dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Here, we showed that HBD is a data source that allows predicting the ILI activity as well or even better than internet data. This can be done using two types of models with similar performance-SVM (a machine learning model) and elastic net (a model of regularized regression). This is a promising way for monitoring ILI incidence rates at the national and local levels. HBD presents several advantages compared with internet data. First, they are real health data and can give information about patients (sex, age, and comorbidities). This could allow for making predictions on ILI activity targeted to a specific group of people. Second, hospital data can be used to determine the epidemic severity by taking into account the number of patients who were admitted in intensive care or died as a result Finally, HBD could also be used to estimate the incidence rates of diseases that do not generate internet activity.
in the Northwest area of France associates six University Hospital centers (Angers, Brest, Nantes, Poitiers, and Rennes et Tours) and Orleans Regional Hospital Centre, thus collecting data on patients in the Bretagne, Centre-Val de Loire, and Pays de la Loire regions. This corresponds to 15. 5% of Metropolitan France and 14. 4% of the entire French population. Another way to aggregate patient data could be a cloud-based platform, and we are currently setting up this kind of architecture; this platform will integrate two University Hospital centers, Brest and Rennes, the French health reimbursement database (Système national d'information interrégimes de l'Assurance Maladie) and registries, such as the birth defect registry or cancer registry.
Acknowledgments We would like to thank the  French National Research Agency  for funding this work inside the Integrating and Sharing Health Data for Research Project (Grant No. ANR-15-CE19-0024 ). We thank  Magalie Fromont Renoir  and  Ronan Le Gue´vel  from the University of Rennes 2 who provided insight and expertise that greatly assisted the research. We also thank the  French Sentinelles network  for making their data publicly available.
g. ,  Madau & Dickinson 2004) , and possibly earlier for (proto-)cluster galaxies  (Chiang et al. 2017; Overzier 2016). The two sources have an angular separation of 0. 72 arcsec inferred from their archival Hubble Space Telescope (HST) image (i. e. , 6 kpc at the redshift of the sources) and belong to the distant cluster ISCS J1426. 5+3339. They are located at an angular separation of ∼1 arcmin (∼500 kpc) from the cluster center. The CO detection reported in this work is the first one resulting from a wider search for molecular gas in distant cluster galaxies. In Sect. 2, we describe the two targets; in Sect. 3 we describe the observations and data reduction; in Sects. 4 and 5 we present and discuss the results, respectively; in Sect. 6 we draw our conclusions. Throughout this work, we adopt a flat ΛCDM cosmology with matter density Ω m = 0.
Observations and data reduction We observed the two sources with eight antennas using the compact array D-configuration of the NOEMA interferometer (P. I. : Castignani). Such a configuration provides the lowest phase noise and the highest surface brightness sensitivity. It is therefore best suited for detection experiments such as ours. Our observations aim at detecting CO(2-1) from the two sources at observer frame frequency ν obs = 106. 239 GHz (i. e. , ν rf = 230. 538 GHz in the rest frame), given the spectroscopic redshift z = 1. 17  (Zeimann et al. 2013). At an angular resolution of ∼ 3 arcsec, the two target sources are unresolved. The phase center of our observation is R. A. = 14h:26m:26. 090s, Dec. = 33d:38m:27. 600s, corresponding to the J2000 coordinates of the northernmost galaxy among the two  (Zeimann et al.
The fact that the two unresolved galaxies are detected in CO(2-1) by NOEMA both in projected space at the source coordinates and in frequency at the source redshift further supports the reliability of both our detection and the S/N estimate. We stress that the noise associated with interferometric observations is not Gaussian in the projected space. In fact, spurious peaks due to the side lobes can occur at coordinates that are not coincident with the phase center of our observations, also considering the low S/N of the our detection. These aspects prevent us from estimating the S/N directly from the intensity map of Fig. 2a. Instead, similarly to previous work  (Walter et al. 2016; Decarli et al. 2016) , we need to rely on a positional prior: we cannot search for CO detections within the entire data cube (rms=0.
Acknowledgements. This work is based on observations carried out under project number  S17BJ  with the  IRAM NOEMA Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). GC thanks  Cinthya Herrera  ( LOC at NOEMA, Grenoble ) and all IRAM staff for the hospitality in Grenoble and precious help concerning data reduction. We thank the anonymous referee for helpful comments that led to a substantial improvement of the paper. GC thanks  Paola Dimauro  for the help concerning the use of SExtractor. CC acknowledges funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No. 664931.
In this respect, the approach is different from  [18]  where two interfaces where used for the local/global coupling. In addition, the proposed method can treat non-conforming geometries in a more robust and non-intrusive way when compared to  [32, 33]. The proposed method manages all the numerical complexities that arise with multiscale aeronautical concerns: local non-linear model, non-conforming geometries, shell-to-solid coupling, fully non-planar structure, etc. According to our knowledge, this is the first time, in the context of non-intrusive coupling, that a numerical strategy has been implemented to solve all these inconveniences together. A demonstrator of the method is developed in the Abaqus v6. 14 software in the aim of encouraging its dissemination to the industrial world. The article is organized as follows: a general presentation of the proposed methodology is made in "The proposed coupling strategy" section.
"Implementation" section then gives a more detailed description of the implementation of the strategy in the industrial software Abaqus. "A first validation example" and "Application to industrial realistic examples" sections are devoted to the validation of the approach through numerical experiments. First in "A first validation example" section an academic test case was considered, with assumed geometrical simplicity and low numbers of dofs, locally and globally; even if not representative in terms of size and complexity, it does incorporate critical issues to be addressed (non-planar geometry, shell to solid transition, composite material damage and failure). Then in "Application to industrial realistic examples" section the multiscale strategy is exemplified on industrial models derived from actual engineering practices.
More Fig. 2  The iterative non-intrusive exchange procedure. Global and local problems are solved alternatively and only displacement and force exchanges at interface are required than a technique to couple different models, the method also offers the possibility to simply couple different numerical codes. Indeed, the global and local problem being solved alternatively, an industrial code can be used for the large-scale computation while a dedicated research code can be set up to solve a specific local behavior. The price to pay to reach non-intrusiveness is the number of iterations but this one can be deeply reduced by means of accelerations techniques, such as based on an Aitken's Delta Squared method or a Quasi-Newton method (see, e. g. ,  [10, 20, 34] ). Remark 1 It may be noticed that the first iteration of the non-intrusive algorithm exactly corresponds to the so-called submodeling approach (see, e.
Co-simulation derived implementation path The co-simulation technique is an analysis feature proposed by SIMULIA that allows coupling different FE analysis and managing data exchanges between user-defined domains. Quantities to be exchanged and time-synchronization during analysis can be defined by the user in a specific co-simulation configuration file. Abaqus v6. 14 co-simulation native capabilities have been extended through additional specific developments (user subroutine and Abaqus python script for time synchronization of global/local jobs). This environment was used for replicating a non-intrusive algorithm, while accounting for meshing strategy exposed hereabove. The proposed meshing strategy is applied through a main python script for Abaqus GUI pre-processor (CAE), that handles global and local model preparation (see Fig.
5 ). Then the computation is managed by the Abaqus co-simulation engine and additional features mentioned hereabove, implementing the non-intrusive coupling strategy, defined by algorithm (  8 )-(  9 ). As stated earlier, the convergence of the iterative algorithm is tested regarding the equilibrium of reaction forces at the interface of the models, also known as residual efforts. The main script also contains in its beginning the instantiation operation of the implemented classes, namely the coupling classes, the global model class and the local model class. Those operations in the beginning of the script correspond to the resolution of the grid intersection problem, the transition mesh generation, the global and local model data initialization and the determination of the connectivity between both models.
"Transition mesh generation" section is dedicated to explain with more details the operations related to the transition mesh generation. After the initial operations, the non-intrusive coupling algorithm itself begins. The exchange procedure exactly follows the one described in Fig. 2. The first step is the resolution of the global problem  (8). In order to do so, a new input file for the global model is created so that the loading corresponding to the interface forces is considered, as this problem has Neumann conditions on. Then, the script calls upon a series of Abaqus functionalities in order to create and submit a job containing the required analysis, as well as to access the postprocessing tools required to find the interface displacements so that they can be imposed as boundary conditions in the Dirichlet problem that follows.
After solving the global problem and before entering the local one, a projection is necessary in order to translate the interface displacements from the global model into the local one by using the information of connectivity between both models previously assembled. The next step is the resolution of the local problem  (9). Once more, a new input file is created, but now the local model with the transition mesh added is considered, so that the interface displacements are taken into account as boundary conditions, as this problem is a Dirichlet one. Again, the script calls upon a series of Abaqus functionalities in order to create and submit a job with the required analysis, as well as to find the reaction forces at the interface so that they can be imposed as loading in the Neumann problem in the next iteration.
Transition mesh generation The transition mesh is generated through the Abaqus scripting interface so that the procedure is automated. A Python script includes several commands that call a series of Abaqus functionalities. For illustration purpose, we consider a structure with a cylindrical geometry (see Fig. 5 ). Once the patch is determined (see Fig. 6 ), the operations for the generation of the transition mesh are applied. The method begins by accessing the model database corresponding to the local model and creating an object that contains all the information relative to the model. Knowing that a model in Abaqus is constituted by individual parts, the initial local model information was present in a single part. Then, a new part is created in order to generate the transition mesh, which is assembled in a later operation.
In this new part, an offset plane is defined according to the cylinder radius, corresponding to the projection of the cylinder into a plane. Only the patch needs to be projected. A sketch is created using the plane just defined and the edges corresponding to the inner and the outer boundaries of the transition region are generated. The inner boundary is characterized by the initial local model considering its mid-plane while the outer boundary is defined by the interface corresponding to the global model patch border. All of those boundary edges were previously determined by the script and, in the case of the cylinder, they were projected into the plane in order to allow a proper mesh generation (see Fig. 7 ), that is subsequently reprojected into the cylindrical geometry. After the generation of the shell geometry by the planar sketch (see Fig.
7a ), each edge is seeded with a fixed number of elements (see Fig. 7b ). When the ratio between global and local elements becomes too large, meshing constraints may appear. This may lead to either a distorted or oversized transition mesh. In order to avoid these constraints, we use non-matching meshes at the new coupling interface. This can be performed very conveniently using Mortar operators, that are explicit here, since the meshes are nested. Then, the mesh is generated in that new part (see Fig. 7c ) and both parts are assembled. An input file is written by an Abaqus job, then the Python script reads it in order to perform the projection of nodes and elements to the cylindrical geometry, manually   The last procedure is the implementation of the Abaqus based shell-to-solid coupling (see Abaqus documentation  [36] : "Shell-to-solid coupling" section), what requires the knowledge of the shell edges corresponding to the inner boundary of the transition mesh and the outer elements of the local solid model.
For the sake of minimal intrusiveness, the shell/solid coupling is managed through features available in commercial software (shell-to-solid coupling). In this work, an important step towards the transfer of the approach to the industrial environment was achieved: a demonstrator of the method was developed in Abaqus v6. 14 FE code in order to facilitate its dissemination to Airbus Group: a dedicated Abaqus python script automates the preparation of global and local models; native Abaqus v6. 14 co-simulation capabilities have been extended through specific developments. Such developments were successfully applied to solve representative use-cases extracted from engineering practices. In particular, the global/local simulation of an aircraft fuselage section subjected to engine burst has been achieved.
Top-k keyword and top-k document extraction are very popular text analysis techniques. Top-k keywords and documents are often computed on-the-fly, but they exploit weighted vocabularies that are costly to build. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present T 2 K 2 , a top-k keywords and documents benchmark, and its decision support-oriented evolution T 2 K 2 D 2. Both benchmarks feature a real tweet dataset and queries with various complexities and selectivities. They help evaluate weighting schemes and database implementations in terms of computing performance. To illustrate our benchmarks' relevance and genericity, we successfully ran performance tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand.
However, most prominent big data benchmarks  [6, 7, 8]  focus on MapReduce operations and do not specifically model text-oriented workflows. Very few benchmarks do, but still focus on Hadoop-like architectures and remain at the methodology  [9]  and specification  [10]  stage. Moreover, computing weighting schemes at the application level can prove inefficient when working with large data volumes, because all the information must be queried, read and processed at a different layer but storage. A better approach is to process the information at the storage layer using aggregation functions and then return the response to the application layer. Hence, we introduced the Twitter Top-K Keywords Benchmark (T 2 K 2 )  [11] , which relies on database storage and features a real tweet dataset, as well as queries with various complexities and selectivities.
We also designed T 2 K 2 to be somewhat generic, i. e. , it can compare various weighting schemes, database logical and physical implementations and even text analytics platforms in terms of computing efficiency. In this paper, we further this work with the following contributions. 1. Since T 2 K 2 's data model is generic enough to handle any type of textual documents (not only tweets), we complement its workload model with top-k document queries. 2. We illustrate T 2 K 2 's relevance and genericity by performing tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand. 3. Since T 2 K 2 queries are analytical by nature, we hypothesize that a star (data warehouse) schema should improve query response and propose an evolution of T 2 K 2 named T 2 K 2 D 2 , where the last two Ds stand for Dimensional and Documents, respectively.
4. We complement our first experiments with top-k keyword and top-k document benchmarking with T 2 K 2 D 2 , still testing the TF-IDF and Okapi BM25 weighting schemes, as well as the Oracle, PostgreSQL and Mon-goDB database systems. We designed our benchmarks' queries by logging computational linguists working on a text analysis platform  [12, 13]  on real-world data. After analyzing and clustering similar user queries, we ended up with 8 queries (4 for T 2 K 2 and 4 for T 2 K 2 D 2 ) that we consider generic enough to benchmark any similar system. Moreover, note that our benchmarks do not apply to Information Retrieval (IR) system evaluation. They rather target database systems built on top of an IR system, with the aim of facilitating text mining, e. g. , text classification, topic modeling or event detection.
Benchmark queries are relevant to machine learning tasks, where it is important to analyze different subsets of the dataset to extract knowledge. In contrast, IR systems do not handle subsets of the initial corpus well, nor datasets whose size changes in time, because they compute weights only once. However, this creates two result reproducibility problems  [14]. First, when using subsets of the initial corpus, weights are not recomputed, which induces errors when computing the ranking functions for top-k keywords and documents. Second, weights must be recomputed each time the size of the dataset changes, inducing more write operations that are computationally demanding. The database approach can solve these result reproducibility problems by dynamically computing weights at search time using fielded data.
Although some IR systems do use fielded data  [15] , as they systems compute weights only once, ranking functions do not update weights when subsets of the initial corpus are used or when the volume of data changes  [16] , which induces errors during the extraction of top-k documents and keywords. Moreover, computing weights dynamically is ideal for analyzing subsets of the initial dataset or corpora whose size changes over time, e. g. , when analyzing stream data  [17]. Database systems are designed to manage large volumes of data with a high throughput and can easily manipulate changing volumes of data by using optimized CRUD (Create, Read, Update, Delete) operations. Thus, they are better suited for handling datasets whose size changes over time and analyzing subsets of the initial corpus.
Benchmark Specifications Text analysis deals with discovering hidden patterns from texts. In most cases, it is useful to determine such patterns for given groups, e. g. , males and females, because they have different interests and talk about disjunct subjects. Moreover, if new events appear, depending on the location and time of day, these subject can change for the same group of people. The queries we propose aim to determine such hidden patterns and improve text analysis and anomaly detection. Typically, a benchmark is constituted of a data model (conceptual schema and extension), a workload model (set of operations) to apply on the dataset, an execution protocol and performance metrics  [24]. In this section, we provide a conceptual description of T 2 K 2 and T 2 K 2 D 2 , so that it is generic and can cope with various weighting schemes and database logical and physical implementations.
End users can preprocess data as they see fit and then store the information in the database using the models provided by the benchmark. In our preprocessing step, words that are abbreviated are considered words in their own sense, i. e. , they are not expanded. For instance, we consider "omg" as a word on his own. Misspellings are currently retained, although in a next version of the preprocessing step, we plan to address this problem. Moreover, abbreviations and misspellings only affect the selectivity of queries, as the vocabulary stores them too. The preprocessing steps we apply on raw texts to construct the weighted vocabulary follow. 1. For each tweet, we extract tags, i. e. , hashtags (which make good keywords), attags, and remove links. 2. We expand contractions, i. e. , shortened versions of the written and spoken forms of a word, syllable, or word group, created by omission of internal letters and sounds  [26].
Data Model All the information and metadata extracted during the preprocessing step are modeled using the entity-relationship presented in Figure  1. Let us describe all entities in this conceptual model. • Author stores information about a tweet author, e. g. , unique identifier, firstname, lastname, and age. • Gender is used to store the authors gender and to remove duplication in the database. • Document entity stores all the textual information and metadata for a tweet, e. g. , the unique identifier of the tweet, the date, and the original and the processed text of the text, i. e. , original text (RawText), clean text (CleanText) and lemma (LemmaText). • Writes is the many-to-many relationship between an author and a tweet. • to eliminate duplicates and minimize the storage space, the Geo Location is used to store the geo-location for each tweet.
Performance Metrics and Execution Protocol We use query response time as the only metric in both T 2 K 2 and T 2 K 2 D 2. It is detail for each query as t(Q i )∀i ∈  [1, 4]. All queries Q1 to Q4 are executed 40 times for top-k keywords and 10 times for top-k documents, which is sufficient according to the central limit theorem. Average response times and standard deviations are computed for t(Q i ). All executions are warm runs, i. e. , either caching mechanisms must be deactivated, or a cold run of Q1 to Q4 must be executed once (but not taken into account in the benchmark's results) to fill in the cache. Queries must be written in the native scripting language of the target database system and executed directly inside said system using the command line interpreter.