dataesr/scientific-paragraphs-categorization huggingface dataset:
As a consequence, the overall idea of MegaM@Rt2 is to scale up the use of model-based techniques by offering proper methods and related tooling, interacting with both design time and runtime, as well as to validate the designed and developed approach in concrete industrial cases involving complex ECS. To this intent, MegaM@Rt2 proposes an overall model-based approach combining existing and novel techniques. A fundamental challenge notably resides in providing efficient traceability support between the two levels i. e. , from design models to runtime ones and back. Moreover, modern large-scale industrial software engineering processes require thorough configuration and model governance to provide the promised productivity gains. Thus, a scalable megamodelling approach is required to manage all the involved artifacts e.
A set of current engineering practices based on SysML, AADL, EAST ADL, but also Matlab/Simulink, AUTOSAR and Method B or Modelica, each one producing as set of specific design models, requirement specifications and resulting software (and sometimes also hardware) artefacts, are integrated into a global system model providing a complete view of the cyber-physical system, and detailing the components, behaviour and desired quality properties of the system. These properties are then object of exhaustive continuous testing and monitoring in the runtime environment (thanks to the configuration of the target platform and the injection of probes in the software or also in the hardware  [18] [19]  [20] ) to detect deviations in real-time. These deviations, plus all the traces information collected in the process, are analyzed to detect the impacted components in the integrated view of system models.
Industrial Impact The ECSEL 2  program seeks to invest in projects that strengthen the industrial competitiveness, enable economic growth and improve sustainability. Europe has a reasonably strong position in the world embedded market (30%), but this is falling as other geographies grow -some at the vanguard and others catching up. The MegaM@Rt2 consortium argues that investment in capability of the software development tools market, although only a fraction, has a very large pay-off. We have seen that the software component of the systems is increasingly more growing in importance. As the hardware becomes commoditized, the added value will rapidly shift to the software. Achieving technological and competitive superiority in software development tools will allow European firms to participate with greater dominance in the overall software market.
Specifically, MegaM@Rt2 achieves this in part through reducing development and exploitation costs and in part by allowing mastery of more complex systems. Reducing development costs and time-to-market is a competitive ad-vantage, allowing on, one hand, greater innovation in each product and allowing faster reaction to hardware changes or new usage scenarios on the other. As the Cyber-Physical Systems' world evolves, the agility to react rapidly to new opportunities is a critical success factor for businesses. Mastering ever more complex systems allows new usage scenarios to emerge, based on optimization of greater problems or more optimized solutions for existing ones. Improved software will allow the bigger players to better position their overall solutions and engender small businesses fulfilling niche needs for high end bespoke software.
They will equally improve their consultancy offering with the MegaM@Rt methodology. • Research organisations will integrate MegaM@Rt results in their software/service engineering courses, which will place the MDD approach as a well-established method for development, maintenance and evolution of large complex industrial systems in the education of Europe's next generation of software engineers, and raise their profiles in the areas of software and service modelling. This will impact the subjects of work in the basic research, contributions in standards communities and technology transfer to industrial partners via research cooperation and publications. (Sweden), which also has an extensive experience in both, participating and managing, EU projects. A suitable management strategy has been evolved by bringing together partners that know each other and have already collaborated in the past  [21].
Camea -CAM (CZ) provides the case study in vision-based intelligence. AinaCom Oy -AINA (FI) will provide a case study in the communication gateway domain. The technological domains and the applicationa areas of the case studies in the project are summarized in Table  1. Space Systems Finland Ltd. -SSF (FI) will contribute to the MegaM@Rt2 framework with the LIME toolset for runtime monitoring of the implementations and automatic test generation, which was partially funded by SSF. SSF will work on integrating the toolset to other MegaM@Rt2 tools. SSF will also participate in the application of the tools to the case studies provided by other Finnish partners. Additionally, SSF will share its extensive knowledge of verification and validation methods for safety-critical systems. Conformiq Software Oy -CON (FI) will contribute to model-based functional test generation in all stages of software process, and to model-based test validation by functional coverage and test correctness analysis with respect to system models.
WP2 -MegaM@Rt2 System Engineering This WP gathers the activities related to the definition of the required Domain Specific Languages (DSLs) to support model-based system design, and of the methods and tools to develop integrated system models. One of the strongest points of model-based approaches lies in the support for separation of concerns and definition of specific architectural views. Specific views focus on specific areas of the development from system to software level, including the system functional, logical and physical decomposition, identification of software and hardware components, definition of functional and non-functional properties, software architecture, data, behavior and algorithmic modeling. This WP concentrates on all the modelling and tooling aspects of MegaM@Rt2. The goal is first to provide the foundations for WP3 and WP4, and later, to design, develop and support the MegaM@Rt2 system engineering tool set to be used by industrial partners in WP5.
Example of already produced deliverable in WP3 Foundations for Model-Based Runtime Methods: This deliverable provides a succinct overview of the foundations of model-based runtime methods and technologies in order to support the innovation tasks of WP3 of the MegaM@Rt2 project. The deliverable discusses how runtime artifacts are obtained from design artefacts and from execution logs. In the first category, we overview approaches for generating run time code and models from design models via code generation and, respectively, model transformations. In the second category, we discuss approaches for creating or improving the runtime artifacts by analyzing the runtime execution logs of the system via methods like machine learning and data analytics. We also discuss how runtime artifacts are used at runtime either by executing them as part of the system at runtime or by using them to generate tests or monitor the system during its operation.
Throughout this deliverable, we have scrutinized the state-of-the art, the state-of-practice, and the baseline technologies which are available for the project participants. To this extent, the deliverable has investigated current methods and tools for their benefits and existing limitations. The results of this deliverable are meant to lay the basis for defining new concepts, methods and tools for coping with these limitations and successfully deploying runtime methods to industrial settings. This deliverable also provides input for the specification of the MegaM@Rt2 runtime tools to support automated code generation and model execution, log analysis, runtime verification and testing activities. The deliverable also includes a collection of relevant solutions and tools provided by the MegaM@Rt2 consortium members as baseline technologies in the project.
WP7 -Management This work package gathers all the activities related to the management of the MegaM@Rt2 project and its consortium. This mostly includes the mandatory official monitoring and reporting tasks (to the ECSEL Joint Unit and the European Commission). The overall objective is to ensure a smooth running of the project and efficient collaborations between all the involved partners. As fundamental to the success of the project, this WP will notably coordinate the establishment of a proper quality plan to be applied to all MegaM@Rt2 results. It will also deal with the important risk management and Intellectual Property (IP) issues that may appear during the course of the project.
Conclusion This paper presented the MegaM@Rt2 ECSEL project. It notably provided the global context and motivation for this project, introduced its mission and targeted objectives, described its general organization in terms of work packages and detailed the composition of its large supporting consortium. As explained in this paper, MegaM@Rt2 mainly intends to create a scalable model-based framework for dealing with the continuous development and validation of the software parts of large and complex industrial CPSs. This framework will notably focus on relating together the actual executions of these systems (i. e. , runtime) with the way they are currently specified, developed and maintained (i. e. , design time). While there is already quite a lot of support for these two dimensions separately, there is currently no real support for an efficient integration and feedback loop between design time and runtime.
The simulations can be conditioned by local values if they are known (hard data). The advantage of that approach is that it is flexible. The same code can generate all kind of geological structures (channels, lobes, braided systems, fractures, etc. ) and therefore it can be applied to a very wide range of inverse problems and applications. Like most sampling techniques, PoPEx produces iteratively new parameter fields (the samples) using a geostatistical technique (see for example the book of  Chilès and Delfiner, 2009) , then runs the forward problem (in our case a groundwater flow and transport simulation, but it could be any forward operator), evaluates the misfit and likelihood for that solution, and accumulates novel knowledge. At each iteration, the geostatistical simulation algorithm is controlled by PoPEx: the general mechanism is to condition the simulation of the parameter fields with a set of punctual values (hard data) selected preferentially from previous models having a high likelihood.
Problem Setup For this example, the conceptual model for the geological heterogeneity is derived from a 3D simulation of the geological processes occurring in a fluvial plain using the FLUMY software  (Lopez et al. , 2009). This tool combines a process-based approach with a stochastic component. A meandering river crosses an alluvial valley with a given slope and causes erosion and deposition of sediments. Over time, the river migrates and alter the topography of the alluvial plain. This process generates complex geological patterns with a realistic and highly heterogeneous architecture. However, the thickness of the alluvial sediments is usually negligible with respect to the horizontal dimensions of the plain. It is therefore reasonable to reduce the complexity of the problem and neglect the vertical component of the flow.
The terrain is exposed to a natural slope of 4‰ in the x-direction, while the basin is closed at y = 0 (m) and y = 500 (m). Corresponding boundary conditions are: fixed head values of 4 (m) (left) and 0 (m) (right) together with no-flow on the upper and lower boundary. A constant tracer concentration of 1(kg/m 3 ) is enforced at (250, 250) throughout the time period. For any given model, the subsurface water flow together with the tracer expansion is computed by the GroundWater simulation software  (Arpat and Caers, 2007). At days 2, 4, 6, 8, 10, 12, 15, and 20 the solute concentration is recorded at the pumping well. This provides a set of 8 observations and represents all the data constraints used for conditioning the inverse problem. For modeling the spatial structures, we used the training image shown in Figure  2  and the DeeSse multiple-point statistics software  (Straubhaar, 2011 ).
Tracer Breakthrough Curve The PoPEx method has been trained to run the above problem for a total of N = 20, 000 models with n max = 25. Three random realizations are shown in Figure  4. The prior facies probabilities in Q were computed from 500 unconditioned MPS models. For each realization in the PoPEx chain, the algorithm computed the tracer concentration at the pumping well, extracted 8 data points and compared them to the reference data in Figure  3B. Together with the weights from Equation (  11 ), the posterior distribution of the tracer breakthrough curve can be computed. Figure  5  shows the 2. 5 -  97. 5% (dashed), 25 -75% (full) and average (blue) curves of the prior and the posterior tracer concentration at the pumping well. The red dots indicate the extracted reference data. It is clear that for any sampling strategy a critical measure is the required computational effort, which usually is proportional to the number of samples.
Simulated datasets In silico validation data were generated using the QuantumCat method from package QuantumClone. For the validation of the QuantumClone method, we generated a phylogenetic tree for each simulated tumor, which was used to compute observed alternative allele read count given the cell fraction of the clone, the ploidy, and the depth of coverage at this position. The following parameters varied within realistic ranges: depth of sequencing (100Â to 1000Â), fraction of contamination by normal cells (from 0% to 70%), number of variants used for the clonal reconstruction (from 50 to 200), number of tumor samples used for each patient (from 1 to 5) and number of distinct clones per cancer type (from 2 to 10) (Fig. 1 ). For the pipeline validation, we simulated variants coming from six clones observed in two samples per patient, with a purity of 70% for the first sample and 60% for the second.
Neuroblastoma WGS data We used WGS data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. Data for 15 patients were taken and reanalyzed from the previous study  (Eleveld et al. , 2015). Additional four were profiled with illumina paired-end sequencing. In total, we had DNA from 11 cases sequenced using Illumina HiSeq2500 to an average depth of coverage of 80Â by Beijing Genomics Institute or the Centre National de Genotypage (CNG) and 8 cases sequenced by Complete Genomics with an average read depth of coverage of 50Â (unpublished data, Supplementary Table  S1 ).
Accuracy in hyper-diploid cancers or cancers with highly rearranged genomes In order to validate QuantumClone on rearranged or hyper-diploid genomes, we simulated variants in loci of genotype AB, AAB, AABB and in a nearly diploid genome, where all possible genotypes can be observed (Fig. 2 ). In addition to QuantumClone, we tested the performance of pyClone (Section 2). We excluded sciClone from this experiment as it cannot use variants from non-diploid regions, and phyloWGS as in addition to somatic variant read counts it required to generate a complex input dataset: read coverage on single nucleotide polymorphisms along the genome. In all types of regions, QuantumClone and pyClone performed equally in terms of NMI (Fig. 2A ), but QuantumClone outperformed pyClone in terms of mean L2 error with an improvement of 31% (Fig.
High fidelity variants, because they have a lower dispersion of observed VAF compared with other variants, are applied to define clones, i. e. high fidelity variants serve as input to QuantumClone or to an alternative method. Functional mutations are defined here as variants that can possibly alter protein function as predicted by commonly used annotation tools  (Adzhubei et al. , 2013; Khurana et al. , 2013; Ng and Henikoff, 2003)  and that can affect either genes reported in the Cancer Census List  (Futreal et al. , 2004)  or genes from gene modules/signaling pathways that are enriched in deleterious variants (Section 2). At the last step of our framework, functional variants are mapped to the clonal structure inferred from high fidelity variants based on the likelihood values. Here, we demonstrated that having the proposed two-step approach allows for a better reconstruction of the tumor, as well as an important decrease in computational time (Fig.
Annotation of functional mutations in each sample based on the global pathway enrichment analysis In our framework, we assumed that functional mutations (i. e. putative drivers) in a given cancer type should target-specific signaling pathways or pathway modules (Fig. 3,  Step 2). We attributed annotated deleterious variants obtained with tolerant filters (Fig. 3 , Section 2) to the ACSN maps and detected recurrently altered gene modules using the ACSNmineR package  (Deveau et al. , 2016). Overall, six general gene maps (apoptosis, cell cycle, DNA repair, EMT/cell motility, cell survival and neuritogenesis) and their 53 gene modules were found to be enriched in mutations (Supplementary Table  S2 ). The enrichment of pathways in ACSN was corroborated by enrichment of similar pathways from two other methods  (Huang et al.
Of note, many of these genes may never have been associated with oncogenesis previously. Moreover, any variant of a user's choice can be mapped to the inferred clonal structure using the function 'Probability. to. belong. to. clone' of the QuantumClone package. We applied the proposed analysis framework to decipher clonal structure in neuroblastoma and assign to clones possible driver mutations. In neuroblastoma, until recently no biopsies were performed in case of high risk relapse due to absence of curative We define the functional mutation rate as a ratio of the number of functional mutations to the number of high fidelity variants. For a given gene module the number of functional mutations in each patient is supposed to linearly depend on the product of the module size and the total number of detected variants.
These requirements are usually met by WGS or whole exome sequencing datasets. Our simulation results show that increasing the number of mutations used for clonal reconstruction above 50 does not improve significantly the clonal reconstruction accuracy provided that mutations specific for every clone are present in the input. We highlight however that our simulated data were generated for six subclones only. Inferring a more complex clonal structure may require a higher number of input variants. Our framework will be extremely useful in settings when tumor samples have been sequenced with a limited read depth-from 100Â to 200Â-and when the contamination level by normal cells is non-negligible, i. e. 10-70%. Our method also runs much faster than previously published methods. A limited number of very high confidence variants can be used to reconstruct the clonal structure; then, in a matter of seconds, all other variants of interest can be mapped to this structure (Fig.
Funding This work was supported by  Annenberg Foundation  and the  Nelia and Amadeo Barletta Foundation ,  SiRIC/INCa [INCa-DGOS-4654  to G. S. ], the  CEST of Institut Curie , the  Associations Enfants et Sante, Association Hubert Gouin Enfance et Cancer ,  Les Bagouz aManon ,  Les amis de Claire , the  ATIP-Avenir Program , the  ARC Foundation  [ RAC16002KSA -R15093KS  to V. B. ],  Worldwide Cancer Research  [ WCR16-1294 R16100KK  to V. B. ], the ' Who Am I?' laboratory of excellence   ANR-11-LABX-0071  funded by the  French Gouvernement  [ ANR-11-IDEX-0005-02 ], the  ABS4NGS  project of the  French Program 'Investissement d'Avenir' ,  US National Institutes of Health  [ RC1MD004418  to J. M. and the  TARGET consortium , and  CA98543  and  CA180899  to J. M. and the  Children's Oncology Group ], Federal funds from the  National Cancer Institute, National Institutes of Health  [ HHSN261200800001E  to  J.
Reset-free Trial-and-Error Learning Algorithm Connecting all the pieces together, RTE first generates an action repertoire with the MAP-Elites algorithm (Algo. 3, lines 2-3); then, while in mission, it re-plans at each episode using MCTS and the current belief of the outcome of the actions (prediction of the GPs), taking into account the uncertainty of the predictions and potential final states (e. g. , collisions with obstacle) (lines 9-13); at the end of each episode, the GPs are updated with the recorded data (lines 14-15). Algorithm 3 Reset-free Trial-and-Error Learning 1: procedure RTE 2: Create Action Repertoire, A, with MAP-Elites (Sec. 4. 2).
Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high.
In eastern Amazonia this was particularly severe because NHCs showed a large lack of occurrence in comparison with plot data but also proper locations were rejected by a kernel density estimate (KDE), because of the huge amount of palm occurrence data from the Aarhus University Palm Transect Database in western Amazonia. Some of the species affected were Attalea butyracea (Fig. S3_8A ), Euterpe precatoria (Fig. S3_60A ), Iriartea deltoidea (Fig. S3_72A ), Oenocarpus bacaba (Fig. S3_113A ), Oenocarpus bataua (Fig. S3_114A ) and Socratea exorrhiza (Fig. S3_150A ).
NHCs data cleaning treatment and MaxEnt map building. All 227 hyperdominant species had records excluded by the data cleaning treatment, the consequence of records that either lack geographic information, are duplicates at the used grid cell resolution of 0. 5 degree or were outliers based on a kernel-density estimate (Appendix S4). An average of 50% of the records was excluded. The first twelve species with the most excluded records were palms, with a mean of 96% of excluded records. The total average of excluded records decreased to 43% when palms were taken out of the analyses (Appendix S4). This high percentage is due to the huge amount of palm occurrence data of the Aarhus University Palm Transect Database  26. At this moment this database contains 543,000 records, all available in GBIF.
Much attention has been given to the possible impacts of spatial bias on the performance of presence-only SDMs, with some showing a negative impact on these models  [13] [14] [15]  , and others arguing for the robustness of MaxEnt against spatial bias  17, 27. However, little attention has been given to the second issue. With our plot dataset, we addressed the relationship between collection localities and the predicted spatial abundance distribution. In 66% of the cases we found that a higher local relative abundance indeed increased the chance of being collected, although the correlations were very weak (Fig. 1A ), and the majority of collections originated from areas with a low relative abundance due to the large areas where a given species' abundance is low. Even hyperdominant species are usually only dominant in one or two of the six regions of Amazonia, most hyperdominant species have a large geographic extent of occurrence but are habitat specialists  24.