dataesr/scientific-paragraphs-categorization huggingface dataset:
We chose to explore 4-week periods because most employed people in France receive their income once a month. It is therefore important to capture at least 4 weeks per period to avoid any artificially enhanced gambling activity resulting from a possible effect following receipt of income. Money and time spent in the preceding 4 weeks were the 2 outcomes of interest and were defined as follows: (a): time spent was obtained by summing all session durations in the last 4 weeks. Session duration was obtained by subtracting session end date from session starting date. Money spent in the last 4 weeks was defined as the net losses in the previous 4 weeks, obtained from all cash game and tournament gambling data at table level for players using real money. Table net loss was obtained from the reverse of winnings.
Winnings were computed from table data (cash game winnings + prize amount for tournaments-buy-in-plus-rake for tournaments). Account-duration was defined as the time between opening the account on the website and the self-exclusion date, or the self-exclusion date of the matched self-excluders for matched gamblers. Money and time spent in the last 4 weeks on poker on the website were calculated at the self-exclusion date (or self-exclusion date of the matched self-excluders for matched gamblers), and at 3, 4, 6 and 12 months after the end of the self-exclusion period (or after self-exclusion date of the matched self-excluders for matched gamblers). We intentionally took the 12 months period after the end of the self-exclusion period into account to explore any possible changes in patterns over time when gambling was again accessible on the website, the self-exclusion period itself being of no interest for the variables studied, since gamblers were prevented from gambling.
Discussion This is the first real life study, reporting comparative follow-up data on voluntary self-exclusion on the initiative of gamblers and including non-self-selected gamblers. This retrospective study analyzed prospectively registered account -based gambling data. The aim was to assess the efficacy of self-exclusion in the long term in term of time and money involvement. The analysis of account-based gambling data for all first-time self-excluders on a website over 7 years confirmed the efficacy of self-exclusion on gambling outcomes in the long term. The exhaustiveness of this data is a strength that ensures representativeness and power for the statistical analyzes. However, the effect of self-exclusion among the most heavily involved gamblers was found only for the time spent, and not for the money spent, despite a very high level of expenditure before self-exclusion in this subgroup  [14].
The efficacy of short self-exclusions among the most heavily involved gamblers was not supported by our data. This is in line with recent experimental data among problem gamblers suggesting no efficacy of very short self-exclusions on gambling outcomes  [10]. Another qualitative study reported a preferred duration to ensure efficacy of 12 months from the perspective of problem gamblers who self-excluded  [22]. This study presents some important limitations. First, we included only poker gamblers. As discussed in the introduction, poker gamblers present particular cognitive profiles. Moreover, online poker gamblers are younger than other gamblers  [4, 12] , and their history of gambling and associated damages could differ, as well as their motivation to change. The presented results could reflect some of these particularities and not be true in other gambling activities.
Data analysis of visual recognition task The percentages of correct recognition as well as the percentages of other meanings attributed to the action were measured. Among the 360 ASFGs, a total of 20 were excluded from subsequent analysis because they had a mean recognition rate less or more than two standard deviations (2 SD) away from the mean recognition rate per action. To maximize approximation to normality for generalized linear model analysis, arcsin transformation was performed on rates of correct recognition  [26]  (data in S1 Dataset). Results from both statistical analysis (with ratios data and using arcsin transformation) will be presented. Preliminary main effects ANOVA showed no significant effect of gender (Ratio: F(1, 68) =. 86, p =. 35, η p 2 =. 01/Arcsin transformation: F(1, 68) =.
67, p =. 41, η p 2 =. 01) on rates of recognition of sighted participants (data in S1 Dataset). Consenquently, data were further collapsed across this factor. In order to control block effect, an 18 (each action) x 4 (survey response blocks) factorial ANOVA was performed on the mean of action recognition with the actions as within-subjects factor and blocks as between-subjects factors. Block conditions were significant, but only using arcsin transformations (Ratio: F(3, 268) = 2. 30, p =. 07, η p 2 =. 02/Arcsin transformation: F(3,268) = 4. 23, p =. 005, η p 2 =. 04). However the interaction between actions and blocks conditions was not significant in both cases (Ratio: F(51, 268) =. 57, p =. 99, η p 2 =. 09/Arcsin transformation: F(51, 268) =. 66, p =. 95, η p 2 =. 11). Given the fact that the rate of recognition of all actions is the same for all blocks, results were further collapsed across blocks.
A one-way ANOVA was performed on the mean of action recognition (ratios data and after arcsin transformation) with the actions as within-subjects factor. A cross-tabulation was also performed to examine the relationship between the original intended meaning of actions (i. e. , ASFG produced) and the meaning attributed to the ASFG by participants (i. e. , ASFG chosen from the list of possible answers). The significance threshold was. 05; effect sizes are given in partial eta-squared η for ANOVAs.
Results and discussion To facilitate readability, only means of ratios data will be presented in this section. The overall results with ratios data and arcsin transformation are available in S1 Appendix. On average, action simulations produced by blindfolded sighted were very well recognized (M = 80. 5%, SD = 13. 8%). An 18 (each action) one-way ANOVA by means of recognition showed a significant effect for action condition (Ratio: F(  17  We observed that mean rates of recognition were very high for most of these: 9 simulated action simulations were recognized at a rate between 80-100% (grey bars), 7 between 60-80% (dark blue bars) and only 2 between 60-40% (light blue bars). A cross-tabulation (Table  2 ) showed relationships between the intended meanings and attributed meanings for the 9 actions recognized at less than 80%.
Data analysis of visual recognition task ASFGs with a mean rate of recognition less or more than two standard deviations (2 SD) from the global mean recognition rate by action for their profile group (late blind and early blind) were excluded. As in Experiment 1, arcsin transformation was performed on rates of correct recognition (data in S2 Dataset) and results from both statistical analysis (with ratios data and using arcsin transformation) will be presented. Preliminary analysis of variance (ANOVAs) was performed to control for gender on rates of recognition of sighted participants in the online survey. Once again, results showed no significant effect on the rates of recognition (Ratio: F(1,85) =. 03, p =. 85, η p 2 = <. 001/Arcsin transformation: F(1,85) =. 01, p =. 92, η p 2 <. 001) and data were further collapsed across these factor.
Results and discussion Factorial ANOVA on mean of action recognition by groups showed that the group condition was significant (Ratio: F(1, 253) = 24. 75, p <. 001, η p 2 =. 09/Arcsin transformation: F(1, 253) = 28, p <. 001, η p 2 =. 10). Means of recognition with ratios data and arcsin transformation are availables in S1 Appendix. On average, action simulations produced by late blind individuals were better recognized (M = 77. 0%, SD = 16. 6%) than those produced by early blind individuals (M = 64. 5%, SD = 18. 5%). Figs  3  and 4  present the order of recognizability of ASFGs produced by late blind and early blind respectively. Factorial ANOVA also showed that the action condition was significant (Ratio: F(17, 253) = 9. 58, p <. 001, η p 2 =. 40/Arcsin transformation: F(17, 253) = 13, p <.
Experimental condition In total, each judge looked at 663 actions (e. g. 37 ASFG x18 actions = 666 minus 3 missing ASFGs in the group of blind individuals as indicated in Experiment 2). First, they were instructed to look at all the videos of ASFGs for each action set: 37 ASFGs in total by action set with 20 produced by sighted, 10 by early blind and 7 by late blind participants in each action set). They were not preliminary informed about the visual status of ASFG encoders. Then, they were asked to classify each ASFG as typical or atypical in regard to their similarity with a prototypical exemplar. For ASFGs judged as atypical, judges were asked to write attributes that cause problems (data in S3 Dataset). Global percentage agreement between the two judges was 79%. As expected, the first result, the 18 ASFGs that obtained a high level of recognition in Experiments 1 and 2 by action, has been judged as a prototype (Jumping on one leg, Playing on a swing, Walking backward).
Learning tasks Learning tasks-general features. All tasks were implemented using MatlabR2015a (MathWorks) and the COGENT toolbox (http://www. vislab. ucl. ac. uk/cogent. php). In all experiments, the main learning task was adapted from a probabilistic instrumental learning task used in a previous study  [32]. Invited participants were first provided with written instructions, which were reformulated orally if necessary. They were explained that the aim of the task was to maximize their payoff and that gain seeking and loss avoidance were equally important. In each of the three learning sessions, participants repeatedly faced four pairs of cues-taken from Agathodaimon alphabet, corresponding to four conditions of a 2×2 factorial design. In all tasks, one of the factors was the Valence of the outcome, with two pairs corresponding to reward conditions, and two to loss conditions (the stimuli visuals were randomized across subjects).
The models were estimated using Matlab's fitglme function, which maximize the maximum likelihood of observed data under the model, using the Laplace approximation. Modelled confidence ratings (i. e. confidence model fits) were estimated using Matlab's predict function. Because the REDUCED models are nested in the FULL model, a likelihood ratio test can be performed to assess whether the FULL model gives a better account of the data, while being penalized for its additional degrees-of-freedom (i. e. higher complexity). This test was performed using Matlab's compare function. To assess the specificity of V(s) we run two additional glmes including ∑Q t = Q t (s,b) + Q t (s, a) and the reaction time, respectively as model-based and model-free variables affected by the valence factor. We tested whether in these glmes V(s) still predicted confidence rating despite sharing common variance with these variables.
Note that confidence is often explicitly modelled as the probability of being correct  [17, 18, 23]. In our dataset, replacing ΔQ t with the probability of choosing the correct option (P t (s,correct)) in the FULL confidence model gave very similar results on all accounts. Bayesian model comparisons indicate that these two models (i. e. including ΔQ t or P t (s,correct) as independent variables) are not fully discriminable, but that the FULL model using ΔQ t appears to give a slightly better fit of confidence ratings (exceedance probability in favor of GLM1, experiment 1: 77. 95%; experiment 2: 69. 79%).
Hence, the REDUCED model 2 omits the confidence observed in the previous trial as a predictor of confidence. Left: pattern of confidence ratings observed in the behavioral data. Middle-left: pattern of confidence ratings estimated from the FULL model. Middle-right: pattern of confidence ratings estimated from the REDUCED model 1. Right: pattern of confidence ratings estimated from the REDUCED model 2. In red are reported statistics from a repeated-measure ANOVA where the alternative model fails to reproduce important statistical properties of confidence observed in the data. Connected dots represent individual data points in the withinsubject design. The error bar displayed on the side of the scatter plots indicate the sample mean ± sem.
Method The study is based on a secondary analysis of data from six waves of the 1970 British Cohort Study (BCS70). BCS70 is following up over 17,000 children born during one week in the UK in 1970, with data collected soon after birth (first wave)  [38, 39]. Since then, additional waves have taken place in at age 5 (n = 12,939), 10 (n = 14,350), 16 (n = 11,206), 26 (n = 8654), 30 (n = 10,833), 34 (n = 9316), 38 (n = 8874) and 42 (n = 9717) years  [40] [41] [42]. The surveys cover health status; health behaviours; wellbeing; educational attainment; employment and occupation; financial status; social and civic participation; social support; family formation and crime. Anonymised data from ages 5, 10, 26, 30, 34 and 42 follow-up surveys were downloaded from the UK Data Service for this study  [43] [44] [45] [46] [47] [48].
Childhood adversities Data collected at age 5 and age 10 follow-ups were reviewed to identify variables that indicated exposure to low socio-economic position and specific adverse childhood experiences as included in a recent systematic review  [1]. We identified 25 variables (13 at age 10, 12 at age 5). Of these, 19 were based on 11 indicators of social and/or material deprivation (living in a poor area, living in damp housing, living in overcrowded housing, living in rented housing, low parental educational attainment, low social class, low household assets, low income, living in a workless household, living in a single parent family, potential maternal psychiatric morbidity). All but three of these (living in damp housing, living in overcrowded housing, low income) were collected at ages 5 and 10.
The remaining six indicators related to three healthrelated adversities (accident requiring medical treatment, hospital out-patient attendance at ages, hospital inpatient admission) each collected at ages 5 and 10. Initial inspection of these data indicated that the indicators based on social and/or material deprivation performed well as a simple additive scale (alpha = 0. 80), but inclusion of the indicators based on health-related adversities significantly reduced the scale's internal consistency. As a result, we created a separate additive scale for health related adversities (alpha = 0. 53).
Statistical analysis In order to maximise use of participants' data and to reduce potential bias resulting from exclusion of partial non-respondents, missing cognitive test data for partial respondents were imputed using multiple imputation routines in IBM SPSS 22. Five parallel data sets were imputed and then averaged to create the final imputed data. Principal components analysis was used to establish the presence of a general cognitive ability factor across tests and standardised scores on the first component were extracted as a proxy indicator for IQ  [49] [50] [51]. At age 10, the first extracted component accounted for 59% of the variance of initial eigenvalues with all tests loading positively on the component (loading range 0. 55-0. 88). For the respondents with missing data on cognitive testing at age 5 we followed the procedures outlined above to: (1) impute partially missing cognitive test results; (2) establish the presence of a general cognitive ability factor across tests (g); (3) use standardised scores on g as an indicator of IQ at age 5.
At age 5, the first extracted component accounted for 41% of the variance of initial eigenvalues with all tests loading positively on the component (loading range 0. 47-0. 76). Exploratory analyses indicated significantly higher attrition rates among participants with BIF than those without BIF. We addressed attrition by imputing missing data (arising from either wave or item non-response) as previous analyses of BCS70 had indicated that imputation models were preferable to the use of sample weights  [60]. We used sex, BIF status, child behaviour problems at ages 5 and 10 and available responses to the Malaise Inventory at ages 26, 30, 34 and 42 to impute missing adult mental health data. Firstly, we used simple descriptive statistics to characterise the associations between: (1) BIF and exposure to childhood adversities; and (2) exposure to childhood adversities and adult mental health.
In Model 4, we included BIF*childhood adversities interaction terms into the model to estimate whether BIF status moderated the association between exposure to childhood adversities and adult mental health problems. Prevalence rate ratios were estimated using Poisson regression with robust standard errors  [61, 62]  using fully imputed datasets and datasets in which missing data on adult mental health outcomes were not imputed. We primarily report analyses using fully imputed datasets, with comments on any notable variation between analyses conducted with the imputed and non-imputed data.
Funding The BIF Consensus Group was sponsored by the Institut d'Assistència Sanitària, the Government of Catalunya (RN-A, LV, JC), the NCD-Lifespan (TW98045) and Global RD0C (TW009680) Fogarty/NIMH grants at the Boston Children's Hospital, Harvard Medical School (K Munir), and the Mental Health Policy Unit, the Centre for Disability Research and Policy Faculty of Health Sciences, University of Sydney (L Salvador-Carulla, E Emerson). The funding body did not have any part in the design of the study, the analysis and interpretation of data and in writing the manuscript. Ethics approval and consent to participate NHS Research Ethics Committees ethical approval was sought and granted for BCS70 follow-ups from 2000 onwards.
(DSM-IV-TR) criteria  (American Psychiatric Association, 2000) ; and the ability to speak French. Mobile mental health outreach teams recruited patients from August 2011 to April 2014 in the street, emergency shelters, hospitals and jails. Psychiatrists and research assistants performed the evaluations during face-to-face interviews in the offices of the mobile mental health outreach teams, which were located in the downtown area of each city. The current analysis comprised only baseline data (t0, before any intervention) for HSB patients who have completed the EQ5D-3L questionnaire.
Data collection The following data were collected: -Pain measure (EQ5D-3L questionnaire). The EQ5D-3L is a patient-reported outcome measure including five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression, and each dimension has three levels (no problem, some problems, extreme problem). This questionnaire was developed by the EuroQol Group, a network of international multidisciplinary researchers devoted to the measurement of health status  (van Hout et al. , 2012). The EQ5D has been validated in SZ individuals  (König et al. , 2007). The subjects were classified in the "pain group" if they reported moderate to extreme pain level (EQ-5D-3L pain score ≥2)  (Fond G. , et al. , 2018). -Sociodemographic information: gender, age, education level, duration of lifetime homelessness, number of days in the street in the last 180 days.
Statistical analysis All variables are presented using measures of means and dispersion (standard deviation) for continuous data and frequency distribution for categorical variables. A multivariate logistic regression including age, gender, number of days in the street during the previous 180 days and all variables associated with self-reported pain in univariate analysis (with p<0. 2) was performed to estimate the adjusted Odds Ratio (aOR) and its corresponding 95% confidence interval (CI) for an association between demographic and clinical characteristics of patients with moderate to extreme pain levels. All of the tests were two-sided. Statistical significance was defined as p < 0. 05. Statistical analysis was performed using the SPSS version 20. 0 software package (SPSS Inc. , Chicago, IL, USA).
Introduction Domestication is one of the most important developments in human history  [1]. Beginning during the Late Pleistocene with dog domestication  [2, 3] , it has irrevocably impacted human history, demography, and evolution leading to our current civilizations  [1, [4] [5] [6]. Domesticated species play important roles for humans in many aspects of our daily life by providing food, biological control agents, pets, sporting animals, basic materials, and laboratory models  [1, 7, 8]. This considerable importance in our culture, survival, and way of life has always aroused the curiosity of scientists and nonscientists. An extraction from the database Scopus of articles and reviews published since 1960 in Life Science Area (i. e. , agricultural and biological sciences; biochemistry, genetics and molecular biology; environmental science; multidisciplinary) for which the term "domestication" is cited in the title, the abstract, or the keywords inventories 6199 documents (database accessed on  August 31, 2018).
The domestication process, pathways, and consequences on plants (e. g. ,  [1, 37, 43] ), mammals (e. g. ,  [1, 10, 26] ), birds (e. g. ,  [44, 45] ), and fishes (e. g. ,  [12, 28] ) have been the focus of an abundant research from Darwin's works  [46]. However, insects' domestication has comparatively received far less attention to date  [47]. Yet, insects are the most common animal group on Earth: they make up about 75% of all animal species  [48, 49]. They play an important role in pollination, waste bioconversion, biocontrol, raw material supplying, food production, medical application, and human cultures. Strangely, major reviews on domestication give the impression that so few have been domesticated  [10, 11, 15, 25, 26]. An overview of current literature shows how insect domestication has been overlooked: the database Scopus inventories only 68 papers that focus on it and most of them on only two species (i.
mellifera. Moreover, differentiations in traits facilitating beekeeping are observed at the subspecies level. Subsequently, some particular subspecies were preferably domesticated by humans. For instance, non-African subspecies have been more widely used by most beekeepers since they can survive in temperate regions, have a low tendency to swarm, and low aggressiveness  [101]. Domestication history of honey bees has been investigated through molecular datasets that highlight several domestication events followed by introgression between subspecies  [90, 113, 114]. Although the honey bee domestication history has been regarded as a directed pathway  [10] , the evolution from early beekeeping practices to modern apiculture practices can been seen as similar to the prey pathway in which game-keeping strategies turns into control over movements, feeding, and reproduction.
, 2015)  as well as of the effects of stimuli when dealing with metacognitive learning  (Bannert et al. , 2015). Consistently with the above literature-based evidences, the opportunities coming from the data contained in log files appear to be promising also to support the analysis of design protocols. The following sections aims at exploring and providing initial evidences to support the following research questions, so that further studies can be planned, if the early results are promising:  Can log file data be used, as proficiently as in other domains, to gather relevant information about the design process? (i. e. could these data be correlated to typical behaviours in design cognition/design creativity?);  What are the efforts required to run a log analysis in terms of time and human resources? ICED19 3 RESEARCH APPROACH.