dataesr/scientific-paragraphs-categorization huggingface dataset:
Training and validation datasets As mentioned in paragraph 3. 1, Table  3  represents the dataset their dispersion, size and distribution to be able to validate our model and have a better accuracy. 5 Organization and structure The structure of the paper is as follows:  First, we have training and validation images that we have processed, normalized.  Train the model and make sure we have a good accuracy, then made predictions with images taken by the webcam in real time.  Finally analyze the errors and display the performances, restart the training if they are not very efficient. A better overview of our model in represent on Figure  4.
Confusion matrix We want to measure the quality of an classification system of the images represented by the FER+ database  [14]  as well as the accuracy on a scalable model. Images are classified into seven classes as explained previously. Suppose our classifier is tested. For this, we want to know: -how many images will be falsely estimated as another emotion (false alarms) by the classifier and -how many images will be considered as the right emotion by the classifier. To analyze the performance and predictions of the model, a confusion matrix is displayed on the Figure  10. as a technique for summarizing the performance of a classification algorithm used in this case study.
Conclusion The proposed paper is designed to develop a real time system to detect, recognize and classify human face emotions. Training a neural network can take a long time, ranging from a few hours to a week, depending on the size of the data source and the complexity of the model. The limitations of our datasets are most related to the variable settings of the FER+ database. In particular, the assumption that this dataset is large and diverse enough to indicate which model settings are generally preferable. The model used in this paper allowed us to have a good classification of the images according to the emotions on the faces. The technology of facial expression recognition has enormous market potential and, in the near future, it will enhance most human computer interfaces.
Participants Participants included 162 children and adolescents recruited from four primary and secondary schools in Québec City, Canada. School recruitment was conducted by contacting school principals by email. Participant recruitment was conducted via personal contact. Inclusion criteria were being aged between 8 and 15 years, francophone and having no significant learning difficulties (e. g. dyslexia), which was screened by teachers at schools before recruitment for participants. A total of 17 participants did not complete the study because they were absent from school during the data collection period. Thus, the final sample comprised of 145 individuals.
Dietary intake and diet quality One 24-h dietary recall was completed for each participant during the first visit by three registered dietitians. Detailed information on all foods and beverages consumed during the previous day was collected using the USDA five-step multiple-pass method  (33, 34). To increase the accuracy of food portion estimations, food models and standard kitchen measures were used. Dietary recalls were analysed with Nutrific nutrient analysis software version 2015 (Université Laval, Québec, QC, Canada) which is based on the Canadian Nutrient File version 2015  (35)  , to obtain energy, macronutrient and micronutrient intakes. Two 24-h dietary recalls were excluded from the analysis due to the lack of information provided by the participants during the interviews. Diet quality was assessed using the Nutrient-Rich Food Index 9•3 (NRF 9.
Statistical analysis The complete database was examined for missing data. For each questionnaire, imputation was performed when there was a maximum of two missing items per factor per participant. Imputation procedure consisted in replacing the missing data with the mean value of its associated item. Mean data imputation was performed since it is an acceptable imputation method when proportions of missing data reach up to 10 %  (38)  and that only small proportions of missing data were found in this study. For the CTFEQ questionnaire completed at the first and second visits, 1•3 % and 0•6 % of the data were missing, resulting in 25•0 % and 92•9 % of the missing data being imputed, respectively. For the EAT-26 questionnaire, 1•5 % of data were missing and 39•3 % were imputed. Prior to the analyses, distributions of the quantitative variables were evaluated based on the skewness and kurtosis indices and by visual inspection.
Reliability Cronbach's α coefficients based on polychoric correlations were used to evaluate internal consistency reliability for each factor of the models tested (i. e. adequate internal consistency as Cronbach's α ≥ 0•70)  (42, 43). Intra-class correlation coefficients (ICC) were assessed with the Macro % ICC9 for SAS two-way mixed-effects  (44)  for each factor using data from the two completions of the questionnaire to determine test-retest reliability. The 95 % CI for each ICC was used to determine the level of reliability  (45). Values less than 0•50 show poor reliability, values between 0•50 and 0•75 show moderate reliability, values between 0•75 and 0•90 show good reliability and values above 0•90 show excellent reliability  (45).
The cutoffs were exponentiated since EI distribution is skewed. Confirmatory factor analysis and Cronbach's α coefficients calculation were performed on EQS Multivariate Software, version 6. 2. All the remaining analyses were performed on JMP software, version 14. 0 from Statistical Analysis System (SAS Institute) and SAS software, version 9. 4 (SAS Institute). Results are presented as mean (SD) or SD. Statistical significance was set at P < 0•05.
Acknowledgements Acknowledgements: We thank the schools for their availability and their interest to participate in this research. We also acknowledge the help of  Francine Pérusse  for her role in the coordination of data collection. We also thank  Diane Drolet ,  Manon-Bordeleau ,  Laurence Deslauriers ,  Maurine Dubeuf ,  Corentin Clemot ,  Sabrina Labrecque  and  Lucie Allard  for data collection. We also thank  Eric Frenette ,  Joël Gagnon -and  Christian Couture  for their help with statistical analysis. I. F. is a recipient of a M. Sc. scholarship from the  Canadian Institutes of Health Research. R. J. is a recipient of a Ph. D. scholarship from the  Canadian Institutes of Health Research  (Fellowship number: 430872). Financial support: This study was funded by the  Québec Heart and Lung Institute.
It had three objectives. First, to estimate associations of early childhood out-of-home placement with income and social welfare use in adulthood using linked registries data from a 30-year longitudinal cohort in Finland. Second, to test whether background differences between placed and non-placed children in terms of family and individual characteristics can explain the difference in income and social welfare use relying on a strong design-propensity score matching. Third, to establish whether, and to what extent, mental health problems in adolescence/young adulthood and high school educational attainment explain the association between out-of-home placement and adult income and social welfare use by conducting mediation analysis.
Study sample Data came from the 1987 Finnish Birth Cohort (FBC-87), a birth cohort including all Finnish children born in Finland in 1987 who survived the perinatal period and is composed by linked administrative nationwide databases  (N = 59,476). Detailed information about the cohort are described elsewhere  (Paananen & Gissler, 2012). Data covered the period 1987 to 2015 (i. e. , birth to age 28 years). For the present study, we used information on all children having data on placement, outcomes (income and social welfare), and mediator variables (school grades and mental health problems), resulting in an analysis sample of N = 54,724. Prior to listwise deletion, 7. 0% of the subjects who were placed had missing primary school grade point averages, compared to 3. 9% among those who were not placed, suggesting that missingness in this measure may not be independent of placement status.
Mediators: Primary school grade point average and mental health problems Primary school grade point average (GPA) at age 16 years was extracted from the National Board of Education register and measured as a continuous variable (range 4-10). The variable was scaled and cantered using the mean and standard deviation from the entire cohort with GPA data available. Mental health information was obtained from the Finnish Health Information System, which includes all information on inpatient and outpatient visits at public hospitals, as submitted to the Finnish Hospital Discharge Register. We considered all psychiatric diagnoses made during inpatient or outpatient visits to specialized hospital units in accordance with the International Statistical Classification of Diseases and Related Health Problems (ICD; 10th Revision codes F00-F99  and ICD 9th revision codes 290-319) between 2005  and  (ages 18 to 25).
Association of out-of-home placement with social welfare use Among placed children, 24. 1% used social welfare in adulthood, compared with 4. 9% non-placed children (OR, 6. 16; CI, 4. 87-7. 81; Table  2 ). When analyses were restricted to the matched sample, we observed a reduction of these differences (24. 2% placed children vs. 13. 3% matched non-placed children; OR, 2. 09; CI, 1. 43-3. 04). We found that both GPA (proportion mediated, 27%; CI, 22-31%) and mental health problems (proportion mediated, 29%; CI, 24-33%) mediated the association between out-of-home placement and social welfare use in  All characteristics are presented as number (%). a Propensity score matching: Using the R package MatchIt, we performed nearest neighbour matching-without replacement-using a caliper of 0. 1 times the standard deviation of the logit-transformed propensity scores, and exact matching on sex.
Strengths and limitations This study has some notable strengths, including the use of a large representative sample with data from birth to 28 years of age, the reliance on data from high-quality linked population registers, and the use of propensity score matching. These factors allowed us to investigate a rare phenomenon, test long-term associations, and find suitable matches for the placed children among a very large pool of potential non-placed controls. Despite those strengths, the following limitations must be acknowledged. First, due to reliance on administrative databases, we were unable to distinguish between missingness caused by incomplete registration of events from missingness due to an absence of events, therefore we could not estimate the effect of missingness on the results.
For example, the difference in missing data on school grades between children placed vs not placed may not be completely at random, which could introduce bias results. Second, mental health problems were measured using hospital records, which only reflects service use among patients with mental health problems severe enough to warrant a diagnosis. Lack of consideration of less severe problems might have resulted in a biased estimate of the mediating role of mental health problems. Third, propensity score enables to balance participants only on measured confounders, therefore bias for potentially unmeasured confounders cannot be excluded. Specifically, we have no data on maltreatment and neglect, which are more likely to be overrepresented in the placed groups compared to the non-placed groups.
Acknowledgements Data were obtained from official Finnish government records. The  Academy of Finland  (grant number  308556  PSYCOHORTS and  288960 Time Trends in Child and Youth Mental Health ,  Service Use  and  Wellbeing Cohorts ) provided support for the salary of MM and TR and had no role in the study. Dr. Orri  receives a grant from the  European Union 's  Horizon 2020 research and innovation program  (# 793396 ).
Socio-demographic and clinical questionnaires were used in this study for describing the participants and demonstrate the psychological problems. To assess psychological problems, Test of Psychological Problems (TPP) Likert scale was employed. This questionnaire contains forty-four items (44 Items) that were scored using a 5-point scale (0 = never; 1 = rarely, 2 = occasionally, 3 = often, 4 = very often). The items from 1 to 17 were about mood disorders; for example: "I feel very happy or act silly in a way that's unusual". Items 18 to 30 were about psychotic disorders; example: "I feel inappropriate laughter or crying". Items 31 to 44 were concerned on anxiety disorders and mental health related disorders; example: "I feel worry about developing serious illness". Concerning the validity and reliability of the used psychometric measurements, before collecting data using the current psychometric measurement in the target setting, these scales were validated in the participants whose characteristics were the same to the target population of this study.
At Baseline, 1,056 MTurk respondents participated in the study. Seventeen individuals were excluded from analyses due to suspicion of data invalidity (e. g. , double MTurk ID; survey completion in less than five minutes); thus, the final sample size was N = 1,039. Table  1  reports characteristics of these participants. At Wave 2, 649 participants yielded valid data (data from seven individuals were excluded), at Wave 3, there were 642 participants with valid data (seven individuals were excluded), there were 547 participants at Wave 4 (nine were excluded), and 462 participated in Wave 5 (one was excluded). Effect sizes were not anticipated prior to data collection. Measures. A critical aim of this study was to capture attitudes and behaviors as quickly during the pandemic as possible. Our approach was to select and use brief face-valid measures.
Study 2 The data we collected for Study 1 only allowed us to test the mediating role of perceived risk. However, health belief models also specify the perceived consequences of an infection and effectiveness of health-protective behaviors predict outcomes and both of these factors may have been politicized in the U. S. Additionally, in Study 1, we did not test whether the association between political orientation and health-protective behaviors was specific to the situation in the United States. We did, however, address these questions in Study 2. We also extended the health-protective behaviors we assessed to include wearing a face covering in public as it became more clearly recommended by the WHO (and the CDC). Although vaccines had not yet been approved for use at the time of conducting this study, we anticipated (correctly) that vaccination would become a politically polarized topic, and thus also investigated vaccination intentions  [44].
Method Participants and procedure. Participants from the U. S. , as well as from 114 other countries were recruited for a longitudinal survey; S2 Table reports the most frequently represented countries at Baseline. Assessments began on March 19 th , 2020 and the current results are based on data collected up to July 13 th , 2020 from 62,909 individuals. The survey was distributed online through a combination of convenience sampling and snowball sampling. Members of the research team distributed the survey using social media campaigns, academic networks, and press releases. This convenience sample was supplemented with age and gender paid representative samples from 25 countries (collected only at baseline). On completion of the survey and debrief, a final screen invited respondents (both paid and unpaid) to distribute the survey to their networks and to participate in weekly (unpaid) follow-ups.
To maximize data collection while minimizing participant strain, follow-up surveys with rotating questions were administered from March 19 th to July 13 th , 2020. As new themes emerged in the discourse surrounding COVID-19, additional items were included in the survey. For instance, attitudes and behaviors pertaining to the wearing of masks/face coverings were added as the WHO amplified its support for their use. Participants were eligible to enroll in the study by completing baseline at any point. Demographic characteristics of participants at Baseline are reported in Table  1. Most participants (75. 43%) enrolled in the study between March 19 th and April 18 th ; see S1 Fig for a histogram of date participants completed the Baseline survey. Following completion of the Baseline survey, participants received invitations to complete follow-up surveys at fixed time points (no follow-up surveys included participant payment).
S. , consistent with our hypothesis regarding the unique effects of political orientation in the U. S. , and reflecting the potentially unique discourse surrounding masks in the U. S. Ancillary analyses. In the analyses described above, we compared associations across US and non-US participants living in 114 other countries. To examine the possibility that our findings could be an artifact of aggregating the data across these 114 countries, we also explored these effects in a (sub-)sample of individual countries. We focused our attention to comparison countries in which we recruited the most participants into the Baseline survey (Spain [n = 3156], Romania [n = 2696], Netherlands [n = 2992]), Indonesia [n = 2407], Greece [n = 2870], and Republic of Serbia [n = 2118]). Additionally, we evaluated responses in Canada (n = 1531) because residents of Canada might be expected to be exposed to political messaging from the U.
Inventory of methods Based on earlier overviews  [27, 28]  and previous reviews,  [11, 12]  of response shift methods, we identified 11 methods. These include the then-test and appraisal method, socalled design methods  [3, 5, 29] , semi-structured interview as an example of a qualitative method  [30] , the Patient Generated Index (PGI)  [31] , and the Schedule for the Evaluation of Individual Quality of Life (SEIQoL)  [32]  to illustrate individualized methods, where we will further focus on the SEIQoL, and vignette studies to exemplify preference-based methods  [33]. The remaining methods rely on different statistical methods. These are identified based on the previous scoping review  [11]  and an expository review  [12]. The most recent scoping review was based on the search of seven library databases (Biomed, CINAHL, EMBASE, Medline, Pro-Quest, PsycINFO, and Web of Science) for the use of the terms "response shift," "response shift effects," "longitudinal measurement invariance," or "retrospective bias" and was limited to articles written in English and published or accepted for publication up to December 31, 2016.
An additional way forward is to apply different methods to the same dataset to see how the methods compare with respect to detecting the same type of response shift. While this seems to be a valuable approach, it has two main caveats. First, because we do not know the "truth" (i. e. , whether response shift is present or absent in the population), the findings will not tell us whether the different methods are sensitive to detect response shift. Second, different methods require different data. In a head-to-head comparison of three statistical approaches using a single dataset of patients with multiple sclerosis, it was necessary to accommodate the data to meet the requirements of each statistical approach, thereby hampering direct comparisons  [21]. Perhaps a more promising path forward involves the conduct of simulation studies, which can provide insight into the performance of the extant and future methods, given known response shift effects and based on requisite simulated data that enable the comparison of the methods  [55]  (e.
Contact networks We use traces for three different places, that are available from the SocioPatterns project (http://www. sociopatterns. org). The project collected longitudinal data on physical proximity and face-to-face contacts between individuals in several real-world environments. 1. A primary school (see  [16, 17] ) where 242 persons participated in 2009 over 2 days (coverage of 96% among children and 100% among teachers). In our baseline scenario with no strategy, the probability of outbreak equals 27. 2%. Each row reproduces results under specific strategies. In the left column, we study the sensitivity of this quantity with respect to potential variants showing increased transmission capacity compared to the reference strain (multiplicative factor on the y-axis), by assuming that such variants would circulate in partially immune populations (percentage of immune people on the x-axis); immune people do not get infected nor transmit the virus.
For each day on which data was gathered we extract a graph aggregating the data for that day: a node corresponds to an individual, an edge corresponds to a face to face contact within 1. 5 meters within a 20 seconds time interval (interactions were measured using active radiofrequency identification devices (RFID)), and the weight of the edge is the number of such short contacts during the day. For comparison, we also generate a synthetic random graph, calibrated so that its main parameters (total number of nodes, of edges, and of contacts) match those of the high school contact network: more precisely, each edge is generated by selecting uniformly at random two nodes with one associated contact (rejecting loops and already generated pairs) and each of the remaining contacts is associated to an edge selected uniformly at random among the previously generated edges.
Stochastic simulation engine We use an agent-based model with discrete time where the time step corresponds to a day. Each person is an agent whose state is either S, E, I, or R; according to the SEIR transmission model described above. Every day, the state of each agent can change according to the number of contacts with other agents in the contact network for that day, the states of these agents, and random coin flips based on the transmission probability. The state of an agent also depends on when it had a previous state change and on what random value was obtained for the duration of the current state. A dedicated C++ program was developed for that purpose and is made available. The model parameters are summarized in Table  2. For each infected individual, the duration of the incubation period is randomly drawn into a Gamma distribution with mean 5.
Funding: LO received funding from the  Fondation de France  (grant  106059 ) as part of the alliance framework " Tous unis contre le virus ", and the  UniversiteParis-Saclay  (AAP Covid-19 2020); C. M. received research funding from the  "Fonds d'urgence MESRI   Covid19 ", https://www. enseignementsup-recherche. gouv. fr/,and from the  French National Research Agency  (grant  ANR-19-CE48-0016 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. L. O. has received research funding from  Pfizer  (through her research unit) on research.
Implementation is available at https://gitlab. inria. fr/miticov/mitigating-covid19-outbreaks All contact graphs are available at: http://www. sociopatterns. org/wp-content/ account uploads/2015/09/primaryschool. csv. gz http://www. sociopatterns. org/wp-content/uploads/2015/07/ High-School_data_2013. csv. gz http://www. sociopatterns. org/wp-content/uploads/2018/12/tij_ InVS15. dat_. gz.
Twenty young adults with insomnia disorder (aged 27. 7 ± 8. 6 years) and twenty age-matched control participants (26. 7 ± 7. 0) without insomnia disorder were included, and all provided written informed consent. The study was approved by the local ethical committee (Comit e de Protection des Personnes Sud-Ouest et Outre Mer III  [CPP] ) and registered as a clinical trial (Clinical-Trials. gov. identifier: NCT02821234). All experimental procedures were conducted according to the Helsinki Agreement (World Medical Association General Assembly, 2014).