dataesr/scientific-paragraphs-categorization huggingface dataset:
Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Virtual geoscience education resources". It is not associated with a conference. Acknowledgements. The authors would like to thank all the researchers, technicians, and crew involved in the ODEMAR and SUBSAINTES cruises, whose findings are open for educational purposes. We thank Christine Deplus and Nathalie Feuillet for sharing the large-scale 25 m DEM from Bathysaintes, and Aurélien Arnaubec, Klemen Istenic, and N. Gracias for their role in processing 3D data used here. We thank Julia Bozzinio and Loïc Dornel for their participation in the development of Minerve. We are grateful to L3 students from 2019-2020 and 2020-2021 promotions for their feedback and resilience during this very difficult period.
Special thanks are due to Felix Moquet and Nora Van Reeth from the ICAP team (U. Lyon) for their patience in explaining VR to beginners, to Antoine Triantafyllou for useful discussions and perspectives, and Amandine Minjollet for help with the figures. The thorough comments of Gaël Caravaca and an anonymous reviewer helped us in improving the manuscript. This project was partially supported by ANR Sersurf (ANR-17-687 CE31-0020). We also thank the scientists, crew, and officers of the different cruises that acquired data used here. We also thank the QGIS development team, and we use base map and data from OpenStreetMap and the OpenStreetMap Foundation. Financial support. This research has been supported by the Agence Nationale de la Recherche (grant no. ANR-17-687 CE31-0020). Review statement.
They are compositions of the micro-entities below them (e. g. , forests made of trees or molecules made of atoms). This characterisation of types of macro-entities further explains our broad definition of entities. People, insects, culture, a database, or an abstract idea of the world can all represent macroentities if they are associated with collective information which leads to the adaptation of micro-entities, which, in turn, may be different entities or hosted within the same entity. Different types of macro-entities can also co-exist within the same system. For example, we may consider a three-level system composed of individuals (micro-entities), their political identity (micro-distributed macro-entity), and the leader of each political group (exogenous macro-entity). In all cases, a macro-entity provides collective state information about the micro-entities it gets data from, and shares that collective state information with micro-entities, who adapt accordingly.
Blackboard (BB) strategy The Blackboard strategy can be configured with two parameters: the random error for the downward propagation of feedback (random-param) and the probability of worker task switching (P switch ). The feedback cycle between workers and their parent managers proceeds as follows: 1. State information collection: Workers (at L 0 ) send the task id of their selected task to their parent manager (at L 1 ); 2. State information abstraction: Each parent manager calculates the sum of collected task ids , for each task type (collective-state[]). The sum is updated upon reception of each task id , so collected data is stored directly in abstracted form. This abstraction is also sent (as individual-state[]) to the higher-level manager (L 2 ). 3. Information processing: Each manager (L 1 ) uses the error correction received from its parent manager (at L 2 ) (via individual-control[]) to determine the error correction proportion to send to its workers (collective-control[]).
Goal variation in task distributions The first set of experiments compared the strategies' sensitivity to variations in the goal task distribution (i. e. , how many workers must perform task 0 and how many workers task 1 ). We selected a small scale of 16 workers with 2 children-per-manager (|W | = 16, C = 2, N = 5), allowing to rapidly explore several distributions. Fig. 5  summarises the average convergence results for the Blackboard,  4  Rewards, Model, and ModelRew strategies. The Basic strategy is not shown in the graph as its convergence for unequal task distributions was several orders of magnitude larger than for the other strategies. The convergence data for all strategies, including the Basic one, is included in the Appendix, under Table  4  (rounded to one decimal). Generally, as strategies use random task selection -in the initialisation phase and for some strategies also when feedback is available -they all converge faster for equal task-distributions (e.
Acknowledgements We are very grateful to two anonymous reviewers for their constructive insights. We would also like to thank all the participants of the  Complex Systems Summer School (CSSS ) of 2018, organised by the  Santa Fe Institute (SFI) , for all the insightful discussions and ideas related to the presented topic. Finally, we would like to thank  Dr. Jean-Louis Dessalles , from  Telecom Paris , for lucrative comments on information abstraction.
2 we introduce the dd calibration and imaging problem, together with the existing software that we use to tackle it. We describe our dde calibration and imaging strategy (ddf-pipeline-v2) in Sec. 3 (for completion in appendix A we describe ddf-pipeline-v1 that was presented in detail in  Shimwell et al. 2019). In Sec. 4 we use ddf-pipeline-v2 to synthesize deep images over the Boötes and Lockman Hole extragalactic fields and present these deep low frequency images. The subsequent papers in this series will present the deeper ELAIS-N1 data products  (Sabater et al. 2020, in prep. ) , the multiwavelength cross matching  (Kondapally et al. 2020, in prep. )  and the photometric redshifts and galaxy characterisation  (Duncan et al. 2020, in prep. ).
2018; O'Sullivan et al. 2018 ). However, in the fields containing these bright polarised sources the ddf-pipeline-v2 output becomes unreliable for polarised sources. This limitation likely arises from assuming Q = U = V = 0 Jy for a field in the di calibration step. While only a few percent of fields are strongly affected, the exact extent of this issue is being investigated further through simulations, where bright polarised sources are inserted into existing LoTSS uv-datasets. Possible solutions will be tested in future pipeline developments.
10GHz, giving 32 physical compute cores. The dashed area is a quadrant representing a day, while the inner pie shows the total contributions of the imaging, calibration and miscellaneous tasks. using the SharedArray 12  module. As explained by  Tasse et al. (2018) , this pythonic approach minimizes the process interconnections for both the kms and ddfacet software. This paper considers the application of ddf-pipeline-v2 to the LoTSS-Deep Fields. The pipeline is also being used to process data from the wider and shallower LoTSS survey. The LoTSS project is presently observing at a rate of up to 1,500 hrs every 6 month cycle which corresponds to approximately two 8 hr pointings (observed simultaneously) each day. The ddf-pipeline-v2 compute time is roughly split equally between calibration and imaging tasks (see Fig.
9 ). The total run time for an 8 hour pointing is ∼ 5 days (on a node equipped with 192 GBytes RAM and 2 Intel Xeon Gold 6130 CPU@2. 10GHz, giving 32 physical compute cores), and takes an extra ∼ 30% of computing time to completion as compared to ddf-pipeline-v1. Hence 10 compute nodes are sufficient to keep up with the observing rate. However, in practice more compute nodes are used because LoTSS has been observing since 2014 and as of June 1st 2019 over 1,000 pointings exist in the archive. Over ∼ 1000 pointings and ∼ 12 PB of averaged and compressed LOFAR data (∼ 40 PB uncompressed) have now been processed with ddf-pipeline-v2. A&A proofs: manuscript no. paper.
Image synthesis The Lockman Hole and Boötes fields data have been both reduced using Alg. 2. In this approach we first build a wide-band di+dd self-calibrated sky model x ν from a single wide band ∼ 8 hours observation using Alg. 1. This model is then used to di+dd calibrate all the n p pointings (with n p = 10 and n p = 12 for the Boötes and Lockman Hole datasets respectively) following Alg. 2. This amounts to repeating Steps 1. 13 to 1. 18 of Alg. 1 on a larger dataset. A comparison between the images synthetised from 8 and 80 hours datasets is presented in Fig. 11. On a single node equipped with ∼ 500 GB of 2. 4 GHz RAM and 2 Intel Xeon CPU E5-2660 v4@2. 00GHz with 14 physical cores each, Alg. 2 took ∼ 21 days to process the 80 hours of Boötes data. Estimating the noise in radio maps is not straightforward since noise is correlated and non-Gaussian.
Cataloguing In order to extract astrophysical information we build a catalogue of radio sources from the images produced by Alg. 2 and the data described in Sec. 4. Even in the apparent flux maps, because of the imperfect calibration and imaging, the LoTSS-deep     The colorscale is the same on all panels, and diplayed using an inverse hyperbolic sine function to render both the low level artifacts and some bright sources. The stripy artifact seen in the zoomout Fig. 13b  seems to be produced by the residual deconvolution and calibration errors of a few 10 mJy. beam -1 bright sources that are a few degrees away from the center of the field.
Acknowledgements Data  The data processing strategy of the LoTSS first data release (DR1) has been extensively described by  Shimwell et al. (2019). Since addressing the issues described in Sec. 2 involves making improvements relative to this approach, we give here a brief description of the data reduction strategy in ddf-pipeline-v1 (the various steps are outlined in Alg. 0). As discussed in Sec. 2, the calibration and imaging problem is non-convex and ill-posed. Beyond the computational issues, the great difficulty of the calibration of the dde is sky incompleteness, because the dd-C-rime non-linear system can be subject to ill-conditioning. This is due to the fact that the extended emission (i) is hard to model in the deconvolution step, and (ii) is seen by only the shortest baselines, and therefore sky incompleteness biases the Jones matrices in the calibration step.
Comparison with deep factor image synthesis The image of the Boötes field based on 55 hours of LOFAR HBA data and presented Retana-Montenegro et al. (2018) reaches an unprecedented noise level image of ∼ 55 µJy. beam -1 at 150 MHz. To achieve such high sensitivity, Retana-Montenegro et al. (2018) have applied third generation calibration and imaging to correct for the dde using the factor package (developped by van Weeren et al. 2016, see Sec. 2 for more detail). Because the set of LOFAR datasets used by Retana-Montenegro et al. (.
4. 3. 3 Comparison with deep factor image synthesis The image of the Boötes field based on 55 hours of LOFAR HBA data and presented Retana-Montenegro et al. (2018) reaches an unprecedented noise level image of ∼ 55 µJy. beam -1 at 150 MHz. To achieve such high sensitivity, Retana-Montenegro et al. (2018) have applied third generation calibration and imaging to correct for the dde using the factor package (developped by van Weeren et al. 2016, see Sec. 2 for more detail). Because the set of LOFAR datasets used by Retana-Montenegro et al. (.
Methodology to compute the parameters of the IM model using FEM for a generic case FEM software allows to create accurate IM models which consider the non-uniform air-gap due to stator and rotor and other asymmetries due to faults. Therefore, in opposition the simulation requirements for healthy machines, the whole geometry of a faulty machine has to be considered in the simulation which results in a much more time-consuming task. Besides, a large number of simulations are required to compute the coupling parameters matrix. The method proposed in this paper assumes linear conditions for the computation of the inductance matrix of the eccentric machine. From a diagnostic point of view, saturation generates fault harmonics which are different from those generated by the eccentricity fault. Indeed, the purpose of the presented approach is to develop an efficient method for computing an inductance matrix that accurately captures the effect of the eccentricity fault.
The proposed paper aims at developing an analytical model able to run in real-time with high accuracy (based on FEM results) but a much lower cost. In this case, considering only the linear, incremental problem, balances the results with the computational costs obtaining a reasonably accurate solution for eccentricity fault diagnosis purposes. Nevertheless, if the saturation effects are also to be reproduced, there are several methods presented in the technical literature where the inductance matrix obtained with the proposed approach can be used. One of such methods is the "incremental permeability" solution proposed in  [33] , which is used in several FEM software packages to save computation time. In this solution, first, the nonlinear Fig. 2. Analytical model using the system equations (  3 )-(  7 ) using Simulink blocks ready to be run in a HIL system for real-time simulation.
Particularizing the method to compute the coupling parameters to the IM under study To illustrate the cost in terms of computing power, memory resources and processing time, the method introduced in Section 2 and illustrated in the Fig. 3  has been applied to obtain the coupling parameters matrix L [ ] of the IM whose main characteristics are shown in Table  1. The software used for the FEM simulations is the open source femm 4. 2 and the computer used has an intel processor (R) Core (TM) i5-6400 CPU@2. 70 GHz 2. 20 GHz and 16 GB of RAM memory. The first step, is to decide the rotor movement steps ( = rd K 2 / ) to be used. This term is related to the accuracy, in terms of rotor position, needed to compute the coupling parameters L [ ]. Usually, the result of multiplying the number of stator slots by the number of rotor bars for cage IM (or rotor slots in case of wound rotor IM) provides a good value of K: [ ], for one machine and with only one severity degree for a given fault, would require 11 days 21 h and 36 min and 376.
The Sparse Subspace Learning (SSL) and the Hierarchical Lagrange interpolation (HLI) The SSL in combination with a hierarchical Lagrange interpolation (HLI) as polynomial basis introduced in  [28]  reduces the computing time needed to solve parametric problems in FEM software. It proposes a collocation strategy to reduce the time computing requirements of parametric models. The SSL strategy selects specific sampling points in which the simulation has to be performed; thereafter, the output of the deterministic solver is used to obtain a HLI polynomial basis which allows to compute an approximate low-rank parametric solution at new sampling points. This approximated solution is used to initialize the FEM solver which speeds up the convergence of the iteration process as the predicted solutions are very close to the FEM solution; in fact, in some cases, the iteration process is not even required to be run.
In this paper, the proposed method has been illustrated for the static eccentricity fault with different fault degrees from incipient levels or even inherent levels of the machine to severe scenarios which would help in the aim of correlating the amplitude of the fault harmonic components with the fault severity degree. After that, the models have been tested with the coupling parameters computed and the fault diagnosis results (fault harmonic components) have been compared with those obtained with the models where the coupling parameters have been fully computed with FEM software. Besides, these results have been confirmed by experimental validation. Following the same reasoning the method could be extended to other types of faults or even to simultaneous faults. Finally, it must be highlighted that the proposed method would have a major impact in the fast development of IM models with different types of fault and/or with different degrees of severity of a given fault.
While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in data generation, ingestion, and materials state-aware machine learning. High-throughput experiments and automated computational workflows are addressing the challenge of data generation, and capitalizing on these emerging data resources requires ingestion of data into an architecture that captures the complex provenance of experiments and simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence and interrelationships among events occurring in a simulation or experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments performed using various modalities such as serial, parallel, multi-modal experimentation.
Our data architecture tracks the evolution of a material's state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with state-changing processes in addition to the typically used composition data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in enabling several aspects of accelerated materials discovery such as dynamic workflow design, generation of knowledge graphs, and efficient integration of theory and experiment.
Introduction Accelerating materials discovery is critical for a sustainable future and the practical realization of emergent technologies. Datadriven methods are anticipated to play an increasingly significant role in enabling this desired acceleration, which would be greatly facilitated by the establishment and community adoption of data structures and databases that capture data from the broad range of materials experiments. In computational materials science, automated workflows have been established to produce large and diverse materials datasets. While these workflows and associated data management tools can be improved to facilitate capturing of a materials' state and enable easy capture of re-configurable analysis methods, their current implementations have facilitated a host of materials discoveries,  [1] [2] [3] [4]  emphasizing the importance of continued development of materials data architectures.
In case of experimental materials science, the majority of the data remains in human readable format and is not ingested into a database. In cases where the databases exist, they are either large with limited scope (ICSD, ICDD, which contains hundreds of thousands of x-ray diffraction patterns) or are diverse but have limited data. [5] [6] [7]. This has limited application of machine learning for acceleration of experimental materials discovery to specific datasets such as microstructure data, x-ray diffraction spectra, x-ray absorption spectra, or Raman spectra. [8] [9] [10] [11]  Recent application of high-throughput experimental techniques have resulted in two large, diverse experimental datasets: a) High Throughput Experimental Materials (HTEM) dataset, which contains synthesis conditions, chemical composition, crystal structure, and optoelectronic property measurements (> 150,000 entries), and b) Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the corresponding property and performance metrics.
12, 13  These datasets contain thousands to millions of data entries for a given type of experimental process, but the experimental conditions or prior processing of the materials leading up to the process of interest can vary substantially. The multitude of process parameters and provenances results in datasets whose richness could be fully realized and utilized if the context and provenance of each experiment were appropriately modeled. In contrast to computational data, where the programmatic workflows facilitate provenance tracking, experimental workflows generally experience more variability from many on-the-fly decisions as well as environmental factors and evolution of the instrumentation. Sensitivity to historical measurements is generally higher in experiments since any measurement could conceivably alter the material, making any materials experiment a type of "processing.
" Factors rang-ing from instrument contamination to drifting detector calibration also may play a role. Therefore, a piece of experimental data must be considered in the context of the parameters used for its generation and the entire experimental provenance. The importance of sample and process history of the experimental data makes it challenging to identify which measurement data can be aggregated to enable data-driven discovery. The standard practice for generating a shareable dataset is to choose data that match a set of process and provenance parameters and consider most or all other parameters to be inconsequential. This method is highly subjective to the individual researcher. For both human and machine users of the resulting dataset, the ground truth of the sample-process provenance is partially or fully missing.
In addition, an injection of assumptions prior to ingestion into a database creates datasets that do not adhere to the Findability, Accessibility, Interoperability, and Reusability (FAIR) guiding principles  14  resulting in lack of interoperability, creation of data silos that cannot be analyzed efficiently to generate new insights and accelerate materials discovery. As a result, the data's value is never fully realized, motivating the development of data management practices that closely link data ingestion to data acquisition. Given the complexity and variability in materials experimentation, several tailored approaches such as ARES, AIR-Chem, and Chem-OS have been developed to enable integration between data ingestion and acquisition for specific types of experiments. [15] [16] [17]  Recently, a more generalizable solution for facilitating experiment specification, capture, and automation called ESCALATE was developed.
18  Such approaches aim to streamline and minimize information loss that occurs in an experimental laboratory. We focus on modeling the complete ground truth of materials provenances that could operate on structured data resulting either from a specialized in-house data management software or a more general framework such as ESCALATE. We use an event-sourced architecture for materials provenances (ESAMP) to capture the ground truth of materials experimentation. This architecture is inspired by event-sourced architectures used in software design wherein the whole application state is stored as a sequence of events. This architecture maintains relationships among experimental processes, their metadata, and their resulting primary data to strive for comprehensive representation of the experiments.
We believe that these attributes make ESAMP broadly applicable for materials experiments and beyond. We discuss database architecture decisions that enable deployment for a range of experiment throughput and automation levels. We also discuss the applicability of ESAMP to primary data acquisition modes such as serial, parallel, and multimodal experimentation. Finally, we present a specific instantiation of ESAMP for one of the largest experimental materials databases (MEAD) consisting of more than 6 million measurements on 1. 5 million samples. We demonstrate facile information retrieval, analysis, and knowledge generation from this database. The primary use case described herein involves training machine learning models for catalyst discovery, where different definitions of provenance equivalence yield different datasets for model training that profoundly impact the ability to predict catalytic activity in new compositions spaces.