ∼ N (12) where y i ( j) is the j th concentration entry in experiment i's responses y i.
The choice of prior distribution will have an effect on the posterior distributions (via Equation (  6 )). However, the more information that is contained in our data, the less effect we expect our prior distribution to have on our posterior distribution. For example, Figure  9  shows how the marginal posterior distribution for the 'top-level' parameters correspond to their respective prior distributions in the case of synthetic data, where we fit to different numbers of experimental datasets. This synthetic data were generated by sampling pIC50 values from a logistic distribution with μ = 6 and s = 0. 1, and Hill coefficients were sampled from a log-logistic distribution with α = 1 and β = 5. Normally-distributed observation noise with standard deviation σ = 1 was added to the dose-response model at every concentration.