dataesr/scientific-paragraphs-categorization huggingface dataset:
Land use and orography are extracted from the modified International Geosphere Biosphere Pro- gramme (IGBP) Moderate Resolution Imaging Spectroradiometer (MODIS) 20-category database with 30 s resolution  (Friedl et al. , 2010). CHIMERE is a Eulerian 3-dimensional regional chemistry-transport model, able to reproduce gasphase chemistry, aerosols formation, transport, and deposition. In this work, the 2017 offline version of CHIMERE is used  (Mailler et al. , 2017). The model configuration is described in Table  A1 , with the same horizontal domain as for WRF applied on 30 vertical levels up to 150 hPa. Emissions from the EDGAR HTAP v2 (Emission Database for Global Atmospheric Research Hemispheric Transport of Air Pollution version 2) inventory are downscaled and split in time down to daily/hourly rates following the methodology of  Menut et al.
Acknowledgements. The chemistry-transport simulations were performed using the high-performance computing resources of  TGCC (Très Grand Centre de calcul du CEA ; allocation no. GEN10274 ) provided by  GENCI  (Grand équipement national de calcul intensif). The authors acknowledge the editor and the two anonymous reviewers for their valuable comments and feedback. Financial support. This research has been supported by the  Agence de l'Innovation de Défense  (grant no. NETDESA 2018600074 ). Review statement. This paper was edited by  Aurélien Dommergue  and reviewed by three anonymous referees.
Participants We surveyed 14,478 participants from 45 countries (compare  Conroy-Beam et al. , 2019). The global study protocol was approved by the Ethics Committee of the Institute of Psychology, University of Wroclaw and local collaborators obtained additional permits when this was legally required. All participants provided informed consent prior to their inclusion in the study. We conducted the data collection among both community members and university students (the maximum was 50% per sample per country) to create as diverse sample of inhabitants as possible for each study site. The participants were required to be literate and between 15 and 75 years of age. For further analysis on affective touch behavior, we only included data from participants who reported having interacted at least once with an intimate partner, male friend, female friend, or their own child in the week preceding the data collection.
Cultural-Level Predictors of Affective Touch Individual-level scores of Parasite History, Conservatism, and Religiosity were aggregated at the Country-Level to form an index for each country (see Supplementary Table  S2  for descriptive statistics and country-level values and Supplementary Table  S3  for intercorrelations between the Country-Level factors). Temperature. Annual average temperatures reported on http://www. weatherbase. com (retrieved January 10, 2017) provided Temperature data for each Country. Collectivism. The 178-nation index of Ingroup Favoritism from Van de Vliert (2011) was used as a proxy for the collectivism score for each Country.
If there were two or more groups collecting data in one country, they arranged translation and back translation between groups and then used the same version of the questionnaire. For data collection, great care was exercised to ensure similar recruitment methods across all study sites. This was achieved by detailed protocols, including (a) a standardized instruction to conduct the data collection face to face via paper-and-pencil or electronic survey; (b) standardized study information (i. e. , desired sample size and composition [see the "Participants" section], description of study aims, hypotheses, methodology, and predicted data collection time); and (c) detailed scoring instruction and standardized scoring sheets. Data collection was conducted simultaneously across all locations in 2016-2017.
Data Analysis First, we computed all Affective Touch Indices and descriptively analyzed the Affective Touch Prevalence per interaction partner and country. Second, we tested whether Touch-type-specific Prevalence differed between touch types and between interaction partners. Therefore, a repeated measures ANOVA was conducted with the country-level Touch-Specific-Prevalence Scores as dependent variable and the touch type and interaction partner as within-subject factors. A full model was calculated with Bonferroni corrected post hoc analysis. Effect sizes are presented as partial eta-squared (η 2 ). Third, we conducted multilevel model analyses to determine whether Country, Individual-Level, and Cultural-Level predictors influenced Affective Touch Diversity with a Partner, a Female Friend, a Male Friend, and own Child.
, 2014; Duncan et al. , 2015; Hertenstein et al. , 2006; Kusano & Kemmelmeier, 2020). We also recommend using a variety of our touch indices as dependent variables in future multilevel analyses. All data used in this research are published in an open-access repository and we encourage further explorations and hypotheses generated on the basis of the wealth of information we collected. For example, our indices of affective touch behaviors that were computed prior to the COVID-19 pandemic can be especially relevant to explore multiple social consequences of this pandemic, or to conduct post hoc analyses of the disease spread.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was funded from statutory funds for AS, PS and TF. During the project, AO was supported by the  Polish Ministry of Science and Higher Education  scholarship for years 2018-2020 (# 626/STYP/12/2017 ). Marina Butovskaya  and  Daria Dronova  participated in this study in line with the research plan of the  Institute of Ehnology and Anthropology, Moscow.
Statistics Bioinformatics analysis for miRNA NGS data was performed using Partek® Genomics Suite® software  52. Heat maps and PCA were performed to identify differentially expressed miRNAs between the groups and at various time points. Norm nder was used to identify miRNAs with best stability factor, to be suitable endogenous control miRNAs  53. Relative quanti cation of the candidate miRNAs were performed using 2 -ΔΔCt method  54. Hierarchical clustering was performed using R program  55. All other data analysis was performed using GraphPad Prism 8. All statistical tests were 2-tailed with a signi cance level set with an alpha of 0. 05. For continuous variables, statistical signi cance between three patient groups was compared using one-way ANOVA with Dunnett's post hoc test when comparing with the control group.
Acknowledgements We are grateful to the neonates and the families who participated in BIBiNs study and thank the medical and nursing staff on all the neonatal units for their support. We would like to thank  Ms Nicola Openshaw-Lawrence ,  Nicky Holland ,  Vicky Payne  and  Karen Few  for assistance with patient recruitment and data coordination at  Royal London ,  Ashford & St Peter 's, Southampton and Norwich hospitals respectively. We are also grateful for the work done by  Dr Jane Evanson  and  Dr Olga Kapellou  in reporting the MRI brain scans. We are very grateful to  Barts Charity, London, UK  for funding support for the study.
Statistics The statistical analysis was performed using the software "MedCalc" (Ostend, Belgium) with a significance level of p < 0. 05. The exact 95% confidence interval (CI) of rates was calculated. The qualitative variables were compared using the chi-square test, and the quantitative variables were compared using the Kruskal-Wallis test. Differences between the modalities of a variable were only search if the global test was significant. A multivariate stepwise logistic regression was used to assess the association between PET/CT results (positive vs. negative or equivocal) and variables whose results were available before PSMA-11 PET/CT. Receiver operating characteristic (ROC) curve analysis was carried out and the area under the curve (AUC) was determined to evaluate the predictive value of the multivariate criterion on PSMA-11 PET/CT positivity.
Acknowledgements The authors want to pay tribute to the radiopharmacists of the 6 PET centres: without their skills and their work no 68 Ga-PSMA-11 would have been available for their patients to benefit of PET/CT:  Drs. Valérie Nataf ,  Timofei Rusu ,  Pierre Baumgartner ,  François Hallouard ,  Sophie Levesque ,  Marion Tempier ,  Frédérique Blanc-Béguin ,  Simon Hennebicq ,  David Kryza. Although  it is not possible to list here all the clinicians who referred the 1052 patients of this cohort, the authors want to testify how they are indebted for their support and their confidence in PET/CT, the most recent functional and biological imaging modality, and in the rapid progress of radiopharmaceuticals for PET/CT. The contribution to this article of two nuclear physicians,  Dr. Nassima Younsi  for the bibliographic search and  Dr.
Study population We used the data from the Research project for prospective Investigation of health problems Among Survivors of the Great East Japan Earthquake and Tsunami Disaster (RIAS) for analysis. The study is a longitudinal cohort study to determine the long-term health impact of the disaster. 4] [15]  The survivors were identified their addresses based on municipal government's information. All residents aged 18 years or older were recruited by sending out notifications of the health survey and by announcing in a community bulletin board (total, 42,831). A total of 10,081 participants who provided written informed consent comprised the original cohort (23. 5% of the total population). After excluding people those who lack data for at least one variable of sex, living condition and body weight of the 2011 (n = 172), we analyzed the participants regardless of their participation=nonparticipation during any given study wave between 2011 and 2015 (n = 9,909; mean age, 61.
status (severe economic status=usual economic status) + β17 × psychological distress (psychological distress=no psychological distress) + β18 × insomnia (insomnia=no insomnia) + β19 × low level of social network (low level of social network=high level of social network) + β20 × low level of social capital (low level of social capital=high level of social capital). We showed an estimated marginal mean of body weight in living conditions in each survey point. Next, we performed the same analysis with the first wave participants (n = 9,909), and conducted the following analyses in these subjects. We also conducted analysis with the missing covariate data in 2011 by being imputed in multiple imputation using Markov Chain Monte Carlo method by creating five imputed datasets. We combined the results from each imputed dataset to obtain the final estimates.
We combined the results from each imputed dataset to obtain the final estimates. In subgroup analysis, we demonstrated the coefficients of body weight in the interaction effect between living conditions and time points stratified by obesity in the 2011 survey. All P-values were based on two-sided tests, and P-values <0. 05 were considered statistically significant. The Statistical Package for Social Sciences version 24. 0 (IBM Corp, Armonk, NY, USA) was used for all analyses. The research plan was approved by the Ethics Committee of Iwate Medical University (approval no. H23-69).
ACKNOWLEDGEMENTS The authors would like to thank the participants of the study, as well as the many health care workers. This project was conducted with the support of the  Takemi Program in International Health at the Harvard T. H. Chan School of Public Health. We thank  Prof. Ichiro Kawachi  for contributing to the preparation of the manuscript, and supervision of the lead author during his fellowship. The work was supported by a  Health Labour Sciences Research Grant  from the  Ministry of Health, Labour, and Welfare of Japan  ( H23-Tokubetsu-Shitei-002 ;  H24-kenki-sitei-001  and  H25-Kenki-Shitei-001 (Fukkou)). The work was also supported by  JSPS KAKENHI  Grant Numbers  JP20K18858.
Nowadays, the PHM framework has moved from conventional-based health monitoring (HM) to a deep learning approach. Therefore, the previously hand-designed features extracted from raw data are automatically determined by the first layers of a deep neural network (DNN). Additionally, in the extensive review of deep learning by  [1] , visualizing time series behavior as pictures is an ongoing research topic. Time series dynamics can be extracted by the Gramiam Angular Field (GAF) representation of  [2]  or the Recurrence Plot (RP) of  [3]. These representations can be fed into a DNN for better classification of data. In our work, a similar approach is used for visualizing time series behavior. In  [4] , Jain defined clustering as "the unsupervised classification of patterns into groups". While this exploratory analysis technique is widely used in spatially organized datasets, it is scarcely used in time series where data are organized by time.
The famous C-MAPSS dataset from  [10]  and its recent enhancement by  [11]  contain run-to-failure data from an aircraft engine, without maintenance operations between several flight cycles. Furthermore, this hypothesis was already clearly made in  [12]  (Hypothesis 12). Otherwise, one could face two configurations. First, if the maintenance completely rejuvenates the system, the method still applies after moving the origin of the time series before processing. However, if it is not the case, the residual part of useful life gained after maintenance must be evaluated. The latter is out of the scope of this work. Thus, with Hypothesis 1, every cluster found can be associated with a fault severity degree. As the clustering is done on the time series, each new cluster increases the severity value of the underlying default.
• Secondly, build a structure to reveal the different square shapes and eliminate the noisy background from images automatically, thus conserving only relevant information for clustering. To that extent, deep neural structures are studied. One drawback of such architectures is the need for a tremendous amount of training data. In industry, and specifically in aeronautics, relevant training data are scarce and costly to obtain. Consequently, Ref. [13]  has developed an architecture, U-NET, for cell images segmentation specialized in small data samples to alleviate this limitation. Note that the dataset dimension should be compared with usual ensembles used for training state-of-theart neural network architectures. To give an order of magnitude, one could cite Microsoft's COCO dataset  [14]  and its 3 × 10 6 images.
U-NET's goal is to segment every distance matrix G j∈ 1;p. As stated earlier, the network has to learn to recognize square patterns alongside each matrix diagonal. Consequently, it has to be trained to do so. Like many industrial environments, aeronautics lacks an appropriate amount of high sampled training data. An artificial dataset is generated to alleviate this problem. Four types of training patterns are produced, all sharing the same underlying structure: a set of squares with black ones along the diagonal. The images from Figure  2  are randomly created by calculating the distance matrices of constant continuous piecewise functions (CCPF), linear continuous piecewise functions (LCPF), and adding Gaussian noise. The first row of Figure  2  represents input training data, whereas the second row represents its associated mask.
At the end of each forward pass, the neural network compares its output to the mask and backpropagates the gradient accordingly. The best training procedure is empirically determined. First, U-NET is trained with clean square images (CCPF). Once the loss function is converged, the training of U-NET is reinforced by fuzzy data (LCPF). The operation is then repeated, and Gaussian noise is added to each input to further increase the neural network generalization capacity. Finally, a new dataset is generated with a field-specific bias to help the convergence of the training to the study goal. This bias comes from another fundamental hypothesis made in this work: Hypothesis 2. The aging process is a compounding effect and can only accelerate through time. Similarly to Hypothesis 1, Hypothesis 2 is commonly made in literature.
1)), the empirical density can be expressed in Equation  (5). p(x) = 1 nh n ∑ i=1 K x -y i h (5) K is a kernel function in Equation (  5 ), applied to the centered variable of the sample time series and scaled by a factor h. Even if the Gaussian kernel defined in Equation (  6 ) is not a computationally optimized one, it is chosen for its theoretical properties. K(x) = 1 √ 2π exp -1 2 x 2 (6) The best property in this context is the ability to chose a simple bandwidth parameter from the state of the art. As detailed in  [19]  (Equation (3. 31)), the bandwidth h of a Gaussian kernel can be expressed as h = 0. 9An -1/5 , with the constant A = min(σ, inter-quartile range/1. 34). Finally, gathering all the steps in this Section 2, a function estimating the empirical likelihood of cluster frontiers is created for the studied dataset.
Dataset and Studied System The dataset comes from four different electromechanical actuators, each made of an asynchronous electrical machine and a ball screw. A controlled test bench is constructed to monitor the vibration behavior of each actuator. The goal of the procedure is to detect any precursor of a jamming defect in each ball screw of the different actuators. That is why an accelerometer is placed on every monitored structure, as shown in Figure  5. This configuration allows the collection of vibration signals coming from the longitudinal axis of the actuation device. Every actuator is stressed through 1130 cycles, specially designed to emulate a realistic environment. With this protocol, each one reaches the end of life at the last cycle. The signal measured is sampled at 1 kHz, standardized, and normalized between 0 and 1.
Time Series Encoding As stated in Section 2, p statistical and model-based descriptors are computed from the raw data measured on the monitored actuator. Every descriptor X j has 1130 samples. Among all p features, four are chosen. They are referred to as {X 1 , X 2 , X 3 , X 4 }. Each is standardized and normalized between 0 and 1. They are represented in Figure  6. The four descriptors are selected to evaluate the method on signals that exhibit various dynamics but are still coherent for extracting a health monitoring information extraction. Obviously, from Figure  6 , the features are rather noisy and not strictly monotonic. However, a general ascending or descending trend is recognizable. In Figure  7 , each pixel corresponds to one value of the distance matrix. Note that pictures are colorized to better highlight symmetries, but they are in grayscale with original values between 0 and 1.
Discussion In this subsection, the clustering method is compared to other state-of-the-art algorithms. The latter are summarized in Table  1. They are taken from the Python library scikit-learn  [21]  except Kmeans, which comes from the tslearn  [22]  package. Table  1. Clustering methods considered in our study. These methods were selected according to two criteria. The first is the ease of implementation to our benchmark scope, as they are present in standard machine learning libraries in Python. The second is the representativeness of the diversity of the clustering algorithms. Indeed, according to  [23]  (Figure  6 ), they can be separated into six different approaches. At least three of them have been studied: partitional, hierarchical, and modelbased. Each method in Table  1  was fed a data structure with five columns and 1130 lines.
Conclusions Prior work in time series partitioning for condition monitoring in aircraft systems did not consider the temporal dimension of data, as illustrated by  [7]. It used K-Nearest Neighbors or the Support Vector Machine between others to cluster data without considering its time precedence relation. The work of  [12]  is the most recent attempt, to our knowledge, to partition time series while using and keeping time information. However, the latter required the user to have prior knowledge of the data. By doing so, the risk is for the user to add their own biases into the procedure. Moreover, as developed in  [9] , it is challenging to find a cluster indicator that could assess the clustering quality algorithm, as it is highly dependent on the hypotheses made in the research and the dataset used.
This study developed a new clustering method for time series with a deep neural network core. The whole procedure consists of three main steps. First, temporal data are encoded into pictures to be fed to the DNN. Then, the segmented images are processed with consistent signal processing algorithms. Finally, the information from all the descriptors is fused to obtain the general data partitioning profile. The whole algorithm empowers us to find cluster frontiers with a likelihood measure from a set of time series. It was designed specifically to consider the precedence relation of elements and to avoid temporal consistency incoherence. It was applied to an industrial dataset to assess its relevance. Note that the whole algorithm could be used with multivariate time series, as the nature of the data is irrelevant to the results.
Data Analysis The survey was attempted by 4313 individuals; of these, 3483 answered the COVID-19 impact open-ended question (80. 3% completion rate) required for the current study. Qualitative data were analyzed to detect sentiment and key themes using advanced natural language processing tools. We used the Stanford CoreNLP sentiment annotator  [26] , a machine learning model that uses recursive neural networks to perform sentiment analysis and classify input text on a 5-point scale of very negative, negative, neutral, positive, and very positive (higher scores indicate more positive sentiment). In addition, we analyzed data using the Valence Aware Dictionary and Sentiment Reasoner (VADER), a lexicon and rule-based sentiment analysis tool that produces a unidimensional measure of sentiment for a given sentence that reflects a summed score for each word in that text  [27].
Results The majority of the participants were female (see Table  1 ). The participants' mean age was 57. 1 years (SD 12. 2), and participants reported an average COVID-19-related financial stress score of 1. 9 (SD 1. 5) out of 7, with lower scores equating to less financial distress. The qualitative data set contained 3483 COVID-19 impact stories, comprising a total of 86,642 words. The average length of the impact stories was 25 words; however, men provided shorter stories on average (mean 18. 4, SD 24. 8) compared to women (mean 26. 4, SD 27. 3). Table  1. Participant characteristics among a sample of Australian adults in a cross-sectional study investigating the psychosocial impacts of COVID-19 in May 2020 (N=3483). For all variables, a higher score indicates a higher level of that variable.
Sentiment Toward COVID-19 Impacts The average Stanford CoreNLP sentiment score fell into the neutral category (mean 2. 91, SD 0. 76). Neutral classifications were most common, representing 44. 3% of impact stories (n=1544/3483). There was also a relatively balanced prevalence of negative (n=1136/3483, 32. 6%) and positive (n=802/3483, 23. 1%) sentiment scores. Approximately 1% (n=38/3483) of all COVID-19 impact stories were categorized at the extreme ends of the sentiment distribution (very negative or very positive). The distribution of the VADER sentiment scores is depicted in Figure  1. Representative quotes from the data that exemplify each of the sentiment score categories are provided in Multimedia Appendix 1.