dataesr/scientific-paragraphs-categorization huggingface dataset:
Blueprints API The Blueprints API is composed of a set of Java classes to manipulate graph databases in a generic way. These classes are wrappers for database-level elements, such as vertices and edges, providing methods to access, update, and delete them. A Blueprints database is instantiated using a GraphFactory, that takes a configuration file containing the properties of the databases (type of the underlying graph engine, allocated memory. ) and creates the corresponding graph store. The Blueprints Vertex class provides the methods addEdge(String label, Vertex otherEnd) and removeEdge(otherEnd) that allow to connect/disconnect two vertices by creating/deleting an edge between the current vertex and oth-erEnd with the given label. Blueprints also defines the vertex method property(String key), that retrieve the value of the vertex property defined by the given key.
In addition, the Blueprints API provides the traversal() method, that allows to send Gremlin traversals to the database and return the subgraph resulting from that query. A complete reference of the Blueprints API is available in  [26]. The final step in our UMLtoGraphDB process is the database and code artifacts generation. Figure  6  presents the infrastructure generated by the Graph2Code transformation. In short, the generator processes the GraphDB model to retrieve all the VertexDefinition elements and, for each one, it creates a corresponding Java class with the relevant UMLtoGraphDB has been implemented as a collection of open-source Eclipse plugins, available on Github  7. UMLtoGraphDB takes as input the UML and OCL files (defined, for instance, using Eclipse-based UML editors such as Papyrus 8  ), that are then translated, respectively, by the Class2GraphDB and OCL2Gremlin ATL transformations seen before.
Related Work Mapping conceptual schemas to relational databases is a well-studied field  [19]. A few works also cover schemas that include (OCL) constraints. For example, Demuth and Hussman  [9]  propose a mapping from UML (augmented with OCL constraints) to SQL that covers most of OCL and implement it via a code generator  [10]  that automates the process. Brambilla et al. [4]  propose a methodology to implement integrity constraints into relational databases recommending alternative implementations based on performance parameters. While these approaches are well-suited for relational databases, they all rely on the generation of database constraints. In a NoSQL environment, and especially for graph databases, there is a lack of support for built-in constraint constructs, and data validation must be delegated to the application layer as UMLtoGraphDB does.
Li et al. proposed an approach to transform UML class diagrams into a HBase data model  [18] , by mapping classes to tables, attributes to columns, and providing transformation rules for associations, compositions, and generalization. Still, it is only applicable to column-based datastores, and does not support the definition of custom OCL constraints and business rules. More specific to NoSQL databases, the NoSQL Schema Evaluator  [20]  generates query implementation plans from a conceptual schema and workload definition. For now, the approach is limited to Cassandra, but authors intend to adapt it to different data models, such as key-values and document stores. However, this solution does not take into account constraints specified in the conceptual model. Sevilla et al. [25]  presented a tool to infer versioned schemas from NoSQL databases.
The resulting model is then used to automatically generate a viewer and validator for the schema but they do not aim to provide support for a full-fledged application nor consider the addition of constraints on the reversed schema. Bugiotti et al. [5]  propose a database design methodology for NoSQL databases. It relies on NoAM, an abstract data model that aims to represent NoSQL systems in a system-independent way. NoAM models can be implemented in several NoSQL databases, including key-value stores, document databases, and extensible record stores. Instead, we focus on generating NoSQL databases from higher-level UML models, and thus, designers do not need to learn a new language/platform. Nevertheless, NoAM could be integrated in our approach if we manage to extend it with constraint support.
Conclusion and Future Work In this article we have presented the UMLtoGraphDB framework, a MDA-based approach to implement (UML) conceptual schemas in graph databases, including the generation of the code required to check the OCL constraints defined in the schema. Our approach is specified as a chain of model transformations that use a new intermediate GraphDB metamodel. This metamodel can also be regarded as a kind of UML profile (and could be easily reexpressed as such) for graph databases. As future work, we plan to provide refactoring operations on top of the GraphDB model to allow designers to tune the data representation according to specific needs, such as query execution performance or memory consumption. We also plan to extend our approach to cover reverse engineering scenarios, by adapting existing work on schema extraction from relational databases  [7]  to graph databases.
Another ongoing work pursues adapting our framework to cover multiple database types. More precisely, we aim to support conceptual schema fragmentation between several databases (even mixing NoSQL and SQL ones). This requires a mechanism to evaluate constraints over several persistence solutions and query languages. Apache Drill  [12]  or Hibernate OGM  [17]  could be reused for this. Finally, we plan to reuse existing work on the integration of incremental constraint checking  [6]  as part of the code-generation phase so that the scalable performance of the graph database is not hampered by the constraint evaluation phase.
As research into innovative forms of automated transportation systems gains momentum, it is important that we develop an understanding of the factors that will impact the adoption of these systems. In an effort to address this issue, the European project CityMobil2 is collecting data around large-scale demonstrations of Automated Road Transport Systems (ARTS) in a number of cities across Europe. For these systems to be successful, user acceptance is vital. The current study used the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the factors which might influence acceptance of ARTS vehicles, which were operational in two locations in Europe. The results indicate that the UTAUT constructs of performance expectancy, effort expectancy and social influence were all useful predictors of behavioural intentions to use ARTS, with performance expectancy having the strongest impact.
CityMobil2 has worked with 12 city partners who were interested in the implementation of the ARTS systems, including 5 cities that have been selected for vehicle demonstrations. One of the main aims of the CityMobil2 project is to gain an understanding of the factors that might impact on people's use of ARTS vehicles. With this in mind, the current study focused on developing and assessing a model of user acceptance adapted for ARTS, based on data collected in two of the CityMobil2 demonstration cities -La Rochelle in France and Lausanne in Switzerland. Alessandrini et al. (2015)  have put forward a vision of how ARTS can enhance safety and improve the efficiency of current transport systems. However, in order for this vision to be realised, it is essential that the public actually uses the systems put in place.
Procedure The questionnaire was administered on a tablet-based application using iSurvey (www. harvestyourdata. com). Data collection was conducted in the vicinity of the CityMobil2 vehicle demonstrations (see Figure  2 ), and carried out by students of L'Ecole d'Ingénieurs en Génie des Systémes Industriels (EIGSI) in La Rochelle, and École Polytechnique Fédérale de Lausanne (EPFL) in Lausanne. The questionnaire was translated into French by the La Rochelle team, and was independently checked by a bilingual colleague in Leeds to ensure that the meanings had been correctly translated. The Lausanne team also cross-checked this translation for accuracy. All respondents in La Rochelle responded in French, while participants in Lausanne were given a choice of responding in French or English. To ensure that respondents had some knowledge of the demonstrations, only members of the public who had come across the ARTS vehicle in operation at least once were asked to complete the questionnaire.
Questionnaires were largely self-administered, apart from a few cases where respondents had difficulties operating the tablets, in which case the students captured responses. Data from La Rochelle were collected in blocks of 1. 5-3 hours in two waves between 9th-20th February and 13th-24th April 2015, while in Lausanne the time blocks ranged from 2-10 hours on dates between the 20th May and the 3rd June 2015. The information was recorded anonymously and no compensation was offered to complete the questionnaire. Each questionnaire took between 8 and 10 minutes to complete.
Discussion As research into automation gains momentum, and increasing amounts of money are invested into adopting innovative automated transportation solutions, it is very important that we gain an understanding of the factors that will impact their adoption. This is something which has rarely been explored in the literature to date. The purpose of this study was to use UTAUT to learn more about the levels of importance placed by potential ARTS customers on performance expectancy, effort expectancy, and social influence in two locations (La Rochelle in France, and Lausanne in Switzerland). This was the first study to explore how user acceptance variables might influence the use of a public automated transport system, and a particular strength of the study was that data was collected on-site during the demonstrations, thus ensuring that first-hand experience was measured.
CODALEMA is currently the only radio experiment able to access such a wide band at once. The high frequencies could be of paramount interest to sample the Cerenkov ring. As shown on Fig. 2  from  [13] , close to the axis the expected Cerenkov signal in [120-200] MHz is much higher than in the usual band [30-80] MHz (with which it competes, giving a "double-peak-like" structure), and furthermore its variation with the distance to the axis is faster. Unless a high antenna density is used (LOFAR case), it is difficult to correctly sample the electric field profile in [30-80] MHz, while using the information above 120 MHz could greatly help determining the later with only few antennas spaced of few tens of meters (CODALEMA case). This is currently studied on real detected events simulated with the SELFAS code  [5].
We therefore chose to reinvestigate low frequency measurements, by adding dedicated antennas within CODALEMA and using the SELFAS simulation code to figure out what could be the emission mechanism. Especially, we will focus on the signal predicted by SELFAS at low frequencies (< 5 MHz) and appearing when the shower front hits the ground. This is the goal of the EXTASIS project (EXTinction of Air Shower Induced Signal). The new mechanism proposed in  [9]  is the coherent emission of the secondary electrons in excess in the shower front when reaching the ground level. These particles suffer a sudden deceleration when they disappear below the ground level; we propose to call this mechanism the sudden death mechanism, which creates the sudden death pulse (SDP). This signal could be of several interests: 1) Let t = 0 be the time of impact at ground.
Implementation We implemented the classical model in Python programming language using NumPy and SciPy packages. A major issue in the classical model consists in deciding the moment and the place of the next initiation. In Snow and Snow simulations, time progression is continuous and the initiation time is unknown in contrast to the Hofmeister model where time periodicity is imposed. To simulate continuous time progression, small time steps dt must be used, which may lead to markedly long simulation times. To speed up the simulation time, we implemented a dichotomic initiation time search. Based on a initially large time step Dt, we first calculate the inhibition field every Dt unit of time. Let E min denote the minimum value of inhibition at the periphery of the central zone at time t. Once the inhibition minimum at the periphery of the central zone is below the inhibition threshold, i.
Implementation We developed two different versions of the model. In the first version, called SMPmicro, every cell on the peripheral zone is considered as a potential initiation site and therefore the Poisson processes are simulated for each cell. In the second version of the model, SMPmacro-max, the local minima indicate the potential initiation sites and therefore the Poisson processes are simulated at these sites assuming that a primordium is triggered if at least one of the cells spanned by the local minimum valley switches its identity to primordium. The models are implemented using the Python programming language using the NumPy and SciPy packages. In the following, we present the pseudo code of the model to generate n primordia. We use the non-homogeneous Poisson simulation algorithm as presented in 4 Interpreting phyllotaxis phenotypes using the stochastic model.
Acknowledgements The authors would like to thank  Olivier Hamant  and  Benoit Landrein  for making original data from paper (Landrein et al. , 2015) available to them,  Arezki Boudaoud ,  Henrik Jo ¨nsson ,  Olivier Hamant  and  Jan Traas  for their insightful comments on the manuscript, and to acknowledge funding from the following grants:  HFSP  grant  RGP0054-2013  BioSensors ( Human Frontier Science Program ) to TV and CG,  Inria  Project  Lab Morphogenetics  and  ANR-funded Institute of Computational Biology (IBC)  to CG,  Jan Traas 's  ERC Morphodynamics & INRA CJS  to YR.
Cluster Analysis of AE Data AE events recorded during tensile tests on composite materials were subjected to cluster analysis, which resulted in identification of clusters of AE events. A Cumulative AE event of a selected cluster will be correlated with image processing results. This will allow identification of the failure mechanism which caused an AE event. Cluster analysis is a statistical methodology to analyze AE sources. Before performing cluster analysis, the first and crucial step is to choose the AE features in order to eliminate irrelevant and redundant signals  [5]. The selection of the relevant features was done using Laplacian score and correlation coefficients. Thus, from the nine AE features (time domain and frequency domain) selected using the AEwin software, four (amplitude, number of counts, peak frequency, and frequency centroid) were chosen as AE descriptors.
Among cluster algorithms, the most frequently used methods are K-means, self-organized map combination with k-means and fuzzy-c means algorithm. K-means is the simplest and most effective method for AE signal clustering. To perform cluster analysis of the AE events recorded during the tests, Principal Component Analysis (PCA) and K-means++ algorithm were used to gather similar AE events into clusters. The cluster analysis was performed using the Statistics toolbox and functions in the SOM toolbox in Matlab™ software. The optimal number of clusters is chosen based on the values of both the Davis-Bouldin index (DB) and the Silhouette Coefficient (SC). The optimal number of clusters leads to a highest value of SC and a lowest value of DB. With these values, the clusters are dense and well separated, which corresponds to the standard concept of a cluster  [6].
1. 4b). AE events of Cluster#5 (peak-frequency lower than 100 kHz and a number of counts greater than 200) were associated with intra-laminar crack damage. Cumulative AE events of Cluster#5 during the tensile test were used to both assess and plot the evolution of normalized crack density. Figure  1. 4d shows a good correlation between the normalized crack density evaluated with acoustic emission (red triangles) and the one evaluated with image processing (blue circles). Figure  1. 5 shows the results of cluster analysis on AE data from Infused specimens [0 2 ,90 2 ] s. For both methods, image processing and cluster analysis, the intra-laminar crack evolution is evaluated in terms of normalized crack density (Fig. 1. 5d). On Fig. 1. 5, the cluster analysis presented shows that the optimal number of clusters is three (Fig.
For both methods, image processing and cluster analysis, the intra-laminar crack evolution has been evaluated in terms of normalized crack density. To achieve automatic image processing and crack counting, a program was developed with Matlab™ software. The program processed the images to highlight the cracks, with a filter based on a Gaussian function convolution. The cracks were then counted automatically by differentiating levels of grey on a vertical line defined in the program (based on 90 plies location). AE events recorded during the tensile tests on composite materials were subjected to cluster analysis, which resulted in identification of clusters of AE events. A Cumulative AE event of a selected cluster was correlated with image processing results. This allows the failure mechanism which caused an AE event to be identified.
Activation of CD4 + T cells requires the recognition of peptides that are presented by HLA class II molecules and can be assessed experimentally using the ELISpot assay. However, even given an individual's HLA class II genotype, identifying which class II molecule is responsible for a positive ELISpot response to a given peptide is not trivial. The two main difficulties are the number of HLA class II molecules that can potentially be formed in a single individual (3-14) and the lack of clear peptide binding motifs for class II molecules. Here, we present a Bayesian framework to interpret ELISpot data (BIITE: Bayesian Immunogenicity Inference Tool for ELISpot); specifically BIITE identifies which HLA-II:peptide combination(s) are immunogenic based on cohort ELISpot data. We apply BIITE to two ELI-Spot datasets and explore the expected performance using simulations.
Implementation In order to find the posterior density P(H|D), we implemented the Metropolis-Hastings algorithm  [11] , which is explained in more detail in S1 Text, to construct Markov chains of length 100,000, as these were found to return the same marginal distributions as chains of length 10^6. The model was implemented in R version 3. 1  [12]. The graphical output was generated with the R package ggplot2  [13]. The code is available on http://github. com/liesb/BIITE as an R package. It can be installed using the R library devtools. In R, use the command install. packages("devtools") to install devtools. Then, to install BIITE, load devtools using the command library(devtools); next, use devtools::install_github("liesb/BIITE") to retrieve BIITE from its github repository. To load the package, use library(BIITE).
This threshold is arbitrary and can be adjusted by the user. To obtain a single estimate for E j , we propose using the mode of the marginal posterior distribution, but other summary statistics could be considered. In our analyses, using either the mean or median of the marginal posterior distribution yielded very similar results. These three summary statistics are all provided in the output of the algorithm (see S8 Table  for  an example where we have retained only the mode). Our interest lies in determining, for a given peptide, which HLA molecules were responsible for the observed positive ELISpot assays in the dataset, i. e. , which HLA has the highest explanatory power. This can be done as follows. Based on the output file, we can rank the HLAs from highest posterior mode to lowest posterior mode; the HLAs ranked highest are the ones identified by BIITE to be the most immunogenic.
Validation on a Burkholderia Pseudomallei dataset The bacterium Burkholderia Pseudomallei is the causative agent of melioidosis. PBMCs from a small cohort (N = 38) of exposed sero-positive blood donors were used to test 17 overlapping 20-mers of the alkyl hydroperoxide reductase protein (AhpC, BPSL2096) by IFNγ ELISpot assay  [14]. Results that were 2 standard deviations (SD) above the mean of medium only control were considered positive. Each subject was HLA genotyped at the HLA-DRB1 and HLA-DQB1 loci. The observed alleles and their frequencies in the cohort are listed in S1 Table. We applied the algorithm to each of the 17 peptides separately, both with and without the inclusion of prior data. We used two datasets for validation. In the first dataset, immunogenicity of pHLA complexes for 6 HLAs (DRB1 Ã 01, DRB1 Ã 04, DRB1 Ã 15:01, DRB1 Ã 15:02, DQB1 Ã 06, DQB1 Ã 08) was determined using transgenic mouse models that express a single (human) HLA-II molecule (and no mouse MHC class II) and were challenged with the 17 different peptides in turn; IFNγ ELISpot was then used to determine whether peptides were immunogenic (number of SFCs larger than mean of SFCs numbers in medium-only + 2SD)  [14].
The second dataset consisted of relative peptide binding affinities for all 17 overlapping 20-mers of the protein with respect to 10 HLA-II molecules; this was evaluated with competitive ELISA using an automated workstation. A peptide was considered to be binding to the HLA molecule when the relative binding affinity was less than 100  [14]. The immunogenicity of each pHLA combination was calculated as the mode of the marginal posterior distribution. We constructed ROC curves to compare these values to the pHLA binding data and transgenic immunogenicity data.
Validation on a Pseudomonas Aeruginosa dataset The bacterium Pseudomonas Aeruginosa (PA) is an environmental organism that can cause infection in damaged lung or the immune compromised host. PBMCs from a small cohort (N = 58) of patients with a chronic lung disease associated with recurrent lung infection and progressive lung damage, called bronchiectasis, were used to test 34 overlapping 20-mers of the OprF protein using IFNγ ELISpot assays  [15]. We used a cut-off of 2SD above the mean of medium only control to decide whether a particular ELISpot result for an individual was positive or negative. Analogously to the Burkholderia dataset, we had access to a immunogenicity data derived from transgenic mouse models and binding data from peptide-binding assays  [15]. Transgenic mouse models were available for DRB1 Ã 01, DRB1 Ã 04 and DRB1 Ã 15; binding data was available for 7 HLA-DRB1 alleles.
Validation on synthetic datasets In order to assess the validity of the method and the effect of sample size on the outcomes of the algorithm, we resampled (with replacement) HLA class II haplotypes from the Burkholderia dataset (n = 10, 30, 50, 70, 100, 150, 200). We randomly assigned each peptide:HLA molecule an immunogenicity value E j 2[0,0. 55]; whether a peptide:HLA combination was considered immunogenic was decided by a cut-off on E j ; we considered a range of different cut-offs (0. 1, 0. 2, 0. 3, 0. 4, 0. 5). We then simulated ELISpot data in the following way: for each subject, the probability of having a positive ELISpot was calculated using eq (1) above, whether the individual had a positive or negative ELISpot was then decided by drawing a random number from the unit interval and comparing it to this probability; if the random number was above the computed probability then the individual had a negative ELISpot, if it was below they had a positive ELISpot.