dataesr/scientific-paragraphs-categorization huggingface dataset:
Value of testing The development of docstring and unit tests within Landlab was motivated by following software development best practices (e. g. ,  Wilson et al. , 2014 Wilson et al. , , 2017)). That is, our focus was on ensuring that the package behaves as described and, where an analytical solution exists, that Landlab correctly solves it. While using a testing suite is standard in many software development contexts, it is relatively uncommon in scientific software development (e. g. ,  Prabhu et al. , 2011). Tests do not ensure that elements of the Landlab software represent the truth or guarantee that a model is appropriate for a specific application; in other words, Landlab cannot and does not attempt to validate (sensu  Schlesinger et al. , 1979)  the assumptions of its components. Instead, the tests verify  (Schlesinger et al.
Collaborative development of research software requires many skills Scientific software development requires distinct skills. Based on working with community user-developers and onboarding new members of the core development team, we describe the set of skills that are needed to interact with a project like Landlab as a user-developer. Our intention here is to document a concrete example so that efforts to create scientific software development curricula can be based on usecases. In the case of Landlab, the skills required to contribute to the project include: 1. Python programming, including functions, classes, and basic package organization. 2. Fundamental elements of version control using git (branching, commits). 3. GitHub for collaboration (issues trackers, merging, pull requests, managing forks, code reviews).
How do I get started? We highly encourage all contributions to Landlab. The package is designed as an extensible piece of community software, and we look forward to it growing to meet community needs. Common ways that an interested individual might get started include the following: identifying or making improvements to the documentation and example notebooks, finding and fixing bugs, and describing and creating desired features -such as new components. For information about how to get started, including source code and prepackaged binary installation (via PyPI or conda-forge), visit the website at https://landlab. readthedocs. io/ (last access: 12 May 2020).
Conclusions Landlab v2. 0 provides the community with a robust and extensible package for modeling Earth surface dynamics. It is distributed as source code and as prepackaged binaries for Linux, MacOS, and Windows. An extensive set of unit tests ensure reliability of the code base. This version provides substantial improvements over v1. 0 including (i) a revised set of model grid classes, (ii) updates to the component interface, (iii) 31 new components, (iv) expanded and consolidated documentation, and (v) a tool for identifying appropriate citations. The backward-compatibility-breaking changes made in Landlab v2. 0 reflect changes necessary based on use and development of the package. The modular design of Landlab means that developers only need to create the new piece they need, and researchers can mix and match components to create a desired model.
As a tested, version-controlled, and documented software package, Landlab reduces barriers to computational modeling and supports reproducible research. edged in the next section. In addition, we recognize support and guidance from the Community Surface Dynamics Modeling System. We thank In this spirit, we thank all community members who have asked questions, made issues, commented on documentation that did not make sense, and contributed code to the package. Below we list the results of our best efforts to compile all non-author community contributors to the Landlab package source code. The are as follows (in alphabetical order): Guiseppe Cippolla, Jon Czuba, Vanessa Gabel, Rachel Glade, Jenny Knuth, Abby Langston, David Litwin, Amanda Manaster, Allison Pfeiffer, Francis Rengers, Charlie Shobe, and Rhonda Strauch.
Financial support. This research has been supported by the US National Science Foundation  (grant nos. 1147454, 1450409, 1147519, 1450338, 1148305, 1450412, 1246761, 1725774, 1902600, 1226297, and 1831623) , the Marie Curie/Sêr Cymru II Cofund Research Fellowship (grant no. 663830-CU-035), a Software Sustainability Institute Fellowship, and a Tulane University Oliver Fund Scholar Award. Review statement. This paper was edited by Simon Mudd and reviewed by Tristan Salles and Wolfgang Schwanghart.
Conventional CNNs demand a great deal of resources in terms of processing time and disk space. Therefore, CNN classification with superpixels has recently become a field of interest. In this work, a CNN-based deep learning framework is proposed that combines sticky-edge adhesive superpixels. The results indicate that a cheap camera-based video monitoring system is a suitable data source for coastal image classification, with optimal accuracy in the range between 75% and 96%. Furthermore, an application of the method for an ongoing case study related to Sargassum monitoring in the French Antilles proved to be very effective for developing a warning system, aiming at evaluating floating algae and algae that had washed ashore, supporting municipalities in beach management.
Sticky-Edge Adhesive Superpixels In this work, over-segmentation processing is adopted by using sticky-edge adhesive superpixels  [39]. Data fed into the input layer of CNN classification must be images with a fixed size, whereas the segmented regions generated from traditional superpixel partitioning methods vary greatly in size and shape. The selection of the superpixel algorithm and the choice of parameters greatly affect the final results of image classification. Theoretically, the selected over-segmentation algorithm favors boundary adherence and generates superpixels with high compactness. This chosen method is built on an iterative approach motivated by SLIC  [24] , but with superpixels snapped to the edges of the image, resulting in higher-quality boundaries. SLIC generates superpixels by clustering pixels based on the color similarity and proximity in the image plane.
In this work, a transfer learning methodology is employed using the pre-trained CNN MobileNet-V2 architecture  [43]  implemented within TensorFlow-Hub  [45]  in order to classify coastal oblique images. As an alternative to building a model from scratch, here we explored the possibility to improve a previously trained model which had already been trained to solve a similar (larger) classification problem, specifically, on the Imagenet database  [46]. It is surprisingly effective to take advantage of features learned by a bigger database  [46]. So, a new final classifier is added to a pre-trained "frozen" model, and only the weights of this layer on top of the network are updated during training-a fully-connected layer that determines the image class given the set of features extracted from the convolutional base network.
Contrary to training large models, less regularization and data augmentation techniques are used since small models have less trouble with overfitting  [42]. CNNs require input data to be small square image regions (patches) due to the shape limitation of the network. Since the shape of superpixels is irregular, superpixels cannot be directly used as input data to feed CNNs. Therefore, the center pixel of each superpixel is selected and a square centered on the superpixel center is used as the input data. After the classification of squares, the resolving class label is assigned to the associated superpixel. The patch images based on the superpixel centers are the data used by the MobileNet classifier, and the superpixels mine the specific pixels that are labelled in the resolving classification.
Workflow of STICKY-CNN-CRF Classification The workflow of the superpixel-based CNN classification is shown in Figure  2 , which includes two main phases-training and testing, including the application of the semantic segmentation. Firstly, by sticky-edge adhesive superpixels partitioning, the original SolarCam images from the training data are over-segmented into several regions, which are used as the spatial processing units for CNN classification. The image is manually labelled by following a modified methodology from  [18]. Next, transfer learning enables the retraining of the experimental network, based on MobileNet V2 convolutional neural network, described in Section 3. 4. The samples of labelled regions are used to retrain the CNN parameters, based on resolving the accuracy of the classification.
The patch size used is equal to 96 pixel square. Finally, with regard to the semantic segmentation phase, the testing dataset from the original images are firstly over-segmented by means of the sticky-edge adhesive algorithm. The superpixel patches are classified using the pre-trained network. Then, the fully-connected CRF is applied for smoothing the results and better capturing the boundaries of the regions. SolarCam images are used for superpixel partitioning, CNN training, superpixel-based classification with CNN, and finally refining via fully-connected CRF. As an example, for the explanation of the main steps related to the semantic segmentation phase, Figure  3  shows the original image, over-segmented in superpixels (Figure  3a, d ), the CNN-based superpixels classification with class labels as mentioned above (Figure  3b ,e), and finally the result from the CRF refining mechanism used to smooth the segmentation map (Figure  3c, f ).
Results In this section, the performance of the method described in Section 3 is evaluated on the basis of 72 images in the testing dataset, based on six Solarcam cameras (12 images per camera), with full resolution of 3264 × 2448 pixels. Around 3000 superpixels for each image were used as processing units for CNN training. The metric used in this work for analyzing the performance of the methodology presented is mainly based on a confusion matrix, a table with rows and columns reporting the number of false positives (FP), false negatives (FN), true positives (TP), and true negatives (TN), which allows for more detailed analysis than the mere proportion of correct classifications (accuracy). "True" or "false" indicates if the classifier predicted the class correctly, whereas "positive" and "negative" indicate if the classifier predicted the desired class.
Experimental Results During the CNN transfer training procedure, the main hyperparameters were kept constant: the learning rate at 0. 01, the training steps fixed as 2000. The batch size of the SolarCam classification datasets of coastal images was set to 100. The codes ran on a computer with Intel R Core TM i5-7300HQ CPU @ 2. 50 GHz, 16 GB RAM, and Python 3. 7. The gradient was computed via batch gradient descent, employing only the CPU. TensorFlow  [48]  was chosen as the deep learning platform. Two models were trained, related to the categories mentioned above (i. e. , beach morphology and harbour-type land cover). The classification accuracy was evaluated with respect to the superpixel-based CNN classification, and then by inspecting the pixel-scale results, following CRF refinement. Table  1  summarizes the CNN prediction accuracies (F1-score) at superpixel scale.
The running time decreased by a factor of 1. 8. With regard to the overall dataset, the superpixel classification (F1-score) achieved slightly lower (2. 64%) accuracy for the final classification, obtained by averaging all types of classes. It can be concluded that sticky-edge adhesive algorithm performed slightly better than SEEDS with almost the same running time, but with benefits from the shapes of superpixel regions. The main reason is related to the fact that sticky-edge generates superpixel regions whose boundaries, derived from an edge detector, are closer to practical land cover regions. Conversely, superpixel regions generated by SEEDS could cross the border of two land covers, especially for those with blurred borders. Moreover, the non-uniformity in the size and shape of superpixel regions, more noticeable in SLIC, is supposed to improve the generalization capability of CNN, which could prevent over-fitting.
Subsequently, the computation of boundaries and areas of each spot is given. Finally, the determination of an expected warning is based on site-specific temporal thresholds. Geo-referenced data are being used, where available, to report data in meters rather than pixels, by means of a standard method for georectification  [51]. A location map where the framework is being tested is shown in Figure  7. With respect to all sites monitored, presented in Section 2 (shown as points), the map marks the warning system stations in red and green. Specifically, the green and red colors indicate the percentage of manually validated data with respect to the excluded ones, respectively. This means that a percentage is affected by different issues, mainly related to acquisition problems, loss of data, blurred conditions (i.
Moreover, it allowed a smoothing much more adherent to the reality (interfaces of water-sand or algae-sand). Particularly relevant for improving Sargassum classification, we observed an increase in classification accuracy. On the basis of 20 images from the same testing database, as above, the F1-score increased by 2. 3% compared to the results discussed in Section 4 with respect to beach-type morphology. The great advantage of employing such models could be straightforward for several applications related to coastal monitoring research and case studies. Because only a few images are required for training since the superpixel partitioning creates a broad and adequate database for CNN transfer learning (i. e. , the number of training superpixels in Table  1 ), easy prototyping of models can be realized.
Experimental results indicate that for beach and harbour-type image categories the average accuracies were in the range of 75% to 96%, across a dataset derived from six cameras. By means of a superpixel over-segmentation method at a variety of scales, in coastal-specific environments, the effectiveness of deep features extracted by CNN was evaluated and the optimal scale was discussed. A sensitivity of final classification accuracy to the CRF hyperparameters space is proposed. For some images with few small land-cover spots or some simple artificial regions, the methodology can suffer from ambiguities. A case study related to the implementation of a warning system for Sargassum monitoring is presented to exhibit the capabilities and potential applications of the trained models. Particularly, when a further post-processing procedure based on superpixels region-based majority voting was applied, the F1-score related to the Sargassum class reached 91%, with respect to images of beach-type morphology.
The presented procedure promotes the use of camera-based images for applications related to automatic feature recognition and coastal quality monitoring. Further experiments are being planned with a larger dataset (including other Lesser Antilles islands) and aiming at the application of the method to other sources of data, such as videos. Moreover, further studies are focused on processing the time-series data derived from the warning system in order to retrieve parametrization of the meteo-oceanographic forces useful for describing the probability of occurrence of algae degradation over time, disintegration due to wave impact, or removal due to tide oscillations.
Acknowledgments: The video system used in this study was deployed as part of the Posidonia beach litter monitoring project funded by  BRGM  and  DDTM 2B  ( Dir. Dep. Des Territoires et de la MEr de Haute-Corse ), and the  Sargassum monitoring network of Martinique  funded by  BRGM ,  ADEME (Ag. De L'environnement et de la maîtrise de l'Energie)  and  DEAL (Dir. Environnement, Aménagement et Logement) de Martinique. We particularly thank  Thibaut De Cathelineau  for his fundamental support in web-cam deployment. We thank the three reviewers for their constructive comments that improved the quality of the paper.
HMCS-Collab is composed of five modules: • Matching Tool (MT): ensures model matching via two sub-modules: (1) Assisted Matching Tool (AMT) that allows defining correspondences at metamodel level (meta-correspondences) and (  2 ) Refining Tool (RT) which propagates meta-correspondences to models level by generating the Cartesian product of instances of meta-elements involved in a meta-correspondence, then filtering them thanks to the semantics of their relationships. • Consistency Management Tool (CMT): ensures the consistency of the system in case of partial models evolution via three sub-modules: (1) Change Detection Tool (CDT) that contains a listener of a fixed set of changes, (2) Consistency Checker Tool (CCT) which analyses the performed changes and their impacts on inter-model correspondences and (3) Inconsistencies Resolver Tool (IRT) which contains a repository of inconsistencies resolutions and recommendations.
• Collaboration Tool (CollabT): ensures collaboration mechanisms (e. g. communication, group management and group-awareness) through Communication Tool (CommT), Group Management Tool (GMT) and Group Awareness Tool (GAT). • Decision Management Tool (DMT): contains a set of decision policies and the implementation of their aggregation methods. It is divided in two sub-modules: (1) Decision policies Repository Tool (DRT) which implements decision making policies and (  2 ) Decision Aggregator Tool (DAT) which assesses the evaluation to obtain the collective decisions about proposals. This module and CollabT are invoked by both MT and CMT. • Transformation Tool (TT): supports two kinds of transformation: Model to Text (M2T) and Text to Model (T2M). MT and CMT use Eclipse Modeling Framework (EMF 3 ) which is a modeling framework and code generation facility for building tools and other applications based on a structured data model.
They both essentially use (i) Graphical Modeling Framework (GMF 4 ) for graphic editors generation, (ii) EMFCollab 5 for allowing multiple users to simultaneously edit a single EMF model, (iii) Connected Data Objects (CDO 6 ) for data persistence, and (iv) Xtext 7 for developping domainspecific languages by using a powerful grammar language. CollabT and DMT use Eclipse Communication Framework (ECF 8 ) which is a set of frameworks for building communications into applications and services. It provides a lightweight, modular, and fully-compliant implementation of the OSGi Remote Services standard. ECF Shared Object provides basic services for creating replicated objects within a distributed container. The presence API provides services for remote file retrieval and peer-to-peer file transfer.
Illustrative example To illustrate the GDM enactment, we conduct a part of the alignment process on a Conference Management System (CMS) (only the matching part). The collaborative matching and the CMS were presented in  (Bennani et al. , 2018). We do not detail either the process nor the CMS models here, but we can say, briefly, that CMS has been designed from three points of view, leading to three heterogeneous models : • Software Design (SD) model: represents classes, their attributes and methods. • Business Process (BP) model: describes roles, activities and products. • Persistence (PS) model: describes a relational database with tables for data storage. Concerning the collaborative matching process, it works at two levels. First, the actors define correspondences among elements of metamodels called meta-correspondences (MCs).
Materials and methods Strain culture, Library preparation and DNA sequencing We continuously cultivated A. vaga individuals from AD008 strain (i. e. same strain as in  3  , COI sequence accession number is KM043184) since 2007 in Petri dishes using Spa water, feeding them with sterile extract of lettuce juice and stocking well-grown cultures at -80°C. A. vaga individuals were thawed before proceeding to DNA extraction using QIAGEN Gentra Puregene Tissue Kit. Genomics Core (UZLeuven) produced PCR-free 250-bp paired-end Illumina reads that were sequenced with a coverage of approximately 350x on a HiSeq 2500 sequencing platform. The same procedure was followed in order to obtain high molecular weight DNA using Macherey-Nagel NucleoBond HMW procedure that was subsequently sent to the Genomics Core (UZLeuven) to generate a coverage of 200x of PacBio RSII sequencing data.
Around 30 µg of high molecular weight DNA was also extracted from living A. vaga individuals using the QIAGEN Gentra Puregene Tissue Kit and then sent to the Genoscope sequencing center (François Jacob Institute of Biology) which produced 5 ONT libraries, each starting from 2 to 5 µg of DNA, using the 1D ligation sequencing kit (SQ-LSK108) and R9. 4 (or R9. 4. 1) flowcells. This resulted in a coverage of 125x long-reads using Oxford Nanopore Technology (ONT). Samples ID and SRA accession numbers are detailed in Supplementary Table  S1. Chromosome conformation capture The Hi-C library construction protocol was adapted from  48, 49. Briefly, individuals from the A. vaga AD008 strain were chemically cross-linked for 20 min at room temperature and 30 min at 4°C (with gentle stirring) using formaldehyde (final concentration: 5% in milliQ water; final volume: 50 ml).
Bwise assembly The Bwise assembler v0. 1 (https://github. com/Malfoy/BWISE) was used on high-coverage Illumina data (sample ID GC047403, see Supplementary Table  1 ) to produce a draft phased genome assembly. We selected a Kmer size parameter of 63 (-k 63) as this produced the most contiguous assembly over the range of tested Kmer sizes: 63, 73, 101, 201. Other parameters were left as default. Bwise rests on a different paradigm than most assemblers: it starts by generating a de Bruijn graph from the reads to assemble  51  , then cleans the graph by removing tips caused by sequencing errors  52  , remaps the initial reads on this corrected de Bruijn graph  53  , transforming them in super-reads  54. Finally, the resulting super-reads are assembled in a greedy fashion whenever they overlap unambiguously by one or several unitigs.
FLYE assembly The Flye genome assembly software version 2. 5 14 was run using both PacBio (350 X coverage) and ONT (125 X coverage) long reads, with default settings (see Supplementary Table  1 ). Flye is a OLC (overlap-layout-consensus) longread assembler especially designed to handle repeats. During the layout step, it uses a repeat graph allowing for approximate sequence matches, therefore tolerating long reads errors. Briefly, at the beginning of the layout step the initial disjoint, error-prone contigs (disjointigs) are concatenated in a random order. An assembly graph (the repeat graph) is subsequently constructed from the repeat plot of the concatenates and each edge of the graph is classified according to its multiplicity (unique or repetitive). Mapping the reads on that repeat graph allows resolving bridged repeats and subsequently unbridged repeats.
FALCON assembly The de novo assembly of Adineta vaga genome was carried out with diploid-aware long-read assembler FALCON version 0. 7. 0, FALCON-Unzip and partial FALCON-Phase (only FALCON-Phase Workflow steps 1, 2 and 3)  15. Prior to the assembly, Canu error correction module was used for read error correction based on raw PacBio reads. The FALCON software is highly optimised for eukaryote genomes, and uses hierarchical genome assembly process (HGAP). More specifically, reads longer than 15 kb were selected by Falcon as "seed" reads to generate consensus sequences with high accuracy. The pre-assembly steps in FALCON uses DALigner to do all-by-all alignments of the corrected PacBio reads. Long reads were then trimmed at regions of low coverage with FALCON sense parameters (-minidt 0. 70 -mincov 4 -maxnread 200) and sensitive DALigner parameters were selected (-h60 -e.