dataesr/scientific-paragraphs-categorization huggingface dataset:
In the dataset used to test whether the engagement in mutual exits depends on species and activity type (model 5), we were unable to include exit types that; a) were cases of the pilot study for which no exit gaze categories had been coded (N = 28), b) where no gaze category could be coded due to the absence of an exit (N = 142), and c) where gaze categories could not be determined because one of the two partners was not sufficiently visible to assess whether or not mutual gaze existed (N = 42). This meant that out of a total dataset of N = 1,195 interaction endings (see Table  1  results), we could analyze mutual exits from a final dataset of N = 983 exits (Table  S2 ).
ACKNOWLEDGMENTS We thank  Adrian Baumeyer  at  Basel Zoo ,  Dean Gibson ,  Mike Bates , and  Kim Livingstone  at the  San Diego Zoo ,  Emmanuel Le Grelle , and the  Conservatoire pour la Conservation des Primates at La Vallee des Singes ,  Antoine Joris , and  Marielle Vandenbunder-Beltrame  at  La Reserve Africaine de Sigean , and all the dedicated animal keepers for support during study periods. We are also grateful for the research assistance of  Cindy Maslarova. Research was funded by  Swiss National Science Foundation  (Grant No. CR31I3_166331  awarded to A. B. and K. Z. ).
, 2019; Petelle, Martin & Blumstein, 2019; Santicchia et al. , 2020). Research on animal personality aims to understand why some individuals respond consistently in a different manner compared to others in a given context. Personality traits can be quantified for each individual by carrying out specific tests. For example, the largely used open field tests and novel-object tests provide data on exploration behavior, activity or boldness  (Dammhahn, 2012; Dammhahn & Almeling, 2012; Highcock & Carter, 2014; Perals et al. , 2017; Savidge & Bales, 2020) , but other experiments like handling  (Verdolin & Harper, 2013)  and tonic immobility  (Erhard & Mendl, 1999)  are used to quantify aggressiveness in various taxa. Aggression and sociability, as personality traits, have also been addressed using mirror tests; for example, in yellow-bellied marmots (Marmota flaviventris;  Svendsen & Armitage, 1973; Armitage, 1986)  or in rainbow kribs (Pelvicachromis pulcher;  Scherer, Buck & Schuett, 2016) , making MIS a valuable tool in this area of investigation as well.
For the T context, a live conspecific from the same age and sex class was in an identical box adjacent to the subject's box. Individuals that were tested together were not familiar to each other, meaning that they did not live in the same aviary. This context (i. e. , conspecific) allowed us to collect data on two animals at the same time to maximize data collection. We selected a sample including pairs of individuals of same sex, age and corpulence for this purpose. We recorded behavioral tests using a digital camera (SONY Handycam DCR-SR75) with the infrared (IR) mode activated, as we filmed in the dark. We added an IR spotlight to gain visibility. We proceeded to a blind analysis (meaning that the scorer did not know the identity or sex of the individual) to quantify behaviors using the software VLC media player.
Statistical analysis We first conducted a principal component analysis (PCA) on the eight variables and subsequently conducted a VARIMAX rotation on the recorded behaviors to reduce the number of behavioral variables to a smaller number of independent variables  (Budaev, 2010). The first three principal components -named PC1, PC2 and PC3-accounted for 47. 3%, 13. 4% and 27. 0% of the behavioral variance, respectively. We kept only three components as jointly they explained more than 80% of the total variability. We used the acp. varimax() function provided by ''psych'' package  (Revelle, 2019)  with R software version 3. 6. 0 (Rstudio Version 1. 1. 456). Subsequently, we estimated principal component repeatability as an intraclass correlation coefficient, which is the ratio of the interindividual variance to the sum of inter-and intraindividual variances  (Nakagawa & Schielzeth, 2010).
For that purpose, we ran a linear mixed effects model, with only individual as random effect and no fixed effect. We used the rpt() function in the ''rptR package''  (Nakagawa & Schielzeth, 2010). We used a non-parametric two-way ANOVA with context as a repeated factor and sex as an independent factor. We used the package nparLD and the f1. ld. f1 function  (Noguchi et al. , 2012). When the ANOVA was significant, we conducted a Friedman test to detect between which test contexts a difference was observed (for both sexes, and for females and males separately) followed by a post-hoc correction with the pgirmess package  (Giraudoux et al. , 2018)  and the friedmanmc function (proceeding to pairwise comparison followed by the observed difference between the rank sums (Sidney, 1957)). We used α = 0.
| Material and measures 2. 2. 1 | Cardiac vagal activity indexed via heart rate variability CVA was indexed via RMSSD, calculated from HRV. HRV was measured via an electrocardiography (ECG) device (Faros 180°, Bittium, Kuopio, Finland), at a sampling rate of 500 Hz. We used two disposable ECG pregelled electrodes (Ambu L-00-S/25, Ambu GmbH, Bad Nauheim, Germany). The negative electrode was placed on the right infraclavicular fossa (just below the right clavicle), whereas the positive electrode was placed on the left side of the chest, below the pectoral muscle in the left anterior axillary line. The Kubios software (University of Eastern Finland, Kuopio, Finland) was used to extract RMSSD and the other HRV parameters. The ECG signal was visually inspected for artifacts and corrected manually if needed (<0.
001% of the total heartbeats)  (Laborde, Mosley, et al. , 2017). In order to provide an overview of the different HRV parameters, following  Laborde, Mosley, et al. (2017) , we also extracted heart rate and the standard deviation of the NN interval (SDNN) for the time domain and the frequency domain (fast Fourier transform), low frequency (LF: 0. 04-0. 15 Hz), high frequency (HF: 0. 15-0. 40 Hz), and the LF/HF ratio. Finally, we also extracted the respiratory frequency from the ECG signal, based on the ECG-derived respiration algorithm of Kubios  (Tarvainen et al. , 2014). 2. 2. 2 | Slow-paced breathing with and without biofeedback A 5-min video showing a ball moving up and down at the rate of 6 cpm, based on the EZ-Air software (Thought Technology Ltd. , Montreal, Canada), served as a breathing pacer.
An additional nine children refused to participate in the training session (mock-scanner) to familiarize children with the fMRI apparatus, resulting in 77 children participating in the passive fMRI experiment at kindergarten age (T1). Data from one child could not be analyzed due to technical problems during the scanning session and 22 children had to be excluded due to excessive movement artifacts. Consequently, the fMRI sample consisted of 54 children, 16 of these children were at family risk of developing dyslexia; that is, at least one relative was affected by developmental dyslexia as reported in a parental questionnaire  (Moll & Landerl, 2010). The remaining 38 children had neither first-nor second-degree relatives with developmental dyslexia. In a second session at T1, children were also tested for cognitivelinguistic prereading skills.
To assess literacy skills, all children were invited back at the end of the second year of primary school (T2). At this point, three children from the original sample of 77 children withdrew their participation. Two children had to repeat the second grade and were retested 1 year later. Forty-eight of these children had successfully completed the fMRI session at kindergarten age (T1). For these children, we were able to link the neural processing before onset of literacy to actual reading performance. All following results are based on the fMRI sample of 54 (concurrent analysis of cognitive-linguistic prereading skills) and 48 (longitudinal prediction of reading) children. A summary of the demographic and psychometric data is given in Table  1.
In any case, when asked afterwards whether they noticed the mascot, all of these children confirmed that they had. As such, the data from those children was kept for further analyses. Catch trials were followed by a jittered fixation cross (300-600 ms) and then replaced by the first stimulus. Visual stimuli were shown for 500 ms and auditory stimuli were presented for 500-600 ms. Each stimulus was followed by an inter-stimulus interval of 200 ms (fixation cross). Three to five nullevents were included in each run to allow the blood-oxygenation-level dependent (BOLD) signal to return to baseline (2-14 s, 26 s per run). Each run took about 136 s and was followed by a short break to improve compliance and maintain attention of the children (see Figure  S1  for a schematic overview of the fMRI design).
| fMRI data analyses Thus, we decided to run a more focal region of interest (ROI) analysis to capture more subtle effects of the brain-behavior relationship. Literature-based ROI analysis was computed for the bilateral fusiform, MTG, and the STG; the left IOG, and the SPL. All ROIs were anatomically defined using the aal atlas of the wfupickatlas  (Maldjian, Laurienti, & Burdette, 2004; Maldjian, Laurienti, Burdette, & Kraft, 2003; Tzourio-Mazoyer et al. , 2002). The significance threshold for the ROI analysis was set to p <. 001 (uncorrected) and k > 10 voxels. To account for the number of regression models, we report the results at a stricter threshold of p corr <. 0005 for the concurrent brain-behavior analysis (1 fMRI contrast Â 2 cognitive-linguistic skills). For the longitudinal prediction of reading fluency, no further correction was required (1 fMRI contrast Â 1 reading skill).
| fMRI stimuli During the passive fMRI paradigm, five different sensory stimuli were presented, targeting visual symbol and spoken language processing. Auditory stimuli consisted of spoken words (N = 60) generated with MAC OSX voice Anna (female). Visual stimuli encompassed horizontal checkerboards (N = 15), houses (N = 60), faces (N = 60), and written words (N = 60). To match the visual angle across conditions, all stimuli were presented in gray scale and uniform size in the middle of the screen. Faces (male and female) were taken from the Karolinska Directed Emotional Faces (KDEFF; Lundqvist, Flykt, & Öhman, 1998) and Radboud Face Database (RaFD; Langer et al. , 2010) databases. All faces had a neutral expression, forward gaze and were Caucasian. Visually and auditory presented words were highly frequent (absolute type frequency: spoken words range = 61-1971, M = 233, SD = 305; written words range: 60-1,355, M = 180, SD = 179) mono-syllabic nouns of four to six letters and were taken from the childLex database, which provides age-specific norms for children (www.
ACKNOWLEDGMENTS We thank all research assistants of the  Department of Experimental and Neurocognitive Psychology at the Freie Universität Berlin  and the members of the  CCNB  for help with the experiment. Grant information: J. Z. was supported by the  Alexander-von-Humboldt Stiftung  and the  Institute of Language, Communication  and the  Brain  ( ANR-16-CONV-0002 ). The study was supported by the  German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF ; grants  FKZ 01GJ1010A  and  01GJ1404. Open access funding enabled and organized by Projekt DEAL.
Funding information  FKZ, Grant/Award Numbers :  01GJ1404 ,  01GJ1010A ;  Bundesministerium für Bildung und Forschung ;  German Federal Ministry of Education and Research ;  Institute of Language, Communication  and the  Brain, Grant/Award Number :  ANR-16-CONV-0002 ;  Alexandervon-Humboldt Stiftung; Freie Universität.
2. 3 3 | fMRI stimuli During the passive fMRI paradigm, five different sensory stimuli were presented, targeting visual symbol and spoken language processing. Auditory stimuli consisted of spoken words (N = 60) generated with MAC OSX voice Anna (female). Visual stimuli encompassed horizontal checkerboards (N = 15), houses (N = 60), faces (N = 60), and written words (N = 60). To match the visual angle across conditions, all stimuli were presented in gray scale and uniform size in the middle of the screen. Faces (male and female) were taken from the Karolinska Directed Emotional Faces (KDEFF; Lundqvist, Flykt, & Öhman, 1998) and Radboud Face Database (RaFD; Langer et al. , 2010) databases. All faces had a neutral expression, forward gaze and were Caucasian. Visually and auditory presented words were highly frequent (absolute type frequency: spoken words range = 61-1971, M = 233, SD = 305; written words range: 60-1,355, M = 180, SD = 179) mono-syllabic nouns of four to six letters and were taken from the childLex database, which provides age-specific norms for children (www.
As noted above (see hypothesis iii), we did not intend to statistically compare the strength of these associations across sites, which requires a different methodology  [29]. Rather, we explored whether or not these associations were statistically significant for each site. A significance level of 5% (two-tailed) was set for all the above analyses, performed using the Statistical Package for Social Science version 25. 0 (SPSS, Inc. , Chicago, IL, USA).
The fully adjusted analyses took into account all selected covariates. In each model, to account for missing information on covariates, namely psychiatric disease history, we used the multiple imputation-by-chained equation method. Briefly, we performed 10 imputations and averaged the variable estimates to produce a mean estimate. Finally, we verified that the relative efficiency of the imputation for each variable was greater than 95%. All analyses were performed using SAS version 9. 4. Two-sided P values < 0. 05 were considered statistically significant.
Acknowledgements The i-Share and CONFINS team are currently supported by an unrestricted grant from the  Nouvelle-Aquitaine Regional Council (Conseil Regional Nouvelle-Aquitaine)  (Grant No  4370420 ) and by the  Bordeaux 'Initiatives d' excellence' (IdEx) program  of the  University of Bordeaux  ( ANR-10-IDEX-03-02 ). M. Macalli was supported by a PhD grant of the  Nouvelle-Aquitaine Regional Council  (Grant No  17 EURE-0019 ) and by the  PhD Digital Public Health Graduate School Program  supported within the framework of the  PIA3 (Investment for the Future ) (Project Reference:  17-EURE-0019 ). This research was supported by the  Agence Nationale de la recherche (ANR)  (Project  N°ANR-21-COVR-0029-01 ), the  Direction Générale de l'Offre de Soins (DGOS) , as part of a research program on " Suicide and its prevention 2020 ", organized by the  Direction de la Recherche, des Études, de l'Évaluation et des Statistiques (DREES)  as part of the work of the  Observatoire National du Suicide (ONS)  (Grant No  2103306249 ).
An inertial measurement unit including an accelerometer and a gyroscope, attached to the chest belt, is used to record movement and sleep positions in order to extract movement features. Next, a linear discriminant (LD) classifier was used for the automated classification of sleep stages for Wake, REM, light sleep and deep sleep, compared to the PSG. For a more detailed overview of sleep monitoring systems, readers may refer to the review paper  12  published by our team. In literature, most research adopts supervised machine learning methods that usually require large amounts of learning data to train the classifier and computation to implement the model. However, some works adopt unsupervised methods such as k-means clustering to achieve a sleep stage classification. It is generally based on signals directly related to sleep stages such as the EEG signal which is very intrusive and not easy to collect in home environment.
In this study, we try to use the proposed methods to detect the time of falling asleep and waking up, as well as the time spent in each sleep stage during sleep. This information will be used as important reference provided to users or medical staff to determine whether the sleep is healthy. We propose a threshold-based method and a k-means clustering method using only the acceleration data from a wrist-worn 3-axis accelerometer to achieve first detection of the falling asleep and waking up moments and a classification of sleep stages into four classes: awake, light sleep, deep sleep and REM. The results of the proposed methods will be compared to "Fitbit" and "Withings" devices, namely the "Fitbit Charge 2" and the "Withings Sleep Analyzer". Both methods require a relatively smaller amount of computation  13  which could make the implementation of the algorithm easier and more efficient for real-time applications.
The sensing device and settings We use a smart module (Fig. 1 ) already designed in our team as a detection device. The smart module is an embedded system powered by a button battery (3V) whose basic components are: a NRF51822 microcontroller containing a 32-bit ARM Cortex M0 processor and a 256kB flash memory, a 2MB non-volatile FRAM memory for data backup during sleep, a low-power tri-axial accelerometer ADXL362. Programs are written in C using Keil μVision. ADXL362 accelerometer on smart module is adopted to acquire acceleration data in the experiment. The parameters of ADXL362 are: -2g ~2g measurement range, 12. 5 Hz output data rate (ODR) and 8 bits output resolution. Acceleration data is collected every second and stored in FRAM. A study showed that the average activity of the dominant wrist was higher than for the non-dominant wrists for all behaviors  14.
K-means clustering As a classical machine learning method, k-means clustering  22  has been widely used in fields as diverse as image segmentation, data compression, wireless sensor network routing, data mining, etc. It is an efficient method for automatically classifying a dataset into k-groups based on the similarity of the features of each dataset. For the k-means clustering algorithm, the selection of the initial cluster centers could significantly affect the final clustering result. As the initial clustering centers are randomly selected, the clustering result also has some uncertainty. During the experiments, we found that the final clustering results using randomly selected cluster centers usually do not change much, but in few cases the final clustering results were very far from each other, as also reported in  23.
Experimental setup One adult male without subjective sleep complaints was recruited for the tests. He is 28 years old and has a BMI (body mass index) of 18. 3. A total of 10 overnights sleep data acquired in real conditions were tested using four sleep stage classification methods: two commercial products including "Fitbit" and "Withings", two proposed methods including the "Threshold" method and "MK-means" method. Both the "Threshold" method and the "MKmeans" method are implemented solely based on wrist movement data. The proposed algorithms are all implemented on the same computer with "Intel i7-2600 CPU @ 3. 40GHz, 8GB RAM" on "MATLAB R2011b".
Conclusion In this study, we propose two sleep monitoring algorithms based only on wrist movements acquired by a 3-axis accelerometer. The proposed algorithms include a "Threshold" method and a "MK-means" method. The "Threshold method" uses three thresholds to achieve falling asleep and waking up detection and provides classification into four sleep stages ("awake", "light sleep", "deep sleep" and "REM"). The "MK-means" method also achieves classification into four sleep stages. The commercial products "Fitbit" and "Withings" are used as a reference device to validate the proposed methods. Experimental data are acquired from 10 overnights sleep of a volunteer. For the detection of falling asleep and waking up time, the proposed method shows a deviation of 5. 6±2. 4 mins and 10. 3±12. 4 mins respectively compared to the reference.
The milk in the cup under the unrewarded bowl was refreshed between piglets. On A1 and A2 of the acquisition stage, piglets were allowed to go back and forth until they reached the rewarded bowl. The maximum time allowed per trial was 5 min. From A3 to A6 of the acquisition stage and from R1 to R3 of the reversal stage, piglets were no longer allowed to travel back and forth. Four options existed to stop a trial: (1) to select the right arm and drink milk for 10 s, (2) to select the right arm but turn back before reaching the rewarded bowl, (3) to select the wrong arm (piglet allowed to reach the end to check the bowl is empty), and (4) to do not choose any arm for the 60 s after the start of the trial. Video recordings were analyzed using the XP Observer 14 software (Noldus, The Netherlands) by a single trained observer blind to treatment.
1. 39. 3 standard operating procedure (SOP) designed for MiSeq data  17  ; the mothur MiSeq SOP (version 132) was accessed in August 2018. The SILVA-based bacterial reference alignment was used to identify the taxonomy of OTUs at a cluster cutoff of 97% sequence identity. Measures of richness, evenness and diversity were determined in mothur. Richness can be defined as the number of OTUs observed per sample, evenness represents the uniformity of the distribution of OTUs amongst a community across the multiple observed OTUs, while α-diversity is a concept combining both richness and evenness  18. Richness and α-diversity were determined through the coverage, the number of OTUs observed, the Chao1, the ACE, the Shannon, the Simpson, the inverse Simpson estimators and β-diversity metrics were calculated using the Yue and Clayton's theta metric (thetaYC, as implemented in mothur) distances  19.
Statistical analysis. Statistical analyses were performed with the software R 3. 4. 3  20. The variables of time to drink during the habituation to the bowl of TMAZE, the BARR test variables, as well as the mean times to make a choice and to touch the rewarded bowl were normalized by logarithmic transformation before statistical analysis. Other variables were normal without transformation. In all the statistical analysis, p < 0. 05 was considered statistically significant and 0. 05 ≤ p < 0. 1 as a trend. Linear model with the treatment as fixed effect was used to test the time for the piglets to touch the bowl the first time during habituation to the TMAZE test, as well as for the variables of the SOR test. For the familiarization phase of the SOR test, the fixed effect was also replaced by the object type and the arena of the test.
A mixed effects model for repeated measures with the treatment, the trial or the day and the interaction between the two factors as fixed effects and the animal being included as random effects were used to test the effect of habituation variables to isolation and the TMAZE bowl, as well as the barrier test and the TMAZE test variables. The statistical unit for the previous traits was the animal. These analyses were done with the function lmer from the R package "lme4" 1. 1-7. The emmeans function from the R package "emmeans" 1. 2-2 was used to perform pairwise comparisons with the FDR correction when interactions were significant (p < 0. 05). A generalized linear model (family: Poisson, link: log) was used to analyze the number of trials needed to succeed in the reversal stage of the T-maze, with the treatment as fixed effect and the side of the rewarded arm as a random effect.
The reception of each message was notified with an auditory warning to avoid any misses while the Pilot was focused on flying the aircraft. Then the Pilot had to click on one of the following possible responses "Yes visual", "No / No visual", "Reword lost" or "OK Copy" to indicate to the JTAC whether he/she understood the instructions or not. These reply-messages were then notified to the JTAC with an auditory warning. JTAC and Pilots were equipped with in-ear headphones. The aerofly fs2 software was used to simulate the DR400 aircraft, displayed on three 19-inch screens. Pilots had a joystick to control the roll, pitch, yaw and thrust of the simulator. Lastly, a JTAC-Bot and Pilot-Bot were implemented using basic AI techniques. These two artificial agents were programmed to behave like real JTACs or real Pilots (i.