dataesr/scientific-paragraphs-categorization huggingface dataset:
Entity-fishing Entity-fishing is a service implementing the task of entity recognition and disambiguation using both Wikipedia and Wikidata. It is designed to be domain agnostic, thus giving the flexibility to be upgraded to support disambiguation of specialized entities with minimal effort. It currently supports five languages: English, French, German, Italian, and Spanish. Entity-fishing can process raw text with optional pre-identified mentions or entities which are used to help resolve ambiguities. It supports search queries-short text with very minimal context-and PDFs. PDF support is provided by the GROBID library  (Lopez 2009). The service provides a web interface (fig. 1 ) and a REST API (4. 4) for third-party service integration.
• Acronyms extraction 15 List of Wikipedias, accessed July 24, 2020, https://en. wikipedia. org/wiki/List_of_Wikipedias. 16 GitHub repository, accessed July 8, 2020, http://github. com/kermitt2/grobid-ner. 17 "Classes and Senses," GROBID NER User Manual, accessed July 8, 2020, http://grobidner. readthedocs. io/en/latest/class-and-senses/. An acronym is a word (usually a name) composed as an abbreviation from the initial components in a phrase, usually from individual letters (as in NATO or laser) but sometimes also from syllables (as in Benelux). Acronyms and initialisms (abbreviations formed like acronyms but pronounced letter-by-letter) are widely used in research articles in order to optimize space and to communicate concepts and methods more effectively (e. g. , humoristic acronyms are usually easier to remember).
Data Storage The data storage layer is responsible for maintaining the large amount of data which cannot be kept in memory. The service is handling the entire contents of the Wikipedias (see sec. 4. 6) for our different languages (each consisting of several gigabytes of raw data) and precomputed data (such as the number of links per article source and destination, or the number of documents). During normal service operations, entity-fishing reads only from data storage, which "simplifies" the requirements (e. g. , no risk of dirty reads and/or performance issues). We evaluated different storage technologies: NOSQL Databases such as MongoDB were discarded because of their complexity. We selected a light key-value database with memory-mapped files: Lightning Memory-Mapped Database (LMDB). Developed by Symas 18  , it is fast storage for the OpenLDAP project that fully supports ACID semantics, concurrent multithread read/write access, transactions, and zero-copy lookup and interaction, and is available with bindings for several languages (Java, Python, etc.
Wikidata As stated on its web site, "Wikidata is a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia, Wikimedia Commons, the other wikis of the Wikimedia movement, and to anyone in the world. "  25 ikidata is a collaboratively edited knowledge base hosted by the Wikimedia Foundation  (Vrandečić 2012). It is intended to provide a common source of data which can be used by Wikimedia projects such as Wikipedia, and by anyone else, under a public domain license (CC-0). Wikidata has become widely used: for example, Google decided to migrate its knowledge base Freebase into Wikipedia  (Pellissier Tanon et al. 2016). Wikidata is the largest collaborative database in the world, administered by only six staff members with more than 18,000 human collaborators and several (semi-)automatic bots  (Steiner 2014).
Motivation and Rationale The decision to use Wikipedia and Wikidata as data sources is justified by the fact that both are generic and provide a basic, stable, and fairly complete knowledge set that can be systematically enriched by specialist domain data. Compared with other sources of information, they are the most complete available to use, reuse, and redistribute. Wikipedia is released under a CC-BY (attribution) license while Wikidata has chosen CC-0 (full copyright waiver). We would like to stress here the importance of the licensing choice in the long term, particularly when dealing with scientific knowledge. Many people have shown concern about the fact that Wikipedia, being a collaborative source of information, could be biased by the contributors' opinions. This concern has a real foundation, without any relevant impact on the results from the entity-fishing process.
The way entity-fishing exploits Wikipedia relies on the graph network of concepts rather than the deep meanings of the articles themselves. Finally, the use of externally managed data sources is strongly motivated and well advocated in software engineering best practices: maintainability and independent management, respectively. First, the amount of information managed is too big to be handled internally by the entity-fishing project itself. Second, having an independent body (the Wikimedia Foundation) administering the sources assures the relevant competences will be available for management, engineering, and content.
Basic Organization In this section, we examine how the data have been organized and integrated into the knowledge base. Earlier in this paper (sec. 4. 3. 2 and fig. 3 ) we mentioned that the knowledge base is divided into two main areas corresponding to language dependent and language independent information, respectively. The language independent part, corresponding to the generic data provided by Wikidata, contains metadata across all languages and is accessed through the UpperKnowledgeBase object. Language dependent information, representing a Wikipedia in a specific language, is offered via the LowerKnowledgeBase object (fig. 6 ). The data are accessed by means of the language-independent component (UpperKnowledgeBase) to get the general concept and further drawing on available specific language resources through the LowerKnowledgeBase.
This component provides convenient access to all of the Wikipedia content through the programmatic API. Technically, the infrastructure itself is hosted in a large computing centre in Lyon 30. A long-term preservation facility hosted at the CINES data centre 31 based in Montpellier is also used. In addition, a group of correspondents in the MSH 32 (Maison des Sciences de l'Homme) network all over France is in charge of relaying information about Huma-Num's services and tools.
largest and most popular general reference work on the internet. Owned and supported by the Wikimedia Foundation, a non-profit organization supported by donations, it was launched on January 15, 2001, by Jimmy Wales and Larry Sanger and initially only supported the English language 22. Other languages were quickly developed in the months following the launch. With over 6 million articles as of this writing, the English Wikipedia is the largest of the Wikipedia encyclopedias 23. Wikipedia has reached a high level of completeness and popularity, with more than 50 million articles in over 300 different languages. Statistics 24 from the Wikimedia Foundation show popularity in the order of billion page-view and million of unique visitors each month. Its level of coverage and reliability has made Wikipedia a reference database for many information extraction processes.
Acknowledgments We would like to address our warmest thanks to  Patrice Lopez , who designed, demonstrated, and implemented the first version of entity-fishing during the  CENDARI H2020  project. Patrice is also the author of GROBID 44 (GeneRation Of BIbliographic Data) (Lopez 2009), a machine-learning library for extracting, parsing, and restructuring raw documents such as PDF into structured TEI-encoded documents. We would like to thank our colleagues within the  HIRMEOS  project, especially  Open Edition  and the  University of Göttingen State Library , for the particular support they provided in testing and disseminating entity-fishing. Finally, we would like to thank  Huma-Num  for hosting the service within their infrastructure.
Acknowledgements We would like to thank  D. Andriot ,  N. Bobev ,  C. Eloy ,  A. Guarino ,  M. Gutperle ,  O. Hohm ,  G. Larios ,  H. Nicolai ,  C. Nunez ,  B. Robinson ,  E. Sezgin ,  A. Tomasiello ,  J. van Muiden ,  O. Varela ,  D. Waldram , and  N. Warner  for useful discussions and comments. We acknowledge the  Mainz Institute for Theoretical Physics (MITP) of the Cluster of Excellence PRISMA+  (Project  ID   39083149 ) for hospitality while this work was initiated. EM is supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) via the  Emmy Noether program  " Exploring the landscape of string theory flux vacua using exceptional field theory"  (project number  426510644 ).
Reconstruction method and model inputs Reconstructions were made with the help of GPlates, a free plate kinematics software developed by EarthByte (http:// www. gplates. org; Boyden et al. , 2011). GPlates works with "features", polygones or polylines (mostly polylines in our case), that are rotated about Eulerian rotation poles. For reconstructing the Western Mediterranean, and integrating these reconstructions in the whole Mediterranean framework, we used more than 400 such features (Fig. 2 ). Several tables are provided in the Supplementary Materials of this paper. Supplementary Material #1 contains (i) the geological, geophysical and paleomagnetic constraints used for the reconstructions, (ii) the associated list of references and (iii) the list of features (rigid polygons) used for the reconstructions.
, 2016). Additional information comes from the pressure-temperature-time constraints obtained on exhumed metamorphic rocks that are used as a proxy for burial as a function of time, bringing some insights on the thermal regime and active geodynamic processes. Hence, HP-LT and HT-LP metamorphic events are diagnostic of syn-and post-orogenic environments, respectively. Kinematic indicators in the exhumed MCCs are also used to construct the kinematic model. We avoided using any a priori dynamic ideas to construct the kinematic model, using only published kinematic data for the large plates surrounding our study area and geological and paleomagnetic data for the internal kinematics. We thus consider that our kinematic model is as independent as possible of any dynamic considerations and can then be used to discuss dynamic processes.
Acknowledgments. This paper is a contribution of the  OROGEN  project, a common venture of Total,  CNRS  and  BRGM. Stimulating discussions with the Orogen Community helped shaping the manuscript. Special thanks are due to  Emmanuel Masini ,  Sylvain Calassou  and  Olivier Vidal  as well as to the co-PI's of the project,  Thierry Baudin ,  Isabelle Thinon ,  Mary Ford ,  Gianreto Manatschal ,  Frédéric Mouthereau ,  Benoit Issautier ,  Adnand Bitri ,  Eric Lasseur  and  Abdeltif Lahfid  for stimulating brainstormings during the course of the project. Discussions with  Dominique Frizon de Lamotte  and  Rémi Leprêtre  were very useful at an early stage of this project. Constructive reviews by  Francesc Sàbat ,  Jaume Vergès  and  David Pedreira  are much appreciated. The full GPlates project can be obtained from  Laurent Jolivet  (laurent.
In the era of immunotherapy and precision medicine, higher resolution sequencing data are required to characterise heterogeneous tissues and complex diseases such as chronic liver disease and cancer. 4] [5] [6]  In scRNA-seq, liver tissue is dissociated, single cells captured, and RNA sequencing is performed using several workflows (Fig. 1, 2 ). ScRNA-seq generates very large datasets of thousands of gene transcripts per cell. These datasets are usually represented in a compressed 2D space, e. g. t-distributed stochastic neighbour embedding (t-SNE) map,  7  where each cell is a dot and the distance between cells is a function of their similarity (Fig. 1A ). In this 2D space, cells can be clustered according to their similarity and single or multiple genes can be plotted on separate t-SNE maps.
22 ovoSpaRc assumes that physically apposed cells probably share similar transcriptomic profiles and that physical distance can be a function of transcriptomic difference. The algorithm can reconstruct, in a virtual space, the organisation of symmetric tissues, e. g. normal liver and intestine, but also early embryos and charts of complex tissues such as the cerebellum and kidney. However, novoSpaRc has not yet been used to investigate liver spatial organisation and function. One of the challenges of inferring spatial information from standard scRNA-seq data is the requirement for careful, follow-on validation by direct spatial techniques such as immunohistochemistry, immunofluorescence or FISH. ] [25]  These systems generally consist of a special slide covered by beads carrying oligos composed of a polyd(T) tail for RNA capture, a spatial barcode defining bead position, a UMI for transcript count, promoters and adaptors for cDNA synthesis, amplification and sequencing, and a cleavage site to detach the oligos from the slide (Fig.
Distributed algorithms offer challenges in checking that they meet their specifications. Verification techniques can be extended to deal with the verification of safety properties of distributed algorithms. In this paper, we present an approach for combining correct-by-construction approaches and transformations of formal models (EVENT-B) into programs (DISTALGO) to address the design of verified distributed programs. We define a subset LB (Local EVENT-B) of the EVENT-B modelling language restricted to events modelling the classical actions of distributed programs as internal or local computations, sending messages and receiving messages. We define then transformations of the various elements of the LB language into DISTALGO programs. The general methodology consists in starting from a statement of the problem to program and then progressively producing an LB model obtained after several refinement steps of the initial LB model.
Introduction EVENT-B is a formal modelling language developed by Abrial  [1]  offering key features such as the use of set theory as a data modelling notation, the use of refinement to relate system models at different abstraction levels and the use of mathematical proofs to verify consistency between refinement levels. Moreover, the language is supported by the environment RODIN  [2]  which is extensible through the mechanism of plugin. Previous works  [3, 14, 10, 11]  illustrate the correct-by-construction design of distributed algorithms using EVENT-B models and refinements; those works show that at an adequate level of concretization of models, one can derive a distributed algorithm in a pseudo algorithmic notation. However, the derivation of concrete EVENT-B models requires to develop a methodology related to a given class of problems.
For instance, we have produced a plugin EB2RC  [12, 5]  which automatically generates a recursive algorithm from an EVENT-B model derived by analysis of a problem such as Floyd's algorithm, or search algorithms, or sorting algorithms. The transformation of an EVENT-B model into a recursive algorithm was based on the definition of a class of (concrete) EVENT-B models satisfying constraints making the transformation automatic. In the current paper, we study the systematic transformation of concrete EVENT-B models into the DISTALGO  [8]  programming language. In fact, the design of a distributed algorithm using the correctby-construction approach starts by expressing the required computations in a very abstract EVENT-B model (AM) and then progressively refining the model into a final concrete model (CM) very close to an algorithmic expression of the distributed algorithm.
The main advantage of such a refinement-based process is the preservation of safety properties of the different models: the refinement is checked by discharging a list of proof obligations. We do not describe the process for developing the model CM which is supposed to be a local EVENT-B model and which could be translated into an algorithmic distributed notation. We focus on the transformations required for obtaining a DISTALGO program from a local EVENT-B model as indicated in Figure  1 : the program program. da is generated from CM and CONTEXT-CM. We will not provide the proof of correctness of the translation but we will give enough details for trusting it. The proof will be given in a future work. An overview of the integrated development framework Figure  1  provides an overview of our integrated development framework for refinement-based program verification of distributed algorithms.
The general methodology starts by stating the problem to solve by listing the requirements (i. e. the contract) attached to the problem; the requirements can be either expressed in a formal language or in an informal textual language. One has then to specify the EVENT-B machine AM translating the main requirements for the given problem. Then a list of formal EVENT-B refined machines are produced to obtain a final EVENT-B machine and context, CM and CM-CONTEXT. Finally, the translations of these final context and machine into DISTALGO components and programs are generated in two main steps: the automatic compilation of CM and CM-CONTEXT into a DISTALGO program, and the manual tuning of the obtained DISTALGO components (if some configurations were not specified in the model). The refinement block (with nodes AM, CONTEXT-AM, CM and CONTEXT-CM) in Figure  1 , illustrates the mechanism for deriving machines via refinement.
The result of the refinement is the EVENT-B machine CM, which contains the refined events and the proof obligations that must be discharged in order to prove that the refinement is correct. Transformations of this EVENT-B machine CM into a DISTALGO program is based on the extraction of information concerning the network and the process classes from the context CONTEXT-CM, and on the analysis of the localization of the different variables. The events of CM are supposed to be local which means that they are using only local instances of variables. For instance, pc will be a local variable with an instance pc(p) for the process p. We will define precisely the localization process from the code of EVENT-B models. Finally, some constants whose values are not defined in the context are instantiated during the configuration phase.
Related work We described a simple extension of the call-as-event paradigm  [9, 12]  to handle the design of concurrent programs in the coordination-based approach but we do not target a specific programming language as DISTALGO. The EB2ALL (http://eb2all. loria. fr) framework provides a list of transformations of EVENT-B models into classical programming languages (C, C++, Java,. ) but it does not consider distributed algorithms. The current work can be considered as adding a new target programming language but with the target of a distributed program like it was proposed in Visidia (http://visidia. labri. fr) together with EVENT-B with the plugin B2VISIDIA relating the local EVENT-B model and a VISIDIA program. However, the VISIDIA approach addresses distributed programs defined as set of rewriting rules of graphs, which is less concrete and effective than DISTALGO programs.
Code generation from classical B models are supported by the Atelier B (http://www. atelierb. eu) tools but those transformations do not consider distributed programming models. Atelier B supports code generation into Ada, C, C++. Moreover, it is defined over Classical B software models restricted to the B0 language which is a computable subset of the B language but without communications features. An EventB2Java  [4]  tool for RODIN has been developed for translating any EVENT-B specification into (sequential) JML or Java code. Finally, a Tasking EVENT-B  [7]  for RODIN extends the EVENT-B language to provide features for specifying concurrent multi-tasking systems. A model is decomposed into several tasking machines which schedule and perform tasks involving shared machines which correspond to protected resources accessed by tasking machines.
The plugin provides a tool support for translating a tasking specification into ADA code. The generated programs are not distributed ones and consider only a subclass of the ADA language. Our work focuses on generating DISTALGO programs from local EVENT-B models and provides a way to preserve powerfull safety properties from the local models. Overview of the paper In the next section, we briefly present the two languages EVENT-B and DIS-TALGO. Section 3 shows how distributed programs can be modelled in the sub-language called LB for Local EVENT-B. Finally, in Section 4 we define the transformation of LB models into DISTALGO programs. Our paper then concludes with the results and future work. A more detailed description of the translation as well as the complete definition of the LB models and of the DISTALGO program of our example are available in  [6].
The DISTALGO Distributed Programming Language DISTALGO  [8]  is a programming language used to develop distributed algorithms by providing high level programming mechanisms such as communication primitives for the exchange of messages between a set of processes. A DISTALGO program is composed of several process classes managed by a main module (see Example 4. 1). A process class is made of a setup method which initializes the class attributes, a run method for carrying out the main execution flow, several receive methods for handling the reception of messages and other user defined methods that may be called by the run method. For each process class PC, the main module uses a statement of the form pset=new(PC,num=n) to build the set pset of n processes running the algorithm specified for PC.
A graphical representation of the message exchanges is given in Figure  2. Since DISTALGO is implemented as a PYTHON module all the data structures and primitives of the latter can be used. In our translation we use, in particular, lists, sometimes built using the function range which creates a list interval of integers, and sets, sent ⊕ (m, q) ch(p, q) ch(p, q) ⊕ m mq ⊕ (m, p) yield point ch(p, q) received ⊕ (m, p) send(m,to=q) exec. receive handlers process p process q Figure 2: Comunications in DISTALGO: the communication channels ch, as well as the message queues mq cannot be accessed explicitly in DISTALGO; only the sent and received messages can be accessed using the sent and received primitives. which can be built from a list or using the function setof(expr(x 1 ,. , x n ), x 1 in S 1 ,.
, x n in S n , pred(x 1 ,. , x n )) which is a set comprehension with expressions built out of elements in the sets S 1 ,. , S n and satisfying a predicate. PYTHON dictionaries are also used; these can be updated with the elements of another dictionary using the method update and cloned with the function deepcopy which copies an object and the objects it contains recursively. The DISTALGO boolean functions each and some acting as a universal quantifier and an existential quantifier respectively, are also used.
Modelling Distributed Algorithms in EVENT-B We use the modelling technique of G. Tel  [13]  and express a distributed algorithm as a set of local algorithms, each local algorithm being able to do an internal action, or to send a message, or to receive a message. The final context CONTEXT-CM and machine CM in Figure  1  model such a distributed algorithm using a subset of the modelling language EVENT-B, denoted LB (Local EVENT-B). We use the simple distributed algorithm introduced in Example 3. 1 to explain the methodology for modelling algorithms following Tel's technique and the restrictions imposed on LB. Example 3. 1. We consider a distributed algorithm where each process q in a set of processes Q sends its stored value to a central process p who previously made the corresponding requests.