dataesr/scientific-paragraphs-categorization huggingface dataset:
Big, heterogeneous data (to be also made available to the CY-BELE industrial test beds) are made available through HPC powered repositories. Prior to the check-in and storage, as demonstrated on the middle left part of the architecture, the data pass through a data quality check pipeline in order to address the data veracity and timeliness challenges that are typically associated with big data. From the moment that data are collected in CYBELE, quality checks are performed to discover inconsistencies and other anomalies in the data and eventually ensure their integrity and completeness, to be followed by a number of steps associated with data cleansing procedure (ranging from data filtering and cleaning to normalization). Finally, the CYBELE Data Provenance Service puts in place the necessary mechanisms to record all relevant information that influence the "incoming" data of interest.
In CYBELE, the Data Provenance Service is intrinsically linked to the Data Policy and Assets Brokerage Engine that facilitates the data sharing and trading features that will be offered by the platform to link data providers and data consumers. Checked-In data will also be semantically annotated and harmonized so as to promote data interoperability and reuse. Since the data will come from a multitude of physically distributed data sources, a common semantic data model will be created which will be used to semantically describe and annotate the data. The model will be used as a common language to annotate data and the messages exchanged between the components, so as to facilitate the pipelining and enable the seamless communication of the various heterogeneous components. The clean and semantically uplifted (open and proprietary data) will be then made available for querying, analysis and visualization.
In order to enable simulation execution, a dedicated Experiment Composition Environment will be designed and delivered, as demonstrated on the upper right part of the architecture. The Experiment Composition Environment aims to facilitate the detaching of the design, development and execution of the big data analysis processes, supporting embedded scientific computing and reproducible research. The analysis process will be based on the selection of an analysis template, where each analysis template will represent a specific algorithm with the associated software and execution endpoint, and will provide to the user the flexibility to adjust the relevant configuration parameters, including input parameters for the algorithm, execution parameters, parameters associated with networking and computing resources constraints, as well as output parameters.
The Experiment Composition Environment will support the design and implementation of data analysis workflows, consisted of a series of data analysis processes, interconnected among each other in terms of input/output data streams/objects. Upon the execution of an analysis template, the outcome could constitute the input for another analysis template. The output of the analysis template execution will be a session object that contains on memory all output values. In order for the Experiment Composition Environment to run, it requires input datasets (training and/or evaluation datasets). For this reason, an Advanced Query Builder will be designed and developed which will provide users an intuitive environment to define and execute queries on data available in the CYBELE platform. After input datasets have been selected and the workflows have been designed, the advanced analytics on top of big data need to be executed.
In the case of CYBELE, advanced analytics algorithms will be provided to the stakeholders with the ability to visually explore the different kinds of data, while discovering and addressing new patterns. Machine learning and predictive modelling techniques will be updated in order to be able to manage the predictive life cycle of data preparation, exploration and analysis, for achieving better deployment and monitoring. However, the execution of advanced analytics on top of big, diverse data, raises the need for strong computational power and increased computing memory so as to be able to not only extract insights, but to do this within a reasonable time frame. The execution of the demonstration cases selected raises the need for several HPC attributes including Storage Intensity, Computing Intensity, Memory Intensity, Throughput Intensity and Short Turnaround Time.
Towards this end, analytics workflows designed, will be sent for execution on well-known HPC and Big Data frameworks, which will run on HPC resources abstracted to the user, as demonstrated on the lower part of the architecture. CYBELE relies heavily on HPC infrastructure, to provide the compute power required to advance models and methods and will take a two-fold approach; a) focus on tuning the HPC software stack to allow for efficient execution of Big Data processing frameworks on top of HPC resources, b) target the HPC resource management layer and its interface with Big Data processing frameworks and orchestration engines, thus bridging the gap between the HPC and Big Data worlds. In addition to that, the efficient execution of analytics workflows and the man- agement of resources comes with multiple challenges.
In the context of CYBELE, optimization of workflows on different frameworks will be examined for performance, along with tasks scheduling on resources to meet resource constraints/performance constraints/ time constraints. Depending on the workflow and the frameworks in use, CYBELE consortium will explore the abilities of the framework's scheduler to optimize the workflow and guarantee performance, as well as the deployment through an orchestrator such as Kubernetes, which can optimize the performance of a workflow over multiple frameworks with its highly modular architecture and ability to run multiple and customized schedulers. The analysis results will then need to be visualized and presented to the end users in a precise, coherent, and user-friendly way. Towards this end, as demonstrated on the upper left part of the architecture, adaptive visualization / user interfaces will be delivered, improving the way in which information is presented, for example by converting the raw data into interactive visualizations and dosing the information available at any given time.
(HPC & Big Data storage & computing) resource abstraction CYBELE relies on HPC infrastructure, to provide the compute power required to advance models and methods for PA. In this direction, CYBELE will take a two-fold approach; first, we will focus on tuning the HPC software stack to allow for efficient execution of Big Data processing frameworks on top of HPC resources. Second, we will target the HPC resource management layer and its interface with Big Data processing frameworks and orchestration engines. This approach is necessary to bridge the gap between the worlds of HPC and Big Data, which reflects to the system architecture, the software stacks and the resource abstraction, and stems from having their roots in different classes of problems. We should note that HPC infrastructure used in CYBELE includes both typical HPC resources, as well as partitions of resources built for data analytics and other data-intensive applications.
However, within CY-BELE, we envision to explore the full scaling and computing capabilities of all available HPC resources. A core issue to be addressed within CYBELE is efficient I/O and data movement, to handle the volume and velocity of data. On HPC infrastructure, storage resources and compute resources are decoupled, and compute resources use a parallel file system, such as LUSTRE  [28]  or General Parallel File System (GPFS) [29] , and Portable Operating System Interface (POSIX) [30] semantics to perform I/O operations, or a network file system (NFS) and name-space sharing. As a result, storage is optimized for infre-quent I/O of large batches of data, typically to instantiate simulations or store results or checkpoints. On the other hand, traditional Big Data infrastructure relies on low-latency and reliable local storage, with a distributed file system, such as the Hadoop Distributed File System (HDFS)  [31]  , handling the batches of data and guaranteeing fault-tolerance.
The mismatch between the two architectures can cause severe performance degradation when executing big data frameworks over HPC resources, as big data frameworks are designed with the principle of data being close to the processing unit. We will work to alleviate this mismatch using local file systems mapped by the shared file system and will explore software layers that eliminate overheads related to POSIXlike semantics  [32]. We will additionally enable efficient mechanisms for Big Data frameworks to use near storage, such as Solid State Drives (SSDs), to enable scaling up to multiple nodes avoiding the latencies involved from frequently accessing the shared file system  [33]. Besides techniques for efficient "vertical" data movement, within CYBELE, we will procure Remote Direct Memory Access [34] (RDMA)-enabled data analytics frameworks  [35]  , to enable efficient "horizontal" data movement, i.
e. efficient communication between nodes, since modern HPC infrastructure that allows for such deployments. Container technologies (e. g. Docker) will allow for seamless adaptation of the software stack to the needs of the application, without affecting the HPC applications executing on the HPC systems. CYBELE will also work on efficiently coupling the resource management for Big Data frameworks over HPC resources. HPC resource managers (SLURM [36] , Torque [37] , etc. ) interact with the user, who submits a "job", only once, to grant the requested resource allocation that remains unmodified until completion of the application. In contrast, Big Data resources managers (MESOS [38] , YARN [39] , Kubernetes [40] , etc. ) embrace the concept of elastic resource allocation: they either allocate resources to meet application demands, or negotiate with applications for resources.
The challenges are different for compute-intensive workflows, as are deep learning tasks on high-dimensional data: such workloads require high computational power and large scale. To this direction, CYBELE will work with best practices and optimized software, coming from the world of HPC, to enhance performance through scaling and efficient utilization of all available compute resources (CPUs, GPUs, FPGAs, etc. ). For non-critical data processing tasks with low demands on resources, we will explore the potential of using YARN or we will directly use the built-in schedulers of frameworks. An important issue CYBELE will need to address is workflow scheduling across different system partitions and/or resource allocations, as provided by the HPC resource manager. For example, a workflow may include some data streaming tasks and some data analytics tasks, which, for reasons of performance, should run on different partitions of a system and be composed of different frameworks.
The environment will also support the application of different execution modes, e. g. sequential or parallel execution of R algorithms for smaller data streams or in clustering mode by the HPC and big data frameworks (e. g. Spark, Hadoop, Kafka etc. ) provided through the parallel execution management layer over the managed HPC clusters abstracted through the Resource Abstraction Layer. The analysis process will be based on the selection of an analysis template after the input datasets (training and/or evaluation datasets) have been queried and provided to the Experiment Composition Environment by the Advanced Query Builder. Each analysis template will represent a specific algorithm with the associated software and execution endpoint, and will provide to the user the flexibility to adjust the relevant configuration parameters, including input parameters for the algorithm along with their description and their default value, execution parameters that denote whether an analysis should be realized in a manual or automated way, as well as the periodicity factor for the latter case, parameters associated with networking and computing resources constraints, as well as output parameters along with their type (text, image, data, html).
What is more, machine learning and predictive modelling techniques will be updated in order to be able to manage the predictive life cycle of data preparation, exploration and analysis, for achieving better deployment and monitoring. Through CYBELE, the provided advanced analytics algorithms will semi-automate, or even automate the already developed processes that exist to improve data performance, thus gaining quicker and more efficient results. Moreover, the data mining process will be streamlined, creating both high-performance and accurate models, while predictive models will be possible to be built using traditional statistical, data mining or text mining algorithms. As a result, the latter will lead to cost and latency reduction, since CYBELE will automate in-database scoring to improve model performance and get faster results.
An innovative concept that well summarises the need for improvement is to optimise the protein production instead of optimising the production of soybean in general, meaning that the inputs brought into the system should aim at producing as much protein as possible. Using the dataset acquired through crowdsourcing, we will derive methods for predicting yield and protein-content maps based on satellite imagery and additional information (if available) concerning electromagnetic soil scans, drone images and sensory data. Time-series of satellite images are very indicative of the relative yield and protein content on the field, i. e. they can pinpoint the areas in which soybean grows better and in which it grows worse. By knowing the absolute value of the yield and protein content on the whole farm, we can "reverse-engineer" these traits in these specific areas of the field and derive the corresponding maps.
Due to the lack of sufficient processing capacity and adequate database systems, nor fishers e. g. to optimise their operational decisions, nor fisheries managers make optimal use of these data. Solutions to be explored within the context of CYBELE include: 1) Hidden Markov Modelling combined with nonparametric methods (e. g. interpolation of vessel tracks) will be used to analyse vessel trajectories, whereupon the various states will be coupled to landing data. This will provide information about the occurrence of hotspots and provide better insights into the targeting behaviour of the fleet, as well as the spatial distribution of fish. 2) The data of all the sensors on board of a fishing vessels will be merged whereupon multivariate analysis will be used to increase the value of the information and provide advice to skippers.
Conclusions The scope of the current paper is to introduce CYBELE, a platform aspiring to safeguard that the stakeholders involved in the agri-food value chain (research community, SMEs, entrepreneurs, etc. ) have integrated, unmediated access to a vast amount of very large scale datasets of diverse types and coming from a variety of sources, and that they are capable of actually generating value and extracting insights out of these data, by providing secure and unmediated access to large-scale HPC infrastructures supporting advanced data discovery, processing, combination and visualization services, solving computationally-intensive challenges modelled as mathematical algorithms requiring very high computing power and capability. The CYBELE project officially started on January 2019, thus no scientific results have yet been made available.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (2009). Prior to co-founding Suite5, she worked as a R&D Project Manager at the School of Electrical and Computer Engineering in the National Technical University of Athens (NTUA) and acted as an adjunct lecturer in the post-graduate programmes of NTUA and the University of Aegean. During the last 12 years, she has successfully led the team's research and management activities in a series of EU-funded R &D projects in multiple domains (e. g. Big Data, Factories of the Future, Cloud Computing, eGovernance) related to data interoperability, modelling and analytics (e. g. ICARUS H2020 Technical Coordinator, EOSChub H2020, UTILITEE H2020, UPTIME H2020, AEGIS H2020, UNICORN H2020, PSYMBIOSYS H2020, FITMAN FI-PPP, OPENi FP7), managed 3 Coordination and Support Actions (as the overall project manager for FutureEnterprise FP7, ENSEMBLE FP7 and CROSSROAD FP7) and was involved in the research activities of numerous initiatives (e.
Within 48 hours Figure  1. Design criteria and mechanical properties for stiffness-tunable substrates. (a) Tissue stiffness of normal and diseased placenta (log scale, shear modulus values), using data obtained from literature  [26] [27] [28] [29] [30]. (b) Measured shear modulus of synthesized polyacrylamide hydrogels measured over an applied strain of 10%. Loss modulus measurements were negligible. of culture, cells adopted near-spherical structures on sub-physiologically soft substrates and flat, well-spread morphologies on normal and pathologically stiff substrates (Fig. 2 ), consistent with many other adherent cell types  35, 40. Also as expected, projected nuclear area decreased with spread area (data not shown). This effect was observed in both single cells and in the average cell area within small colonies (Fig.
image collection and analysis. Imaging was performed on an inverted fluorescent Olympus microscope (Olympus, IX73) outfitted with an sCMOS Flash 4. 0 Camera and Metamorph software (version 7. 8. 13. 0). All images were taken at randomly selected locations at 20X magnification. Subsequently, the obtained images (f-actin, E-cadherin, or hCG with DAPI) were merged and analyzed using ImageJ (NIH). Cell morphology was characterized based on fluorescent images obtained after 24-hours of culture with mitomycin C inhibition to prevent proliferation. The ratio of cells expressing hCG were analyzed based on the co-localization of hCG and DAPI-labelled nuclei signals in merged immunofluorescence figures. Fusion was assessed in BeWo cells after 48-hour forskolin induction and vCTBs after 72-hours of primary culture based on immunofluorescent localization of E-cadherin and DAPI-labelled nuclei.
Statistical analysis. Comparative data analysis and standard deviations of fusion ratios were based on results obtained from 3 independent experiments conducted on different days. Statistical significance of fusion ratios, cell spreading area and β-hCG expression level amongst cells cultured on substrates with different stiffness was analyzed using Student's t-test or a two-tailed, one-way ANOVA with a Holm-Sidak post-hoc pairwise comparison test. All statistical analyses were conducted in SigmaStat 3. 5 (Systat Software Inc. , San Jose, CA).
Acknowledgements This work was supported by the  Fonds de Recherche du Québec (FRQ) -Nature et technologies  (Grant # 205292 , to C. M. and C. V. ),  Natural Sciences and Engineering Research Council of Canada (NSERC Discovery   RGPIN-2015-05512  to C. M. and  RGPIN-2019-06778  to C. V. )), and the  Canada Research Chair in Advanced Cellular Microenvironments  to C. M. Authors gratefully acknowledge personnel funding from  NSERC   Postdoctoral fellowship  (L. S. F. ).
This model is implemented in COMSOL Multiphysics® using the Rotating Machinery, Magnetic (RMM) interface in the AC/DC module, which uses this mixed formulation of V m and A. An appropriate gauge is chosen such that the scalar electric potential (see equations (  22 ) and (  37 )) vanishes and only A has to be considered (equation (  12 )). The H-formulation is implemented in the Magnetic Field Formulation (MFH) interface, also in the AC/DC module. In addition to the H-A coupling described above, the in-built Mixed Formulation Boundary node in the RMM interface imposes continuity between V m and A on either side of the magnetic scalar/vector potential boundary, such that   1 1 1 m V m V       A n H n H n (14) and 2 2 2 m V      n B n B n A A ( 15 ) where the surface normals, n 1 and n 2 , are antiparallel (n 1 = -n 2 ).
Integral equation (IE) The current distribution along a segment representative of an infinitely thin (1D) superconducting layer (as per Section 3. 4) can be given by an integral equation that can be easily solved by the finiteelement method. This approach was proposed in  [27] , and later extended to consider interacting HTS wires in  [47, 48]. For the problem analysed here, the integral equation is written in the Partial Differential Equation (PDE) module of COMSOL in 1D and takes the following form ( ) s J f Q K C      (29) with ( , ) ( , ) x t n a K x t H t d       (30) and 1 ( , ) ( , )ln 2 a t s a Q x t J t x d         ( 31 ) where ρ is the power-law resistivity (equation (  1 )), J s is the sheet current density (current per unit width, A/m) in the z direction, f is the thickness of the superconductor (see Figure  1 ), a is the half-width (i.
Discussion As shown in Figures  3 4 5 , all of the models produced the expected benchmark solution with excellent qualitative and quantitative agreement. In this section, a critical analysis and comparison of each of the modelling frameworks is presented. Table  II  lists the key metrics assessed for each benchmark model: the number of mesh elements in the HTS wire; the total number of mesh elements in the model; the number of DOFs; the relative and absolute tolerance settings (for FEM-based models), tolerance for the mutual interaction matrix (MEMEP; programmed in C++) or ode23b solver relative tolerance (VIE; programmed in MATLAB); and the approximate time taken per cycle for each model. In the interest of a fair comparison, all of the FEM-based models were run on the same computer under the same conditions (e.
 The use of the scalar potential (H-A, T-A (1D) SP and T-A (2D) SP) -with first-order (linear) elements -improved stability and spurious oscillations/noise, as well as computational speed, in comparison to the VP models. Using this mixed scalar-vector formulation is a potentially useful alternative for modelling such dynamos, as well as superconducting rotating machines in general. For 3D models in particular, the scalar potential formulation introduces fewer DOFs and can ensure a more accurate coupling of the magnetic field. Indeed, although all of these models were specifically created for the HTS dynamo benchmark, many of the findings are equally applicable to and useful for modelling superconducting rotating machines.  In terms of ease of use, all models (except for the MEMEP and VIE methods, which are selfprogrammed using C++ and MATLAB, respectively) were implemented in COMSOL Multiphysics™.
COMSOL is a popular commercial software package with a reasonably gentle learning curve and is currently used by dozens of research groups worldwide to model superconductivity-related problems  [56, 57]. Many shared modelling examples, most associated with peer-reviewed publications, are available on the HTS Modelling Workgroup website  [58]. COMSOL now has a dedicated superconductivity interface (the MFH interface; see Section 3. 1. 1) with dedicated technical support.  However, there is a significant disadvantage when using commercial software in that users do not have complete control over its implementation: some of the programming cannot be accessed easily, if at all. It is of particular note that the SEG-H and H+SC models, which were built and optimised in COMSOL version 5. 4, ran significantly slower when opened and run in version 5.