dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical data are usually collected by specialized institutions (ministry of health, health agency, etc. ) and must be transmitted to WHO daily as a notifiable disease. Scientific output on this emerging disease is outstanding, with thousands of articles published in just 6 months, in one form or another, often in electronic pre-publication form. Most of these publications deal with the Far-East, Europe, America, but few with Africa. A search on Medline database on "coronavirus disease 2019 (COVID-19) epidemiology" on November 07, 2020, gave 687 references for Africa out of 20,373 in the world (3. 4%)  (Medline, 2020). There are several reasons for this difference: Africa was affected somewhat later than other continents, the medical infrastructure and research are less developed there, and it seems that, at least so far, the virus is less prevalent and less lethal than elsewhere (WHO database 2020).
Country Databases Data on the epidemics (reported cases and deaths) are those transmitted to WHO, published in the daily reports  (WHO, 2020). The cumulative numbers of cases and deaths were used each week from February 16, 2020, to August 15, 2020, thus covering the first 6 months of the outbreak. This database covered the 54 African countries (classified in the AFRO zone or in the EMRO zone), as well as two territories located in the Indian Ocean (Reunion and Mayotte). Only one territory did not declare any case by August 2020: The island of Saint Helena, located in the middle of the Atlantic Ocean, one of the rare territories to have escaped the Spanish flu pandemic of 1918-1919 because of its geographic isolation  (McSweeny, Colman, Fancourt, et al. , 2007). Demographic data for these countries were taken from the United Nations Population Division database: Population in 2020, age structure, mortality, fertility, and urbanization  (United Nations, 2014; United Nations, 2019).
The data on country area, allowing the calculation of the population density, came from the FAO database  (FAO-Stats, 2019). This was added a database on the geographical distribution of the population, but which does not cover the islands  (Linard, Gilbert, Snow, et al. , 2012). The economic data came from the World Bank database (World Bank, 2019), which were supplemented when necessary by the Index Mundi database (Index Mundi, 2020): GDP per capita in purchasing power parity and in constant dollars; air traffic (in passengers per million populations).
The fact that these factors point to the transmission of the disease indicates that data are probably reliable, which indirectly validates the statistics, although they may be questioned in some countries. These variations between countries seem to be greater than in Europe. However, in Europe too, there were large variations in incidence, from 11,363 (Luxembourg) to 475 (Slovakia), that is, a ratio of 24 to 1 among the 48 European countries, excluding the former USSR (WHO database 2020). These large variations in Europe remain poorly explained, and the unexplained part could be due, in addition to population density and economic development, to random phenomena, chaotic dynamics, or quality of surveillance systems. Variations in case fatality were much smaller, and Africa as a whole appeared fairly homogeneous.
These results again point to good data quality, even if it seems surprising that some countries report so few deaths given the number of reported cases (Ghana, Guinea, Cote d'Ivoire in West Africa; Burundi, Rwanda, and Uganda in Central Africa; and Botswana and Namibia in Southern Africa). In Europe, there were also large variations in case fatality between countries, ranging from 16. 3 (France) to 0. 5 (Iceland) per 1000 reported cases, that is, a ratio of 32 to 1, variations which remain largely unexplained (WHO database 2020). Variations in case fatality in Africa could be explained in part by levels of mortality, by the effectiveness of treatments in the case of severe forms of the disease, by better inclusion of mild cases in the denominator, and perhaps by notification bias, or by other unidentified factors.
Numerical results This section is devoted to simulation tests in order to show the feasibility of our approach. Note that the difficult question of time integration of the resulting non-linear DAE is not treated in this work, where the Backward Differentiation Formula (BDF) of order 6 provided by the PETSc library  [53]  has been used, with a maximal time step ∆t = 0. 01. In particular, the fully discrete system is not a discrete pHs a priori, implying that the discrete power balance is only satisfied up to the time scheme error. Videos of the simulations will be available on the website of the journal as supplementary materials. The codes are available as published examples on the github https://github. com/g-haine/scrimp of the SCRIMP environment used for the simulations. For the two following tests, continuous Lagrange finite elements have been chosen to approximate all quantities (both scalar-fields and vector-fields).
Ribbing et al. (2007)   [11]  developed a LASSO algorithm in PsN and showed that it outperformed the PsN's SCM algorithm in predicting covariate models on small datasets by examining the effect of data size, number of covariates and starting model. Sibieude et al. (2021)   [14]  compared machine learning approaches (random forest and SVM) with PsN's SCM algorithm and Monolix's COSSAC algorithm, in terms of covariate detection performance by varying the number of covariates, the correlation and the effect size. Svensson et al. (2022)   [7]  demonstrated that the number of runs and objective function evaluations were considerably reduced with PsN's SCM+ algorithm compared to PsN's SCM algorithm, and that both algorithms performed equally on covariate detection performance (which can be improved by adding a stage-wise filtering stage).
Yngman et al. (2022)   [20]  compared the FREM algorithm newly implemented in PsN with the full model approach and showed that they both performed equally well on covariate effects estimation accuracy using different covariate-parameter relationships, covariate distribution and level of correlation. Finally,  Amann et al. (2023)   [21]  found that using PsN's FREM algorithm followed by a backward elimination procedure improved covariate identification (higher power), estimates accuracy and precision compared to PsN's SCM algorithm, particularly for sparse datasets, by varying the number of individuals, the covariate correlation, the covariate effect sizes; however, without backward elimination, FREM produced imprecise estimates for sparse data. Going as far as quantifying the clinical relevance of covariates is key to perform dose adjustment for patients.
Simulation study Population PK models A population PK model has been developed by  Retout et al. (2020)   [28]  to characterize the PK of emicizumab in adult and pediatric patients ( >1 year of age) with hemophilia A using a database of 𝑁 = 389 individuals. Patients were included in 5 phase I/II and phase III clinical studies (see Table  S1. 1 in the Supplementary File 1). The PK sampling scheme was either rich (phase I/II or run in phase of one of the phase III study) or sparse (phase III studies with mostly trough samples) and 3 dosing regimens were investigated: an administration every week, every 2 weeks or every 4 weeks. In Retout et al. (2020)  [28] , from 383 evaluable PK profiles, it was found that a 1-compartment model with a first-order absorption and a linear elimination best described the emicizumab PK data.
Estimation and implementation The entire analysis was run using NONMEM version 7. 4. 3 on a High Performance Computing (HPC) environment. The HPC had 66 compute nodes using each 2 x Intel® Xeon® Platinum 9242 Processor (48 cores per processor) @ 2. 3 Ghz, 96 cores and 768 GB DDR4-2933 of RAM memory. The datasets were simulated with PsN version 5. 3. 0 using the sse tool. A total of 24 cores were allocated and the seed was fixed. For the parameter estimations, each run was launched with 24 cores, 6 threads using PsN version 5. 3. 2. The first-order conditional estimation with interaction (FOCEi) algorithm was used. SE were derived from the covariance matrix computed as 𝑅 -1 𝑆𝑅 -1 , with 𝑅 and 𝑆 the Hessian and the Cross-Product Gradient matrix, respectively. The reference model was launched using the execute PsN tool.
Covariate parameters estimation accuracy and uncertainty evaluation The accuracy of covariate parameter estimates was assessed by calculating the estimation errors (EE) for the simulated covariates where 𝛽 = 0, and the relative estimation errors (REE) for the simulated covariates where 𝛽 ≠ 0: 𝐸𝐸(𝛽 ̂) = 𝛽 ̂-𝛽 𝑅𝐸𝐸(𝛽 ̂) = 𝛽 ̂ -𝛽 𝛽 × 100 (in %) where 𝛽 ̂ and 𝛽 are the estimated and simulated covariate parameters, respectively. The uncertainty of covariate parameter estimates was evaluated by comparing the SE for the simulated covariates where 𝛽 = 0, and RSE for the simulated covariates where 𝛽 ≠ 0, with empirical SE and RSE, respectively. The RSE, empirical SE and empirical RSE were computed as follows: 𝑅𝑆𝐸(𝛽 ̂) = | 𝑆𝐸(𝛽 ̂) 𝛽 ̂| × 100 (in %) 𝑆𝐸 𝑒𝑚𝑝 (𝛽 ̂) = √ 1 𝑆 -1 × ∑(𝛽 ̂𝑠 -𝛽) 2 𝑆 𝑠=1 𝑅𝑆𝐸 𝑒𝑚𝑝 (𝛽 ̂) = | 𝑆𝐸 𝑒𝑚𝑝 (𝛽 ̂) 𝛽 ̂| × 100 (in %) where 𝛽 ̂𝑠 is the estimated covariate parameter for the simulated dataset s and 𝑆𝐸(𝛽 ̂), the estimated SE of the covariate parameter.
For categorical covariates, the ratio between the covariate effect value of one category and the parameter covariate effect value of the reference category was considered. The simulated and estimated ratios were then expressed as follows: 𝑟 = 1 + 𝛽 and 𝑟̂= 1 + 𝛽 ̂ The 90% CI of the estimated ratio was then expressed as: 𝐶𝐼90 𝑟̂= 𝑟̂ ± 𝑧 0. 95 × 𝑆𝐸(𝛽 ̂) The accuracy of covariate ratio estimates was assessed by calculating the REE: 𝑅𝐸𝐸(𝑟) = 𝑟̂ -𝑟 𝑟 × 100 (in %) The relative root mean square error (RRMSE) were calculated to characterize the bias and the variance of covariate ratios over the 200 simulations as follows: 𝑅𝑅𝑀𝑆𝐸(𝑟) = √ 1 𝑆 × ∑ ( 𝑟̂𝑠 -𝑟 𝑟 ) 2 𝑆 𝑠=1 × 100 (in %) where 𝑟̂𝑠 is the estimated covariate ratio for the simulated dataset s. Coverage rates were calculated to check the validity of the covariate ratios confidence interval.
RESULTS Results were presented for the reference model and the 3 evaluated approaches i. e. the full model, SCM and SCM+ applied to simulated datasets under the base and the covariate model. First, the models selected by SCM and SCM+ were evaluated. Then, the covariate parameter estimates and their associated uncertainty accuracy were evaluated. In addition, the covariate ratio estimates accuracy, precision, and uncertainty were studied. More importantly, the clinical relevance of the different covariates was assessed, which is the main objective of the article. Finally, the computational efficiency of the 3 approaches was compared. In this section, covariate-parameter relationships for which an effect was simulated (i. e. 𝛽 ≠ 0) were referred to as "simulated covariate with an effect". Similarly, covariate-parameter relationships for which no effect was simulated (i.
Model selection using SCM/SCM+ Fig. 2  summarizes model combinations obtained with SCM and SCM+ on datasets simulated with the base and the covariate model. SCM and SCM+ mostly selected identical models (99. 5% and 98. 5% with the base and the covariate model, respectively). Therefore, only SCM results were discussed further. The "true" model (i. e. the simulated model) was selected by SCM in 82% and 59% of the cases under the base model and the covariate model, respectively. Under both simulated models, BW was consistently included in the final model on CL/F and V/F. Regarding the other simulated covariates with an effect, they were retained in the final model in most instances, with percentages ranging from 85% (e. g. RACE on V/F) to 99% (e. g. ALB on CL/F). Simulated covariates with no effect were infrequently selected in only up to 4%, especially on KA.
Covariate clinical relevance Fig. 6  shows the results of the covariates clinical relevance, with the different conclusions defined in Fig. 1. To provide a concrete illustration of the proposed analysis, Supplementary File 4 summarizes the results (in terms of parameter estimates and forest plots) obtained with the reference model, the full model and SCM for the dataset n°1 simulated with the base and the covariate model. Under both simulated models, BW was found relevant with the full model and SCM in almost all cases, as with the reference model. In the minority of cases where the covariate was not found relevant, i. e. in between 0 and 1. 5% of the cases depending on the approach and the simulated model considered, there was insufficient information to identify its clinical relevance. Of note, none of the methods identified that BW was not-relevant.
Computational efficiency The full model outperformed SCM/SCM+ in all aspects (total runtime, number of NONMEM runs and number of objective function evaluations), considering the simulated dataset n°1, especially under the covariate model, see Table  S5. 1 in the Supplementary File 5. Under the base model, the full model was 4 times faster or took about the same time than SCM and SCM+, respectively. Furthermore, under the covariate model, the full model was 4 to 20 times faster than SCM/SCM+. The number of NONMEM runs was always equal to 6 with the full model due to the implementation settings (i. e. number of retries fixed to 5) while SCM/SCM+ required much due to the way the algorithms work. Regarding the number of objective function evaluations, the full model required 2 times fewer functions than SCM/SCM+ under the base model and 5 to 10 times fewer than SCM/SCM+ under the covariate model.
Moreover, SCM+ outperformed SCM: (i) the number of runs was reduced by about 20% with SCM+ compared to SCM, considering the 200 datasets simulated with both models, while  Svensson et al. (2022)   [7]  reported 44%, (ii) the number of objective function evaluations was reduced by around 15% and 50% with SCM+ compared to SCM, considering the dataset n°1 simulated with the base model and the covariate model, respectively, while  Svensson et al. (2022)   [7]  reported 70%. These differences can be explained by the higher number of covariates and covariateparameter relationships tested in their study, with several covariates having a strong effect versus one for our work. Moreover, they simulated twice as many covariates having an effect, inducing in our case that the forward ends in fewer steps, which reduces the differences in the number of runs between SCM and SCM+.
ACKNOWLEDGMENTS This work was financed by a  CIFRE  agreement ( Conventions Industrielles de Formation par la Recherche)  and was conducted under the supervision of the  ANRT (Association Nationale de la Recherche et de la Technologie). The CIFRE agreement is a partnership between a public laboratory and a company, here the  UMR (Unité Mixte de Recherche ) 1137 and  INSTITUT ROCHE , respectively. The authors are grateful to  Kamill Jaworski  for his technical support in the simulation implementation.
Data 1. 1. 1. COVID-19 hospitalizations, deaths, and cases We used four types of observational data, aggregated at the departmental level, published by Santé Publique France. As a result, no ethical regulations were applicable to this study. For each department, we obtained daily numbers of COVID-19-related hospital admissions and occupancy from the SI-VIC database as of March 1st, 2020,  11  COVID-19 deaths in hospitals from SI-VIC as of March 18th, 2020,  11  and PCR-confirmed COVID-19 cases from the SI-DEP database as of May 13th, 2020. 12  More information on epidemiological data can be found in the Supplementary Methods. We excluded the two Corsican departments entirely from the analysis, and department 5 from February 17, 2021, onwards, due to missing weather data. We smoothed the hospital admission, death and case time series with a centered 7-day moving average to account for the day-of-week effect.
The population adherence to these measures was parameterized based on surveys of barrier gesture adoption in France by Santé Publique France  13  as a continuous variable ranging between 1 (indicating full population compliance) and 0 (no compliance). A more in-depth description of NPIs can be found in the supplementary methods. Due to identifiability issues, we did not succeed in quantifying the effect of bar and restaurant closures, workplace closures, bans on large public events, travel bans, enhanced testing, or contact tracing. Some of these effects may thus be absorbed in lockdowns, curfews, and barrier gestures. Vaccination The proportion of the population vaccinated with one or two doses was obtained from the VAC-SI database. 14  We did not consider additional vaccine doses as the proportion of people who received a booster by the end of our analysis period was low (2.
7% of the population). The effects of vaccine doses were lagged by 21 days to account for the time needed to develop immunity after vaccination. Weather To account for the potential impact of climate on SARS-CoV-2 transmission,  15  we extracted daily weather data from the National Oceanic and Atmospheric Administration database using the R package worldmet. The data was collected from all meteorological stations located in France. We calculated a daily weather variable for each department combining temperature and humidity (see Supplementary Methods).
Acknowledgements We thank  Lixoft SAS  for their support. Numerical computations were in part carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS (LABRI  and  IMB) ,  Université de Bordeaux ,  Bordeaux INP , and  Conseil Régional d'Aquitaine  (see https://www. plafrim. fr). IG is supported within the framework of the  PIA3 (Investment for the Future) , project reference:  17-EURE-0019 , and by a doctoral award from the  Fonds de recherche du Québec-Santé.
The evidence in these two figures is F. J. Fabozzi, D. Huang, F. Jiang et al. Fig. 7. This figure plots the out-of-sample certainty-equivalent return (CER, in % per year) for a mean-variance Bayesian investor with risk aversion equal to 3. 𝑐 is the maximum value of risky positions that can be established per dollar of wealth. 𝜔 is the confidence level the investor places in HXZ or FF5. The risky assets include 10 anomalies that cannot be explained by either HXZ or FF5, five factors in FF5, and investment and profitability factors in HXZ. We use an expanding window approach in calculating the out-of-sample CER, where the initial window is 120 months. In each month 𝑡, we use data from month 1 to month 𝑡 to compute the various portfolio rules, and apply them to determine the investments in the next month.
The trees T K are easy to determine, since T K is obtained by removing one leaf to T K+1 in [see p. 284-290  Breiman et al. , 1984]. The penalization constant λ can be chosen using a test sample or k-fold cross-validation. In the first case, data are split into two parts before making the tree grow (a training data of size n and a test sample which is not used in computing the tree). In the second case, the dataset is randomly split into k parts which successively act as a training or a test sample [see e. g. Allen, 1974 , Stone, 1974].
The database we consider was obtained through a partnership with the FFA, in particular with one of its dedicated technical body, the association of French insurance undertaking for natural risk knowledge and reduction (Mission Risques Naturels, MRN). It consists of all 3 100 flooding events that have been granted the status of natural catastrophe in France from 1999 to 2019 (let us note that the status "natural catastrophe" is a French specificity, with some legal consequences when an event receives this label [see  Charpentier et al. , 2021 , MRN, 2016] ). This database is fed by 13 contributors including the major French insurance companies, allowing this database to cover 70% of French non-life insurance market. The database gathers information regarding each flooding event (its cost, the meteorological region, the season, the number of affected hydrological regions, the number of individual houses and the number of professional business premises in flood-risk area).
Note that, since the purpose of this database is the fast prediction of the cost of a flooding event (as soon as possible after its occurrence), the variables that are registered correspond to quantities that are available before the event, or soon after it. The variable of interest, the total cost of a flooding event, is highly volatile. Indeed, it ranges between 0 and 394 376 000 euros with an empirical variance equal to 1. 77e + 14. Figure  1  shows the average of the costs of the 10% most onerous flooding events within each meteorological region. This highlights the heterogeneity of the severity of the most severe events. Furthermore, the top ten most onerous events represent 43% of the total cost of this database and the top hundred 80%. Now, let us recall that our goal is to understand the heterogeneity of the total cost of the most severe flooding events, that is of extreme flooding events.
As explained in Section 2. 1, the definition of extreme events consists in choosing a threshold u, which should be chosen as a bias-variance trade-off. We chose a value of u = 100 000 based practical considerations and validated by sensitivity analyses (shown in the supplementary material, Section B). This yields 1 100 extreme events, that is for which the cost is larger than u. The GP regression tree was performed on the database corresponding to the flooding events extracted from the original database for which the total cost is larger than u (=100 000 euros). The variables of this database and their characteristics are summarized in Table  2. Again, it can be noticed that the cost, the variable of interest, is highly volatile. The tree obtained from GP regression procedure is shown in Figure  2  (the quantile-quantile plots of the GP fit in each leaf are shown in the supplementary material, Section C).
The tree is composed of 6 leaves, with three splits according to only 3 covariates: the number of individual houses, the number of professional business premises in flood-risk area and the number of affected Fig. 1  Cartography of the cost of flooding events in France from 1999 to 2019. For each meteorological region, the average of the costs of the 10% more onerous events is shown. The lighter red color suggesting a small cost while a darker color suggests a large cost. Table  2  List of quantitative and categorical variables in the database and their characteristics. For the quantitative variables, Table  a ) shows the minimum, the first quartile, the median, the mean, the third quartile and the maximum, and for the categorical variables, Table  b ) the number of observations per category.
Agrochemical formulation data submissions Data for 192 agrochemical formulations were submitted by CropLife Brasil companies (BASF, Bayer Crop Science, Corteva Agriscience, FMC, Ihara, Ourofino, Sumitomo Chemical and Syngenta) to Inotiv, a contract research organization that created a database inclusive of all submitted data and conducted the analyses presented herein. Data on 192 formulations with retrospective in vivo data were used to evaluate the GHS CT approach; of those 158 had corresponding in vitro data (Table  1 ). No overlap in tested formulations was noted between companies, based on the information provided. Fifteen formulations, submitted by a single company, were tested in two in vitro test methods; 14 were tested using the BCOP and RhCE test methods and one formulation was tested using RhCE and ICE test method.