dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , < 5 years, 5 to 10 years, > 10 years) in the occupation as fixed factors. Because the interclass correlation coefficients indicated that the variance in perceived stress could be attributed to differences between hospitals and occupational status (respectively, 7. 9% and 7. 6%), and because the likelihood ratio test statistics (LR) for each variable were significant (p < 0. 05), we included hospitals and occupational status as random effects to take account of heterogeneity across clusters of participants. We selected the final model with the best fit and maintaining model parsimony using the Akaike Information Criterion (AIC)  [31]. Finally, to explore the relation between stress intensity and mental health across zones with different levels of epidemic intensity, we performed causal mediation analysis of multilevel data via the lmer function in the lme4 package  [29]  and the mediate function in the mediation package  [32].
2. Garment design process dataset (garment patterns, parameters of real and virtual (digital) fabrics with various colors, 3D human models adapted to the target population, and pattern adjustment parameters). Consumer profile data and the predefined design attributes of garments(men's formal shirts) have been collected from an online e-shopping platform. The 3D CAD tool has been utilized to collect the data associated with the designer's activity of the customized garment creation process. Then, the association rules over the collected data generated by exploiting the relations between consumer data and data on design attributes of shirt like the fabric collar, cuff, etc. , so that the best combination of garment design elements with higher confidence level can be determined as insight for the garment buyers as well as the designers.
• Build the CF Tree: The first step is building the CF Tree and loading data into a cluster feature tree (CF Tree). The CF Tree represents or keeps data in a compressed form in memory. The BIRCH algorithm becomes highly efficient by using summary statistics for minimizing large data sets. The CF Tree is built with CFs and each CF is composed of three summary statistics  [19] : - The N represents the number of data points in the cluster -→ X i where i = 1, 2,. , N.
- The squared sum is the sum of squared data points and helps to measure the spread of the cluster, where -→ X i is a cluster. SS = N ∑ i=1 ( -→ X i ) 2 (2) A cluster feature tree is a tree structure composed of CFs. A CF tree represents a compressed form of data, preserving any structure in the data. A CF tree has the following parameters: -Branching Factor B. It determines the maximum children allowed for a non-leaf node. -Threshold. It gives the upper limit to the radius of a cluster in a leaf node. -Number of Entities in a leaf Node L. • Global Clustering: The second step is clustering the sub-clusters. After the creation of the CF Tree, the existing clustering algorithm on the CF Tree Leaf nodes(sub-clusters) is applied to combine sub-cluster into clusters.
Building Prediction Model by Applying BIRCH Incremental Clustering Algorithm The customized men's shirt order dataset contains 5291 records, which includes both males and females. The order dataset has 4898 records for customized men's shirts and 393 women's shirts data. Therefore, in total 4898 records have been utilized to train the model. In this step, we trained an unsupervised incremental clustering model, permitting a biometric profile corresponding to the chosen customized garment (men's shirt) to be classified according to the user biometric profile. First, we acquired the data from fashion e-shopping digital platform, which are specifically dedicated for men's customized shirt. A three-dimensional input vector (height, weight, collar size) was extracted from the user's biometric profile data.
Building Shirt Fitting Style Prediction Model In the previous step, we segregated the user's profile by using the biometric parameters as per the collected data. Further, in step 2, N ARF classifier models were trained where N is the number of clusters found in step 1. The existing dataset was separated into two clusters by applying the unsupervised BIRCH clustering algorithm. This model has been used to find the most relevant fitting style to a specific biometric profile of customized garment; here, men's formal shirt has been taken as a customized garment. First, we set up the ARF classifier by supplying input vector [X, Y] where X consist of three biometric parameters (height, weight, collar size) and Y contains fitting type (Super Slim Fit, Comfort fit, Regular). Table  1  shows the three different types of fitting style with its count value.
Second, we performed an interleaved test, then trained the method to evaluate the ARF classification model  [21]. The incremental classification prediction model cannot be evaluated by applying cross-validation measures, unlike the static and batch learning classification. The prequential evaluation is sequential analysis where the sample size is not fixed initially and the model is evaluated as new data is collected. The prequential evaluation first uses each instance to test the model and then train the model. Table  2  shows the mean performance using accuracy and kappa score measures for individual classifiers trained over sub-datasets processed in step 1. The kappa score ranges between 1 and -1, where 1 implies perfect agreement and values less than 1 indicates less than perfect agreement  [22].
3. Then, filtering the rules based on the threshold value assigned for support, confidence, and lift. Let I = i 1 , i 2 , i 3 ,. , i d be the set of all features of customized garment dataset and T = t 1 , t 2 , t 3 ,. , t N , where each transaction t i contains subset of features chosen from I. Let A,B be sets of items; an association rule is a derivation of the form A ⇒ B , where A ⊂ I , B ⊂ I, and where A and B are disjoint itemsets, that is , A ∩ B = ∅. The strength of extracted rules can be measured by calculating the support and confidence  [24] : • Support (s) determines how often rule is applicable to a given dataset. s = P(A ∩ B) (3) • Confidence (c) of the association rule A ⇒ B is the measure of accuracy of rule, which is determined as the percentage of transaction in dataset containing A that also contains B.
c = P(B|A) = P(A ∩ B) P(A) (4) • Lift (l) computes the ratio between the rule's confidence and the support of the itemsets in the rule consequent  [25]. l = P(A ∩ B) P(A)P(B) (5) The authors of  [25]  have interpreted the lift measure as follows: Finally, let c j be the count of g j. Thus ∑ n i=1 c i represents total number of frequent pattern set tables generated by step 3. Similarly, in continuation, a similar number of table schema corresponding to each frequent pattern in the relational database management system are created via a service. Li f t =      = 1 if.
• From the frequent item sets generated in previous step, the rule generation step extracts the rules satisfying minimum support and confidence condition. To generate frequent item sets from the transaction database, we have used the FPgrowth algorithm. The FP-growth algorithm has been used successfully in large transaction database, because of the use of compact data structure FP-tree to get frequent item sets efficiently and quickly without generating the candidate sets explicitly unlike an a priori algorithm  [26]. The following steps have been followed to generate the frequent feature sets of the customized shirts transaction database.
Therefore, the existing insights for similar drape fabric can be used for the new fabric at designing phase of the virtual garment. Thus, to achieve the integrated solution for designers and consumers, fabric drape data need to be considered as an essential part. Moreover, although existing CAD tools for virtual garment creation process contain a huge library for virtual fabrics of different types, we have used the fabric drape image analysis process to utilize the correlation between the fabric drape and its technical parameters in order to find the existing virtual fabric from the database, which can be compared to the real fabric based on the drape image profile. The following parameters have been considered as per the fabric drape image profile literature: • Amplitude (A) is half the difference between maximum and minimum radii.
• Step 5: After the construction of virtual shirts ease data collected for key body parts of shirt (across back, across front, bust, waist, and pelvis). Each key body part's five ease values obtained corresponding to 5 chosen fabrics to see the influence of fabric on the garment ease. • Step 6: All the 8 human body measurements, 8 different shirt sizes, and 5 fabrics with different fiber composition are utilized to evaluate the 3D ease of virtual shirt. This experiment resulted to 320 records to make the knowledge base, obtained records then further utilized to implement 3D ease non linear functional approximation prediction model. Steps to train the prediction model has been formalized as follows: Let F = { f 1 , f 2 ,. f n } where n = 5 are involved fabric set. Let F j P = { f j p 1 , f j p 2 ,.
1. The system creates a new BIRCH cluster object without affecting the old clustered data and should append the new data to the existing cluster, If new data do not form a new cluster. 2. The system introduces a new adaptive random forest classifier object, if a new fitting type is introduced in the data. 3. The system creates a frequent pattern table in the SQL Server database automatically corresponding to new fitting type, which may occur in new or old BIRCH cluster object. 4. The system creates association rule table in SQL server corresponding to new fitting, which may occur in new or old BIRCH cluster object. 5. The system integrates real fabric to existing knowledge base by parsing the fabric drape image, which is extracted from the drape meter and fabric drape image profile shall be created and saved into system.
[20]iled description of clustered data. version of random forest classifier. ARF classifier has been utilized well to build the classification model over streaming data. Also, to build real time data mining classifier model. Contrarily traditional random forest classifier needs to be retrained fully by merging new set of data to the existing dataset or just override the previously trained version with new one. Previous research work has shown the significant use of ARF classifier, which is described as follows[20]:.
Participants The data were collected by administering, in online format, a multi-questionnaire survey to partner nurseries in France and Switzerland. The survey was completed by mothers of children aged between 6 months to 5 years old, and was available between 5 May 2020 to 6 June 2020. Participants first had to read and agree to a detailed consent form before they could begin the survey. The survey was completed anonymously, in which participants chose an alias. Participation in the study was voluntary and unpaid. The experimental design of including participants living in different countries (here France and Switzerland) provided an opportunity to account for a potential interaction effect based on the differing severity of lockdown conditions between countries. For example, the imperative lockdown in France imposed much more time at home, e.
Materials and Design As detailed below, the multi-questionnaire survey included validated scales for measuring sleep disturbance, anxiety, and behavior issues in mothers and/or their young children, and non-standardized questionnaires to collect data on lockdown variables. The Insomnia Severity Index (ISI  [62] ) was used to measures the degree of insomnia in mothers. The ISI consists of 7 items, each on a 5-point Likert scale (from 0 to 4), and thus in total, possible scores range from 0 to 28, where higher scores indicate more severe insomnia. A score of 14 is considered a threshold, and scores above it suggest a pathological level of insomnia. Herein, a Cronbach's α = 0. 84 was observed for the ISI (see  [63]  for more information on this statistic). The Sleep Disturbance Scale for Children (SDSC  [64] ) was used to measure the degree of sleep disturbance of children.
Data Pre-Processing The data were analyzed and pre-processed using the R programming language, with Rstudio as the coding environment (Version 1. 1. 463). Outliers were identified based on a linear model-based approach using Cook's distance (values > 0. 04  [69] ). In order to satisfy modeling and statistical test criteria (e. g. , normality), the noncategorical variables were optimally normalized via the Yeo-Johnson transformation  [70]. Preliminary analyses (Student's t test for continuous/interval variables, Wilcoxon rank sum test for ordinal variables, and χ 2 frequency test for binary variables, as well as principal component analyses) were used to reveal significant differences between the French and Swiss participants, ascertaining that the differing lockdown conditions of the two countries (a confound) leads to their respective different interactions along the variables.
Third, a high-performance hierarchical clustering of the participants was obtained by directly using the principal component information (HCPC package in R). Finally, path analysis was performed within the structural equation modeling (SEM) framework in order to obtain an integrative network model that details the relationships between the variables for each country (lavaan package in R). No latent explicative/composite variables were added into the model. The resultant networks were obtained through a data-driven approach: optimizing a Bayesian network structure learning algorithm (bnsl package in R). In each case, the simplest SEM network structure that appropriately satisfied the standard reference SEM diagnostics (e. g. , Tucker-Lewis Index (TLI), Bollen's Incremental Fit Index (IFI), Root Mean Square Error of Approximation (RMSEA)); see Table  S2  in Supplementary Materials) was selected.
Procedure and participants We developed and validated the scale through successive steps according to contemporary methodological recommendations  [19, 20] : tests for clarity and relevance, dimensionality, reliability tests, and construct validity. Participants were recruited in several specialized CF centers in France. They were all adults with CF (age > 18 years). The exclusion criteria were people: (a) under guardianship or trusteeship; (b) in acute exacerbation phase; and (c) with cognitive impairment. The participants' sociodemographic data are presented in Table  1. They were divided into four samples for the different steps of validation. The questionnaire was administered in paper form with relay persons (nurses and trainees) or online using LimeSurvey CE, version 2. 06 + (LimeSurvey CE).
Adaptation to SCD Data were collected using the three sections. We used the IPQ-R with wording modification (replacement of the generic term 'disease' by 'sickle cell disease') to adapt to SCD in the Beliefs scale. The Identity scale was not modified. The Causal attribution scale was extended to reflect the specific beliefs adults with SCD could have following previous work by  Gernet, Mestre, and Runel-Belliard (2011) and Oudin-Doglioni et al. (2019). A medical validation of the questionnaire was carried out, in the same way, the comprehensibility and the feasibility of the questionnaire were verified with two patients leading therapeutic patient education programme. We followed a careful cross-cultural translation and validation methodology  (Cha, Kim, & Erlen, 2007). Discrepancies emerging from the back translation were discussed, and adjustments to the translation were made (Table  3 ).
Statistical analysis Descriptive statistics summarised the demographic and medical characteristics of the sample using R. For the Beliefs scale, confirmatory factor analysis (CFA) was conducted using R with package Lavaan  (Rosseel & Jorgensen, 2019)  under Jamovi (The Jamovi Project, 2020). We used the two-index presentation strategy  (Hu & Bentler, 1999) , which includes using the maximum likelihood (ML)-based standardised root mean squared residual (SRMR) with a cut-off value under. 08, and supplementing it with the root mean square of approximation (RMSEA) with 90% confidence intervals and cut-off value close to. 07  (Steiger, 2007). Following advice from Hooper, Coughlan, and Mullen  (Hooper et al. , 2008 , Table  4 ), we added three indicators of goodness of fit: the Wheaton et al. 's relative/normed chi square with a range from 2 to 5  (Wheaton et al.
, 2002) , which would allow an ambitious restructuring of all dimensions. Especially since a meta-analysis of 23 datasets from 30 studies using IPQ-R in chronically ill patients  (Brandes & Mullan, 2014)  indicates moderate to high heterogeneity for all dimensions apart from timeline, coherence and emotional representations, with funnel plots indicative of bias. In a context of reproducibility issue  (Wingen et al. , 2019) , the robustness of the CSM to meta-analytic investigations should be acquired  (Doyle & Mullan, 2017). This work on the psychometric properties of IPQ-R in an adult population suffering from sickle cell disease has made it possible to adapt its use to this population after the adoption of data-driven modifications. The data are specific to the population in which they were obtained and cannot lead to a generalisation in other chronic pathologies even of genetic origin.
Acknowledgments All authors received conference support from  Neurim Pharmaceuticals SARL  for a videoconference consensus meeting related to the under consideration for publication. Dr. Banaschewski  served in an advisory or consultancy role for  ADHS digital, Infectopharm, Lundbeck, Medice, Oberberg GmbH, Neurim Pharmaceuticals, Roche , and  Takeda. He  received conference support or speaker's fee by  Medice and Takeda. He received royalities from  Hogrefe, Kohlhammer, CIP Medien, Oxford University Press ; the present work is unrelated to these relationships. Dr. Bruni  reports personal fees and non-financial support from Neurim pharmaceuticals for advisory board consulting. Dr. Fuentes  has received research support from  Policlinica Gipuzkoa Foundation (PGF) ,  Servier , and  AIMS-2 Trials/Project ID 777394.
In section 3, we present our generalized Pareto interpolation method, which is based on a transformation of b(p). In section 4, we test its precision and compare it to other interpolation methods using individual income data for the United States and France covering the 1962-2014 period. In section 5, we analyze the error associated with our interpolation method, and provide formulas that give approximate bounds on this error in the general case. In section 6, connect our findings to models of income growth and wealth accumulation with multiplicative random shocks. We believe that the approach and the tools that we develop in this article will be useful to researchers who want to improve the way we construct and analyze data on income and wealth inequality, and we indeed plan to use them to keep expanding the World Wealth and Income Database (WID.
Namely, the very top experience higher growth and/or more risk, meaning the processes that generate income and wealth are not fully scale invariant. We believe that more empirical work -especially a careful use of administrative data sources -is necessary to study those dynamics in a fully satisfying way. We hope that the interpolation method presented in this paper will allow future researchers make progress in that direction. To that end, we made the methods presented in this paper available as a R package named gpinter, and also in the form of an online interface that can be used without any installation or knowledge of any programming language. Both are available at http://wid. world/gpinter. Abstract This appendix supplements our paper "Generalized Pareto Curves: Theory and Applications".
It provides technical details on the interpolation method, extended tables for comparison of interpolation methods, and the proofs of the theorems. ⇤ Thomas Blanchet: thomas. blanchet@wid. world. Juliette Fournier: jfournie@mit. edu. Thomas Piketty: piketty@psemail. eu. All R programs developed in this paper are available at http://apps. wid. world/ gpinter, where we also provide a set of online tools to estimate and manipulate distributions of income and wealth on the basis of simple tabulated data files (such as those provided by tax administrations and statistical institutes) and generalized Pareto interpolation methods. We acknowledge financial support from the European Research Council under the European Union's Seventh Framework Program, ERC Grant Agreement n. 340831.
SPIN-CHAT is a 4-week (3 sessions per week) videoconference-based group intervention that provided education and practice with mental health coping strategies, and provided social support to reduce isolation. Groups included 6-10 participants. The primary outcome analysed in the intention-totreat population was anxiety symptoms (PROMIS Anxiety 4a version 1. 0) immediately post-intervention. This trial is registered with ClinicalTrials. gov, NCT04335279 and is complete. Findings Of participants who completed baseline measures between April 9, 2020, and April 27, 2020, 560 participants were eligible and 172 participants were randomly assigned to intervention (n=86) or waitlist (n=86). Mean age was 55•0 years (SD 11•4 years), 162 (94%) were women, and 136 (79%) identified as White. In intention-to-treat analyses, the intervention did not significantly reduce anxiety symptoms post-intervention (-1•57 points, 95% CI -3•59 to 0•45; standardised mean difference [SMD] -0•22 points) but reduced symptoms 6 weeks later (-2•36 points, 95% CI -4•56 to -0•16; SMD -0•31).
Research in context Evidence before this study We referred to a living systematic review that is evaluating interventions to address mental health during COVID-19 by searching seven databases, including two Chinese language databases, plus preprint servers. As of Jan 16, 2021, the review had identified 21 eligible reports of trial results, including 18 from China, one from Iran, one from Sweden, and one that enrolled Amazon Mechanical Turk volunteers without reporting country. Of these, 18 addressed acute psychological distress among patients in the hospital owing to COVID-19 (n=15) or another condition (n=3). Two others were trials of standard psychological interventions (eg, mindfulness and self-affirmation writing) not specific to COVID-19. In all of those trials, reporting was poor, and risk of bias unclear or high.
One well-conducted trial tested an online self-help cognitive behavioural therapy intervention for dysfunctional worry related to COVID-19 in the Swedish general population (n=670) and found a medium to large reduction (0•57 standardised mean difference, 95% CI 0•40 to 0•73) after 3 weeks. No trials tested any multi-faceted interventions that incorporated components recommended by international public health organisations to address COVID-19 mental health, and no trials were done with medically vulnerable people outside of the hospital. We also sought to identify pre-COVID-19 mental health trials in systemic sclerosis via an ongoing living systematic review of non-pharmaceutical interventions (PROSPERO CRD42020219914), which is searching six databases. As of Jan 16, 2021, no randomised trials of any mental health intervention with at least 30 total participants had been done.