dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements  Canadian Study of Health and Aging (CSHA) :  Kenneth Rockwood  (study leader). Funding from the  National Health Research Development Program of Health Canada  ( 6605-3954-MC[S]). Chinese Longitudinal Ageing Study (CLAS): Shifu Xiao (study leader ),  Juan Li ,  Xia Li. The authors extend their thanks to the main investigators from across the different regions of China:  Tao Wang ,  Muni Tang ,  Wei Chen ,  Feng Bao ,  Huali Wang ,  Yuping Wang ,  Ying Liu ,  Yaping Wang ,  Yefeng Yuan ,  Xiaoyun Zuo ,  Zhongming Chen ,  Xulai Zhang ,  Lijuan Cui ,  Wenyuan Wu ,  Mingyuan Zhang. Funding from a  China Ministry of Science and Technology  grant ( 2009BAI77B03 ). Cognitive Function and Ageing Studies (CFAS):  Carol Brayne  (study leader),  Fiona E Matthews ,  Blossom CM Stephan.
, 1985) , and crosslinguistic difference and relativity  (Slobin, 1985; Bowerman & Choi, 2001). Most of the examples are in English and most of the research discussed is about the acquisition of English. The examples might have been more engaging and vivid if they had all been taken from actual children's and adults' data for which the community now has an impressive database through CHILDES  (MacWhinney, 2000). The authors are of course well aware of these issues and databases to which they themselves have contributed. However, in this book they have chosen to present the topics for which clearly opposing views could be contrasted, as such it might have been worth making that even more explicit in the introduction and in the choice of the main title. The lengthy explanations of the debates and clear presentations of a large body of research were particularly helpful elements of the book.
METHOD PARTICIPANTS One hundred and seven native French participants (63 females), recruited from the general population volunteered to take part in the study. Participants were recruited via opportunity sampling from the French general public and students from the University of Reims and Paris Ouest Nanterre la Défense (students randomly solicited on the University and personal acquaintances). They were tested in their homes and classrooms in University campus. They were aged between 18 and 30 years, with a mean age of 23. 9 years (SD = 3. 4). These participants had all obtained at least a High School diploma. Participants were excluded if they reported any history of head injury, neurological disease, or psychiatric disorders. These data were collected by an in-house questionnaire, and participants had to fill in past or present disorders.
STATISTICAL ANALYSES Averages are reported as means (with SD) (Table  1 ). Pearson correlations were performed to explore the association between variables. Statistical significance was set at two-tailed p < 0. 05. Data were analyzed using the Statistica statistical package (version 9. 0, Statsoft, France). First, we explored the correlations between the emotional/cognitive variables and Machiavellianism. Second, we analyzed the Machiavellianism total score and the three subscales as subdimensions assessed using the Mach-IV, and analyzed the predictors (i. e. , affective and cognitive variables) that could indicate the core emotional composition of this particular personality.
Image processing MRI data were segmented, normalized to a pediatric sample of the NIH (N = 324, age range = 4. 5-18. 5 years,  Fonov et al. , 2011) , and modulated using the VBM5. 1 toolbox  (Ashburner and Friston, 2005)  implemented in the Statistical Parametric Mapping 5 (SPM) software (Wellcome Trust Centre for Neuroimaging, London, UK) to obtain maps of local gray matter volume corrected for brain size. Finally, each image was smoothed with a Gaussian kernel (FWHM = 12 mm). Furthermore, we used a gray matter explicit mask in the voxelwise analyses so as to restrain the analyses to the gray matter. This mask was obtained by first averaging all segmented gray matter and white matter images, then thresholding the gray matter average to include voxels with a probability higher than 0. 4 and the white matter average to include voxels with a probability higher than 0.
ACKNOWLEDGMENTS We would like to thank  Valerie Gyselinck  and  Maud Markus  for their contribution to the WHAT-WHERE-WHEN paradigm development, and  Nezha Bissara , and  Lionel Allirol  for their assistance with data collection. We are grateful to  Jane Holmes-Bernstein  for her helpful comments on the manuscript. We are also indebted to the participants and institutions that took part in our research.
APPARATUS During trials' execution kinematic data were recorded by means of a Vicon (Oxford, UK) motion capture system. Six passive markers were attached on subjects' right arm (the acromion process, lateral epicondyle of the humerus, the styloid process and the tip of the index finger) and head (external canthus of the eye and auditory meatus). EMG activity of biceps brachii (Bic) and triceps longus (Tri) was monitored by means of an Aurion (Milan, Italy) wireless EMG system. Impedance between the surface electrodes was always checked not to exceed 5 K : in the case of higher values, skin was rubbed by means of an abrasive sponge in order to decrease it. EMG data were amplified (gain of 1000), bandpass filtered (10 Hz high-pass and 1 KHz low-pass) and digitized at 1000 Hz.
DATA PRE-PROCESSING Data were analyzed off-line using customized software written in Matlab (Mathworks, Natick, MA). Kinematic data were lowpass filtered (Butterworth filter, cut-off frequency of 20 Hz). The angular displacement of the elbow was computed starting from the markers' spatial positions. Elbow angular velocity of rotation was obtained by numerical differentiation of the angular position. Mean and peak angular velocities were computed for each trial. The mean velocity was computed as the mean value of the angular velocity over the movement duration. The time instants of movement initiation (t 0 ) and end (t f ) were defined respectively as the instants at which the bell-shaped angular velocity profile of the elbow exceeded and dropped below 5% of its peak value. For the EMG analysis, muscle signals were full-wave rectified, normalized in amplitude with respect to their maximum value recorded across all trials and conditions and low-pass filtered once more with a zero-lag Butterworth filter (cut-off frequency 5 Hz).
The similarity between synergies was then quantified by computing the maximum of the scalar products over all possible time delays of the second synergy with respect to the first. To access however the significance of the values of similarity provided by the scalar products we defined a similarity index (S) between two synergies. This index, ranging from 0 (similarity at chance level) to 1 (perfect matching of the synergies) was defined as follows S = (s data -s chance )/(1 -s chance ) (5) where s data is the scalar product between two synergies extracted from the actual data and s chance is the mean scalar product between 200 pairs of random synergies. We generated the artificial synergies by resampling randomly from the distribution of the activation amplitude of each muscle in the data set from which the synergies were extracted and constructed sequences of random data with the same length of the extracted synergies.
RESULTS To compare systematically the results provided by different synergistic decompositions when characterizing the same EMG data set, we recorded EMGs during a series of elbow rotations and then we extracted and compared synchronous, time-varying and temporal muscle synergies. To illustrate the data, we begin by showing in Figure  2A  the EMGs recorded during a typical trial accomplished by one subject and relative to an elbow flexion in the horizontal plane. Consistent with previous literature  (Berardelli et al. , 1996) , such a movement is characterized by a sequence of three EMG bursts: an initial burst of the agonist muscle having the goal of providing the propulsive force to accelerate the movement, followed by a second burst of the antagonist to decelerate the movement and a third burst of the agonist to dampen the oscillation that other appears at the end of the movement.
In sum, we found that each kind of muscle decomposition provided a set of interpretable synergies. Each temporal component described a temporal phase of the movement. Each synchronous synergy described the simultaneous and coordinated action of a group of muscles (only one in our case) aiming to achieve a specific action goal. Each time-varying synergy related instead to a specific task-related variable (specifically a direction of motion). We used the synergies extracted from each subject to reconstruct the EMG data of each one of the others and assessed the percentage of VAF. The results are reported in forms of confusion matrices (Figure  7 ). The average percentage of VAF computed across subjects was 90 ± 7% when temporal synergies were extracted and used for reconstruction, and 87 ± 4 for the data sets reconstructed by using the time varying synergies.
Search strategy Information sources and search terms Electronic searches were carried using PUBMED database from 1966 to April 2012, PsycINFO database from 1970 to April 2012, SCOPUS database from 1978 to April 2012, PASCAL and FRANCIS databases from 1961 to April 2012, to identify articles published in French or English. Search terms used were child abuse, child maltreatment, battered child syndrome, child neglect, Munchausen syndrome, shaken baby syndrome, child sexual abuse, combined with sensitivity, specificity, diagnostic accuracy, likelihood ratio, predictive value, false positive, false negative, validity, test validation, and diagnosis, measurement, psychodiagnosis, medical diagnosis, screening, diagnosis imaging, physical examination, diagnostic procedure, scoring system, diagnostic, scoring system, score, assessment (Table  1 ).
Data collection process, data items and analysis The first assessment of selected papers was done by MB, and results were discussed in regular meetings by both epidemiologists MB and LRS. To reduce the likelihood that potentially relevant articles were missed, reference lists from relevant articles were checked. From each included study, we abstracted information about study design, population characteristics, number of participants, screening instrument or procedure, abuse or neglect outcome, and estimates of diagnostic accuracy. Results were not mathematically pooled due to varying methods and types of child abuse identified.
Database Search terms PUBMED ("child abuse" [Mesh] or "child maltreatment") AND ("sensitivity and specificity" [Mesh] OR "sensitivity" OR "specificity" OR "diagnostic accuracy" OR "likelihood ratio" OR "predictive value" OR "false positive" OR "false negative") PsycINFO ("battered child syndrome" OR "child abuse") AND ("diagnosis" OR "measurement" OR "psychodiagnosis" OR "medical diagnosis" OR "screening") SCOPUS ("child abuse" OR "child maltreatment" OR "child neglect" OR "battered child syndrome" OR "munchausen syndrome" OR "shaken baby syndrome") AND ("diagnosis" OR "measurement" OR "screening" OR "diagnostic imaging" OR "physical examination" OR "diagnostic procedure" OR "scoring system") AND ("predictive value" OR "diagnostic accuracy" OR "likelihood ratio" OR "sensitivity" OR "specificity").
Study selection Of 2 280 references identified in the databases, 524 were selected from their title, of which 137 abstracts were read; after exclusion of duplicates, 92 full articles were assessed (Figure  1 ). Studies excluded for lack of reference standard were case-control studies with control groups recruited in the general population without verifying if children were abused or not. Studies were excluded when the reference standard was only the opinion of caregivers who had been asked whether their children were abused or not. One study was excluded because the method of the index text, an assessment by primary care clinicians, was not described  [19]. Finally, one study was excluded because an unknown number of children less than fifteen years old examined in a medical center, who should have been tested during the study period, had not received the index test but were not registered  [20].
In a recent study in the Yale-New Haven Children's hospital from 2007 to 2010, the specificity of coding injuries as physical abuse was 100% (95% CI: 96-100). But the sensitivity was low: among the 43 cases determined to be abused by the Child Abuse Pediatrician, four were miscoded as accidents, two as injuries of undetermined cause, and four did not receive any injury code  [34]. In 1991-1992 in California, the sensitivity of hospital E-coded data in identifying child victims of intentional injuries had been estimated at 75% (95% CI: 64-84)  [35]. This classification underestimates the number of abused children, therefore does not seem to be a good reference test. Cases of child physical abuse are considered as accidents and cases classified as physical abuse are not representative of all the cases of physical abuse, because some cases did not receive any injury code.
The final value of features used for screening will also depend on the prevalence of these features. We reviewed studies only in French and English and only published studies in databases, and might have excluded interesting research. Also, one of our inclusion criteria was that the aim of the study was clearly to estimate the diagnostic accuracy of a test identifying abused children. This might have disqualified some studies in which some parameters of diagnostic accuracy could be estimated. Finally, we were interested in all forms of child maltreatment and all types of tools and we have not specified a particular such as emergency departments. Depending on the context, some tools could not be applied: for example a test requiring a specific laboratory result if the laboratory exam cannot be performed routinely.
Greater transposition costs for letters compared with other kinds of familiar visual stimuli, is a natural consequence of models that code for letter-in-string position in a flexible manner. This is the case for models that employ open-bigram coding  [16, 18]  and spatial coding  [15]. Such flexibility in position coding is used in order to achieve a location-invariant (i. e. , independent of viewing position) sublexical representation of orthographic information that codes for position-in-word rather than position-on-retina. However, current implementations of these models would appear to not generate the amount of flexibility required to capture the transposition costs that occurred with 2-character separations. The results of simulations (The match scores were obtained from the MatchCalculator application (v.
The electro-oculogram (EOG) was recorded from 2 electrodes placed on the supraorbital ridge of the left eye and on the infraorbital ridge of the right eye. Muscle activity (EMG) was recorded from 2 electrodes attached to the chin. Electrode impedance was kept below 5 k. The electrophysiological data (EEG, EOG, and EMG) were continuously recorded via a BrainAmp system (Brain Products GmbH, Germany) with an amplification gain of 12,500, a high-pass filter of 0. 1 Hz and a sampling rate of 1000 Hz with an anti-aliasing low-pass filter  (Eichenlaub et al. , 2012 (Eichenlaub et al. , , 2013)).
SLEEP STAGE SCORING Sleep stages were scored off-line, visually by JBE according to standard criteria  (Silber et al. , 2007) , and automatically by ASEEGA software (http://aseegaonline. com/pub/index. html) (Berthomier et al. , 2007)  to derive hypnograms based on 30 s epochs and to determine the vigilance state (wake, rapid eyes movements sleep-REM sleep, sleep stage 1-N1, sleep stage 2-N2 and slow wave sleep-N3) that occurred for every stimulus delivered during sleep. Only the sleep periods for which JBE and ASEEGA scores agreed were considered for analysis. The percentage of agreement between JBE and ASEEGA respective scoring was 82. 9% with a kappa coefficient of 0. 762 (epoch-by-epoch comparison; epochs scored as artefacts were excluded from the statistical analysis,  Eichenlaub et al.
ACKNOWLEDGMENTS This work was supported by a grant awarded to  Dr. Perrine Ruby  from the  French "Agence Nationale pour la Recherche"  ( ANR-07-JCJC-0095 ). This work was performed within the framework of the  LABEX CELYA  ( ANR-10-LABX-0060 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). All authors (Ruby,  Blochet, Eichenlaub, Bertrand, Morlet, Bidet-Caulet ) have no conflicts of interest.
(v) Specific procedure for participant C. While participant B was instructed, participant C was installed in the experimental room (placing of EMG and SCR electrodes, see (vi) Data acquisition) and was told that he will watch two other participants (one after the other) watching different movies with non-social or emotional content. His task was to report on a sheet of paper, after each trial, what he thought the other participant had just seen between the words ''Start'' and ''End''. C was also told to remain silent throughout the experiment. (vi) Data acquisition. Using the acquisition system ADInstruments (ML870/Powerlab 8/30), we continuously recorded the EMG activity of C using Sensormedics 4 mm shielded Ag/AgCl miniature electrodes (Biopac Systems, Inc) (sample rate: 2 kHz; range: 20 mV; spatial resolution: 16 bits).
SCR activity per trial was obtained by extracting the maximal change from baseline level occurring between 1000 to 6500 ms after the video onset. Data for each trial was then natural-log transformed for both EMG and SCR activity. Finally, data were submitted, separately for each physiological measure, to repeated measures ANOVA using Emotion (joy vs. non-social vs. fear) as within-subject factors. Taking into account the sphericity assumption, we adjusted the degrees of freedom using the Greenhouse-Geisser correction where appropriate (e value). Finally, Bonferroni corrections were employed to account for multiple testing. Post-hoc comparisons were also performed for the analysis of simple main effects. (c) Experiment 2 (i) Participants. Three judges (2 female, 1 male, mean age 23. 3 y 62.
The research was supported by a  DGA-MRIS  scholarship and the  Agence National of Research (ANR) "Emotion(s) ,  Cognition, Comportement" 2011 program  ( ANR 11 EMCO 00902 ), as well as an  ANR-11-0001-02 PSL* , and an  ANR-10-LABX-0087. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Remerciements : Je remercie Françoise Decortis pour tous les échanges passés, présents et futurs sur ce thème ;  Pierre Rabardel  et  Cécile Barbier  pour leurs relectures toujours bienveillantes et leurs conseils ; Marie B pour son oeil de lynx. Je remercie également les financeurs et partenaires du projet ANR GAMME et plus particulièrement les animatrices et conservatrice du  Musée des Beaux-Arts de Rennes (Odile, Anne-Sophie, Carole et Laurence ) avec qui ce fut un plaisir de travailler au fil de ces trois années.
DATA RECORDING AND ANALYSIS The participants' movements were recorded using four Oqus infrared cameras (Qualisys System). Kinematics of reach-to-grasp and transport movements were measured by recording the 3D displacement of the five infrared reflective markers that were placed on the index (base and tip), the thumb (tip), and the wrist (scaphoid and pisiform) of the participant. One additional marker was placed on the dowel. Cameras were calibrated before each session, allowing the system to reach standard deviation accuracies smaller than 0. 2 mm, at a 200-Hz sampling rate. From these measures, tangential 3D instantaneous velocity profiles were calculated. All movements (Preparatory Action, Main Action, Repositioning Action) were characterized by two bell-shaped profiles (see Figure  2 ).
Hence, social uncertainty led participants to adopt a general competitive behavior, which has been previously described in paradigms that are, however, usually designed specifically to encourage direct competition  (Georgiou et al. , 2007). Effect of the social context on movement kinematics was also observed during actions for which the object was placed in totally predictive contexts: data showed that participants tended to have longer RTs and movement times, and performed more curved trajectories (e. g. , higher wrist displacement, APH) when they positioned the object for a forthcoming social action performed by a partner in 100% of the trials (Near-passive condition) rather than individually (Nearactive condition). Variations in movement kinematics are then observed when participants place the object knowing that the partner is going to grasp it and when they place the object knowing that they will personally have to grasp it.
F. and O. P. Pers. Obs. ). We chose to standardize our playbacks to a plausible duration, but to make them not too long to minimize disturbance and habituation. Therefore, to have comparable stimuli, we selected and edited 15 seconds of each recording for each type of playback using the software program Audacity 1. 2. 6. All the playbacks were then transferred to an iPod Classic (Apple Inc. , Cupertino, California) in the AAC format and played through a powered portable speaker (Megavox pro 6000, Anchor, Carlsbad, California). All playbacks were calibrated by ear at levels perceived by the experimenters as 'natural' for each source species in order to be perceived as realistic by the animals; this method has been used in several playback experiments done in the field (e. g. ,  [46, 47] ). The sound levels of the playbacks of the lions' roars and impalas' territorial vocalizations were quite similar, while the control playbacks had lower intensities.
Data Analyses To study the responses of the focal female impala to the different stimuli, we extracted from the video sequences the proportions of time spent by females in all vigilance, and in exclusive vigilance and vigilance while chewing separately, during the pre-and the post-playback periods. We also quantified the numbers of bites taken per minute (bite rate), and the numbers of steps per minute (step rate) performed by the focal animals during both periods. To test the effects of the different stimuli on the vigilance, foraging and movements of female impalas, we used linear mixed-effects models for paired samples with (1) the total proportion of time spent in vigilance, (2) the bite rate, and (3) the step rate as the dependent variables and the time periods (pre-playback and post-playback), the types of playback played (Control, Lion and Impala) and their two-way interactions, as independent variables, and individual identity (to pair the samples) within group identity as two nested random factors.
Acknowledgements The Director General of the  Zimbabwe Parks and Wildlife Management Authority  is acknowledged for providing us the opportunity to carry out this research. We are particularly grateful to  Dr. H. Madzikanda ,  Mr. A. Musakwa ,  Mr. G. Mtare  and  Mr Edwin Makuwe. We would also like to thank  Martin Muzamba ,  Stéphanie Périquet ,  Eunice Chapanda ,  Nobuhle Ndlovu  and  Cynthia Mapendere  for assistance in the field as well as the whole HERD team.
This study has been part of the  HERD  project ( Hwange Environmental Research Development (http://www. za-hwange. org/)),funded by the  French "Ministère des Affaires Etrangères " (www. diplomatie. gouv. fr/), the  "Ambassade de France au Zimbabwe " (http://www. ambafrance-zw. org/),the  CIRAD  (www. cirad. fr/), the  CNRS  (www. cnrs. fr/)and the  Agence National de la Recherche  (www. agence-nationale-recherche. fr/): FEAR  project ( ANR-08-BLAN-0022 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.