dataesr/scientific-paragraphs-categorization huggingface dataset:
3). Indeed, the Hamiltonian has to be zero along the optimal state/costate trajectory, which can be exploited to find the solution without resorting to any trial-error procedure and without additional computational complexity. For resolving general optimal control problems (fixed-or free time), we relied here a classical method that consists of transforming the OC problem into a nonlinear programming problem with constraints. More specifically, we used an orthogonal collocation method that is efficiently implemented in MATLAB (The MathWorks), called GPOPS  (Benson et al. , 2006; Garg et al. , 2010; Rao et al. , 2010). The nonlinear programming problem was solved by means of the well-established numerical software SNOPT  (Gill et al. , 2005). We checked a posteriori that the Hamiltonian was either zero (in free time) or constant (in fixed-time) along the result-ing optimal state/costate trajectories.
They also had to support the weight of their arm by themselves and try to move in a transverse plane as much as possible. A familiarization phase consisting of 20 trials was performed before the recording of the 100 trials. Participants were allowed to rest and relax their arm every 20 trials for several minutes (a "pause" word appeared on the screen for that purpose). Data collection and processing materials. Arm and head motion of the dominant arm were recorded by means of a motion capture system (Optitrack device). Ten cameras were used to capture the movement of five retro reflective markers (14 mm in diameter), placed at well-defined anatomical locations on the moving arm and head (acromial process, humeral lateral condyle, apex of the index finger, left and right sides of the frontal bone).
Data analysis. All the analyses were performed with custom software written in MATLAB from the recorded 3D positions of the markers (sampling frequency of 250 Hz). Recorded signals were low-pass filtered using a digital fifth-order Butterworth filter at a cutoff frequency of 10 Hz (MATLAB filtfilt function). Velocity profiles were computed via numerical differentiation. The movement onset time was defined as the instant at which the linear tangential velocity of the fingertip exceeded 5% of its peak and the end of movement as the point at which the same velocity dropped below the 5% threshold. Movement duration was first inferred from those values as it is a traditional method. However, given the purpose of the paper, a second method was used where the velocity profiles were fitted to minimum jerk velocity profiles  (Flash and Hogan, 1985)  to infer the movement duration (in the spirit of  Botzer and Karniel, 2009).
To do so, a global optimization using MATLAB's patternsearch function was performed, and the parameters of the best-fitting minimum jerk problem were identified. On average, both methods gave similar results, but the second method revealed itself to be more robust to variations of motion amplitude and noise, and was therefore much more reliable in general. This second method was thus retained. Finally, the angular position of the arm was also evaluated from the shoulder and finger markers and using some trigonometry. The error between the theoretical amplitude and the one inferred from our recorded data was 2. 0 Ϯ 0. 9 degrees on average across participants. This indicated that the participants fulfilled the task correctly by respecting the required amplitudes and that the stimuli displayed on the screen were correctly calibrated.
To uncover the CoT underlying such self-paced arm pointing movements, experimental data were first gathered and parameters such as movement extent and duration were computed. For all the participants, the relationship between amplitude and duration was approximately linear, in agreement with the existing literature  (Brown et al. , 1990; Gentili et al. , 2007). Figure  4  illustrates this relationship for 4 participants separately, which was quantified via linear regressions based on all the 100 trials recorded and depicted as single gray dots (R 2 Ͼ 0. 75 for all the 20 participants). Even though the task was fairly simple, interin-dividual differences were noticeable. In particular, both the slope and the intercept of the regression line varied across participants. For each single movement, information about its amplitude and duration was eventually used to estimate the value of g for that time.
B. analyzed data; B. B. and F. J. wrote the paper. This work was supported by a public grant overseen by the  French National Research Agency  as part of the  Investissement d'Avenir program , through the  iCODE Institute  project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. We thank  Thomas Deroche ,  Carole Castanier , and  Jean Jeuvrey  for help during the acquisition of the experimental data; and  Francois Bonnetblanc ,  Francesco Nori , and Jeremie Gaveau for useful comments on earlier versions of the manuscript.
Method Participants A total of 2,232 participants aged 14 years across eight European sites were recruited via high-schools. Parents gave informed written consent and adolescents gave written assent to the study procedure prior to inclusion. All procedures were approved by each local institutional ethics committee. Further descriptions of the study design, sample, and recruitment procedure, including data storage and safety can be found in the Supplementary Material and elsewhere  (Schumann et al. , 2010). After data quality control, complete and reliable data sets for 2,144 volunteers with an average age of 14. 39 years (SD ϭ 0. 77) and an even sex ratio (n ϭ 1,093 girls, i. e. , 51%) were included in analyses. Reliable follow-up data was gathered for 1,603 (75%) participants at 16 years.
In all models, gender and language (English vs. other) were entered as covariates (at the level of observed variables). In addition, all models were fit using a complex random effects design to control for testing site as a cluster variable, and used robust maximum likelihood estimation (MLR). MLR has been shown to perform well when modeling low prevalent behaviors or nonnormal data  (Asparouhov & Muthén, 2005). Full information maximum likelihood was used to handle missing data. Once the best fitting model was established, two sets of correlates (personality and cognitive indices) were each entered into the model separately. That is, unadjusted associations were examined by entering the personality and cognitive variables and the psychopathology factors into the same model and allowing them to correlate.
Supported by the  European Union -funded  FP6   Integrated Project IMAGEN (Reinforcement-Related Behaviour in Normal Brain Function and Psychopathology ;  LSHM-CT-2007-037286 ), the  FP7  project  IMAGEMEND (Imaging Genetics for Mental Disorders ), and the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), the  Medical Research Council   Programme Grant  " Developmental Pathways Into Adolescent Substance Abuse " (93558), the  Swedish funding agency FORMAS , and the  Canadian Institutes of Health Research  (grant  114887 ). Further support was provided by the  Bundesministerium für Bildung und Forschung (BMBF  Grants  01GS08152  and  01EV0711 ), the  Deutsche Forschungsgemeinschaft (DFG)   Reinhart-Koselleck Award  ( SP 383/5-1 ), and  DFG  Grants  SM80/ 5-2 ,  SM 80/7-1 ,  SFB 940/1.
To optimize the inclusion of potentially eligible patients, the bedside nurse will have to enter patient demographic data in an anonymized form starting from the day of ICU admission (prescreening process). On the day of ICU discharge, the bedside nurse will find the patient, whose information has already been entered; the nurse will connect to the electronic-specific database and will test eligibility and exclusion criteria by following a stepby-step approach (screening process). If the patient presents eligibility criteria and has no exclusion criteria, then the bedside nurse will have to assess the discomforts perceived by the patient. To ensure adequate training of the nursing staff, the application (access to the specific website and use of a preproduction database) will be used in each ICU for a training period of several weeks with technical and educational support provided by the coordinating investigator and the coordination team before the first patient is included.
Data collection The following ICU-specific parameters will be recorded: type of ICU (medical, surgical, or mixed medical-surgical); number of beds; mean of stay duration; annual mortality rate. Data related to the patient will be recorded from an electronic case report form (eCRF) specifically elaborated for the study. The following data will be collected: demographics (sex, age); health status before the ICU stay using the Knaus score  [34]  and the McCabe index  [35] ; health status at ICU admission using the Simplified Acute Physiology Score (SAPS II)  [36] ; type of admission (medical, scheduled surgical, or emergency surgical); health status from the ICU admission to ICU discharge (use and duration in days of mechanical ventilation, noninvasive ventilation, administration of vasopressors, renal replacement therapy, type and number of invasive procedures, sedation duration, delay between interruption of the sedation and discharge from the ICU); and  self-perceived discomforts (IPREA, see the Endpoints section) assessed on the day of ICU discharge during the specific periods of assessment.
Statistical considerations Sample size, power, and statistical methods The sample size was determined to detect a 25 % difference in the overall score of IPREA between the two groups. This 25 % difference, considered to be clinically significant, was based on a previous report indicating a mean overall discomfort IPREA score of 22 (standard deviation of 14). The intracluster correlation coefficient (ICC) was estimated from the database of the IPREA questionnaire validation study. With a design effect calculated to be 2. 9 (ICC = 0. 1; expected equal cluster sizes = Table  4  The French IPREA questionnaire for assessing self-reported discomforts perceived by the critically ill patients, original version 1 Avez-vous souffert du bruit (alarmes, radios, sonneries de téléphone, conversations) de jour comme de nuit? 2 Avez-vous souffert de la lumière (éclairage trop important dans la chambre ou dans le couloir surtout la nuit? 3 Avez-vous souffert du lit (matelas trop dur ou trop mou, matelas à eau, tête de lit trop ou pas assez relevée, lit trop bas ou trop haut, barrières, mauvais oreillers, etc.
Data analysis The data will be analyzed using SPSS version 17. 0 software and SAS/STAT® version 9. 2. The primary (overall score of IPREA) and secondary (16-item scores of IPREA) endpoints will be analyzed on the individual patient level using cluster level summaries, which will account for the correlation between patients in the same center. Analysis methods based on individual patient-level data perform well when the number of clusters is high  [37]. The overall score of discomfort and the scores for each item will be compared between control and experimental groups. No interim analysis is planned. The primary analysis will be performed on the intention-to-treat population corresponding to all included individuals in each participating ICU. A secondary analysis will be performed on the per-protocol population corresponding to all included individuals who complete the IPREA questionnaire.
The primary endpoint (mean of the overall discomfort scores collected during the 1-month period, B versus E in Fig. 1 ) will be analyzed without adjustment by comparison of IPREA scores between the two groups using Student t-tests or Mann-Whitney tests according to the variable distribution. A set of secondary analyses will be adjusted for covariates of clinical interest using generalized linear mixed regression models including the group (interventional/control), ICUs and individuals (as random variables), a priori covariates (patient age, gender, ICU stay duration, mechanical ventilation duration, and type of admission), and other co-variables selected with a threshold p-value <0. 05 during univariate analysis. These models will be performed using the MIXED SAS procedure (version 9. 2). The same procedure will be performed on the 16 IPREA scores for each discomfort item (B versus E in Fig.
Acknowledgments This work is supported by institutional grants from the  French 2012 Programme Hospitalier Recherche Clinique National. The legal responsibilities are held by  Hôpitaux de Chartres, France ; its role was to control the appropriateness of ethical and legal considerations. The authors are grateful to all the investigators and patients for their participation in the study. The authors also gratefully thank  Bénédicte Mauchien  for her major and invaluable role in providing technical and educational support to all the investigators under the supervision of the coordinator of the study.
Analyses of Mean Response Times and Accuracy Mean RTs and accuracy were analyzed to illustrate the added value of information the diffusion modeling approach provides. We used mixed-effects modeling  (Baayen et al. , 2008)  as implemented in the "lme4"-package  (Bates et al. , 2014)  with crossed random factors for subjects and items. Analyses were run in R version 3. 3. 0 (R Core Team, 2016). We analyzed RTs by linear mixed-effects regression, including main effects and interactions for task and age as fixed factors. Fixed effects were tested for significance using Type III Wald chi-square tests ("car"-package;  Fox and Weisberg, 2011). Accuracy was analyzed using logistic mixed-effects regression. As recommended by  Barr et al. (2013; Barr, 2013) , the random factor structure included intercepts for subjects and items, and random slopes for age within items, as well as random slopes for task within subjects.
Hierarchical Diffusion Modeling Response times and accuracies were fitted to hierarchical diffusion models using the python toolbox HDDM, which provides hierarchical Bayesian parameter estimation of the drift-diffusion model  (Wiecki et al. , 2013). The hierarchical approach allows for simultaneous estimation of diffusion model parameters across participants and the possibility to restrain these parameters according to theoretical assumptions: While some parameters may vary from individual to individual, others are constrained to be equal across participants  (Vandekerckhove et al. , 2011; Voss et al. , 2013). For our purposes, we created a model for each of the subprocesses of reading (i. e. , letter identification, orthographic processing, phonological processing, lexico-semantic processing).
9, SE = 11. 8, t = -21. 4. Generally, younger participants responded faster than older adults, b = -105. 1, SE = 5. 38, t = -19. 5, and highperforming older adults responded faster than low-performing older adults, b = -26. 3, SE = 3. 79, t = -6. 94. The identical agerelated RT pattern was observed within all four tasks (see Table  2  for a detailed summary). Given the size of the data set and all absolute t-values being well above 2, we consider these differences to be significant (cf. Baayen et al. , 2008). (2) = 19. 4, p < 0. 001 as well as the significant interaction of task and age, χ 2 (6) = 29. 7, p < 0. 001. Highest accuracy rates were obtained in the letter identification task, b = 0. 43, SE = 0. 12, z = 3. 62. Participants made fewer errors in the lexical decision task, b = 0. 71, SE = 0.
Assessment of Convergence and Model Fit Model convergence was assessed by visually inspecting the traces of the posterior distributions and by calculating the R-hat statistics for each of the models separately. We neither observed any drifts or large jumps within the plots nor any parameter values above 1. 02 within the R-hat statistic, indicating successful convergence for all models of reading subcomponents. We then compared the simulated with the observed data, again for all models separately. The model fitted our data very well: The correlation between empirical data and model RT quantiles was r = 0. 98 in the letter identification task, r = 0. 94 in the lexical decision task, r = 0. 99 in the phonological decision task, and r = 0. 96 in the semantic decision task (Figure  1 ).
, 1997). We employed a powerful machine learning procedure to examine the connectivity patterns associated with smoking. Such approaches have previously been used to investigate adolescent binge-drinking  (Whelan et al. , 2014)  and intelligence  (Jollans et al. , 2015). This approach has the potential to detect relatively subtle differences, while guarding against spurious findings, using both cross-validation and random-label permutation. We included 206 adolescents from a large multisite study, with a wide spectrum of nicotine use. As our aim was to identify effects associated with smoking frequency, rather than with smoking initiation, we included only adolescents who had smoked on three or more occasions in their lifetime at the point of data collection. In line with a recent review examining resting state functional connectivity in nicotine addiction  (Fedota & Stein, 2015) , which concluded that disruptions in nicotine addiction appear to be focused on the salience network as well as frontal cognitive control systems, we hypothesized that frequency of smoking would be associated with reduced VS connectivity to fronto-parietal cognitive control regions  (Garavan & Weierstall, 2012)  and increased connectivity to regions associated with salience or valuation of stimuli, such as the anterior cingulate and orbitofrontal and insular cortices  (Seeley et al.
Substance use questionnaire Lifetime smoking, alcohol, and cannabis use were measured using the European School Survey Project on Alcohol and Other Drugs questionnaire  (ESPAD, Hibell et al. , 1997) , which was administered using the computerized assessment platform Psytools. Psytools presented questionnaire items and response alternatives on a computer screen. The reliability of individual data was checked in a two-stage procedure: Before every task, adolescents were asked to report on the current testing context including questions about their attentional focus and the confidentiality of the setting. Potentially problematic testing situations were followed-up by research assistants face-to-face in a confidential setting. Exclusion criteria for substance use measures included an indication that the participant was in a hurry, somebody was watching, or an indication to have known or taken the sham drug Relevin.
fMRI Data Acquisition Full details of the magnetic resonance imaging (MRI) acquisition protocols and quality checks have been described previously, including an extensive period of standardization across MRI scanners  (Schumann et al. , 2010). MRI Acquisition Scanning was performed at the eight assessment sites with a 3T whole body MRI system made by several manufacturers (Siemens: 4 sites, Philips: 2 sites, General Electric: 1 site, and Bruker: 1 site). To ensure a comparison of MRI data acquired on these different scanners, we implemented imageacquisition techniques using a set of parameters compatible with all scanners that were held constant across sites, for example, those directly affecting image contrast or fMRI preprocessing. Standardized hardware for visual and auditory stimulus presentation (NordicNeurolabs, Bergen Norway, http://www.
Functional connectivity associated with smoking frequency Data from 92 ROIs based on the AAL atlas  (Tzourio-Mazoyer et al. , 2002)  and two masks for the subthalamic nuclei (x=-12, y=-10, z=-5; x=12, y=-13, z=-5), as well as lifetime alcohol and cannabis use, data acquisition site, sex, and pubertal development status were entered into the analysis. Data were z-scored. The analysis procedure is shown in Figure  2. A similar approach has previously been used by  Whelan et al. (2014)  and  Jollans et al. (2015). To assess the effect of lifetime smoking on VS connectivity, two ROI regularized multiple regression analyses for the left and right VS seed were carried out in Matlab R2014a, via the Elastic Net  (Zou & Hastie, 2005). Regression with Elastic Net regularization is an example of a sparse regression method, which imposes a hybrid of both L1-and L2-norm penalties (i.
e. , penalties on the absolute (L1 norm) and squared values of the β weights (L2 norm)). This allows relevant but correlated coefficients to coexist in a sparse model fit, by doing automatic variable selection and continuous shrinkage simultaneously, and selects or rejects groups of correlated variables. Least absolute shrinkage and selection operator (LASSO,  Tibshirani, 1996)  and ridge regression  (Hoerl & Kennard, 1970)  are special cases of the Elastic Net. We used 10-fold nested cross-validation, in which 10 separate regression models were generated, with the beta weights for all parameters being generated on 90% of the dataset (the training set), and tested on 10% of the dataset (the test set). Within the test set, additional 10-fold cross-validation was used to identify the optimal Elastic Net parameters α and λ.
Beta weights were averaged for each variable. Two null models were also computed using the same method. For these, the same analysis procedure was carried out using random label permutations with the same dataset (i. e. , subjects were randomly assigned to ESPAD scores). These null models yielded average beta weights of 0. 018 and 0. 016, and average correlation coefficients of r=-0. 006 and r=-0. 01. Based on the null models, the threshold for reporting ROIs was set at a minimum absolute beta weight of 0. 048 this was the 95 th percentile of the distribution of beta weights in the null models). The reporting thresholds for the minimum frequency with which ROIs should be included in the regression models across iterations was set at 84% (left) and 81% (right, this was the 95 th percentile of the distribution of occurrence frequency across iterations in the null models).
Bootstrap aggregation was used in this step. (2) The optimal Elastic Net parameters for each main CV fold were applied to the full training set (90% of the data) to generate beta weights for all input variables. These beta weights were then used to generate outcome predictions for the remaining, untouched 10% of the dataset in each main CV fold. The goodness-of-fit was estimated using the outcome predictions for the entire dataset. Functional connectivity between the VS and nodes drawn in blue was negatively associated with smoking frequency. This figure was generated using BrainNet Viewer  (Xia, Wang, & He, 2013).
Methods Participants A power analysis using the "power. t. test" formula in the R package "stat"  [26]  was based on reported MD amplitude (mean = 0. 18 and sd = 0. 17) in the general population  [22]. It indicated that 9 participants in each group would be sufficient to find a difference using a one-sample t test, with a power of. 9 at a. 05 significance level. Twenty adults with ASD and 19 controls were included in the study. Intelligence quotients were determined by the Wechsler Adult Intelligence Scale. No significant differences in age and intellectual quotient (IQ) were found between individuals with ASD and controls. Participants' demographic characteristics are summarized in Table  1. All participants in the ASD group were recruited from the diagnostic clinic at Hôpital Rivière-des-Prairies, Montréal, Canada.
Tasks All tasks were programmed on a PC, using the Cogent 2000 (Wellcome Department of Imaging Neuroscience, London, UK) library of Matlab functions for presentation of stimuli. All participants took part in both a desirabilityrating (test) task and a recognition (control) task. The desirability-rating task included 120 videos (60 object pairs), divided into two sessions. The two objects of a pair always appeared in the same session to limit the effects of temporal fluctuations and of session-wise rating scale anchors. Also, the presentation order of the different videos was randomized for each subject, with the constraint that the first and the second object of each pair should appear in the first and the second half of a session, respectively. To eliminate color preferences at the group level, color were counterbalanced between subjects.