dataesr/scientific-paragraphs-categorization huggingface dataset:
,2016a). Suchfirmswouldthenhavetotaketheinitiative toincludeculturalorientationofcustomersintotheirexistingsystems. Mostonlineservicefirms havecustomerdatabasesthatincludetheirpersonalinformation,demographicandpsychographic details,theirpurchasehistoryandpriorcustomerserviceencounters. Amongthese,demographic informationrelatedtoethnicitycangiveanindicationofculturalorientation. However,forsuchfirms topreciselysegmentandtargettheircustomersbasedonculture,theyneedtohavedataabouttheir individualculturalorientationforamorefine-grainedanalysisasthepresentpaperhassuggested. ThefirmscanemploysurveyresearchusingCVSCALEtoobtaintheindividualculturalorientation datathatcanbeaddedtothedemographicinformationinthecustomerprofilesoftheirexistingCRM andCKMsystems. Tostartwith,thismaybeimplementedforfrequentorhigh-valueshoppersand thendependingontheresultsmayberolledoutforalargercustomerbase.
Method Main Studies and Replication Studies In two separate data collection efforts (the initial investigations ["Main Studies"] and direct replications ["Replication Studies"]), we randomly assigned participants to different sets of study materials designed independently by up to 13 teams of researchers to test the same five research questions. There were 15 teams of materials designers in total, from which up to 13 teams designed materials for each research question (i. e. , not all teams made materials to test all five original hypotheses). The five research questions were gathered by emailing colleagues conducting research in the area of moral judgment and asking if they had initial evidence for an effect that they would like to volunteer for crowdsourced testing by other research groups.
The original materials for Hypothesis 5 were developed by the team of Sowden and Hall, but were much longer than any other materials set, so this team also developed a shorter set of materials for Hypothesis 5 and data were collected using both versions. In all, 64 sets of materials, including the five sets of original materials, were created through this crowdsourced process. The materials and analyses for both studies were preregistered at https://osf. io/9jzy4/ (see also Supplement 1, as well as Supplement 2 for deviations from the preregistered analyses, in the online supplemental materials).
Procedure In the Main Studies, participants were randomly assigned to one set of materials for each of the five hypotheses, and, for designs with multiple conditions, one condition per hypothesis. The order in which the five sets of materials were presented was randomized for each participant. After responding to all five sets of materials, participants completed a demographics questionnaire including questions about their age, gender, and other characteristics. Additionally, a separate subsample of participants was randomly assigned to only complete the full original materials for Hypothesis 5, because of their length. The materials designed by the team of Jiménez-Leal and Montealegre to test Hypothesis 4 were run separately approximately two months after the rest of Main Studies were run, because we discovered that, owing to a coding error, one of the two conditions from these materials was not presented to participants in the original run (new data were therefore collected for both conditions of this design).
3 This model treats each observed effect size y i as a function of the average true effect size , 2 Materials designed by the team of Donnellan, Lucas, Cheung, and Johnson for Hypotheses 2, 3, and 4 employed within-subjects designs, whereas the other materials for these hypotheses employed one-sample or between-subjects designs. To ensure that all effect sizes were comparable in the meta-analyses, the repeated-measures ds for the within-subjects designs were converted to independent-groups ds (see  Morris & DeShon, 2002). bootES does not have a feature to convert between effect size metrics, so custom bootstrapping code was used (see https://osf. io/avnuc/). This custom code returns the same effect size estimates and variance terms for the repeated-measures ds as bootES, and converts the repeatedmeasures ds to independent-groups ds according to Equation 11 in  Morris and DeShon (2002).
All authors revised and approved the final manuscript. Data collection was funded by a research grant from  INSEAD  and a grant from the  Jan Wallander and Tom Hedelius Foundation (Svenska Handelsbankens Forskningsstiftelser)  to  Anna Dreber. Anna Dreber  is also funded by the  Knut and Alice Wallenberg Foundation  (through a  Wallenberg Academy Fellows grant ) and the  Austrian Science Fund (FWF ,  SFB F63 ), and  Anna Dreber ,  Magnus Johannesson , and  Domenico Viganola  are funded by the  Jan Wallander and Tom Hedelius Foundation  ( Svenska Handelsbankens Forskningsstiftelser ) and the  Swedish Foundation for Humanities and Social Sciences. Thomas Pfeiffer  is funded by  Marsden Fund  Grants  16 -.
Analysis Five logically ordered experiments illustrate the value and flexibility of the model for different policy analyses. The first experiment involves the construction and analysis of a Baseline run that builds, through calibration, on the ongoing COVID-19 outbreak using a cross-sectional dataset involving 6 countries. In the second experiment we perform a sensitivity analysis of this Baseline case, centered on policy and citizen responses to the outbreak within one of the countries. In the next two experiments we perform an analysis, within hypothetical regions, about managing deconfinement and virus resurgence. In the final experiment, we explore the relative effect of the virus on vulnerable populations, by varying the symptom severity across population segments.
Experiment 1 -Baseline calibrated to the ongoing COVID-19 outbreak To perform a cross-sectional calibration of the model I constructed a country-level dataset with diverse outbreak data including reported daily new cases, recovery rates, death rates, and testing rates, as well as a metric for social contact rates. The dataset covers a large number of countries from December 31 2019 until May 15 2020. 8 Daily reported new cases and deaths and testing data were retrieved from Ourworldindata. org (Roser et al. 2020). The metric for social contacts is a composite of data on intensity of walking, driving, and public transport use and of data on school closings (See Figure  1  for details and sources). To obtain the social contact data I multiplied a reference normal contact rate value, constant across countries, with the population size.
The full dataset includes over 200 countries and can be customized to select a subset of countries and to aggregate across a subset of countries. The dataset can also easily be updated as new data become available. I then strategically selected six countries for in-depth analysis, aiming for variation in outbreak dynamics and interventions and selecting for the presence of reliable reporting data for calibration. The final set includes the three countries already highlighted in the background section plus three others, combining to: South Korea, Germany, Italy, France, Sweden, and the United States (S-K, GER, ITA, FRA, SWE, and US) (Table  1 ). Within this set, after South Korea, Germany has the lowest cumulative reported cases per capita (though considerably higher than for South Korea). Germany responded relatively quickly, in particular through early testing buildup and isolation of suspect cases.
The Technical Appendix is available in the online Appendix folder: https://www. dropbox. com/sh/x35h4t8mkkybe0v/AAAkBGr0iWlo6lN3tVpmIb-Va?dl=0. The Appendix folder also includes the full model (in Vensim software), the data used for the calibration analysis performed in the paper (as well as for the selection of other regions and countries), and all files necessary for replicating the analysis. I explain the data, calibration process, and estimated parameters here briefly.
14 Here we use a MC approach in which the conditional probability distribution of future states of the treatment selection process depends only on the current state (Markov property); the stationary distribution of this Markov process is regarded as the probabilities of recommending each treatment. The suggested algorithm allows incorporating information other than that of the relative effects through the initial probabilities assigned to each treatment. The rest of this article is structured as follows: in Section 2, we describe an exemplar NMA used to illustrate our ranking approach. Section 3, after a brief description of P-scores, introduces our ranking method and describes how some key characteristics on top of relative effects can be incorporated in treatment selection. Finally, in Section 4, we use the example dataset to illustrate the method and, in Section 5, we discuss the strengths and limitations of our approach.
Conventional treatment ranking Following the original Cochrane review, we performed a random effects NMA using a multivariate meta-analysis approach assuming a common heterogeneity across comparisons. 25 Table  1  shows the P-score percentages and the respective ranks for all treatments for the two primary outcomes (PASI 90 and SAE). These were obtained using the network and network graph packages in Stata. 25, 26 The dataset and the Stata script used to obtain the results can be found in the Supplementary files. The league table of the relative effects and the respective league table with the probabilities p i > j are available in the Appendix (Appendix Tables  1  and 2 , respectively). In Figure  3 , we show the association between P-scores and POST-R for efficacy based only on the relative effects, hence assuming z = 1.
It should be acknowledged that this is an artificial MC process and that the time points t do not have a real meaning. However, we conceptualized the "ideal" scenario (in terms of exploring efficacy of interventions) of someone going through the whole evidence in order to decide upon a final hierarchy of all the treatment options at hand and we assumed that changing treatment is an evidence-based choice driven by the results of the NMA as well as by some prior/external information. In the presence of important aggregate covariate(s) that are substantially different across studies, the probabilities p i > j can be obtained by a network meta-regression model adjusting for these covariate(s). The POST-R approach has been implemented in the sucra command of the network graphs package  26  in Stata  32  which facilitates its use once Q (0) has been specified.
Data sample: 30 sectors of the French economy observed during 1978-2015 The OECD database for structural analysis (STAN database) and the French national accounts system (INSEE) are used to analyse 30 sectors of the French economy (manufacturing, energy, construction, market services and public administration) over the period 1978-2015. 4  The markups are estimated for each of the 30 sectors for seven periods with an average fi ve-year duration, which provides 210 estimated markups. Seven periods are considered: the 1st period  (1978) (1979) (1980) (1981) (1982) (1983) (1984) , the 2nd period  (1985) (1986) (1987) (1988) (1989) , the 3rd period  (1990) (1991) (1992) (1993) (1994) , the 4th period  (1995) (1996) (1997) (1998) (1999) , the 5th period  (2000) (2001) (2002) (2003) (2004) , the 6th period  (2005) (2006) (2007) (2008) (2009)  and the 7th period  (2010) (2011) (2012) (2013) (2014) (2015).
Acknowledgments: We thank the three reviewers for their very constructive suggestions and comments on the manuscript. This work was funded by the  Deanship of Scientific Research (DSR) ,  King Abdul Aziz University, Jeddah , under grant No. G-143-363-1440. The authors gratefully acknowledge the DSR technical and financial support. " The authors wish to correct the grant number in the funding and acknowledgments sections as: "Funding: This work was funded by the  Deanship of Scientific Research (DSR) ,  King Abdul Aziz University, Jeddah , under grant No. D-143-363-1440. Acknowledgments: We thank the three reviewers for their very constructive suggestions and comments on the manuscript. This work was funded by the  Deanship of Scientific Research (DSR) ,  King Abdul Aziz University, Jeddah , under grant No.
Introduction The development of sensors, information storage and data mining makes it possible to collect data from a large number of sources with different characteristics, such as the familiar single points, curves and pie charts. Multiple types of data, referred to as complex data, greatly enlarge the traditional category of variables and provide researchers with an opportunity to understand the behavior of activities more comprehensively than ever before. For example, the public online sentiments measured from social media and intraday stock returns are improving the accuracy and interpretation of predicting trends in Chinese stock market  (Wang et al. , 2019a) , which will be revisited in the real data analysis. Here, the return series are considered as the continuous functions from opening to closing and the investors' sentiments are measured as the compositions constituted by five types of specific sentiments.
However, this IID assumption is problematic in some cases; these complex data may also show typical longitudinal features, which we call longitudinal complex data. For example, as discussed in Section 5, the closing prices of Chinese stock market were collected from hundreds of stocks over several months. The price-limiting mechanism of the market results in a high correlation among the observations of the same stock. Directly applying existing statistical models to complex data without incorporating the correlation structure might yield biased and confusing results. Similar problems have been discussed separately for FDA  (Chen & Cao, 2017; Gertheiss et al. , 2013; Goldsmith et al. , 2012)  and CDA  (Qiu et al. , 2010; Wang et al. , 2019b; Zhang et al. , 2009) , but few of the related solutions show the potential to integrate multiple types of complex data together.
Data on the constituent stocks in CSI300 from January 8 to April 29, 2016 (75 trading days) are collected from the Wind service. The stocks with prices over 50 or active trading days fewer than 40 are removed, and finally 256 stocks are left. Here, both DCP and DV are scalar, IPR recorded every 5 min is described as a series of smoothing curves from opening to closing, and OIS is measured naturally as a compositional structure associated with five types of sentiments labeled ''Anger", ''Disgust", ''Joy", ''Sadness" and ''Fear"  (Zhou et al. , 2018). Thus, the regression model contains two scalar covariates (including the intercept), one functional covariate and one compositional covariate. In the FPCA procedure, the previously given basis system consists of ten cubic B-spline functions determined by seven equally spaced inner knots over ½0; 1, where 0 and 1 indicate the opening (9:30 a.
In surrogate modeling, polynomial chaos expansion (PCE) is popularly utilized to represent the random model responses, which are computationally expensive and usually obtained by deterministic numerical modeling approaches including finite-element and finite-difference time-domain methods. Recently, efforts have been made on improving the prediction performance of the PCE-based model and building efficiency by only selecting the influential basis polynomials (e. g. , via the approach of least angle regression). This paper proposes an approach, named as resampled PCE (rPCE), to further optimize the selection by making use of the knowledge that the true model is fixed despite the statistical uncertainty inherent to sampling in the training. By simulating data variation via resampling (k-fold division utilized here) and collecting the selected polynomials with respect to all resamples, polynomials are ranked mainly according to the selection frequency.
For the so-called intrusive methods, taking the spectral finite element method  [13]  as an example, the classical FEM is combined with the Karhunen-Loève expansion of input random fields and the coefficients are obtained by a Galerkin scheme which results in a system of deterministic equations  [14]. In contrast, without modifying the underlying code, hence as non-intrusive methods, coefficients can be obtained based on an experimental design (ED) by two popularly utilized approaches. While minimizing the mean square error of data discrepancy leads to the solution of regression method  [15] , projection method  [16, 17]  exploits the orthogonality of basis functions, the expansion coefficient being the solution of multidimensional integrations which can computed by quadrature methods. A PCE, as an infinite series, should be truncated for computational purpose.
Estimation of prediction performance The model assessment is often performed by Monte Carlo simulations with a large test dataset, which is independent from the experimental design. Denote M as the surrogate model, the input vector and response of the n-th test data as x (n) test ) -M(x (n) test ) 2. ( 6 ) For an easier interpretation of test , the associated coefficient of determination R 2 test is computed by R 2 test = 1 - test Var(y test ) , (7) where test is to one, the more accurate is the prediction by M. However, in scenarios with high computational cost for a single simulation, it is usually intractable to have a large test dataset. Then, the same data as for training are often reused for model assessment. However, the underestimation of the generalization error is well-known in the case of overfitting  [18].
Cross-validation was thus proposed and is commonly advocated  [39, 40]. Here, leave-one-out cross-validation (LOOCV) is applied and the corresponding cross-validation error reads: LOO = 1 N N n=1 M(x (n) ) -M -(n) (x (n) ) 2 , (8) where M -(n) denotes the surrogate model trained by leaving the n-th data out. Remark that LOO is also known as predicted residual of squares (PRESS) or jacknife error  [41]  and it can be computed fast in single training process  [18]  by LOO = 1 N N n=1 M(x (n) ) -M(x (n) ) 1 -h n 2 , (9) where h n is the n-th diagonal element of the matrix ψ ψ T ψ -1 ψ T. 3 Surrogate modeling based on full PCE and sparse.
Resampled PCE based on LARS or OMP Inclusion frequency is defined as the percentage of replications  [27]  in which a given basis polynomial is selected by LARS or OMP. The variation of training data is simulated by the subsampling technique, k-fold division, considering its efficiency in exploiting the information of original data, i. e. , the training process makes use of all data in k replications. Dividing the whole set of data into k subsets, all with approximately same size. Of k subsets, the l-th subset is left out and the remaining k -1 subsets are used for the PCE construction. Varying l from 1 to k, one has k PCE models built by LARS/OMP and the associated active sets are denoted by A a P,(l) , l = 1,. , k. The subscript "P " and superscript "a" are ignored in A a P,(l) to be A (l) in the followings.
Summation of multivariate polynomials Y = 1 + X 1 + X 1 X 2 + X 1 X 2 2 + X 1 X 3 2 , (16) which is a summation of five multivariate polynomials (including the constant term), is conducted. X 1 and X 2 are independent variables that follow the Gaussian distributions N (0, 1) and N (6, 1), respectively. OMP is used to build a sparse PCE model with 12 data points for training and 10 4 data for independent testing. A total of 100 PCE constructions are made to check the selection frequency of polynomials. Due to the Gaussian distribution of input variables, Hermite polynomials are used to compose the basis, where the bivariate polynomials are indexed by α = (α 1 , α 2 ). The constant term corresponds with α = (0, 0), while the other four terms in Eq. (  16 ) are with , where X 2 , X 1 , X 3  1 are selected as the basis).
Example with varied input dimension To investigate the effects of the dimension of input on the modeling performance, the following test function  [53]  is used, y = 3+ 1 M M k=1 k(x 3 k -5x k )+ln 1 3M M k=1 k(x 2 k + x 4 k ) +x 1 x 2 2 -x 3 x 5 +x 2 x 4 +x M -4 +x M -4 x 2 M , (20) where M denotes the number of variables, which are independent and uniformly distributed in the range of  [1, 2]. To increase the non-linearity, the range of x 20 (when M ≥ 20) is changed as  [1, 3]. The value of M changes from 11 to 41 with a step 5 and the size of experimental design N is fixed as 200 independently of M. For a statistical assessment of the modeling performance (accuracy and efficiency), 50 replications are performed and 10 3 data are used for the independent test at each replication. Remark that, due to randomness of the LHS method, different training and test datasets are used in replications.
From the methodology of rPCE, one knows that the computational cost is proportional to the number of resampled datasets. In Section 5. 1, the suggested (not obliged) setting of k is a set of values, i. e. , k = {3, 5, 10, 20, N }. As a result, the corresponding computational cost would be high when the ED size N is large. Here, a lighter setting of k, k = {3, 5, 10, 20}, is applied and improved modeling performances are still observed as presented by the following results. For the configuration of UQLab, which the running of LARS and OMP is based on, the maximum value of total degree p is set as 5. The effects of M on the modeling accuracy can be observed from the distribution of R 2 test in Fig. 19. When M equals 11 and 16, accurate models are constructed with the three approaches, although outliers appear with OMP and M = 16.
In resampled PCE, a high computational cost may be suffered, especially with the suggested setting of k, i. e. , k = {3, 5, 10, 20, N } and a large N. As shown by the flow chart in Fig. 1 , the possible high computational cost is due to the loop about k and l. However, one should realize that the loop about k and l are not necessarily performed in sequence and can proceed in parallel. All resampled datasets with different values of k can be first easily generated with LHS method. Since the surrogate modeling with respect to different sets of resamples is separable, techniques of parallel computation (e. g. , computation with GPU and distributed computation) can be applied. Moreover, the suggested setting is not compulsory in the running of resampled PCE. Actually, from the results in Section 6, we can see the improved modeling performance has been observed with k = 10, 20, or k = {3, 5, 10, 20}, even better performances can be obtained if with the suggested setting.
AUTHOR PROOF Open access box 1 Example of report reflecting the reporting issues found Please make the following revisions: ► For CONSORT item 8a ('Method used to generate the random allocation sequence'), please report the exact method you used to generate the random allocation sequence. -Example from CONSORT: 'Randomization sequence was created using Stata M. N (StataCorp, College Station, TX) statistical software'. ► For CONSORT item 11a ('If done, who was blinded after assignment to interventions and how'), please specify in 'Trial design and setting' who was blinded in the study and do not just state that it was a double-blind randomised trial. -Example from CONSORT: 'Whereas patients and physicians allocated to the intervention group were aware of the allocated arm, outcome assessors and data analysts were kept blinded to the allocation'.
Introduction Network meta-analysis has become an increasingly popular tool for developing treatment guidelines and making recommendations on clinical effectiveness and cost-effectiveness. However, fewer than 1% of published network meta-analyses assess the credibility of their conclusions  [1]. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach provides an assessment of the confidence in the results from systematic reviews and meta-analyses, and many organisations, including the World Health Organization, have adopted the GRADE approach  [2, 3]. Based on GRADE, 2 systems have been proposed to evaluate the credibility of results from network meta-analyses  [4, 5] , but the complexity of the methods and lack of suitable software have limited their uptake. Here we introduce the Confidence in Network Meta-Analysis (CINeMA) approach.