dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We wish to thank  Emilie Lepage  ( Geriatric Institute of the University of Montreal ; IUGM) and the  Montreal Alzheimer Society  for recruitment of AD participants,  Nadia Jaffer  ( IUGM ) for the recruitment of control participants, and above all, all participants in the study. We are grateful to  Amélie Racette  for experimental materials and  Patrick Bermudez  for his insightful comments on the manuscript. Aline Moussard  received a fellowship from the  NSERC-CREATE program in Auditory Cognitive Neuroscience  and a research award from the  French Mederic-Alzheimer foundation. Preparation of this paper was supported by grants from the  Natural Sciences and Engineering Research Council of Canada  and the  Canadian Institutes of Health Research  to  Isabelle Peretz  and to  Sylvie Belleville , a  Canada Research Chair  to  Isabelle Peretz , and European EBRAMUS funding to  Emmanuel Bigand.
Movement parameters were recorded using a spatial tracking system (Polhemus Liberty, Polhemus Corporation; Colchester, VT-referred to heron as STS), a device which determines position and orientation of its associated sensors relative to a stationary system by means of an electromagnetic field. This STS permits the recording of movements in six degrees of freedom (x,y,z and rotation along the axes, x,y,z). All data was sampled at a frequency of 240 Hz and recorded online using MotionTracker v1. 43 (BIOMETRICS France; Gometz-le-Châtel, Île de France).
PROTOCOL The experimental protocol used here respected the ethical guidelines of the American Psychological Association (APA). Following the provision of clear information on the conditions of participation, each subject gave their written consent. Prior to commencing the experimental procedure, personal data (e. g. , height, weight, age) was collected and anthropometrical features of the striking arm were recorded in order to permit geometric modeling of the upper limb at a later stage. STS sensors were applied to the striking arm with adhesive tape at the dorsal surface of the hand, the dorsal surface of the lower arm, the lateral aspect of the upper arm and the dorsal aspect of the coracoid process of the scapula, reflecting the protocol used by Biryukova and colleagues  (Biryukova et al.
REGULATION OF FUNCTIONAL PARAMETERS Relationships between key functional parameters were calculated using data extracted from the biomechanical model. Maximal kinetic energy was determined with reference to the working point of the hammer according to the formula E k = 1/2 mv 2. Potential energy was calculated with respect to vertical distances between the frustrum and the working point of the tool according to the formula E p = mgh (g = 9. 81 ms -2 ). Ratios of kinetic energy to potential energy (E k /E p ) were also calculated in order to highlight movement strategies in terms of muscular effort (refer to Figure  2 ). Two way t-tests were used to determine if statistically significant differences existed between the four groups in terms of maximal kinetic energy, potential energy, and the ratio of kinetic energy to potential energy.
ANALYSIS OF COORDINATION BY SEGMENTAL CONTRIBUTION Principal component analysis (PCA), one of the more classic statistically driven techniques for recognizing patterns in movement data was applied to kinematic data of striking movements. This method was chosen for two reasons. Firstly the PCA facilitated the comparison of movements through compression of the multidimensional datasets. Secondly, use of the covariation matrix served to represent the data in a way that reflects the underlying movement synergies  (Ting and Chvatal, 2011). The PCA was applied to each striking movement, following the equation ϕ i (t) -ϕ Mi (t) = k w ki ξ k (t), where the vector of temporal variation of joint angles around their mean values is defined as being equal to the sum of the principal components. Using this mathematical technique, each principal component is presented according to its magnitude so that the first principal component (PC1) corresponds with the axis along which the dataset is most spread; the second principal component (PC2) describes an orthogonal axis which describes the next most important data variance and so on.
Use of this method generally permits the description and analysis of a significant percentage of movement data through one or two matrices  (Rein, 2012). Finally, the regularity of joint angle contributions to PC1 was then calculated for each subject's three successful attempts. This was done using by comparing absolute values of Pearson's correlation coefficient from the PC1 loadings across each of the nine degrees of freedom represented in the biomechanical model strikes (successful strike 1 vs. successful strike 2; successful strike 1 vs. successful strike 3; successful strike 2 vs. successful strike 3). The correlation coefficient was considered to be significant at a value greater than 0. 7.
ACKNOWLEDGMENTS The work presented here uses experimental data collected as part of the Masters thesis of  Mr. Ross Parry  ( Master RIM ,  UPMC, Paris ), under the direction of  Blandine Bril  and  Gilles Dietrich. This project was supported by the  EHESS  and funded in part by the  PHC  Programme Sakura, 2013-2014 Bilateral Joint Research Projects between the  French Ministères des Affaires Étrangères et Européennes (MAEE)  and the  Japan Society for the Promotion of Science (JSPS). We wish to thank all the participants for generously donating their time to our study. A special thank you must be extended to  Tetsushi Nonaka  who participated in the recording of a handful of participants and to  Cezary Sieluzycki  for his valuable input during revision of the manuscript.
In the present study we focus on the participants' ratings of their affective states (feeling familiar, feeling safe, or frightened) during the interaction with NAO. These ratings are obtained using the 7-point Likert scale: 1 meaning, e. g. , "not feeling familiar at all," 7 "Highly/intensely familiar. " We added 0 for "Irrelevant statement" to allow a more precise expression. (2) Each experimental session is video recorded using two stable cameras: one is filming the set from behind and gives images of the robot entering the set and of its interaction with the participants. The other is facing the participants and providing images of their movements and facial expressions. This tool is particularly used to collect data on the participants' non-verbal behavior and on their reactions (answer back or not) to NAO's gestures.
The recorded data is reinforced with observation notes taken by the psychologist of our team. (3) Two IMU (Inertial Measurement Unit) are used for each participant. One is fixed on the forehead to capture the head and upper torso movements; the other on the arm -the right arm for X and the left for Y, each being respectively the closest arm to the robot's position and the one to be most likely used (from our observations on a pilot study of 20 candidates) by the participants to fetch the envelope. The IMU measure the longitudinal accelerations and the rotational velocities around 3-axes. Thus, more discrete micro-movement data is recorded giving another level of information regarding the participants' experience and reactions to NAO. Data for two pairs of candidates are unavailable. An example of raw data for the hand is given in Figure  2 , for participants X and Y.
DATA ANALYSIS (1) The motion data are analyzed from the IMU and from the video. From the IMU data, the three components of the rotational velocity and the three components of the acceleration are post-processed separately to obtain three types of information: motion intensity, frequency and smoothness. Only the last two are addressed in the present paper. The data recording starts a few seconds before the robot enters the room. The data are manually segmented using both the raw data collected by the IMU and the synchronized video data in order to refine the segment point. We distinguish several segments: (1) The whole interaction from when the robot enters the room up to when it leaves; (2) the grasping of the envelope starts when the arm movement is initiated and finishes once the envelope has been grasped, the participant's hand is not necessarily back to its original position; (3) the greetings movements are segmented from the initiation of the movement to the end of the waving, the participant's hand is not necessarily back to its original position.
The frequency analysis and the jerk analysis are performed on each segment corresponding to the interaction with the robot (taking the envelope, waving goodbye) of the data, and during the entire interaction. Frequency analysis: First a simple frequency analysis (Fast Fourier Transform) on the hand motion data (angular velocity) is performed during the grasping motions and when answering the robot's goodbye by waving the hand; the frequency (Hz) of the first highest pick is used. Motion smoothness: it is computed during the overall interaction. There are several methods to assess the motion smoothness  (Rohrer et al. , 2002) ; we chose the jerk metrics (1/s 2 ). For that, the accelerations are used to compute the jerk magnitude averaged over overall motion and normalized with respect to the peak speed.
15 he Lifetime Impairment Survey (LIS), which was conducted in 6 European countries, was designed to assess the degree to which ADHD impairs patients' everyday lives and to identify the areas of life most affected by the condition. 12, 16 The LIS, based on the previously mentioned U. S. survey,  15  collected data from adults with ADHD and parents/caregivers of children with ADHD, for comparison with corresponding control groups. Experiences concerning ADHD care, as well as the duration and efficacy of treatment, were also assessed. Outcomes of the study related to experiences during childhood and adolescence, as recalled by adults, are reported here. We hypothesized that adults with ADHD would recall greater functional impairment during their childhood and adolescence relative to adults without ADHD.
Population and subject recruitment Participants for the survey were recruited by e-mail invitation from the GfK Global Online Panel (GfK Healthcare, London), which had a database of 487,533 members. A range of techniques are used to continuously recruit new panel members, who are informed upon enrolment about their rights and responsibilities relating to participation in survey research and then complete a demographic profiling survey. Consent is not obtained separately for each survey that panel members participate in; however, survey participation is voluntary, and panelists may leave a survey at any time. No individual identifiers are held in the database to maintain confidentiality. In exchange for participation in the LIS, responders were invited to nominate a charity to which a donation would be made and had the opportunity to enter into a prize draw.
20 As the survey was conducted as market research, ethics committee approval was not obtained. The survey comprised 55 questions divided into 4 sections: screening and classification (eg, sex, age, educational attainment, relationship status, employment status, income); impairment in childhood/adolescence (eg, impact of ADHD at school and on peer and family relationships) and general well-being (presence of conduct problems and comorbidities); impairment in adulthood (eg, impact on work, social life, relationships, and personal finances); and ADHD diagnosis and treatment (including satisfaction with current prescribed medicine). Adults were asked to recall their own experiences in childhood/adolescence. Data were collected from survey respondents using computer-assisted Web interviewing.
Mean impairment and symptom scale scores were calculated for all respondents who answered at least half of the questions in a particular scale; only respondents with scores on all applicable scales were included in scalerelated analyses. The effect of study group, country, age, and sex on the impairment and symptom scales and scores was analyzed using ANCOVA. Effect sizes are indicated using Cohen's d and partial eta-squared. No formal sample-size calculation was performed; however, a quota of 100 respondents per study group per participating country was applied. Statistical analyses were conducted using IBM SPSS version 16. 0 (Armonk, New York, USA).
Participants Overall, 1365 adults responded to the survey. However, 41 respondents who provided impossible answers (eg, length of time until diagnosis greater than respondent's age) were removed from the database, which left a total of 1324 participants (588 in the ADHD group; 736 in the control group). Overall survey response rates are 1255] 5 12. 5, p , 0. 001, Cohen's d 5 0. 693); however, all significant scale results remained significant when adjusted for group differences using adult age as a covariate, and when younger (, 45 years) and older ($ 45 years) adults were examined separately. Further investigation into respondent age using ANCOVA demonstrated a significant difference in age between countries (F test [df 5, 1131] 5 4. 4; p 5 0. 001, partial eta-squared 0. 019) and between study groups (F test [df 1, 1131] 5 153.
Setting and Immersive Virtual Reality (IVR) equipment The experiment was carried out in the Laboratory of Cognitive Science and Immersive Virtual Reality, Department of Psychology, Second University of Naples, Caserta (Italy). The IVR equipment is installed in a rectangular room (5 m64 m63 m) and includes the 3-D Vizard Virtual Reality Toolkit Devices for Integrated VR Setups and Position Tracking System. Virtual stimuli were presented through the nVisor SX (NVIS, USA) head mounted display (HMD) with two displays providing stereoscopic depth (approximately 30 times a sec. ). The stereoscopic images run at 128061024 resolution, refreshed at 60 Hz. The virtual scenario spanned 60 degrees horizontally by 38 degrees vertically. Graphics card used Vizard softwares (WorldViz, USA). Head orientation was tracked by a three-axis orientation sensor (InertiaCube3; Intersense, USA) and head position by a passive optical tracking system (Precision Position Tracker, PPT-H4; WorldViz, USA).
Graphics displayed in the HMD were updated on the basis of sensed position and orientation of participant's head. Moreover, the Data Glove, a glove equipped with 14 tactile-pressures sensors providing the sense of hand movement, was also used. Graphics modeling were created by 3D Google Sketch Up 7. 0 free-software. The position and orientation tracking systems allowed participants to realistically experience dynamic and stereoscopic visuo-motor input as if they were in front of natural stimuli.
Data analysis We measured the distance at which the participants stopped themselves or the virtual stimuli according to the task (Reachability or Comfort distance) and the condition (Active or Passive). The IVR system tracked the participants' position at a rate of approximately 18 Hz. The computer recorded participant's position in the virtual room by continuously computing the distance between the marker placed on participants' HMD and virtual stimuli. In each condition, this tracking system allowed to record the participant-virtual stimulus distance (in cm). Participant's arm length was then subtracted from the mean distance. Within each block and for each type of stimulus the mean participant-virtual stimulus distance was then computed. The mean distances obtained in the different experimental conditions were compared through a four-way ANOVA including participants' Gender as between-participant factor and Distance (Reachability-Comfort distance), Approach (Passive-Active approach), and Virtual stimuli (male, female, cylinder, robot) as within-participant factor.
This work was supported by a grant from the  Ministry of Research (program ''Investments for the Future '') and managed by the  National Research Agency  under the reference  ANR-11-EQPX-0023 , as well as by European funds through the program  FEDER SCV-IrDIVE. The work was also supported by  Second University of Naples  funds to TI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Having established that cue sensitivities combine according to Equation 2, the model was then fit to data from each individual subject under the assumption of optimal cue combination. Model fits are shown as solid curves for example subject D2 (Figure  3 ), as well as for all other subjects (Figure  3 -figure supplement 1). Sensitivity parameters, bounds, and non-decision times resulting from the fits are also shown for each subject, condition, and coherence (Figure  3 -figure supplement 2). For 8 of 10 datasets, the model explains more than 95% of the variance in the data (adjusted R 2 > 0. 95), providing additional evidence for nearoptimal cue combination across both time and cues (Figure  7A ). The subjects associated with these datasets show a clear decrease in reaction times with larger |h|, and this effect is more pronounced in the visual condition than in the vestibular and combined conditions (Figure  3 , Figure  3 -figure supplement 1).
Behavioral task sequences and data acquisition were controlled by Matlab and responses were collected using a button box. Visual stimuli were generated by an OpenGL accelerator board (nVidia Quadro FX1400), and were plotted with sub-pixel accuracy using hardware anti-aliasing. In the visual and combined conditions, visual stimuli depicted self-translation through a 3D cloud of stars distributed uniformly within a virtual space 130 cm wide, 150 cm tall, and 75 cm deep. Star density was 0. 01/cm 3 , with each star being a 0. 5 cm × 0. 5 cm triangle. Motion coherence was manipulated by randomizing the threedimensional location of a percentage of stars on each display update while the remaining stars moved according to the specified heading. The probability of a single star following the trajectory associated with a particular heading for N video updates is therefore (c/100) N , where c denotes motion coherence (ranging from 0-100%).
Data analysis Analyses and statistical tests were performed using MATLAB R2013a (The Mathworks, MA, USA). For each subject, discrimination thresholds were determined separately for each combination of stimulus modality  (visual-only, vestibular-only, combined)  and coherence (25%, 37%, and 70% for subjects A-G; 0%, 12%, 25%, 37%, 51%, and 70% for subjects B2, D2, F2) by plotting the proportion of rightward choices as a function of heading direction (Figure  2A ). The psychophysical discrimination threshold was taken as the standard deviation of a cumulative Gaussian function, fitted by maximum likelihood methods. We assumed a common lapse rate (proportion of random choices) across all stimulus conditions, but allowed for a separate bias parameter (horizontal shift of the psychometric function) for each modality/coherence.
Confidence intervals for threshold estimates were obtained by taking 5000 parametric bootstrap samples  (Wichmann and Hill, 2001). These samples also form the basis for statistical comparisons of discrimination thresholds: two thresholds were compared by computing the difference between their associated samples, leading to 5000 threshold difference samples. Subsequently, we determined the fraction of differences that were below or above zero, depending on the directionality of interest. This fraction determined the raw significance level for accepting the null hypothesis (no difference). The reported significance levels are Bonferroni-corrected for multiple comparisons. All comparisons were one-tailed. Following traditional cue combination analyses  (Clark and Yuille, 1990) , the optimal threshold σ pred,c in the combined condition for coherence c was predicted from the visual threshold σ vis,c and the vestibular threshold σ vest by For each dataset, we evaluated the absolute goodness-of-fit of the optimal model (Figure  7A ) by finding the set of model parameters φ that maximized the likelihood of the observed choices and reaction times, and then computing the average coefficient of determination, ( ) 2 2 2 1 2 ( ) ( ) + ( ) psych chron R R R = φ φ φ D.
This lapse rate was assumed to be independent of stimulus modality or coherence, and so a single lapse rate parameter is shared among all modality/ coherence combinations. All model fits sought to find the model parameters φ that maximize the likelihood of the observed choices and reaction times for each dataset. As in  Palmer et al. (2005) , we have assumed the likelihood of the choices to follow a binomial distribution, and the reaction times of correct and incorrect choices to follow different Gaussian distributions centered on the empirical means and spread according to the standard error. Model predictions for choice fractions and reaction times for correct and incorrect choices were computed from the solution to integral equations describing first-passage times of bounded diffusion processes  (Smith, 2000).
See Supplementary file 1 for the exact form of the likelihood function that was used. To avoid getting trapped in local maxima of this likelihood, we utilized a three-step maximization procedure. First, we found a (possibly local) maximum by pseudo-gradient ascent on the likelihood function. Starting from this maximum, we used a Markov Chain Monte Carlo procedure to draw 44,000 samples from the parameter posterior under the assumption of a uniform, bounded prior. After this, we used the highest-likelihood sample, which is expected to be close to the mode of this posterior, as a starting point to find the posterior mode by pseudo-gradient ascent. The resulting parameter vector is taken as the maximum-likelihood estimate. All pseudo-gradient ascent maximizations were performed with the Optimization Toolbox of Matlab R2013a (Mathworks), using stringent stopping criteria (TolFun = TolX = 10 -20 ) to prevent premature convergence.
Figure6. Predicted and observed sensitivity in the combined condition. The sensitivity parameter measures how sensitive subjects are to a change of heading. The solid red line shows predicted sensitivity for the combined condition, as computed from the sensitivities of the unimodal conditions (dashed lines). The combined sensitivity measured by fitting the model to each coherence separately (red squares) does not differ significantly from the optimal prediction, providing strong support to the hypothesis that subjects accumulate evidence near-optimally across time and cues. Data are averaged across datasets (except 0%, 12%, 51% coherence: only datasets B2, D2, F2), with shaded areas and error bars showing the 95% CIs. DOI: 10. 7554/eLife. 03005. 011.
variants of the full model (Figure7B) by Bayesian model comparison based on Bayes factors, which were computed as follows. First, we found for each model M and subject s the set of parameters φ that maximized the likelihood, approximated the Bayesian model evidence, measuring the model posterior probability while marginalizing over the parameters, up to a constant by the Bayesian information criterion, k M is the number of parameters of model M, and N s is the number of trials for dataset s, respectively. Based on this, we computed the Bayes factor of model M vs the optimal model M opt by pooling the model evidence over datasets, resulting in converted to a base-10 logarithm, are shown in Figure7B.
The first aim of the present study was to assess the activity of the striatum during odor fear conditioning using 2-Deoxyglucose autoradiography during development in rats. The data showed that although fear learning was displayed at all tested ages, activation of the striatum was observed in adults but not in juvenile animals. Next, we assessed the presence of evidence of interval timing in ages before and after the inclusion of the striatum into the fear conditioning circuit. We used an experimental setup allowing the simultaneous recording of freezing and respiration that have been demonstrated to be sensitive to interval timing in adult rats. This enabled the detection of duration-related temporal patterns for freezing and/or respiration curves in infants as young as 12 days PN during odor fear conditioning.