dataesr/scientific-paragraphs-categorization huggingface dataset:
Type of data Text file (CSV format). How data were acquired The Jupyter Notebooks and Python scripts to collect and process the data is available at https://github. com/breno-madruga/dib-covid-dataset/. Python Web Scraping tools (Scrapy)  [1]  Pymed library to collect PubMed data  [2]  Pybliometrics library to collect Scopus data  [3]  bioRxiv URL ( https://connect. biorxiv. org/relate/content/181 ) arXiv URL ( https://arxiv. org/covid19search ) Data format Raw. The data were retrieved from arXiv [4] , bioRxiv/medRxiv [5] , PubMed [6] , and Scopus [7] databases, processed and made available in a text file (CSV format).
Parameters for data collection The searching parameters and data collection used in the following databases: • arXiv: https://arxiv. org/covid19search • bioRxiv/medRxiv: https://connect. biorxiv. org/relate/content/181 The metadata of articles related to COVID-2019 from January 2019 to July 2020 were collected from arXiv, bioRxiv/medRxiv, PubMed and Scopus databases, and the tools and techniques of Python Web Scraping  [1]  and Pandas Data Wrangling  [8]  were used to build a scholarly production dataset.
Value of Data • This dataset can be used by other researchers to implement automatic mechanisms (through Natural Language Processing, for instance) to extract insights contained on the metadata (e. g. , abstracts and keywords) of scholarly studies; • This dataset can also be used together with other datasets (e. g. Publon and Google Scholar) in order to get a more accurate overview of research related to COVID-19 and identify possible research gaps that have not yet been explored to combat COVID-19; • Several insights can be extracted from the relationships among various entities (e. g. drugs, researchers and their affiliations) applying techniques of Complex Network Analysis in this dataset; • The most influential researchers or research groups can be identified to initiate new possible collaborations or task forces to combat COVID-19 pandemic.
Data Description The dataset available in this paper is composed of 40,212 records of metadata about the publications related to COVID-19. Such data were collected from Scopus [7] , PubMed [6] , arXiv [4] and bioXiv/medRxiv [5] databases, and correspond to productions whose publication year is 2019 or 2020, and were published, indexed or made available until 07/02/2020 (date of data collection).
Feature Description id The identifier key of a manuscript. doi The DOI of a manuscript. title The title of a manuscript. abstract The abstract of a manuscript. publication_date The date of publication of a manuscript. author_affil A list of Python dictionaries that contains the authors and their affiliations' information in a manuscript. source This feature indicates whether a manuscript was extracted from bioRxiv or medRxiv platforms. e. g. ) and merged, as well as the duplicated records were removed, thus, generating our new dataset. A pipeline was created with DVC, the Data Version Control tool 1  , to preprocess and merge these specific datasets, and it also generates our final dataset after the last stage (see Fig. 1  ). DVC provides data science workflow reproducibility and consistency, and it is Git-compatible, offering lock-free, local branching, and versioning.
PubMed and Scopus databases For PubMed [6] and Scopus [7] databases, the set of keywords were defined from Lou et al. [10]  and by the search string "covid" used on the DeSC platform  [11]. The most characteristic and significant keywords were chosen by two health professionals, which were used to collect the data in these databases. The search strings used were: • PubMed: (covid-19 OR coronavirus disease 2019 OR 2019-ncov OR novel coronavirus OR sars-cov-2 OR novel coronavirus pneumonia OR coronavirus) AND (2019[Date -Publication]:2020[Date -Publication]) AND (english[Language]); • Scopus: TITLE-ABS-KEY("covid-19" OR "coronavirus disease 2019" OR "2019-ncov" OR "novel coronavirus" OR "sars-cov-2" OR "novel coronavirus pneumonia" OR "coronavirus") AND PUB-YEAR > 2018 AND LANGUAGE(english).
We also applied filters of year and language, i. e. , the selected articles were published in the years 2019 and 2020, as well as they should be in English. After the definition of Scopus' and PubMed's search strings, the data of these databases were collected from the Pybliometrics  [3]  (Scopus) and Pymed  [2]  (PubMed) libraries. For the step of data wrangling, the Pandas  [8]  library was used to preprocess these data and generate the Scopus' and PubMed's datasets.
arXiv and bioRxiv databases The process of collecting scholarly articles related to COVID-19, which were registered on the arXiv [4] and bioRxiv [5] platforms was performed as follows: in the homepage of the arXiv platform, there are two hyperlinks that point to the listing of these articles ( https://arxiv. org/ covid19search and https://connect. biorxiv. org/relate/content/181 ), and from these hyperlinks, the tools and techniques of Python Web Scraping  [1]  , especially the Scrapy library 3  , were used to collect the metadata of these scholarly studies. Again, the Pandas library was used to preprocess these metadata and generate the arXiv's and bioRxiv's datasets. It is worth mentioning that the bioRxiv dataset has several articles belonging to both bioRxiv and medRxiv platforms. Using the Pandas library, all aforementioned datasets were merged, and the duplicated records were removed.
As the Scopus dataset is the richest in features/columns, it served as the basis for the creation of the final dataset. For records from arXiv's and bioRxiv's datasets, the duplicated articles were identified by the "title" feature, while, for the PubMed dataset, the duplicates were identified by the "title" and "pubmed_id" features. Moreover, a extra preprocessing step was perform at the final dataset after the merging processing, aiming to warranty the data consistency and integrity. As stated earlier, it has enormous potential to extract knowledge and several insights to support in combating the pandemic, for example, in Figure  2  , the main topics of research related to COVID-19 can be extracted using Natural Language Processing techniques. It is important to highlight that researchers that are not familiar with the Python ecosystem for Data Science and version control technologies (Git, GitHub and DVC) can directly access the data in CSV format available at Data Mendeley ( https://data.
mendeley. com/datasets/ kx7wwc8dzp/ ). In addition, for those who are familiar and interested in more details about the acquisition and the preprocessing of the dataset, the pipeline and source codes (Jupyter Notebooks and Python scripts) are available at GitHub ( https://github. com/breno-madruga/ dib-covid-dataset/ ) and mirrored at DAGsHub 4  , having at their disposal the potential and advantages of the Python ecosystem for Data Science and the Git and DVC technologies.
e remainder of the paper is arranged as follows. Section 2 will present the physical and stochastic carbonation process modelling. Hyvert model, used in the first stage of data generation, will be detailed before the presentation of the nonlinear generalized Wiener process. e second stage of our generic method consists in the maximum likelihood estimations (MLEs) of parameters completed by the Expectation-Maximization (EM) algorithm (Section 3. 1). e third stage of durability estimation is developed in Section 3. 2. Section 4 describes the fourth stage of the generic method: how to design optimal CSADDT for the destructive test. e proportion of units allocated to each stress level and test stress levels will be determined based on three optimization criteria. In subsequent Section 5, we propose to apply our method to the case of the qualification of concrete durability under carbonation, with the aim to show the performance of the proposed methods.
(9) M-step: compute the first derivative of Q(Θ | Θ (h) ) with respect to Θ, and set its value to be zero. en, Θ (h+1) can be derived from the following system of equations: 􏽢 μ (h+1) α � 1 N 􏽘 d k�1 􏽘 m k j�1 􏽘 n kj i�1 E α kji 􏼌 􏼌 􏼌 􏼌 􏼌 y kji 􏼒 􏼓, (10) 􏽢 σ 2(h+1) α � 1 N 􏽘 d k�1 􏽘 m k j�1 􏽘 n kj i�1 E α 2 kji 􏼌 􏼌 􏼌 􏼌 􏼌 y kji 􏼒 􏼓 -􏽢 μ (h+1) α 􏼐 􏼑 2 , ( 11 ) 􏽘 d k�1 s k e 􏽢 β (h+1) s k A k1 -e 􏽢 β (h+1) s k A k2 􏼠 􏼡 � 0, (12) 􏽢 σ 2(h+1) B � 1 N 􏽘 d k�1 􏽘 m k j�1 􏽘 n kj i�1 y 2 kji Λ t kj 􏼐 􏼑 -2y kji e 􏽢 β (h+1) s k E α kji 􏼌 􏼌 􏼌 􏼌 􏼌 y kji 􏼒 􏼓 ⎡ ⎢ ⎣ + Λ t kj 􏼐 􏼑e 2 􏽢 β (h+1) s k E α 2 kji 􏼌 􏼌 􏼌 􏼌 􏼌 y kji 􏼒 􏼓, (13) where A k1 � 􏽐 m k j�1 􏽐 n kj i�1 y kji E(α kji | y kji ) and A k2 � 􏽐 m k j�1 􏽐 n kj i�1 Λ(t kj )E(α 2 kji | y kji ), k � 1, 2. e parameter β can be obtained according to the MATLAB function "fzero" or be approximately estimated as 􏽢 β M � 1 d 􏽘 d k�1 1 s k ln 􏽘 m k j�1 􏽘 n kj i�1 y kji E α kji 􏼌 􏼌 􏼌 􏼌 􏼌 y kji 􏼒 􏼓 ⎛ ⎝ ⎞ ⎠ ⎡ ⎢ ⎢ ⎣ -ln 􏽘 m k j�1 􏽘 n kj i�1 Λ t kj 􏼐 􏼑E α 2 kji 􏼌 􏼌 􏼌 􏼌 􏼌 y kji 􏼒 􏼓 ⎛ ⎝ ⎞ ⎠⎤ ⎥ ⎥ ⎦.
5 C2 ′ • P 0 /P atm  􏽶 􏽴 􏼐 0. 67 􏼑 C2 ′ /0. 67 + 1 • P 0 /P atm 􏼁 􏼐 0. 67 + Q1 􏼑 , (1) with (D o CO2 /Q1) ref � 6. 44 • 10 -13 • (36. 4 • e -0. 04f cm,28 ) 2 when D o CO2 is not available. e meanings of the model parameters, their charac- teristic values, and distribution statistical laws, if available, used for the prior database generation are reported in Table.
We test our conceptual model with survey data collected from 471 French adolescents. The focus on adolescents (ages 15 to 18) is topical; this generation, known as "digital natives" or "Generation Z," has grown up in a digitaldriven world and has abandoned traditional computers for smartphones  (Gentina & Delécluse, 2018). Unlike members of previous generations, who are "digital immigrants," digital natives, born in the early 2000s, are the first true digital natives  (Dingli & Seychell 2015). Most studies treat adolescents as a homogenous entity, ignoring potential group differences and notably the distinction between females and males  (Gentina & Delécluse, 2018). Some studies of adolescents include only males or, even more often, females exclusively  (Haytko & Baker, 2004; Yalkin & Elliott, 2006) , and justify this choice with differences in consumption without, however, attempting to contrast them.
The physical presence of the researcher not only limited the bias linked to information dissemination, by controlling the fact that teen informants reply individually to the questionnaire, but also provided efficient answers to teen informants' questions. Participants first completed a task designed to measure youth materialism, followed by process use, social use, problematic smartphone dependency, and relevant socio-demographic data (gender, age, grade). After completing the study, participants were debriefed, thanked for their participation, and asked not to discuss their responses with classmates. Participants completed the study voluntarily and anonymously, without financial reward, which reduced social desirability and "honest" answers  (Joinson, 1999). Adolescence can be divided into three stages: early adolescence, generally ages 12 to 13; middle adolescence; ages 14-15 (both corresponding to middle school) and late adolescence, ages to 18 (corresponding to high school)  (Chaplin & John, 2007).
Since most of this age group are students, we distributed questionnaires to 17 classes in three public high schools in France, and collected data from 471 middle adolescents in high school (grade 10-11 and 12) (58% female). We deleted 6 respondents from the final sample due to missing data, so the results are based on responses from a sample of N=463 respondents (Table  1 ). The average age was 16. 8 years. We also checked that, consistent with descriptive statistics at national level, students in these classes were indeed equipped with smartphones and not mere mobile phones. ------------Insert Table  1  here-----------.
Exploratory and confirmatory factor analyses In the first step, we conducted exploratory factor analyses with SPSS version 24 software. Prior to testing our hypotheses, exploratory factor analyses (EFA, principal components) were conducted using an Oblimin rotation in SPSS in order to evaluate the measures in terms of their dimensionality. We eliminated items from the scales based on their measurement properties (i. e. , items that loaded on more than one factor and items with factor loadings below 5. 0): two items from the youth materialism scale; two items from the process use scale, one item from the social use scale and three items from the problematic smartphone dependency scale. The resulting fourfactor structure (youth materialism, process use, social use and problematic smartphone dependency) offered satisfactory fit indices and coefficient alpha values (.
Mediation Effects (H6a, H6b) We next tested simple mediation hypotheses (H6a, b) using the SPSS macros developed by  Preacher & Hayes (2007)  and accessed from the website of Andrew F. Hayes (Model 4). The effect estimations rely on bootstrapping with 2,000 samples and 95% bias-corrected confidence intervals. The results show a positive and statistically significant indirect effect of youth materialism on problematic smartphone dependency via process-oriented smartphone use (γ =. 210; [. 065;. 288]; p <. 05) with social-oriented smartphone use as a covariate, in support of H6a. However, we find no such indirect effect for the route in which social-oriented smartphone use is considered a mediator (γ =. 016; [-. 031;. 063]; p >. 05), leading us to reject H6b. Youth materialism affects problematic smartphone dependency only via process-oriented smartphone use, not via social-oriented smartphone use-a result with significant implications.
In addition to this reduction in terms of degrees of freedom, a second reduction stage may be necessary for nonlinear problems, this time in terms of integration points. This is called hyperreduction, or operator compression according to the terminology introduced in  [4]. Operator compression methods include the empirical interpolation method (EIM,  [5] ), the missing point estimation (MPE,  [6] ), the a priori hyperreduction (APHR,  [7] ), the best point interpolation method (BPIM,  [8] ), the discrete empirical interpolation method (DEIM,  [9] ), the Gauss-Newton with approximated tensors (GNAT,  [10] ), the enery-conserving sampling and weighting (ECSW,  [11] ), the empirical cubature method (ECM,  [12] ), and the linear program empirical quadrature procedure (LPEQP,  [13] ). In this article, we consider uncertainties on a tensorial input variable which affects the solution of the partial differential equations (PDEs) governing the physical system.
In these works, the set of pre-computed high-fidelity solutions was partitioned into several clusters, each of them being used to build a small cluster-specific reduced-order model. The resulting dictionary of local reduced-order models was then used to adapt the reduced-order basis to the current state of the solution by finding the closest cluster center. This technique works very well when the solution evolves on a low-dimensional manifold. However, for some specific applications where there is no guarantee that the solution lies in a low-dimensional manifold, this technique might be subjected to the curse of dimensionality  [19]. Indeed, in high dimension, the nearest neighbour is almost as far as the furthest point in the dataset because of the loss of contrast in distances, explaining the difficulties of high-dimensional clustering.
The probabilities y pred k are also called membership probabilities. The deep classifier returns the integer corresponding to the class with the highest membership probability, that is: ∀X ∈ X , F K (X) = arg max k∈[[1;K ]] (y pred k (X)) (14) Such classifiers are called classical deep classifiers in this article. The concept of deep classifier used in the definition of a dictionary-based ROM-net can be extended to include not only the classical ones, but also deep clustering algorithms  [34] [35] [36]. These algorithms use encoders to cluster the data in a low-dimensional latent space, avoiding the difficulties of high-dimensional clustering  [19]  As mentioned earlier, the partition of the set X used to define cluster-specific reducedorder models is given by a clustering algorithm. Clustering algorithms generally rely on a dissimilarity measure quantifying the difference between two points in the dataset.
Training procedure for dictionary-based ROM-nets Let K be a positive integer. In this section, we describe the training phase of a dictionarybased ROM-net R K made of a classical deep classifier F K and a K -ROM-dictionary D K. First, an automatic data labelling procedure is presented. It aims at preparing the data for the supervised learning of the deep classifier F K , using simplified numerical simulations and a clustering algorithm. Then, we train the deep classifier F K and build the K -ROMdictionary D K on the grounds of the clusters identified by the labelling procedure. Figure  3  summarizes the main steps for the training of a dictionary-based ROM-net.
ROM-oriented dissimilarity measure Clustering the input space X enables labelling the training data for the deep classifier, and guides the construction of the ROM-dictionary by defining regions of the input space where high-fidelity simulations must be run to build local reduced-order models. The key point is the choice of the dissimilarity measure for clustering. Indeed, clustering aims at grouping points of a dataset that are similar. As shown in the last section of this article, for some specific applications, defining a dissimilarity measure based on a distance between input tensors leads to inaccurate reduced-order solutions. Furthermore, the difficulties of high-dimensional clustering appear when dealing with large input tensors. In the application to structural mechanics presented at the end of this paper, this issue is due to the complex interaction between the thermal and the mechanical loadings.
The clustering algorithm Once the dissimilarity matrix is calculated, a clustering algorithm must be applied to partition the dataset into K clusters. The choice of the algorithm depends on the context. In this methodology, clustering is used for the definition of local approximations of a nonlinear solution manifold. Hence, the algorithm must focus on compactness rather than connectivity when looking for clusters in the dataset. This property is satisfied by k-means algorithm  [38] , the most well-known clustering approach. However, this algorithm needs to calculate clusters' centroids or means, which is impossible when the input data correspond to vector spaces. For this reason, we choose the k-medoids algorithm presented in  [39] , relying on a Voronoi iteration approach like k-means. The algorithm proposed in  [39]  can be summarized as follows: • Initialization step: select K initial medoids from the dataset.
Training of a deep classifier for fast model selection The ROM-net's classifier F K is trained in a supervised fashion from pairs of examples (X i , K K (X i )) given by the clustering algorithm, where the label K K (X i ) ∈ [[1; K ]] is the index of the cluster containing X i. As explained earlier in the article, the true classifier defined by: K K (X) = arg min k∈[[1;K ]] (δ(X, Xk )) = arg min k∈[[1;K ]] d Gr(∞,∞) V(X), V( Xk ) (20) is too expensive because it is based on numerical simulations, which motivates the use of an approximate classifier. In the previous equation, Xk is the medoid of the k-th cluster. The dataset is split into a training, a validation and a test set. For a given deep neural network architecture and for a given set of hyperparameters, the parameters of the classifier F K are calibrated on the training set via backpropagation with Adam optimizer  [40].
T = {T : × [0; 1] → R ∃ i ∈ [[1; n T ]], ∀t ∈ [0; 1], ∀x ∈ , T(x, t) = T + t(T rand (x, θ i ) -T 0 )} ( 31 ) Remark When training the ROM-net, it is assumed that we have no prior knowledge of the data-generating stochastic model. Training data may come from complex aerothermal simulations with random boundary conditions defined by experts. In the present case, in the absence of such data, we use a stochastic model to generate the training data. This stochastic model is parametric, since every random temperature field comes from a random linear combination of 150 fluctuation modes. However, when training the deep classifier for local ROM recommendation, the training data corresponds to nodal values of the temperature fields rather than the 150 random coordinates. This way, in the test phase (or online phase in the model order reduction community), the ROM-net can be applied to thermal loadings coming from unknown stochastic models, complex aerothermal simulations or even experiments, as long as these thermal loadings correspond to perturbations of the reference thermal loading.
k-medoids clustering The ROM-oriented dissimilarity is used to cluster the data with the k-medoids algorithm. The number K of clusters is a hyperparameter provided by the user. Numerous empirical methods have been proposed to estimate the best number of clusters for different criteria. In our case, the dataset is not organized in distinct clusters. Therefore, the clustering algorithm is applied for different values of K , and the quality of clustering results is evaluated using silhouette analysis  [44]. When selecting the most appropriate number of clusters based on silhouette analysis, one must also consider the trade-off between large numbers giving a better approximation of the nonlinear manifold, and small numbers facilitating the classification problem for the deep neural network. In addition, using a large number of clusters increases the cost of the construction of the ROM-dictionary.
Visualization of clusters on the nonlinear manifold The clustering results can be visualized thanks to Multidimensional Scaling (MDS)  [45]. MDS is an information visualization method which consists in finding a low-dimensional dataset Z 0 whose matrix of Euclidean distances d(Z 0 ) is an approximation of the input dissimilarity matrix δ. To that end, a cost function called stress function is minimized with respect to Z: Z 0 = arg min Z (ς (Z; δ)) = arg min Z ⎛ ⎝ i<j (δ ij -d ij (Z)) 2 ⎞ ⎠ (33) This minimization problem is solved with the algorithm Scaling by MAjorizing a COmplicated Function (SMACOF,  [46] ) implemented in Scikit-learn  [47]. Figure  7  shows clustering results with low-dimensional representations obtained by metric MDS. The relative error ς (Z 0 ; δ)/ς (0; δ) is 11% for the 2D representation and 10% for the 3D representation.
Multiclass classification Classifier based on an ensemble of DNNs. Clustering results obtained with the true classifier K K enable training the approximate classifier F K of the ROM-net R K. Among the 10 4 thermal loadings in the dataset, 6400 are used as training data, 1600 as validation data, and 2000 as test data for final evaluation. As the data labelling procedure involves numerical simulations, the dataset contains a limited amount of training examples in comparison with standard image classification problems. Working on a small dataset makes our deep classifier prone to overfitting. To address this issue, we use the ensemble averaging method  [48] , which consists in taking an ensemble of classifiers and averaging their predicted membership probabilities. Ensemble averaging is a common technique in ensemble learning.
Generally speaking, ensemble methods aim at creating a meta-estimator from several base estimators (or models). Combining different estimators leads to more robust predictions and reduces overfitting. In addition, using an ensemble method replaces the task of finding a single very accurate model by the task of building an effective meta-estimator from several models with lower accuracies. The winners of numerous deep learning challenges used ensemble averaging to improve their predictions  [49] [50] [51]. Our ensemble contains N models = 12 different DNNs trained for the same classification problem with the same training data using Keras  [52]  and Tensorflow  [53]  libraries on Python, but with different architectures and loss functions. All of them use the softmax activation function to get membership probabilities.