dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, OpenML  [189]  is an online platform that facilitates the sharing and running of experiments, as well as the benchmarking of models. As of November 2018, the platform already contained one EEG dataset and multiple submissions. The MOABB  [78] , a solution tailored to the field of brain-computer interfacing, is a software framework for ensuring the reproducibility of BCI experiments and providing public benchmarks for many BCI datasets. In  [73] , a similar approach, but for DL specifically, is proposed. Additionally, a few EEG/MEG/ECoG classification online competitions have been organized in the last years, for instance on the Kaggle platform (see Table  1  of  [32] ). These competitions informally act as benchmarks: they provide a standardized dataset with training and test splits, as well as a leaderboard listing the performance achieved by every competitor.
Reproducibility The significant use of public EEG datasets across the reviewed studies suggests that open data has greatly contributed to recent developments in DL-EEG. On the other hand, 42% of studies used data not publicly available -notably in domains such as cognitive monitoring. To move the field forward, it is thus important to create new benchmark datasets and share internal recordings. Moreover, the great majority of papers did not make their code available. Many papers reviewed are thus more difficult to reproduce: the data is not available, the code has not been shared, and the baseline models that were used to compare the performances of the models are either non-existent or not available. Recent initiatives to promote best practices in data and code sharing would benefit the field of DL-EEG.
FAIR neuroscience  [196]  and the Brain Imaging Data Structure (BIDS)  [56]  both provide guidelines and standards on how to acquire, organize and share data and code. BIDS extensions specific to EEG  [136]  and MEG  [119]  were also recently proposed. Moreover, open source software toolboxes are available to perform DL experiments on EEG. For example, the recent toolbox developed by Schirrmeister and colleagues, called BrainDecode  [150] , enables faster and easier development cycles by providing the basic functionality required for DL-EEG analysis while offering high level and easy to use functions to the user. The use of common software tools could facilitate reproducibility in the community. Beyond reproducibility, we believe simplifying access to data, making domain knowledge accessible and sharing code will enable more people to jump into the field of DL-EEG and contribute, transforming what has traditionally been a domain-specific problem into a more general problem that can be tackled with machine learning and DL methods.
Eventually, we would like to indicate which studies have been submitted or verified by the original authors. By updating the data items table regularly and inviting researchers in the community to contribute, we hope to keep the supplementary material of the review relevant and up-to-date as long as possible. [26, 51, 150] ). 2 Clearly describe the data used. Make sure the number of subjects, the number of examples, the data augmentation scheme, etc. are clearly described. Use unambiguous terminology or define the terms used (for an example, see Table  1 ). 3 Use existing datasets. Whenever possible, compare model performance on public datasets. 4 Include state-of-the-art baselines. If focusing on a research question that has already been studied with traditional machine learning, clarify the improvements brought by using DL.
Limitations In this section, we quickly highlight some limitations of the present work. First, our decision to include arXiv preprints in the database search requires some justification. It is important to note that arXiv papers are not peer-reviewed. Therefore, some of the studies we selected from arXiv might not be of the same quality and scientific rigor as the ones coming from peer-reviewed journals or conferences. For this reason, whenever a preprint was followed by a publication in a peer-reviewed venue, we focused our analysis on the peer-reviewed version. ArXiv has been largely adopted by the DL community as a means to quickly disseminate results and encourage fast research iteration cycles. Since the field of DL-EEG is still young and a limited number of publications was available at the time of writing, we decided to include all the papers we could find, knowing that some of the newer trends would be mostly visible in repositories such as arXiv.
Our goal with this review was to provide a transparent and objective analysis of the trends in DL-EEG. By including preprints, we feel we provided a better view of the current state-of-the-art, and are also in a better position to give recommendations on how to share results of DL-EEG studies moving forward. Second, in order to keep this review reasonable in length, we decided to focus our analysis on the points that we judged most interesting and valuable. As a result, various factors that impact the performance of DL-EEG were not covered in the review. For example, we did not cover weight initialization: in  [51] , the authors compared 10 different initialization methods and showed an impact on the specificity metric, with ranged from 85. 1% to 96. 9%. Similarly, multiple data items were collected during the review process, but were not included in the analysis.
These items, which include data normalization procedures, software toolboxes, hyperparameter values, loss functions, training hardware, training time, etc. , remain available online for the interested reader. We are confident other reviews or research articles will be able to focus on more specific elements. Third, as any literature review in a field that is quickly evolving, the relevance of our analysis decays with time as new articles are being published and new trends are established. Since our last database search, we have already identified other articles that should eventually be added to the analysis. Again, making this work a living review by providing the data and code online will hopefully ensure the review will be of value and remain relevant for years to come.
In this review, we highlighted current trends in the field of DL-EEG by analyzing 156 studies published between January 2010 and July 2018 applying DL to EEG data. We focused on several key aspects of the studies, including their origin, rationale, the data they used, their EEG processing methodology, DL methodology, reported results and level of reproducibility. Among the major trends that emerged from our analysis, we found that 1) DL was mainly used for classifying EEG in domains such as brain-computer interfacing, sleep, epilepsy, cognitive and affective monitoring, 2) the quantity of data used varied a lot, with datasets ranging from 1 to over 16,000 subjects (mean = 223; median = 13), producing to 62 up to 9,750,000 examples (mean = 251,532; median = 14,000) and from two to 4,800,000 minutes of EEG recording (mean = 62,602; median = 360), 3) various architectures have been used successfully on EEG data, with CNNs, followed by RNNs and AEs, being most often used, 4) there is a clear growing interest towards using raw EEG as input as opposed to handcrafted features, 5) almost all studies reported a small improvement from using DL when compared to other baselines and benchmarks (median = 5.
4%), and 6) while several studies used publicly available data, only a handful shared their code -the great majority of studies reviewed thus cannot easily be reproduced. Moreover, given the high variability in how results were reported, we made six recommendations to ensure reproducibility and fair comparison of results: 1) clearly describe the architecture, 2) clearly describe the data used, 3) use existing datasets, whenever possible, 4) include state-of-the-art baselines, ideally using the original authors' code, 5) share internal recordings, whenever possible, and 6) share code, as it is the best way to allow others to pick up where your work leaves off. We also provided a checklist (see Appendix B) to help authors of DL-EEG studies make sure all the relevant information is available in their publications to allow straightforward reproduction.
[219]improved their accuracy on the SEED dataset using LeNet ([90]) from 49. 6% (without augmentation) to 74. 3% (with augmentation), from 34. 2% (without) to 75. 0% (with) using ResNet ([70]) and from 40. 8% (without) to 45. 4% (with) on MAHNOB-HCI dataset using ResNet. Their best accuracy was obtained with a standard deviation of 0. 2 and by augmenting the data to 30 times its original size. Despite impressive results, it is important to note that they also compared LeNet and ResNet to an SVM which had an accuracy of 74. 2% (without) and 73. 4% (with) on the SEED dataset. This might indicate that the initial amount of data was insufficient for LeNet or ResNet but adding data clearly helped bring the performance up to par with the SVM. Second, in[219], a conditional deep convolutional generative adversarial network (cDCGAN) was used to generate artificial EEG signals on one of the BCI Competition motor imagery datasets.
The Transkribus platform Various projects have undertaken work on the OCR of early printed materials and experimented with the recognition of handwritten manuscripts  (Bulacu et al. , 2009; Edwards, 2007; Firmani et al. , 2018; Fischer et al. , 2009; Springmann and Lüdeling, 2017; Terras, 2006; Weber et al. , 2018; Van der Zant et al. , 2009). However, there has not yet been sufficient interdisciplinary work applying deep neural network models to manuscript material and more importantly, there was previously no user-friendly platform to make this technology accessible. With Transkribus, historical manuscripts of all dates, languages and formats can be read, transcribed and searched by means of automated recognition. The Transkribus research infrastructure aims to provide a complete and reliable workflow for this process.
Users work with Transkribus to create "ground truth"[8] data that is suitable for machine learning. From submitted images and transcripts, the HTR engines  [9]  learn to decipher (historical) handwritten or printed text from digital images and can then automatically generate transcripts of similar material. Transkribus was conceived and launched in 2015 with funding from the European Commission's Seventh Framework Programme (FP7), as part of the tranScriptorium project (2013-2015)  [10]. Whilst tranScriptorium largely focused on computer science research, the implementation and development of Transkribus was placed at the centre of a successor project called  READ (2016 READ ( -2019)). Funded once more by the European Commission, this time under the Horizon 2020 scheme, READ aims to maintain, develop and promote a functioning online research infrastructure where new technologies can feed innovation in archival research.
, 2015). Digitised images and their transcripts are the main prerequisite for working with HTR. They must be pre-processed in the Transkribus GUI in order to become ground truth data that can be used to train an HTR model to transcribe a specific collection of historical material, either that written by one writer or a set of similar types of writing. There are three main stages to creating ground truth in the Transkribus GUI. The first is uploading digitised images to the platform. The second is using layout analysis tools to segment the digitised images into lines. The third is accurately transcribing the text of each of the lines in a digitised image. Transkribus accepts a range of image formats, and has sufficient server space to process large collections. When a user uploads images to Transkribus, these images remain private to their user account and are not made publicly available.
The neural networks can also learn from normalised transcriptions, where abbreviations have been expanded  (Thöle, 2017). The Transkribus GUI has a suite of tagging tools for those users who wish to create rich transcripts that could form part of a digital edition. At the current time, there is no benefit in marking up transcripts that are being prepared as ground truth. HTR engines are programmed to ignore tags and instead focus on recognising text. However, developments in Named Entity Recognition technology should permit the recognition of tagged content in the near future (Figures  3  and 4 ). In summary, users upload images to the Transkribus GUI, segment each page into lines and then transcribe each page with a high level of consistency. With these three simple steps, ground truth creation is complete.
During the training process, a small selection of pages from the ground truth is set aside as a test set and is not used to train the HTR. This means that Transkribus can provide CERs relating to the automated transcription of previously processed pages, as well as unknown pages from the same data set. The platform also has a comparison function that enables users to compute and generate a visualisation of the accuracy of the computer-generated transcription of any page from the ground truth. In the best cases, HTR can produce automated transcripts of handwritten material with a CER of below 5 per cent (meaning that 95 per cent of the characters are correct). Outputs from models trained on printed material can be even better, reaching CERs of 1-2 per cent. The use of dictionaries will, in many cases, improve the HTR results but the accuracy of neural networks on a purely visual level is high.
The experience of Transkribus users indicates that transcripts with these accuracy rates can be proofread and corrected relatively quickly, with less effort than would be required to transcribe each page from scratch (Alvermann and Blüggel, 2017) (Figure  6 ). If an HTR model is less accurate, with a CER of more than 10 per cent, experiments suggest that automated transcriptions become less useful as a research resource in themselves because correcting myriad errors is more time consuming than manual transcription. However, it does not follow that less accurate results are ultimately useless. Indeed, HTR output can still be a solid foundation for searching and indexing vast collections of digitised documents. The Transkribus GUI provides access to a sophisticated searching technology known as Keyword Spotting  [23].
HTR could provide volunteers with automated transcripts of simple pages to check and correct or help them to decipher complex passages by providing suggested readings of each word on a page  (Seaward, 2016). Passau Diocesan Archives  [26]  are utilising Transkribus to transcribe and search their large collections of sacramental registers  (Wurster et al. , 2017). Experiments with a set of 1,200 images of death registers written in nineteenth-century German (around 400,000 words written by 40 different scribes) have shown that a CER between 17 and 19 per cent can be achieved. Passau Diocesan Archives are also working to improve the automated Layout Analysis of tabular data by sorting tables from their collection into different categories that can be used as training templates. Improved table recognition and the possibility of exporting tabular data will have significant implications for the field, since many archival documents are laid out in tables and forms  (Clinchant et al.
At the time of writing, HTR tends to be strongest for Western scripts because it can draw upon a larger reserve of training data for common languages like English, French or Latin. However, Transkribus users are also starting to generate good results on texts written in non-Western languages. The University of Belgrade Library are working with Transkribus with a view to allowing users of their archive to access transcribed and searchable text. They have used Transkribus to train an HTR model to recognise Cyrillic handwriting from the twentieth century. A training set of some 7,000 words has generated a result where the CER is as low as around 2 per cent on material that the programme has seen before  ( Jerkov and Sofronijevic, 2017). With more words of training data, the recognition of previously unseen material should become stronger.
Transkribus users have also benefited from integrating the platform into the workflow of existing research projects. The Barlach 2020 project at the University of Rostock is working on a digital edition of letters written by the German sculptor and writer Ernst  Barlach (1870 Barlach ( -1938)). They have trained an HTR model with some 42,000 words of Barlach's writing, integrating an earlier edition of Barlach's letters into the training process as a dictionary  (Lemke and Onasch, 2017). The resulting model transcribes pages with a CER of around 9 per cent and the team are now using these automated transcripts as a starting point for 965 Transforming scholarship in the archives scholarly editing. The Centre for Manuscript Genetics at the University of Antwerp[33] is working on a digital edition representing the genesis of works by the Irish writer Samuel  Beckett (1906 Beckett ( -1989)[34])[34].
Automated transcripts allow for an unprecedented scale of access to digitised historical material, providing a basis for scholarly editing and research work. Moreover, it must be acknowledged that all of the statistics presented in this paper are likely to improve significantly following a major update of the technology known as HTR+, which has been developed by the CITlab team at the University of Rostock. HTR+ draws on the Tensorflow[39] software library developed by Google, which means that deep neural networks can be constructed more efficiently than ever before. Experiments on three handwritten data sets suggest that the training of HTR+ is up to ten times faster than previous versions of HTR  (Michael et al. , 2018). Most importantly, this technology can improve the CER of automated transcriptions by between 5 and 10 per cent.
Improving Transkribus The expanding network of Transkribus users has advantages for the usability and efficacy of HTR technology. User feedback and bug-reporting feeds directly into development work on the platform. As a system of machine learning, Transkribus also becomes stronger as more and more data are processed  (Carbonell et al. , 2013). All documents uploaded to the Transkribus GUI remain private and are not publicly shared. In the background however, the neural networks are learning from every piece of ground truth submitted in the system and 966 JD 75,5 consequently becoming better at recognising different hands, scripts and languages. The more Transkribus users there are, the stronger the HTR will be. In the computational science field, the automated recognition of manuscripts on the basis of a sufficiently large set of ground truth is now viewed as a scientifically solved problem.
The growth of a research community will be facilitated in two ways. First, users can already exchange models among themselves or between different collections and this will be made easier in the future. Second, as has been suggested above, the Transkribus team will train global models, which will unite different sets of training data and thus cover a wide variety of document types and writing styles. It makes more sense to adapt existing models, benefiting from the training data that is already in the system rather than training every new model from scratch. In addition, there are future developments that need to occur in the technology behind Transkribus. There remain problems with the recognition of documents with a layout that is tabular or otherwise complex. Research will continue into the recognition of structural elements such as marginalia, headlines, addresses, dates, salutations and signatures.
From this point onwards, Transkribus services will be provided as part of a European Cooperative Society (SCE) based at the University of Innsbruck. This is a legal entity founded with the objective of fulfilling the needs of its members, where profit is shared between members and used to improve services  [43]. The working title for the initiative is READ-COOP. This legal basis is intended to promote cooperation between archives, libraries, universities and the general public. At the time of writing, a freemium service model is planned, with a mixture of free and paid-for services. The availability of documents, tools and data for the members of the network will continue to be a central element of the platform, promoting open research which allows confidence in the results generated from the system.
Preparations for the implementation of READ-COOP were presented at the second Transkribus User Conference in November 2018  (Dellinger, 2018). Regular business operations will begin on 1 July 2019. To become viable in the long term, the platform needs to continue to support its research community, while generating enough resources to cover staffing and infrastructure costs. The alternative, of course, is that commercial digitisation providers will act as gatekeepers to HTR technologies that they can afford to underpin: restricting access to particular collections, and subscribing users (and seldom making their computational methods transparent). These concerns come at a time when HTR is ready to bring potential change to the wider archival environment. Training data currently available in Transkribus can already be used to create models that provide the basis for Keyword Spotting to search substantial parts of the archive stock in English and German.
For other languages, such as Dutch or Finnish, there is also sufficient training data now available to achieve useful results at least for parts of the document stock. In a few years' time, it can be predicted that sufficient training data will be available to make the majority of digitised archival holdings in Europe searchable with this technology. It is therefore imperative that HTR remains accessible to libraries, archives and individuals who would benefit from it, to allow vastly improved access to our written cultural heritage. Transkribus users will always be able to access and export their ground truth data. These data allow users to analyse the assumptions upon which their results were built and consider possible limitations and biases of automated transcriptions. Open discussion of algorithmic provenance and dependencies is important to develop trust in the reliability of research resources generated with artificial intelligence  (Dayhoff and DeLeo, 2001; Samek et al.
Acknowledgements This research was funded as part of the  Recognition and Enrichment of Archival Documents (READ)  project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 674943. This research was previously funded as part of the tranScriptorium project. This project received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under Grant Agreement No. 600707. The authors would like to extend thanks to all users of the Transkribus platform.
Introduction The main goal of the present manuscript consists in introducing to tsunami modelling community some state-of-the-art numerical methods for solving nonlinear hyperbolic equations, which allow to use the available degrees of freedom in the (nearly-) optimal way. Until the last 15 years, tsunami wave modelling has been done in the framework of Nonlinear Shallow Water Equations (NSWE). The numerical simulations have been performed by practitioners essentially with several well-established community codes. To give a few examples, let us mention the code TUNAMI  [50]  based on a conservative finite difference leap-frog scheme on staggered grids. This scheme approximates NSWE on real bathymetries. Another widely used code is MOST, which is based on the dimensional splitting method *  [86, 87].