dataesr/scientific-paragraphs-categorization huggingface dataset:
Specifications, such as the Minimum Information about a Metagenome Sequence, the Minimal Information about a MARKer gene Sequence (MIMARKS) and the Minimum Information about any Sequence (MIxS), have been initiated by the Genomic Standards Consortium (GSC)  (9, 10) , as a result of the need for metadata standardization or extension of the existing standards  (6). More recently, a specification tailored to the metagenomic data derived from hydrocarbon resources (HCRs) (MIxS-HCR) has been proposed  (11). The MIxS-HCR package incorporates the core features of the MIxS standard for marker gene (MIMARKS) and metagenomic (MIME) sequences along with HCR customized environmental package. Currently there are efforts to extent this package to cover other Hydrocarbon Occurrences such as 'Surface or Seabed Geological Structures' and 'Anthropogenic Hydrocarbon Occurrences' (e.
g. Oil/Gas Production Systems)  (11). Through the MIxS-HCR standard, the comparison and better contextualization of investigations pertaining to the development of hydrocarbon recovery processes and management of microbiological issues in petroleum production systems will be better accomplished. The need for the storage of metadata related to metagenomic and genomic projects has been addressed by different web platforms or portals and databases  (12) (13) (14) (15) (16) (17). All these metagenomic-ready platforms are web-based systems accessible publicly or through some user authentication mechanism and-most of them-are hosted at the same institutions as their authors. However, instead of many disjoint databases, there are some obvious advantages in having these systems centralized and publicly available: including the management of the sheer amount of accessible data in them, the ease of data and metadata sharing within the scientific community and the well-supported modern infrastructure.
There are disadvantages in having a non-local repository with respect to both data and metadata. First of all, it is impossible to capture environmental metadata that are not currently covered by these online repositories. For example, none of the publicly available repositories are able to capture environmental metadata pertaining to hydrocarbon-rich environments. As such, contextualization and comparative analysis among datasets derived from these environments is to a certain degree hindered or cannot be fully evaluated. Secondly, online repositories can be 'rigid' with little room for customization (i. e. use of software tools or parameters other than those already supported). Thirdly, transferring data onto a remote system can be a lengthy process depending on the size and the available bandwidth.
Moreover, there may be confidentiality restrictions on the data that could prohibit their transfer and usage outside the institution. This especially concerns private organizations, where such restrictions are a common practice. Furthermore, private organizations may face restrictions on the use of these web platforms for commercial purposes. Users may also wish to use these web resources anonymously without the requirement for registration, which is not an option on some web systems. Finally, the use of a remotely hosted system renders the user dependent on a third-party infrastructure, which is out of one's control. Here we describe metagenomics data management system for hydrocarbon resource (MetaHCR), a standalone and open-source software (OSS) that allows users to store, catalogue, share and analyze metadata and sequencing data originating from metagenomics projects related to hydrocarbon environments.
MetaHCR consists of a relational database management system for the storage of data and a web application for user interaction with the database. Furthermore, it allows the recording of over 300 different attributes or metadata associated with organisms, the environment, samples, hydrocarbon and water chemistry and geological properties. For this purpose, whenever possible, MetaHCR uses a controlled vocabulary (CV) in association with the MIxS-HCR metadata specification  (11)  from the GSC. Finally, a user-friendly web front-end enables the non-expert to easily access, interrogate, update, export and analyze the acquired metagenomic data residing in the relational database. MetaHCR is a metagenome data management system suitable for private and public institutions and laboratories who wish to establish their own webserver for the cataloguing and sharing of metagenomic data and metadata related to hydrocarbon-rich environments.
MetaHCR architecture MetaHCR is a Python application built using the opensource Django web-application framework, and it supports any standard relational database. Django follows the Model-View-Controller architectural pattern. During development, the PostgreSQL database system was used, however, it can be easily replaced by a different database system (e. g. Oracle, SQLServer, etc. ) as there is no PostgreSQL-specific code in MetaHCR. All browsing and search requests are translated into standard Structured Query Language (SQL) statements independent of the supporting database. These SQL statements can be viewed using the Django Debug Toolbar (http://django-debug-toolbar. readthedocs. io/en/stable/).
Deployment of MetaHCR For the deployment of MetaHCR, any web server supporting Python such as Apache, nginx or Microsoft IIS can be used. MetaHCR can be run on any of the major platforms, like Apple Mac OS X, Microsoft Windows and Linux. MetaHCR can be configured to run in a fully open-source environment, for example by using Linux as the operating system, Apache as the web server and PostgreSQL as the back-end, which is also the recommended configuration. In addition, this configuration makes possible the dissociation of the database from the web application by means of two independent servers. MetaHCR can be installed through the available installation scripts following the instructions provided in the 'MetaHCR Installation Guide' included in the software package. The current MetaHCR version described here is v.
1. 1. Once installed, a local copy of the MetaHCR can be tested by starting the built-in web server ('manage. py runserver'), but for security reasons, MetaHCR should be run via a secure web server, such as Apache. Users on the same computer can access MetaHCR by pointing their browsers to the MetaHCR home page: http://127. 0. 0. 1:8000/MetaHCR/home/. The installation guide covers additional use cases, such as deploying MetaHCR on a central production server accessible to every authorized user on the local area network. The database and web server parts are independent and, as a result, they can be located on different physical servers. Moreover, the databases can be located on more than one physical server. In the simplest case, MetaHCR can be deployed on a local desktop computer, provided that this computer includes a database system, a web server, the appropriate Python and Django support (e.
Database structure The database schema (Figure  1 ) uses a traditional model of relational database systems and is structured around a total of 25 distinct tables (Supplementary Table  1 ). Each table has a specific purpose and most of them support the classification of metadata under categories (e. g. 'sample', 'organism', 'hydrocarbon_chemistry', etc. ). Other tables provide more general or static metadata (e. g. 'country', 'contact', 'hydrocarbon_resource') or serve as linkers between two different tables (e. g. 'investigation_sample') (Supplementary Table  1 ).
CV One of the main goals of MetaHCR is to utilize a CV in the form of ontologies. The use of a CV as semantic metadata can be advantageous in terms of avoiding metadata redundancy, improving data accessibility and facilitating integrated search queries for data retrieval. To achieve this goal, the database schema of MetaHCR implements a table named 'attribute' (not shown in Figure  1 ), which categorizes the CV or metadata as attributes and can contain one or many unique values. In addition, it is possible for each metadata value to have further descriptors, such as source type and source details and even a boolean flag in case this metadata value is deprecated. The metadata values from the 'attribute' table can then be referred to by other tables, using their unique identifier integer through a one-tomany relationship.
There are over 849 different metadata attributes in all of the combined tables. During installation of MetaHCR, it is possible to prepopulate the 'attribute' table with a core set of metadata values for the projects, sequencing, organisms, habitats, samples and geology categories. The values of this core set of metadata originate mostly from literature review or biological and biomedical ontologies  (18). For those originating from ontologies, the Open Biological and Biomedical portal (abbreviated as BioPortal)  (19)  has been employed for finding the most appropriate set of metadata values for specific attributes. Accordingly, sequence ontology (20) was used to describe the quality of sequencing, and Habitat-Lite  (21) , an easy-to-use subset of terms from the Environment Ontology  (22) , was used to characterize an organism's habitat.
Web interface MetaHCR is accessible through a user-friendly web frontend interface segmented into various components depending on the desired functionality. These components are available from any part of the web application, through a simple navigation menu. Consequently, the menu gives immediate access to the following components: browse, search, upload, administrate and change databases with their respective sub-components or actions (Figure  2 ). A more detailed description of the web page interface appears in subsequent sections. Additionally, a 'register' link allows a new user to create an account in MetaHCR and a 'login' link lets a user who is in possession of a valid account to login. An icon followed by the name of the database will remind the user which database is connected to at any time.
Browse component In a nutshell, the browse component provides the user with the ability to sort, make simple queries and export the data stored in the most important tables of the database. These are the 'investigation', 'sample', 'analyses', 'organism' and 'HCR' tables. Clicking on the tab associated with each table displays its contents in a table-formatted list. It is possible to sort data by any column in ascending or descending order. Typing values into the search field located at the top of each column starts a real-time search of the data associated with that tables column. The columns that have a restricted set of values (i. e. CVs) feature a drop-down list from which to choose the search value. Clicking on any row brings up an 'infosheet' i. e. a formatted display that is associated with the particular entity (Figure  3 ).
Upload component and data storage The upload component of MetaHCR facilitates the uploading of single gene and metagenome analyses. data  association. Samples associated with a particular investigation can be visualized in the form of a sub-table by clicking on the plus (+) sign. In turn, available analyses for a particular sample can be viewed by expanding the associated sub-table using the plus sign right next to the sample name. Moreover, MetaHCR can also handle the storage of large amounts of data associated with single gene and metagenome analyses in a central location. The location of this storage is configurable by the administrator. As an example, MetaHCR comes configured with the ability to store analyses source files and their upload log files in Amazon's AWS S3 storage-a cloud-based and secure storage facility.
Single gene and metagenomic data cataloguing Taxonomic summary tables produced by amplicon processing pipelines like QIIME and mothur  (23, 24)  are imported The search functionality provides an intuitive way to search for organisms and contextual data. In this example, the organism Flexistipes was searched in an effort to find any samples associated with this organism (steps 1-5). In addition, all organisms identified in a given sample can be listed along with their predicted metabolisms and threat they may pose to the production system (steps 6 & 7). in MetaHCR and can be visualized in the organism panel as well as in the corresponding 'single gene results' infosheet. In addition to the relative abundance of each of the identified taxa, information on the predicted predominant metabolism(s) and the potential threat this taxon may pose to the hydrocarbon production system are also displayed if known.
Metagenomic data captured in MetaHCR rely on IMG's annotation pipeline. Metagenomic information related to predicted genomic features (e. g. gene name, location, orientation, length, scaffold name, etc. ), functional assignments (e. g. COG, Pfam, EC number, KO, etc. ) and taxonomic affil-iations are imported into MetaHCR and exhibited under sample's 'metagenome results' tab (Figure  6 ). Hyperlinks to external resources are also provided where possible (COG, Pfam, EC number and KO). All metagenomic fields are searchable in real-time using the search field provided at the top of each table column. For example, a user can search a metagenomic dataset for potential nitrate-reducing microorganisms by either typing 'nitrate reductase' in the 'gene name' field or by searching for any corresponding identifiers for this gene in the appropriate column (e.
Intended use and benefits The MetaHCR data management system aims to be a platform for the cataloguing, data storage, sharing and searching for microorganisms from environmental samples that have been recovered from HCRs and that are associated with hydrocarbon degradation, biogenic H 2 S production, MIC and methane emissions. More broadly speaking, MetaHCR is a platform for a census of metagenomic data including all the relevant metadata for organisms (and their predicted metabolic functions) that are directly or indirectly related to HCRs. When a database contains a significant amount of valuable data and metadata, it is expected to be used as a knowledgebase in order to gain a better understanding of biological and biochemical processes taking place at these sites. Furthermore, it is intended to be utilized as a central repository for DNA sequences originating from metagenomic sequencing and thus to avoid using local desktops for file storage.
In the long-term, it may also phase out the use of spreadsheets on desktop or laboratory computers for the recording of metadata information, such as those related to a sample, sequencing run or organism. Therefore, MetaHCR aims at being a centralized management system for recording biological and associated metadata while it facilitates easy data sharing among collaborators or third-party institutions, when needed. MetaHCR is built on a clear and well-segmented data model and offers a user-friendly and simple web interface, which makes it accessible even to the non-expert. On the administration side, since the data are centralized, it greatly facilitates the support, maintenance and creation of data backups by simply using the database's built-in backup tools. In addition, migration of data to another server or similar platform is made simple.
Currently, data exchange between different databases can be achieved by either using the database's 'dump' and 'restore' functions or by CSV (comma-separated values) files. Data essential for the proper function and sharing between MetaHCR instances include those in the Attributes table as they comprise the ontological terms (i. e. CV) used in many of table fields in the MetaHCR database. MetaHCR is a standalone data management system and therefore can be deployed locally within a private organization for additional flexibility and security. Alternatively, MetaHCR can be deployed as a distributed database with the maintenance and updating being provided by a central authority. In this case, the implementation of the notion 'Organization' who provides access to only a set of authorized MetaHCR users can be particularly useful.
Performance The Django web development framework, which is used by MetaHCR, is stable, robust and highly responsive. Moreover, it has been designed to support web applications that are secure and scalable. Horizontal scalability for accessing MetaHCR can be realized with the use of multi-threaded web servers such as Apache and Internet Information Services. For databases, such as PostgreSQL, the replication of database servers can be employed. User interaction has been enhanced by using JavaScript on the client side. More specifically, MetaHCR has been built using the AJAX methodology, which makes a web application more collaborative, rapid and dynamic. Further, it reduces the server workload and increases the application's responsiveness. On the database side, the schema has been designed to allow for more efficient queries by splitting data into smaller and logically grouped tables.
On the contrary, MetaHCR does not provide any kind of automated metagenome annotation or community analysis, but it is rather designed to be used in collaboration with other metagenomic tools for automated sequence analysis, such as QIIME for marker gene surveys or IMG/M for metagenomic studies. Owing to open and modular architecture of MetaHCR, components may be added or interchanged. Therefore, additional metadata categories for other types of environmental and metagenomics studies, such as marine microbiology, can readily be added without the need to reprogram the application.
Future improvements One of the improvements from which MetaHCR would benefit most is the incorporation of data visualization tools such as Krona plots  (25)  for a more elegant representation of taxonomic or functional data compared to the current tabular forms. Other improvements pertain to the incorporation of corrosion attributes making it suitable for MIC related samples, enhancements on the way large sets of data are imported in MetaHCR as well as interconnecting multiple MetaHCR instances for synchronous data sharing. The latter would be particularly useful for sharing data between two or more institutions.
Conclusions The software presented here may be helpful for various tasks related to the management and investigation of complex metagenomics data and metadata for hydrocarbonrich environments. MetaHCR greatly facilitates the recording of relevant metadata, as well as the standardization of data, using CV whenever possible. Export of metadata or search results is facilitated by downloading these data in a CSV-formatted file readable by any spreadsheet software. MetaHCR integrates a user-friendly and modern front-end web interface to interact with the database backend, which makes the application accessible to the nonexpert. Moreover, it has been exclusively built using OSS and cutting-edge technologies, such as the Django web application framework. Equally important, the software has been extensively tested with 36 metagenomic investigations pertaining to HCRs retrieved from public raw data repositories.
The Feel++ toolboxes The numerical implementation is performed using the Feel++ -finite element C++ library -  [7] [8] [9]. Feel++ allows to use a very wide range of Galerkin methods and advanced numerical methods such as domain decomposition methods including mortar and three fields methods, fictitious domain methods or certified reduced basis. The ingredients include a very expressive embedded language, seamless interpolation, mesh adaption and seamless parallelization. It has been used in various contexts including the development and/or numerical verification of (new) mathematical methods or the development of large multi-physics applications  [19] [20] [21]. The range of users span from mechanical engineers in industry, physicists in complex fluids, computer scientists in biomedical applications to applied mathematicians thanks to the shared common mathematical embedded language hiding linear algebra and computer science complexities.
Feel++ provides a mathematical kernel for solving partial differential equation using arbitrary order Galerkin methods (fem, sem, cg, dg, hdg, crb) in 1d, 2d, 3d and manifolds using simplices and hypercubes meshes  [7] [8] [9] 22]  : i. a polynomial library allowing for a wide range polynomial expansions including H div and H curl elements, ii. a lightweight interface to Boost. UBlas, Eigen3 and PETSc/SLEPc as well as a scalable in-house solution strategy iii. a language for Galerkin methods starting with fundamental concepts such as function spaces, (bi)linear forms, operators, functionals and integrals, iv. a framework that allows user codes to scale seamlessly from single core computation to thousands of cores and enables hybrid computing. Feel++ provides also an environment for modeling and solving various kinds of scientific and engineering problems.
The framework, implemented in several Feel++ toolboxes, provides a language to describe these models. Based on JSON and INI(we use the. cfg extension) file formats, it is possible to configure and simulate a large class of models by defining the relevant physical quantities -such as the material properties, the boundary conditions, the sources, the couplings and the solvers. In this paper, we have used the MultiFluid toolbox which allows to setup all the necessary ingredients for a multifluid flow simulation. This toolbox, which mainly manages the coupling between the fluids present in the system, is built on others toolboxes inclined toward monophysics problem resolution, namely the FluidMechanics, LevelSet and AdvectionDiffusionReaction toolboxes.
Acknowledgments: The authors would like to thank the  French National Research Agency  ( MN-VIVABRAIN  projects  ANR-12-MONU-0010 ) for their financial support. Thibaut Metivet  and  Christophe Prud 'homme would like to acknowledge the support of the  Labex IRMIA. Most of the computations presented in this paper were performed using the Froggy platform of the CIMENT infrastructure https://ciment. ujf-grenoble. fr, which is supported by the  Rhône-Alpes region  ( GRANT CPER07 13 CIRA ) and the  EquipMeso  project ( ANR-10-EQPX-29-01 ).
Our strategy for the shared task was to take advantage of four main optimization techniques: (a) sequence-level distillation, in particular cross-class distillation from a transformer model  (Vaswani et al. , 2017)  to an RNN, (b) architecture search, changing the structure of the network by increasing the size of the most efficient modules, reducing the size of the most costly modules and replacing default gated units, (c) specialized precomputation such as reducing dynamically the runtime target vocabulary  (Shi and Knight, 2017) , and (d) quantization and faster matrix operations, based on the work of Devlin (2017) and gemmlowp 2. All of these methods are employed in a special-purpose C++-based decoder CTranslate 3. The complete training workflow including data preparation and distillation is described in Section 2.
Training and Distillation Following the shared task setup, we use the data set provided by WNMT 2018, which is a preprocessed and tokenized version of WMT 2014 on English-German translation 5. The training data contains about 4. 5M sentence pairs. We use news-test2013 as the validation set and newstest2014 and newstest2015 as the testing sets. Before training, we trained a 32K joint byte-pair encoding (BPE) to preprocess the data  (Sennrich et al. , 2015)  (actual vocabulary size of 34K). We limit the sentence length to 100 based on BPE preprocessing in both source and target side (excluding only 0. 31% of the training corpus). After decoding, we remove the BPE joiners and evaluate the tokenized output with multi-bleu. perl (Koehn et al. , 2007).