dataesr/scientific-paragraphs-categorization huggingface dataset:
Examples on material and contact non-linearities have been presented above, but this very general methodology can deal with any other type of non-linearity already present in the industrial Simcenter Samcef TM software. This is the strength of this approach to be able to treat any type of finite element, any geometry, any boundary condition, any loading or even any non-linearities all using reduced-order modeling methods in order to circumvent prohibitive computation times. However, at the present time, computations are limited to moderate displacements, outside instability zones. One of our next task will be to overcome this limitation.
A detailed description of numerical underpinnings is given, including a complete mathematical description of the CircAdapt model in a single manuscript that has been lacking so far. Efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. The coupled model is first parameterized and stabilized to a limit cycle representing baseline conditions, and then rigorously tested by demonstrating its ability to predict physiological behaviors under experimental standard protocols altering loading conditions and contractility that are used for the experimental assessment of systolic and diastolic ventricular properties. Transient responses under these protocols are simulated over prolonged observation periods, covering up to 25 beats. The presented framework can be considered a first feature-complete realization of an universal cardiac EM simulator that can be applied, given appropriate parameterization and initialization, under a much broader range of protocols and conditions as any previously reported model.
Experimental data acquisition In a previous study, see  [45] , mongrel dog data were acquired to investigate the influence of different pacing protocols on cardiac mechanics, pump function and efficiency. The animals were handled according to the Dutch Law on Animal Experimentation (WOD) and the European Directive for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes (86/609/EU). The protocol was approved by the Maastricht University Experimental Animal Committee. Anatomical Magnetic Resonance Images (MRI) were acquired on a Philips Gyroscan 1. 5 T (NT, Philips Medical Systems, Best, the Netherlands) using a standard synergy receiver coil for thorax examinations. Images of seven short-axis cross-sections of slice thickness 8 mm with 0 mm inter-slice distance were obtained to capture the whole heart.
(14) Using this approach, the volume V PDE (x, t) can be computed for each state of deformation at time t and the flow can be derived by a numerical approximation using a difference quotient. 2. 4. Lumped ODE model of the circulatory system: the CircAdapt model CircAdapt  [27] , as shown schematically in Fig. 2 , is a lumped 0D model of heart and circulation. It enables real-time simulation of cardiovascular system dynamics under a wide variety of physiological and pathophysiological situations. The entire cardiovascular system is modeled as a concatenation of modules: a tube module representing the systemic and pulmonary arteries and veins (Appendix A. 1); a chamber module modeling actively contracting chambers, i. e. , left and right atria and ventricles (Appendix A. 3), respectively, where myofiber mechanics and contraction is described by a sarcomere module (Appendix A.
2); following Lumens et al. [61]  this also includes inter-ventricular mechanical interaction through the inter-ventricular septum (Appendix A. 4); a valve module representing the aortic, mitral, pulmonary, and tricuspid valves (Appendix A. 8); a module representing systemic and pulmonary peripheral microvasculatures (Appendix A. 6); and a module accounting for effects of the pericardium (Appendix A. 5). The modules are connected by flows over valves and venous-atrial inlets (Appendix A. 7). The whole lumped model consists of 26 ordinary differential equations (ODEs) which are solved using an adaptive Runge-Kutta-Fehlberg method (RKF45) (Appendix A. 9). In Appendix A the mathematical underpinnings of the CircAdapt model are outlined. Briefly, cavity pressures and cavity volumes are interconnected as follows: volumes regulate cavity wall areas, which in turn determine strain of the myofibers in the wall.
Strain is used to calculate myofiber stress, (A. 9, A. 10), which drives wall tension in each cardiac wall  (A. 22). Using Laplace's law, transmural pressure is calculated from wall tension and curvature for each wall (A. 23). Cavity pressures are found by adding the transmural pressures to the intra-pericardial pressure surrounding the myocardial walls (A. 31). Consecutively, cavity pressures are used to update flow over valves (A. 43) and thus intra-cavitary volumes (A. 34). A significant advantage of the modular setup of the model is that a simple 0D module can be straightforwardly replaced by the more complicated finite element (FE) model in Section 2. 2. In this setup CircAdapt provides realistic boundary conditions to the FE problem, see Section 2. 5. The version of the CircAdapt model used for all simulations has been published previously  [44]  and can also be downloaded from the CircAdapt website (http://www.
For the time integration we used a generalized-α scheme, see Appendix C, with spectral radius ρ ∞ = 0 and damping parameters β mass = 0. 1 ms -1 , β stiff = 0. 1 ms. We implemented the coupling scheme in the FE framework Cardiac Arrhythmia Research Package (CARPentry)  [76, 57] , built upon extensions of the openCARP EP framework (http: //www. opencarp. org). Based on the MATLAB code presented in  [44] , which is available on the CircAdapt website (http://www. circadapt. org), a C++ circulatory system module was implemented into CARPentry to achieve a computationally efficient and strongly scalable numerical scheme that allows fast simulation cycles. Execution of the 3D-0D model was sped up by limiting the number of Newton steps to k max = 1 for the initial series of heart beats that were simulated to stabilize the coupled 3D-0D model to a limit cycle.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Action   H2020-MSCA-IF-2016  InsiliCardio, GA No. 750835  and under the  ERA-NET  co-fund action No. 680969  ( ERA-CVD SICVALVES ,  JTC2019 ) funded by the  Austrian Science Fund (FWF) , Grant I 4652-B to CMA. Additionally, the research was supported by the Grants  F3210-N18  and  I2760-B30  from the  Austrian Science Fund (FWF)  and a  BioTechMed Graz flagship award "ILearnHeart" to GP. Further , the project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the ERA-LEARN co-fund action No. 811171  (PUSHCART,  JTC1_27 ) funded by  ERA-NET  ERACoSysMed to JL, FWP, EJV, and GP.
The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization of selected narratives, aimed at validating and revitalizing the transformational and morphodynamic theory and methodology proposed by Claude Lévi-Strauss in his structural analysis of myth. After an analysis of Lévi-Strauss's transformational methodology, we describe in detail how discrete-event system specification models are implemented and developed in the framework of a DEVSimPy software environment.
It provides a formal representation of discrete-event systems capable of mathematical manipulation just as differential equations serve this role. By allowing an explicit separation between modeling and simulation phases, DEVS formalism is one of the best ways to perform a simulation of systems using a computer. The finite probabilistic DEVS is another interesting developed property of DEVS extension  (Seo et al. , 2015). It allows the transition out of a state to be one of a finite set of possible states where the choice is made probabilistically  (Capocchi et al. , 2019)  Several generic tools allowing M&S execution of complex systems are developed in different applications dealing with industrial processes modeling. They deal with industrial tools of which the main representative is Matlab/Simulink  (Choi and Liu, 2016) , or they use modeling languages of which the main representative is Modelica  (Fritzson, 2015) , allowing the simulation of systems containing mechanical, electrical, electronic, hydraulic, thermal, control, electric power, or process-oriented subcomponents.
Overall, they are mainly used for the simulation of industrial processes and they have difficulties to take into account human-centered systems. DEVS formalism and M&S environments allowing the M&S of complex systems  (Van Tendeloo and Vangheluwe, 2017)  are extended in their PythonDEVS kernel to provide an open source python-oriented framework, of which DEVSimPy is the representative environment  (Capocchi et al. , 2011; Capocchi, 2020). In previous papers, we further used such recent elaboration of computer M&S concepts to deal with dynamic variable structures applied in several approaches from social sciences and the humanities to object-oriented models for the analysis of narrative transformations, including the analysis of myths following Lévi-Strauss's structural analysis of myths  (Santucci et al.
In section "DEVS," we summarize the basic notions of DEVS formalism and we introduce DEVS extensions to develop a computer software approach to the structural analysis of myths and to allow the dynamic variable structure modification of models useful for myths visualization. We also introduce the DEVSimPy software environment as a DEVS software framework selected in order to implement DEVS models allowing both the generation of myths and the visualization of a given myth using Google Earth. In section "Implementation of DEVS MS of Levi-Strauss Myth Analysis," we summarize the software approach that we developed in more detail elsewhere  (Santucci et al. , 2020) , regarding the modeling of a given myth issued from the oral literature of a given culture and the simulation of myth transformations as described by Lévi-Strauss.
In section "Visualization of a Given Myth Using Google Earth," we develop a DEVS simulation approach for the visualization of myth in a given geographical context. In section "Validation of the visualization of a given myth," we provide a validation test of the DEVS dynamic formalism allowing to model the structure of a given myth and to sequentially display the visualization of the selected myth on a map. The use of a computational approach and the simulation of myth analysis and validation are based on the development of a DEVS modeling library dedicated to myth transformation and visualization, which can allow the analysis of a great number of myths belonging to a given culture that could be difficult to do manually. Furthermore, the generic simulation features of the formal approach and the resulting software mechanism make it possible to take into account a set of different cultures and could be used to propose the automatic validation of hypotheses set up by researchers in anthropology.
Yet another new Internet research strategy, inspired from big data algorithms of narrative texts, is recently adjusted to more general categories of Bayesian inference and ontological transformativity in anthropology and narrative analysis  (Kockelman, 2013). In particular, existing works have already explored the links between computer science and structural anthropology, focused on the structural study of narratives  (Greimas, 1966) , or on myth transformation modeling  (Petitot, 2011; Jason and Segal, 1977; Richard and Jaulin, 1971; Klein et al. , 2010) , including attempts to formalize not only Lévi-Strauss's structural analysis of myths but also Vladimir Propp's morphology of folktales  (Propp, 1968). Arguably, the software approach to myth analysis needs further developments, which we propose elaborating based on the extension of DEVS formalism.
DEVSimPy environment DEVSimPy (Python Simulator for DEVS models)  (Capocchi et al. , 2011; Capocchi, 2020)  is a user-friendly interface for collaborative M&S of DEVS systems implemented in Python language. The DEVSimPy project uses the Python language and provides a General User Interface based on PyDEVS API  (Bolduc and Vangheluwe, 2001)  in order to facilitate both the coupling and the reusability of PyDEVS models. DEVSimPy allows the extension (or overwriting) of their functionalities in using special plug-ins managed in a modular way. The user can enable/disable a plug-in using a simple dialog window (Figure  1 ). We propose the DEVS modeling of Lévi-Strauss myths analysis method through DEVSimPy by implementing a specific dynamic library. The visualization of a given myth using google Earth is based on the extension of DEVS concepts known as dynamic structural modification of DEVS models.
DEVS implementation of Lévi-Strauss myth analysis In this section, we summarize how DEVS formalism is used in software approach to myth transformation as we detailed elsewhere  (Santucci et al. , 2020). The implementation is realized using the DEVSimPy environment presented in the previous section. In order to model the transformation of myths based on Lévi-Strauss theory we developed a M&S approach allowing to implement the following functionalities: • Modeling of a given myth through a set of mythemes. The software will allow a user to define a myth as an interconnection of mythemes. • Definition of an initial myth as a reference myth. • Generation of a new myth from the reference myth by performing a set of transformations. • Visualization of the set of generated transformations in graph representation.
Visualization of a given myth using Google Earth In this section, we use Google Earth  (Gorelick et al. , 2017)  to present the dynamic visualization on a map of the unfolding of a given myth generated with myth transformation software. In particular, we offer the possibility to locate all the mythemes of a given myth on a map when the localization of any given mytheme is available. The simulation process displays all the numbered mythemes on the map. To this aim, we combine the simulation of DEVS variable dynamic structures involved in the DEVSimPy framework with some features of the Google Earth API.
Google Earth invocation The Google Earth invocation leans on the following atomic models: PointMyth and SIGViewer. The link between the DEVSimPy software and google earth is performed through a KML (Keyhole Markup Language) file  (Wernecke, 2008). KML is a specialized type of XML that enables to build and organize points, lines and other information on a Google Earth map. The δ ext function of the SIGviewer atomic model allows the management of the placement of marks on Google Earth using a KML file as seen in Algorithm 1. The dynamic visualization is performed using the plugin viewMyth of DEVSimPy, which allows the user to associate the initialization of the software towards the dynamic visualization by choosing between Google Earth and Google Map as shown in Figure  9.
Dynamic visualization The dynamic visualization of the placement of points on the map is realized by setting a refreshment of the   KML file associated with the SIGviewer atomic model. The detection of an event is performed through the statement pointed out in line 3 Algorithm 1 of the previously detailed code of δ ext : msg = self. peek(activePort). Then the KML file is modified according to the code presented lines 5 to 25 in Algorithm ??. The KML file can be checked periodically by Google Earth in order to have dynamic refreshment by enabling the auto-update function of the Google Earth API. Using this option, the KML file source is regularly reloaded at a specified interval. One of the main interests of the proposed approach is to offer the possibility of a dynamic printing on map the unfolding of the narrative.
Conclusions We presented in this paper an approach to dynamically visualize the unfolding of a narrative. This approach is based on the concept of mytheme. Each folktale (or myth) is decomposed into a set of mythemes which are the basic elements of a given story. The proposed approach leans on an association of the DEVS formalism and the Google Earth API. We have detailed the set of DEVS models which has been defined in order to: (i) dynamically read a text file containing the story where each line of the file corresponds to a mytheme (ii) dynamically print on a map each mytheme using the Google Earth API. We presented how the DEVSimPy software environment has been used in order to implement the set of DEVS models required for processing the dynamic visualization of myths. We also described the validation of these DEVS models using a concrete folktale from Corsican oral literature.
Introduction Linked-Reads technologies, pioneered by 10x Genomics  (Medsker et al. , 2016) , partition and tag high-molecular-weight DNA molecules with a barcode using a microfluidic device prior to classical short-read sequencing. This way, all the sequenced reads that come from a common molecule contain an identical barcode, thus offering additional data for downstream processing, compared to classical short reads. As a result, Linked-Reads manage to combine the high-quality of the short reads and long-range information which can be inferred by identifying distant reads belonging to the same DNA molecule with the help of the barcodes. Although 10x Genomics has recently discontinued their Linked-Reads products, large volumes of data were produced and still need to be analyzed. Moreover, three other Linked-Reads technologies have been developed and commercialized in the last two years, namely TELL-seq  (Chen et al.
, 2020) , stLFR  (Wang et al. , 2019)  and the open protocol Haplotagging  (Meier et al. , 2021). They have already produced many such data and will likely increase their throughput in the future. For instance, the lower cost of Haplotagging, with respect to long read technologies is very attractive, especially for large-population re-sequencing projects. Linked-Reads have already been efficiently employed in various applications, such as structural variant calling  (Spies et al. , 2017) , but also genome assembly  (Mostovoy et al. , 2016) , phasing  (Zheng et al. , 2016)  and scaffolding  (Yeo et al. , 2017). To benefit from Linked-Reads data, most methods first map the reads against a reference genome, and then rely on the analysis of the barcode contents of genomic regions, often requiring to fetch all reads or alignments with a given barcode.
LRez toolkit The LRez toolkit provides end-users with a suite of command-line programs for indexing and manipulating Linked-Reads barcodes, in BAM, FASTQ, and gzipped FASTQ files, where the barcodes are reported using the BX:Z tag. This tag is classically used by all Linked-Reads sequencing technologies to report barcode information, in both FASTQ and BAM files. In case the input data is not formatted as so, some scripts are provided by LRez, and external programs, such as Long Ranger basic can also be used to properly format the data. Table  1  provides a summary of currently available features of LRez, and we further describe some of these features below. In all features, barcode extraction from BAM files is performed using the BamTools library  (Barnett et al. , 2011).
Compare The compare subcommand computes the number of common barcodes between pairs of regions. counts between all possible pairs of regions without relying on the index. It does so by performing greedy extraction and comparison of the barcode contents of each region. The second feature, motivated by scaffolding applications, compares the ends of a given contig of interest against all other contigs' ends. This time, the index is used. The barcodes from the extremities of the contig of interest are extracted, and the index is queried in order to retrieve other contigs' ends in which these barcodes appear, and compute the numbers of common barcodes on-the-fly. Both functionalities of the compare are illustrated in Supplementary Materials Section 1 LRez compare, Supplementary Figure  S1.
LRez API The LRez API provides C++ programmers with tools allowing to efficiently analyze and manage Linked-Reads data. It is compiled as a shared library, helping its integration to external projects. Moreover, all functionalities are implemented in a thread-safe fashion. Available modules include IndexManagementBam and IndexManagementFastq, to build indexes from BAM and FASTQ files, AlignmentsRetrieval, and ReadsRetrieval, to query the indexes and retrieve alignments or reads tagged with a given barcode, BarcodesExtraction and BarcodesComparison, to extract barcodes from given regions of a BAM file, and compute the number of common barcodes between pairs of regions, and finally computeStats, to extract statistics from a BAM file. Guidelines on how to use the API and code examples are provided on the GitHub project.
Results We tested the index bam and query bam subcommands on multiple files from various organisms and sequencing technologies. The datasets are described in details in Supplementary Materials Section 2 Linked-Reads datasets. All experiments were performed on a cluster node equipped with 150 GB of RAM using a single thread, on a 2. 3 GHz CPU. Indexing results are reported in Table  2 , and querying results are reported in Table  3. We compared the querying results with two naive methods without a barcode-based indexing of the reads. The first naive method we chose was to use the grep -f command, with the list of query barcodes as a parameter, piped to the output of the samtools view command. The second approach , which is less naive but still index-free, is to use a Python script performing a single scan through the input file, with the list of query barcodes stored in a dictionary.
We report the total runtime for performing a 1,000 barcodes query, as well as the average runtime per queried barcode. Additionally, for LRez, we report the runtime of the indexing and querying steps combined, as well as the runtime of the querying step alone. We also provide the command lines that were used to run LRez, both for indexing and querying, in Supplementary Materials Section 3 Indexing experiments, and Section 4 Querying experiments. These experiments show that, on a 61 GB 10x Genomics BAM file from H. sapiens, indexing took an hour, and resulted in an index occupying 9 GB of RAM. With this index, querying time per barcode reached an average of 290 ms. In comparison, using naive approaches without a barcode-based index, querying time per barcode reached 1. 7 minute with the grep-based approach, and 2.
8 sec with the Python-based approach. On an other 204 GB stLFR BAM file from H. sapiens, containing less alignments per barcode, indexing required 26 GB of RAM, and LRez querying time only reached an average of 15 ms, while the naive approaches based on grep and Python required respectively 43 seconds and 2. 5 seconds per query. Additional indexing and querying experiments on FASTQ files are reported in Supplementary Materials Section 3 Indexing experiments, Supplementary Table  S1 , and Section 4 Querying experiments. Supplementary Table  S2. Moreover, it is worth noting that, due to the large main memory size available on the cluster node, the index has always held in the main memory for all experiments, and no additional virtual memory was thus required. Additional experiments showed that, when the size of the main memory is not sufficient to store the index, the indexing process cannot terminate, and users shall thus prefer using naive index-free approaches in such cases.