dataesr/scientific-paragraphs-categorization huggingface dataset:
5). Given that all constructs were measured on a 7-point scale, and following established practice in fsQCA research (e. g. , Leischnig and Woodside, 2017), the thresholds were 7 (for full membership), 1 (for full nonmembership), and 4 (as crossover point), respectively. This calibration method can produce fuzzy-set membership scores of exactly. 5, so to address this ambiguity issue, a constant of. 001 was added to the fuzzy-set membership scores for all conditions below full membership  (Fiss, 2011). The fs/QCA software program  (Ragin and Davey, 2016)  was used for calibration and the same procedure was used for both animosity contexts examined The same software program was used to construct and analyze the truth tables (all truth tables are reported in the Web Appendix). Since fs/QCA only considers one outcome condition at a time, this study created three truth tables (one for each outcome: product quality perceptions, NWOM, product avoidance) for each animosity context (i.
Methods and Results : openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential.
Towards a Common Software In other fields, certain software has been adopted by a critical mass of the community and thus have become the de facto standard for the entire field. Examples of this are Neuron for neural simulation  [55] , and GAMESS for chemistry  [56]. While the need for standardized, open software has been recognized before within the CEP modeling community, no software has achieved such a status. Preceding initiatives in this vein include openCMISS  [18]  and Chaste  [57] , but their success has been mixed so far, as their rate of adoption has been rather marginal. Reasons are multifactorial: For groups developing numerical methods, there is limited demand as they have the capability to build custom tailored frameworks themselves  [23, [58] [59] [60] [61] [62]  that facilitate the implementation of disruptive changes at any time, without the frictional losses involved in community projects where changes have to be agreed upon by various stakeholders.
The main objective of the openCARP initiative is to provide and establish simulation software within the CEP modeling community which fulfills the criteria outlined above. To better meet these user requirements, the openCARP modeling environment is organized as comprising two major software bundles, the actual code for executing simulations, i. e. , the openCARP simulator, and a framework for describing in silico experiments, referred to as carputils (Fig. 2 ). The openCARP simulator has been developed with the following objectives in mind: • Accessibility: The main distribution mechanism of openCARP emphasizes binary packaging for commonly used platforms (currently Linux and macOS), platform-independent Docker containers, and a detailed documentation supporting source installations on all levels of computers including large-scale national HPCs.
Only providing a source installation excludes major parts of the user community who are not sufficiently versed in compiling scientific software, or do not want to manage a complex software stack, especially given the release requirements and potential conflicts with compilers and underlying software libraries. The source code of all openCARP components is maintained in the public GitLab instance • Interoperability: openCARP builds on two decades of CEP modeling experience gained with its proprietary predecessors, CARP  [16]  and acCELLerate  [17] , with a stable user base about 130 registered users. The predecessor software packages have been extensively used by several CEP modeling groups and led to >250 peer-reviewed journal publications, covering the full range of modeling issues including numerical methods  [71] , anatomical modeling and model functionalization  [48, 72]  as well as a broad range of applications such as formation and maintenance of arrhythmias  [73, 74] , EP therapies  [75, 76] , diagnostic  [77, 78]  and therapeutic applications  [79, 80].
The openCARP simulator has been built from scratch in terms of code, but the user interface is consistent with previous proprietary versions of CARP  [16]  to facilitate re-use of a large number of existing experiments. Moreover, openCARP interoperates with relevant community standards by providing input and output interfaces VTK  [70]  and CellML  [51] , for example, either directly or via other tools. All openCARP specific formats and standards are openly available and concisely described, with adequate tools for their management provided as source code. • Performance: openCARP has been implemented from scratch in C++ (2011 standard) but follows the same discretization and solver schemes that have been used successfully in the predecessor code CARP  [16, 71]. Briefly, the openCARP simulator spatially discretizes the partial differential equations underlying the monoor bidomain model (or potentially other physics like mechanics) using the FEM  [81, 82]  with linear basis functions.
Time integration schemes for these ODE systems can be controlled per variable and include Runge-Kutta, Rush-Larsen, Rosenbrock  [89]  and advanced schemes accessible via CVODE  [90]  with adaptive step size and error control. While profiling and benchmarking plays a central role during development, openCARP does not yet employ detailed benchmarking or profiling during integration testing. Currently, integration tests are binned into different execution time groups and tests changing group are marked as failed. • Transparency: The availability as source code will benefit the technically affine model developers in the CEP modeling community by allowing implementation of additional features, the critical revision of numerical schemes, and the identification of weaknesses. This will trigger constant re-engineering of the software and, thus, help improve software quality.
The software is version controlled using git and connected to continuous integration / continuous delivery (CI/CD) services with integration testing of most simulation setups (currently 74 individual simulations). We have opted against unit testing at the current stage of development. Release versions of openCARP  [91]  are automatically archived and uploaded to the RADAR4KIT repository as part of the continuous deployment pipelines  [92]. Submission information packages are built in BagIt format and comprise the source code, the binary versions for the support platforms, the respective version of the documentation and test reports. Metadata is automatically extracted from machine-readable files in the git repositories and provided according to the DataCite 4. 3 scheme  [93]. A DOI is minted for all published and archived versions to ensure long-term accessibility.
• Reproducibilty: By providing mechanisms for easy sharing of in silico experiments, carputils contributes to the concept of Open Science and facilitates reproducibility and reuse. Owing to the complexity of experiments, the ability to share them has been severely hampered, even within the same laboratory, let alone across different ones. We overcome barriers posed by numerous differences in local installations, inconsistencies between code revisions, and the management of high dimensional input parameter spaces defining a setup: meshes, label fields, electrode definitions and pacing protocols, limit cycle state vectors, structural entities such as fibrotic lesions or scars, functional heterogeneities, conduction system topology, and parabolic solver method, to name but a few important ones.
• Quality assurance: Support for regression testing of all major simulator features is built in, including automated nightly building and testing of the entire software stack. carputils encodes a range of verification benchmarks such as the CEP N-version benchmark  [9]  and a suite of additional performance benchmarks of varying level of complexity, ranging from biventricular slices up to whole heart models, for measuring simulator performance, or for investigating the effect of numerical settings. Additional open source software components have been developed to support the modeling, simulation and visualization when using openCARP as shown in Fig. 2. meshtool  [54]  (License: GPL v3) allows generating or interacting with geometric data. carputilsGUI (License: Apache 2. 0) is a web-based graphical user interface to control specific carputils experiments and visualize results within the browser for low-threshold entry to openCARP.
meshalyzer (License: GPL v3) is a tailored visualization program for CEP, even though VTK export allows seamless visualization in ParaView  [94]  as well. openCARP provides documentation for users with different levels of experience. Video tutorials introduce users to the basics of CEP modelling and guide them through their first steps with openCARP. Further videos introduce standard simulation pipelines and progressively advanced features covering the whole gamut from single cell simulations to 3D heart models including pre-and post-processing steps using the carputils framework. carputils examples cover common simulation scenarios and provide a starting point to develop custom carputils experiments in combination with the API documentation. The openCARP reference documentation contains all openCARP parameters to give full control of all aspects.
Additional models can be included using CellML, the XML-based community standard for ionic models. Models downloaded from the CellML repository  [51]  can be converted to EasyML using tailored commandline scripts or interactively via the third-party tool Myokit  [103]. Initial conditions for tissue scale simulations are generated with bench by creating a set of myocyte models representing the CEP heterogeneity at the tissue scale. Each myocyte model is paced at one or more basic cycle lengths based on the activation rates desired for the tissue model until the model settles in to a limit cycle. Initial state vectors of each myocyte model are stored, to be used later to populate the tissue and organ scale model as detailed in the limit cycle initialization example. Tailored experiments for interrogating and tuning of dynamic properties of ionic models such as APD restitution are also provided.
Organ Level For whole chamber or organ level models, meshes must be created that accurately reflect the anatomy of the entity to be modelled. Streamlined workflows for this task are currently under development by various labs  [106] , typically relying on connecting heterogeneous software components, including commercial software, that are specialized for particular processing stages, ranging from multi-label segmentation, meshing, fiber architecture generation  [107] , anatomical reference frame generation  [95] [96] [97]  or topology generation of the specialized cardiac conduction system  [108]. The Python-based carputils is perfectly suitable for creating such workflows  [109]. However, owing to the technological heterogeneity of all this software, a complete integrated workflow is currently not available within carputils, but many basic elements required for building such workflows are.
Bishop & Plank  [128]  showed that the latter approach (referred to as pseudo bidomain) retains accuracy in most cases, particularly with augmentation techniques to recover bath-loading effects close to the tissue-bath interface  [129] , while drastically reducing the computational cost; and iii) An even more simplistic approach is to assume that the cardiac tissue is immersed in a homogeneous volume conductor of infinite size, which allows recovering the extracellular potential Φ e at specific points in space from a given transmembrane voltage distribution by using the integral solution of Poisson's equation. One of the carputils examples introduces all three methods and involves the user in simple experiments highlighting the differences between them. To compute surface potentials in own carputils experiments, the ep module provides the model_type_opts(sourceModel) function to choose between Φ e recovery, pseudo-bidomain and bidomain.
As such, openCARP can be a suitable software solution for a large portion of the CEP community, by contributing to the use, transparency, standardization and reproducibility of in silico approaches. openCARP is designed to appeal to users of all experience levels. New users can easily explore parameter effects in prebuilt carputils experiments, while more experienced users can further extend such experiments to more elaborate scenarios, or build their own complex experiments from scratch. openCARP provides a solution for potential new users in the fields of basic science CEP (integrating experimental data into mechanistic models), cardiology research (understanding arrhythmia mechanisms, diagnostic support, therapy stratification, planning and delivery), pharmaceutical companies (virtual testing arrhythmogenic potency of new compounds), medical device companies (interpreting measured signals, optimizing new device designs  [130] , testing their safety and efficacy, and understanding reasons for device failure  [26] ), educational use (visualizing complex heart function), and regulatory entities (e.
g. , the FDA which is already encouraging the use of numerical models for drug and device testing  [2] ). The central resource and entry point is the project web page www. openCARP. org. From there, the community has access to the user and developer documentation, tutorials, examples, the GitLab projects, and a question and answer system  [131]. Besides the online platform, in-person contact among and between users and developers is recognized as a key factor to build and maintain a strong community. Regular user meetings allow training new users, exchange experiences with the software, and stay close with the community as part of a continuous feedback loop. Within a year from the release of the first public version, more than 250 users opened an account in our user and development community.
openCARP is a meritocratic, consensus-based community project. Anyone with an interest in the project can join the community, contribute to the project design, and participate in the decision making process. Community contributions are highly appreciated and can range from code commits to bug reports, suggestion of ideas and new directions for improving the software, documentation or any aspect of the community platform. Code contributions will enter a review process as a quality control measure and will be integrated in the code base if they meet quality criteria and satisfy a community need. The roles in the openCARP project are users, developers, maintainers and the steering committee. Users are community members who have a need for the project. The openCARP project asks its users to participate in the project and community as much as possible.
Acknowledgments We gratefully acknowledge support by  Deutsche Forschungsgemeinschaft (DFG)  (project  ID   391128822 ,  LO 2093/1-1 ,  GS 1758/4-1 ). We further acknowledge the software contributions of  NumeriCor GmbH, Graz, Austria  who conceived and implemented the openCARP simulation core. We thank  Felix Bach ,  Jochen Klar  and  Robert Ulrich  for developing and providing the openCARP development environment as well as  Michael Selzer  and  Philipp Zschumme  for developing and providing toolcompendium for generation of user documentation. We thank  Prof. Natalia Trayanova  for agreeing to make the EasyML model generator, developed in her lab by  Rob Blake , available to be distributed with openCARP. We thank  Giorgio Luongo  for providing figures of simulation results.
The Loopy Belief Propagation algorithm is often chosen because of its good approximation and execution time. Others approaches exist as Variational methods (the simplest example is the mean-field approximation) or Sampling methods (Monte Carlo); a more efficient approach is called the Monte Carlo Markov Chain (MCMC), and includes Gibbs sampling as a special case. We developed the model in Matlab and used the free library BNT Bayesian Network Toolbox  (Murphy, 2007)  to obtain a suitable generic representation of the Bayesian Network problem and a library of inference algorithms. We first used the Loopy Belief Propagation algorithm which is the fastest one, but found inconsistency in the results probably due to the size of the problem. We therefore used the version of the Gibbs sampling inference algorithm with 200,000 samples.
2016). The metamodel was estimated from samples of the simulator. We chose 60 values for the vector (ϵ, ρ, ν, c pest , γ , q) according to a Latin Hypercube Sampling, obtained by combining ten values for each factor, uniformly sampled in the domain of variation of each factor. We then fitted a Kriging model with constant trend on these 60 samples (km function of the R package DiceKriging). We did not consider a more complex Kriging model since this one was well adapted for each criterion according to classical evaluation methods (see Figures 9-11 in Appendix D). We then used the metamodel to estimate the part of the model variance explained by the pest dynamic and the economic input variables (using Sobol indices, Sobol 2001) for each pest type. The domains of variation of the factors used to build the metamodel and to compute the Sobol indices are reported in Table  2.
In this work, we propose a deep learning algorithm for automatic and accurate MI segmentation from LGE-MRI. The EMIDEC segmentation challenge  [8]  supplies a large dataset to evaluate different methods. We used the EMIDEC STACOM 2020 challenge dataset to validate our model. This paper is organized as follows: Related Works are described in Sect. 2. Material and Method are explained in Sect. 3. Comparative results are outlined in Sect. 4 and Sect. 5 concludes the paper.
3D U-Net  [20]  was developed to extract volumetric spatial and intra-slice information using 3D convolutions efficiently. The proposed model demonstrates remarkable results in comparison with an equivalent 2D implementation performance for the segmentation of the highly variable structures of the Xenopus kidney. Yang et al. [21]  used a 3D U-Net with pre-trained parameters brought from a C3D temporal transfer learning framework  [22]  to produce higher performance and fast convergence of their method. The authors have validated their approach using the Automated Cardiac Diagnosis Challenge 2017 datasets for MR image segmentation. The proposed network is a powerful deep CNNs. Milletari et al. [23]  proposed V-Net deep-based model. As compared to U-Net architecture, V-Net counts more on the convolutional process and evenly moves to a deeper stage by joining a small learnable function at every stage.
Shankaranarayana et al. [27]  proposed a retinal depth estimation model based on a dilated residual inception (DRI) block for multi-scale features extraction. Ibtehaz and Fig. 2. Pipeline of the proposed segmentation network. We first crop the LV region of interest (ROI). A new anatomical network is then used to segment the left ventricular cavity and the myocardium areas from the ROI images. Finally, a pathological network is used to detect pathological tissues (MI and MVO). The red, green, blue, and yellow colors present the left ventricular cavity, the myocardium, MI, and MVO. Rahman et al. [28]  introduced a U-Net with residual inception modules on various multimodal medical imaging datasets. Several medical imaging attempts based on deep learning models have also been included in the 2020 EMIDEC MICCAI challenge to support the clinicians in the early detection of MI and treatment.
Clinical images and metadata The dataset for EMIDEC challenge  [8]  provides 150 exams, including LGE-MRI associated with 12 clinical physiology characteristics. The image data was obtained on Siemens MRI scanners on 1. 5T and 3T. The training set (67 pathological cases, 33 normal cases) and the testing set (33 pathological cases, 17 normal cases) consist of 1/3 normal and 2/3 of myocardial damage subjects. There are no overlapped exams in the training and testing sets. Each exam includes a series of 5-10 short-axis slices covering the whole left ventricle myocardium from the base to the apex. Manual delineations (contours of the relevant cardiac regions, including left ventricular cavity, healthy myocardium, MI, and MVO) are given to challengers for the training set and drawn by a biophysicist with more than 15 years of skill in medical imaging.
Image pre-processing LGE-MRI in the EMIDEC Challenge has an irregular shape. To surmount the shape mismatch and to remove irrelevant anatomical structures, original volumes were cropped to a normalized set whose center was the centroid of the left ventricular cavity annotation. Hence, all present subjects are reshaped to 96 × 96 × 16 by adding empty slices  [35]. A standard adaptive histogram equalization algorithm is further adjusted slice-wise to enhance the local image contrast  [36, 37]. The noise was reduced using the non-local mean denoising algorithm that achieved good results when applied to previous images  [38]. Finally, 16 testing cases were randomly chosen from the training dataset of EMIDEC challenge to validate our approach (Table  1 ).
The residual connections supply smooth learning concerning the input feature maps, instead of learning an unreferenced function  [39]. The proposed network is shown in Fig. 3. The Fig. 4a  presents the proposed Inception residual block. Compared with the original Inception residual module, Batch Normalization (BN) layer has been used after each convolutional layer except for bottleneck layers. We applied 1 × 1 and 3 × 3 kernel, and also proposed 5 × 5 kernel branch as inspired by the DeepLab architecture  [40]. BN layer yields smooth training and may avert gradient vanishing while retaining convolutional layers. The feature maps are aggregated by convolving with three kernels, namely x l+1 = c 1×1 (c 1×1 (x l ). c b (c 3×3 (c 1×1 (x l ))). c b (c 3×3 (c b (c 3×3 (c 1×1 (x l ))))). c b (c 5×5 (c b (c 5×5 (c 1×1 (x l ))))) + x l (1) In the synthesis path, we have introduced EDP block to extract relevant semantic information.
Pathological network The experiments revealed that extracting volume patches of size 12 ×12 ×12 pixels 3 from the EMIDEC training dataset achieve excellent results for diseased myocardial tissues' segmentation. As illustrated in Fig. 7 , our 3D pathological network fuses 3D U-Net with a Super-Resolution (SR) module to constrain prior knowledge shape. 3D Autoencoder aims to correctly encode and minimize the original volume that can be reconstructed from the encoded representation. Thus a pre-trained 3D Autoencoder is efficient to regularize the segmentation result into a realistic shape. Pre-trained 3D Autoencoder is linked to the 3D U-Net and takes the segmented volume as input. A regularization term is introduced for restraining the segmentation output. The 3D U-Net is a classical encoder-decoder segmentation framework in which, firstly, the encoder uses an ensemble of convolution-pooling layers to extract more high-level semantic features.