dataesr/scientific-paragraphs-categorization huggingface dataset:
Current SAS Applications In vivo T1 and T2 relaxation times extracted from MR imaging data are tissue dependent and their modifications may be characteristic of specific anatomical regions and pathological situations. Recently, we used the SAS infrastructure to produce quantitative 3D T1 and T2 maps for the healthy rat brain at 7T. Such maps were defined based on data acquired at two different   centers on twenty Sprague-Dawley rats at GIN (Grenoble, Fr) and twenty Sprague-Dawley rats at CRMBM (Marseille, Fr). We used three different fitting algorithms from three difference centers (CRMBM, MRICen (Fontenay-aux-roses, Fr) and Icube (Strasbourg, Fr) to compute the T1 and T2 values in each voxel and to evaluate the influence of image processing steps on the final maps. To derive relaxation time values per brain area, two multi-atlas segmentation pipelines from MIRCen and Icube were executed.
All data were stored on SAS and image processing pipelines were embedded into Docker containers and executed on the VIP processing platform. With the DTM, processed data were stored in SAS for each individual. This work investigated the influence of data acquisition centers (n = 2) and image processing pipelines (n = 3) on the production of T1 and T2 brain maps. Figure  6  shows the reproducibility of the T1 and T2 relaxometry values computed from two MR acquisitions for each rat in each center (intrareproducibility) and the coherency between the values obtained between the centers (inter-reproducibility). It showed the coherence of the final computed maps when the acquisition sequence parameters are optimized and harmonized between centers  (Deruelle et al. , 2020). Using SAS, the study demonstrates the feasibility of pooling animal data from different centers.
DISCUSSION Similarly to human research, sharing (data and tools) requirements will necessarily grow up in the next future for preclinical (animal) studies. The design of SAS was motivated by the needs of the preclinical research community. The problem of replication and reproducibility in preclinical studies is now well documented  (Sena et al. , 2010; Begley and Ellis, 2012; Tsilidis et al. , 2013)  and can negatively impacted the translation to human studies, leading to waste of resources, unnecessary risks for volunteers and slowing down new therapies development. The first cause of non-reproducibility is the weakness of experimental design including low power  (Begley and Ioannidis, 2015). With SAS, we show that animal population imaging is feasible and that research in this field can be conducted using tools compatible with open sharing and use of automated workflow tools to ensure accessibility and reproducibility of research outputs.
Our first preliminary experiments (see §3. 5) demonstrate the strong interest of the community to promote the federation of multiple sources of information, processing tools and diffusion of knowledge distributed in various centers for preclinical studies. We will promote data preparation and tools for reproducibility and reuse and participation in multicenter or replication studies. Thanks to the FLI-IAM architecture used, we will diffuse our results beyond the national community. Indeed, a recent external survey (30 interviews) indicated that the primary application domain for such an infrastructure was preclinical imaging where solution is missing. Note that developing such a platform requires some efforts: from 2016, up to 15 computer engineers were employed for Shanoir-NG and SAS developments.
Moreover, FLI-IAM is partner of OpenAIRE-connect, for connection with European large scale open science projects. Additionally, SAS will contribute to the promotion of this platform and in return will be internationally visible: data and tools will be interoperable and queryable via international platforms. In using SAS, users strongly advocate for the diffusion of acquired and processed data and dissemination of imaging processing pipelines. They promote Open Science by Design (Committee on Toward an Open Science Enterprise et al. , 2017), meaning that research conducted openly and transparently leads to better science. SAS is a first step for promoting Open science for small animal imaging and a contribution to the valorization of data and pipelines of reference. SAS will also be a good starting point for launching specific working group for animal MR studies at the European (RDA) or International (INCF, ISMRM) levels creating a culture that actively supports Open science by Design.
The idea is to seamlessly integrate SAS with the open science community and to provide researchers a tool for easy sharing in a controlled way. Shanoir-NG and its extension SAS are open-source software, published under the GNU General Public License (GPL), version 3. The source code is freely available on GitHub under 5. The current implementation of SAS is available at 6. Developer documentation is centralized in the GitHub Wiki 7 with code 8 and general user information can be found under http://shanoir. org. The development of Shanoir-NG is driven by a roadmap that is defined by the members of the consortium Shanoir within InriaSoft. Contributions are integrated in form of pull requests. The source code of Dicomifier is available at 9. OpenID Connect (OIDC) is an authentication layer on the top of the authorization framework OAuth 2.
0, a standardized protocol controlled by the OpenID Foundation. This protocol is implemented in Keycloak, an open source software product that implements the OpenID Connect protocol and then is used as the central authorization server. Renater is a highly fast and secured academic European network for internet access and associated services. SAML (Security Assertion Markup Language) defines a protocol for security information exchange. It is based on XML and proposes a unique (single sign-on) identifier. Apache Solr is a search and navigation platform built on the open-source HTTP server, Apache. Docker allows to define containers in a standardized way that encapsulate code and the associated dependencies for an execution on different computing environments. Singularity is an alternative solution to Docker.
Entity linking (EL) aims to recognise, disambiguate, and relate named entities to specific entries in a knowledge base. EL is a challenging task due to the fact that named entities may have multiple surface forms, for instance, in the case of a person an entity can be represented with their full or partial name, alias, honorifics, or alternate spellings  [29]. Compared to contemporary data, few works in the state of the art have studied the EL task on historical documents  [30, 16, 3, 4, 13, 23, 28]  and OCR-processed documents  [20]. In this paper, we present a deep learning EL approach to disambiguate entities on historical documents. We investigate the issues of historical documents and propose several techniques to overcome and reduce the impact of these issues in the EL task. Moreover, our EL approach decreases possible bias by not limiting or focusing the explored entities to a specific dataset.
We evaluate our methods in two recent historical corpora, CLEF HIPE 2020  [9] , and NewsEye datasets, that are composed of documents in English, Finnish, French, German, and Swedish. Our study shows that our techniques improve the performance of EL systems and partially solve the issues of historical data. This paper is organised as follows: we describe and survey the EL task on historical data in Section 2. Next, the CLEF HIPE 2020 and NewsEye datasets are described in Section 3. We detail our multilingual approach in Section 4. Then the experiments and the results are discussed in Sections 5 and 6. Lastly, we provide the conclusion and some final comments in Section 7.
Then, it crawls data from other linked data sets using equivalence links and fuses graphs of homologous individuals into a non-redundant graph in order to select the best candidate. Heino et al. [13]  investigated EL in a particular domain, the Second World War in Finland, using the reference datasets of WarSampo. They proposed a ruled-based approach to disambiguate military units, places, and people in these datasets. Moreover, they investigated problems regarding the analysis and disambiguation of these entities in this kind of data while they proposed specific rules to overcome these issues. The impact of OCR errors on EL systems, to our knowledge, has rarely been analysed or alleviated in previous research. Thus, the ability of EL to handle noisy inputs continuous to be an open question.
Historical Datasets Unlike contemporary data that have multiple EL resources and tools, historical documents face the problem of lacking annotated resources. Moreover, contemporary resources are not suitable to build accurate tools over historical data due to the variations in orthographic and grammatical rules, not to mention the fact that names of persons, organisations, and places could have significantly changed over time. To the best of our knowledge, there are few publicly available corpora in the literature with manually annotated entities on historical documents. Most EL corpora are composed of contemporary documents. Unfortunately, they do not contain the distinctive features found in historical documents. In this work, we focus on two corpora that contain historical documents in English, Finnish, French, German, and Swedish.
The first corpus was produced for the CLEF HIPE 2020 challenge 7 [8]. This corpus is composed of articles published between 1738 and 2019 in Swiss, Luxembourgish, and American newspapers. It was manually annotated by native speakers according to HIPE annotation guidelines  [8]. The second corpus was produced for the Horizon 2020 NewsEye project 8  and it is a collection of annotated historical newspapers in French, German, Finnish, and Swedish. These newspapers were collected by the national libraries of France 9  (BnF), with documents from 1814 to 1944, Austria 10  (ONB) with documents from 1845 to 1945, and Finland 11  (NLF), with Finnish and Swedish documents from 1771 to 1910 and 1920, respectively. Both corpora contain named entities that are classified according to their type and, when possible, linked to their Wikidata ID.
Wikidata 13  is a KB created by the Wikimedia Foundation 14  to store, in a structured way, data generated and used by the different Wikimedia projects, e. g. Wikipedia and Wiktionary. For instance, it has been used to annotate historical corpora, such as those used on this paper, CLEF HIPE 2020 and NewsEye. DBpedia  [19]  is a KB that structures and categorise information collected from different Wikimedia projects, including Wikipedia and Wikidata, while including links to other KBs such as YAGO  [26]  or GeoNames  15. For instance, it was used by  [6]  for annotating mentions of locations in Historische Kranten, a historical newspaper corpus. While  [23]  used DBpedia for annotating historical legal documents. Other examples of EL and DBpedia can be found in the works of  [10, 16]. In this work, we decided to build our own KB consisting of information from Wikipedia.
Nevertheless, rather than just focusing on the English Wikipedia, we make use as well of the versions found in the languages used in the datasets to evaluate: French, German, Finnish, and Swedish. The reasoning behind this is that despite the richness and coverage of the English Wikipedia, on occasion other versions of Wikipedia might contain information that is only found in a specific language. For instance, Valentin Simond, owner of the French newspaper L' Écho de Paris, has an entry only in the French Wikipedia  16.
Experimental Settings In the context of multilingual historical newspapers, documents tend to contain local information that is often specific to a language and one or more related geographical areas. The use of KB in the historical newspaper's language is an obvious choice because it reduces problems of data consistency while decreases noise from entities in other languages. For instance, entities can represent different things according to each KB. For example, the English and the Finnish Wikipedia pages with the title "Paris" do not describe the same entity; in Finnish "Paris" make reference to Greek mythology while the French capital is known as "Pariisi". Therefore, we trained our EL model for the corresponding language of historical newspapers. For the entity embeddings and the entity disambiguation model, we used the pre-trained multilingual MUSE  22  word embeddings with of size 300 for all the languages in the corpora.
The character embeddings are of size 50. As no historical data is available for English, we used the AIDA dataset  [15]  and validated on the CLEF HIPE 2020 data. Based on the statistical analysis of the training data, we defined a Levenshtein distance ratio of 0. 93 to search for other mentions in the probability table if this mention does not have a corresponding entry in the table  23. For the evaluation, we compute precision (P), recall (R), and F-score (F1) measures calculated on the full corpus (micro-averaging). For the mentions without corresponding entries in the KB, EL systems provide a NIL entry to indicate that these mentions do not have a ground-truth entity in the KB.
Evaluation As we previously stated, the semantic textual enrichment of historical documents depends on aspects such as the OCR quality or how a language has evolved. In order to analyse the EL performance on historical data and the impact of our techniques on the of entities in historical data, we present in the Tables  2  and 3  a simple EL baseline (p(e|m)) and different combinations of our EL approach (henceforth MEL). For the filtering experiments (see Section 4. 6), we predicted the five best candidate entities for a mention m based on the probability table (p(e|m)). The configuration MEL+ML+MC+F  24  achieved the best results for French and German languages in CLEF HIPE 2020 corpora (Table  2 ). 25 Our model for English was trained on a contemporary dataset which degraded the performance of the MEL model and, consequently, all the variations.
Despite the lack of historical training data, our model MEL+MC+F achieved the best results for the English data set (Table  2 ). For the NewsEye corpora, the MEL+MC+F version achieved the best results for all languages (Table  3 ). Similar to CLEF HIPE 2020, the MEL version generated the worst predictions. The filter increased the F-scores values of all EL versions. The combination of probability tables had almost no changes in the predictions. Though we generated the embedding representation for the 1. 5M most frequent entities in each Wikipedia language, several historical entities are not so frequent on this KB. As our EL approach only disambiguates candidate entities that contain embedding representations, the MEL version achieved worse results than the baseline (p(e|m)). The major impact of this limitation was on the CLEF HIPE 2020 corpora where our approach had a drop of 0.
It should be noticed that the filter is not free of errors. In some cases, the best candidate was positioned at the end of the rankings because DBpedia's categories did not match the categories defined for the named entity type, e. g. the journal "Le Temps", a product-type named entity, is not classified as a human work in DBpedia 27. As digital library frameworks tend to provide the top N most probable entities for a mention in a context, we analysed the performance of the best two EL approach versions when we provide the top three candidate entities for each mention. These results are presented in Table  4. The MEL+MC+F method achieved the best average F-score, which is remarkable considering that the issues encountered in multilingual historical data can increase the difficulty of this task.
Compared to Tables  2  and 3 , the results are at least 14% better than the top one prediction. Based on all the previous results, we can observe that our EL approach outperformed the baseline for both corpora in all languages. Thus, we can conclude that the proposed techniques partially attenuated the impact of historical data issues. As well, the proposition of the best candidates can accelerate the work of librarians and humanities professionals in the analysis of historical documents in several languages and on different subjects. Finally, despite the recent progress, the EL for historical data is still a challenging task due to the multiple constraints. Examples of these limitations are the lack of annotated training data and the existence of multiple missing historical entities in the KBs, which can limit the training of more robust models.
Multiple NDE technologies, including micromagnetic, electrical and ultrasound-based methods, are developed and applied to these multiple scales of samples in neutron-irradiated condition. The results are to be compared and combined across methods, samples and degradation parameters in order to define a hybrid approach and finally demonstrate it in a modular way. For the first time, a systematic study in terms of correlation of microstructure, mechanical properties, neutron irradiation conditions and non-destructive properties is carried out on a well-characterized set of samples. The aim is not only to extend the existing database, but also to include issues such as reliability and uncertainties of the techniques as well as effects caused by material heterogeneity. Furthermore, the capabilities of the individual NDE techniques and, as result, the performance of the NDE tool regarding the future application in the field will be determined.
HARMONICS HARMONICS (abbreviation for Harmonised Assessment of Reliability of MOdern Nuclear I&C Software) recognized that software can in general not be proven to be completely defect-free, and addressed the issue of reliability and safety of the computer-based systems that implement safety functions in nuclear power plants. HARMONICS had the objective to ensure well founded and up-to-date methods and data for assessing software of computer-based safety systems in Gen-II and Gen-III NPPs throughout the entire system lifecycle. It has taken advantage of the aforementioned advances to propose systematic and consistent, yet realistic and practical approaches for software assessment. The project addressed three key issues: software verification & validation (V&V), software safety justification, and quantitative evaluation of software reliability.
To this, NDE methods based on different physical principles have been developed and successfully tested on neutron-irradiated Charpy samples and thermally aged cladded blocks. TEAM CABLES faces multiple scientific challenges related to polymer ageing, which are in part covered by three PhD collaborations with academic partners. The overall ambition of TEAM CABLES is to allow NPP operators to improve their capacity to safely manage the lifetime of cables and thereby contribute to ensuring the lifetime extension of NPPs to 60-80 yr. To achieve this, a radically new way to predict the lifetime of cables (in terms of mechanical, physical and electrical parameters) is developed, using much more precise information about material composition and more relevant methods for analysing the data based on multi-scale studies of the materials.
End user implication Horizon 2020 focuses on dissemination, which clearly emerges in all ongoing projects. All three projects have designated dissemination work packages. TEAM CABLES pushes this idea particularly far, with a summer school, two end user workshops, a training workshop for NPP operators and researchers as well as a final symposium. ADVISE and NOMAD will hold at least one joint public symposium. To ensure the industrial applicability of models and tools developed in all those projects, end user groups composed of external advisors have been set up, with the main goal to assess the developed models and tools during and by the end of the project. HARMONICS, which was funded by FP7, held two end user workshops in order to establish and maintain a link with stakeholders. All projects set up public web sites with detailed descriptions of the projects and their publications  [2] [3] [4] [5].
The Event Index service of the ATLAS experiment at the LHC keeps references to all real and simulated events. Hadoop Map files and HBase tables are used to store the Event Index data, a subset of data is also stored in the Oracle database. Several user interfaces are currently used to access and search the data, from a simple command line interface, through a programmable API, to sophisticated graphical web services. It provides a dynamic graph-like overview of all available data (and data collections). Data are shown together with their relations, like paternity or overlaps. Each data entity then gives users a set of actions available for the referenced data. Some actions are provided directly by the Event Index system, others are just interfaces to different ATLAS services. In many cases, specialized views are offered for detailed data inspection, such as histograms, Venn diagrams, etc.
Data-oriented Implementation A new web service implementation (under development for Run 3)  [4]  is organized by data entity. Two prototypes were evaluated and Prototype 2 (see 3. 2) has been selected for the final implementation. A very generic dynamical web service GUI is put on top of the core application. This web service, customizable via stylesheets, is in principle re-usable for any graph-like data. The web service offers a global dynamical interactive view of all ATLAS data with relations between data entities and access to all available services. It is user-extensible. Navigation is provided via a dynamic hierarchical graph-like overview of all available data and data collections. Data are shown together with their relations, ownership, containment or overlaps. Some actions are provided directly by the Event Index system, others are interfaces to various external ATLAS services.
Prototype 1 Following the evolution of the Event Index Web Service towards the graph-like dynamical interface, Prototype 1 was developped to evaluate the feasibility of directly using the Graph Database for Event Index data. This prototype stores data directly in a JanusGraph database  [5]  on top of HBase  [6]. Access is provided via the standard Gremlin  [7]  language of the TinkerPop framework  [8]. By using the Graph Database directly, the prototype implementation has reproduced very performant graph-like Web Service with much less code then the previous implementation. It has been abandonned for two reasons: The relaively slow data injection and the incompatibility with other ATLAS database services mostly using SQL implementations. It has been decided to build a new prototype, bringing together SQL and Graph Database features.
Prototype 2 Both databases share the same keys. User sees them both under the same interface, all data corresponding to one key is represented by one element. The additional HBase database is much smaller than the Phoenix one, as only a subset of data are included and only new information is stored there. The Phoenix database is read-only, while the additional HBase database is modifiable. The additional information can contain: • Simple Tags, which can be also used in a search filter. • Extensions with any object, like trigger statistics and overlap, duplicated events list, etc. • Relations to other elements, which serve as a Graph Database emulation and can contain, for example, overlaps between datasets. HBase can also contain elements without their proper Phoenix partner, called Hubs. They represent virtual collections of elements, like dataset tags, stream names, run numbers, project numbers, etc.