dataesr/scientific-paragraphs-categorization huggingface dataset:
Inter-subject: For inter-subject classification in ecological design, a particular leave-n-out cross validation was used, with n = 2. In this procedure, each time only the last 2 blocks of mental workload data (one Low and one High) of 2 participants were selected for testing. The remaining 11 participants along with the initial 2 blocks of mental workload data of 2 testing participants were used for training. Therefore, the combination of training data is used for parameters optimization (grid search) with 5 cross-validations, and following the classifier was trained, and finally tested with the 2 selected participants separately.
Statistical Analysis Statistical analyses were performed on the subjective, behavioral, physiological and classification data using the Statistica software, in order to assess the impact of the experimental conditions and used features and techniques. Paired t-tests and Wilcoxon tests were used for subjective and behavioral data analysis, and for ECG features depending on the respect or not of the data distribution normality assumption. Regarding the EEG data analysis, in order to decrease the dimension of the dataset, 20 electrodes that had a highest magnitude difference between high and low mental workload conditions were kept: Fp1, AF3, F7, F3, FC5, T7, CP5, P7, PO3, O1, Oz, O2, PO4, P8, T8, FC6, F4, F8, AF4, and Fp2. A repeated measure n-way ANOVA was applied on the EEG power features (i.
This could account for the lack of correlation between CM and personality traits. Moreover, we did not compare our results to a control group of depressed patients showing no resistance to treatment, in order to assess whether  mediation through self-esteem is closely related to TRD. In addition to this, the cut-off age we used for geriatric depression was relatively young (60 years of age and above). However, this cut-off is the same as that used in other studies  [1, 6, 7]. In addition, the diagnoses of EOD and LOD were determined retrospectively. This may have introduced a bias and may explain the lack of data in relation to the first major depressive episode. There could also be a recall bias regarding CTQ. Indeed, a recall bias, or greater likelihood of reporting exposure in participants with MDD, has been highlighted due to negative bias in autobiographical memory  [41].
This research was funded by the  Foundation FondaMental, Institut National de la Santeet de la Recherche Medicale (INSERM) ,  AP-HP ,  Astra Zeneca , and by the  Investissements d'Avenir program  managed by the  ANR  under reference  ANR-11-IDEX-0004-02. This funding source had no role in the study design, data Methods from a longitudinal cohort ( FACE-DR) of the French Network of Expert TRD Centres. Results  Antoine Yrondi  received speaker's honoraria (AstraZeneca,  Janssen ,  Lundbeck ,  Otsuka, Servier) , and carried out clinical studies in relation to the development of a medicine ( Janssen, Lundbeck)  unrelated to this work. Christophe Arbus received honoraria (Janssen, Lundbeck, Otsuka) Lilly ,  Astra-Zeneca ,  Janssen ,  Servier  and  Lundbeck. Jean Baptiste Genty  received speaker's honoraria from Servier.
Statistical Analysis All data were analyzed in SAS 9. 4 software (Copyright (©) 2002-2012 by SAS Institute Inc. , Cary, NC, USA). The significance threshold was classically fixed at 5% for all the following statistical analyses. First, preliminary statistical tests were performed to evaluate the effect of the dog's activity (PD vs. AD) on age and sex and determinate if these last should be included in the statistical analysis. For the age variable, comparison between the two groups (AD and PD) was performed with the Wilcoxon/Mann-Whitney test with the NPAR1WAY procedure. Conditions of normality and heteroscedasticity were tested respectively with the UNIVARIATE and TTEST procedures and have not been checked. For sex and activity effects, their association was studied with the Chi-Square test with the FREQ procedure.
Then, for PRL, 5-HT, fOT, and tOT, a multiple linear regression was performed using the GLM procedure. Effects of activity (AD and PD), sex (male and female), age, and the three corresponding interactions were tested in the complete model. Conditions of normality of residues and homoscedasticity were tested using respectively the UNIVARIATE and the GPLOT procedures. For the age effect (quantitative variable), the linearity assumption was verified with the GPLOT procedure. For fOT and tOT, these conditions were verified. For PRL and 5-HT, normality of residues was not verified; a Box-Cox transformation was applied to data using the TRANSREG procedure, which allowed to verify the normality and then the homoscedasticity. The three interactions were removed of the model because they were not significant.
Authors have shown considerable interbreed variation in canine serum 5-HT concentrations  [25, 81, 82]. The population of AD we included in this study all belong to Labrador-related breeds. Therefore, breed and selection features may also fully or partly explain our descriptive data observations. The serum 5-HT concentrations found in PD were similar to those reported by previous studies  [25, 69, 82]  but are higher than those described by others [79-81]. These differences can arise from the variable characteristics (other than the breed and the age) of the dog population used in each study: shelter dogs, obese dogs, dogs with valvular diseases, etc. They can also arise from different preanalytical factors and analytical procedures used among these studies  [80]. For instance, the antibodies' affinity and specificity are crucial features in antibody-based assays, and they can differ across the available ELISA commercial kits, resulting in measurement variability.
Acknowledgments: We thank  Muriel Jochem  and the  Frédéric Gaillanne Foundation  with all its members for their support and help all along the trial. We are very grateful to  Eva Teruel  and  Estelle Descout  from the IRSEA Data Management and  Statistics Service  for their help in the statistical analyses following the reviewing processes and we also sincerely thank the reviewers for their useful suggestions. We would also like to thank all the foster families and all the pet dogs' owners involved in this study for their help, their availability, and their interest in this trial.
This controlled for potential changes in physical practice durations due to the fatigue elicited by the protocol. For recovery trials, we calculated isochrony using the physical practice trial of the walking sequence as the reference for the subsequent MI trial. closer isochrony data approached 0, the closer physical practice and MI durations. The isochrony index was used as an index of the capacity to reproduce the temporal organization of the movement during MI. Isochrony index = (Physical practice duration -MI duration) ( Physical practice duration-MI duration 2 ) * 100.
Electromyography recordings To assess muscle function during the physical practice trials of the walking sequence, we continuously recorded the electromyograms (EMG) from three superficial muscles of the quadriceps: i) Rectus Femoris, ii) Vastus Medialis and iii) Vastus Lateralis. After shaving and cleaning the skin with alcohol, EMG wireless surface silver electrodes (Patented parallel-bar technology, sampling rate 1000 Hz) were positioned based on the "Surface Electromyography for the Non-Invashive Assessment of Muscles" (SENIAM) recommendations  [37]. We used the TrignoTM Wireless EMG System© (2014, Delsys Incorporated) for data acquisition. The EMG signal was rectified offline and filtered using a bandpass of 20-450 Hz and a 0. 5 s sliding time window (Butterworth filter, 4th order). A Fourier fast transform was eventually applied to calculate the root mean square for each trial (EMG RMS ).
Power and sample size considerations We used R and the package pwr to determine the sample size from general linear modeling hypothesis testing  [46]. Due to the repeated measures of the design, for a type 1 error rate of 5 % a n = 14 sample size yielded a statistical power of p 1-β = 0. 78 to detect small effect sizes (i. e. corresponding to 5 % of explained variation) for interaction effects tested by the linear mixed effects analysis of isochrony and vividness scores. The statistical power > 0. 90 for medium and large effect sizes, i. e. corresponding to 10 % of explained variation and higher.
Behavioral data Raw data from mental chronometry measures are provided in Table  2. The linear mixed effects analysis carried on isochrony indexes before and after the fatiguing exercise revealed a main effect of TEST (Rp 2 = 0. 15, χ 2 (1) = 6. 71, p < 0. 01), while other main and interaction effects did not reach the statistical significance threshold. The POST-FATIGUE isochrony index values were closer from the 0 reference value (-0. 75 %, 95 % CI  [-13. 50, 12.
[12]  used a pairwise ranking approach to avoid calibration problems, and a total of 321,684 pairs were used on the ChaLearn Apparent Personality Trait Dataset. Based on Deep Learning, Ventura et al. [13]  used the CNN architecture proposed by Zhang et al. [14] , focusing only on the video modality to estimate the interview. The addition of multiple sources of information has been shown to be a viable approach by merging audio and visual modalities. Zhang et al. [14]  proposed a Deep Bimodal Regression (DBR) framework in which they modify the traditional convolutional neural networks to utilize important visual cues. While Subramaniam et al. [15]  proposed two endto-end trained deep learning models using facial images and audio features for first impression recognition. They trained their network with temporally ordered audio and novel stochastic visual features from a few frames, taking care to avoid overfitting.
[19]  proposed audio-visual deep learning-based architectures. They trained two deep residual networks on the ChaLearn dataset with 17-layers each for audio and visual streams, then an audio-visual stream of a fully connected layer outputting the Big-Five features. Finally, they used a linear regression model to explain the interview decision based on the predictions of these features. Kaya et al. [20]  developed a multimodal approach that combines face and audio features and feeds them into modality-specific regressors to predict revealed personality features using an ensemble of decision trees. They also used another decision tree, combined with a rule-based algorithm, to explain interview decisions. Several teams obtained competitive results with different strategies, the FDMB team used frame differences and Local Phase Quantization descriptors at several fixed image regions with the support vector regression (SVR) technique to predict the interview variable and the Big-Five traits  [10].
Deep features Deep features are generally extracted from one of the last layers of a convolutional neural network (CNN). In our work, we used the VGG16 architecture  [29]  trained on VGGFace dataset  [30] , ResNet-50  [31]  trained on VGGFace2 dataset  [32] , SE-ResNet-50  [33]  trained on VGGFace2 dataset, Additive Angular Margin Loss (ArcFace)  [34]  based on SE-ResNet50 and trained on MS-Celeb-1M  [35]  dataset and MobileFacenet  [36]  which also trained on MS-Celeb-1M dataset. VGG16 is a large CNN network with 16 learnable layers which has about 138 million parameters where we extract the deep features from the FC7 linear layer that produces 4096 features. ResNet-50 is a convolutional neural network based on a residual learning framework, where layers within a network are reformulated to learn a residual mapping rather than the desired unknown mapping between the inputs and outputs.
ResNet-50 produces 2048 features from the global average pooling layer. SE-ResNet-50 has a similar architecture to ResNet50 and it produces the same number of features, however, it uses Squeeze and Excitation modules instead of residual blocks. ArcFace is a convolutional neural network that benefits from ResNet residual blocks and uses discriminative loss functions that directly optimize the parameters. The features of this network are extracted from the last linear layer in which 512 features are produced. MobileFacenet is a small convolutional neural network with less than 1 million parameters, its architecture is derived from MobileNetV2  [37]  and uses ArcFace loss function. It produces the same number of features as ArcFace.
Feature selection For feature selection, we used Neighborhood Component Analysis (NCA), which is a non-parametric learning method for estimating the feature weights. NCA sorts the features according to their relevance by performing feature ranking with regularization to learn feature weights, minimizing an objective function that measures the average leave-one-out classification or regression loss over the training data  [38]. Based on NCA results we aimed to identify the best subset of features. NCA ranked features were used in the selection of the most relevant features for each personality trait separately. Thus, for each personality trait, we select the best features subset based on these ranked feature weights. The selected subset of features is determined by taking all features with high weights until reaching the mode of the histogram of the weights.
Experimental settings In this work, we used the dataset ChaLearn LAP 2016 APA  [6]. This dataset consists of 10,000 short clip video sequences with an average duration of 15 seconds each, the resolution of the videos varies between 682x406 and 720x1280, and the number of frames varies between [49-456]. These video sequences were retrieved from YouTube and include more than 3,000 subjects. The subjects spoke English in front of a camera. The subjects depicted in the clips have different ages, genders, nationalities, and ethnicities. The competition consisted of two phases, a validation phase and a testing phase. In the first phase, participants had access to 6,000 labeled video sequences, representing 60% of the dataset as a training set, and 2,000 unlabeled videos, representing 20% as a validation set.
In the second phase, participants had access to the labeling of the previous validation set, and access to an additional 2,000 unlabeled videos as a test set. For each video in the dataset, the ground truth labels for the Big-Five personality traits and interview variable were given by real values that fit the range [0, 1]. We used mean accuracy to evaluate performance for each personality trait and the interview variable. This indicator was used in the previous challenge  [6]. This is given by P=100(1-MAE), where MAE is the mean absolute error over the tested video sequences. To identify the optimal set of features with PML-COV, we apply other statistical descriptors besides the mean to all feature vectors of each video, such as the standard deviation, skewness, and kurtosis. Table  2  shows the results of these statistical descriptors and the combination of the top two descriptors (Mean & RMS) in terms of accuracy on both validation and test subsets.
The present study was conducted on two of the eight social groups, called Michelin & Utara hereafter, for which we had the most detailed dataset. In March 2020, Michelin group counted 136 individuals in total, while Utara group counted 33 individuals (see Appendix A for the demographic composition of the study groups). However, the social data included in this study concern female network only, including 28 intact females and 13 sterilized females in Michelin, and 6 intact females and one sterilized female in Utara (Appendix B). All studied females were sexually mature thus likely to attempt to breed, and most of them experienced different nursing conditions over the course of the study (Appendix B).
Behavioral Data We used 15-min focal sampling  [58] , with the Animal Behaviour Pro V. 1. 2. app  [59]  to collect a total of 202 h on the 41 females of the Michelin group (mean of 4. 9 h/ind. ) and 30 h of focal observation on the 7 females of the Utara group (mean of 4. 3 h/ind. ). Focal samples were done semi-randomly, i. e. , based on the first-seen individual, independently from its activity, while giving priority to individuals having the lowest cumulative data. We collected duration and direction of grooming interactions as well as the identity of the focal interactor(s). To test our hypotheses related to nursing condition while controlling for the confounding effect of hierarchical rank  [3, [60] [61] [62] , we calculated the rank on weighted matrices generated from unidirectional agonistic interactions (cf.
"winner/looser") with the modified David's' score  [63, 64]  using the steepness R package  [65]. We then standardized the hierarchical rank with the group size. We supplemented focal sampling with scan samples  [58]  every 5 min to collect association data (frequencies) on contact-and ≤5 m proximity neighbors. Proximity scans done in food provisioning areas were excluded from the dataset to limit bias related to food competition. We got a total of 3099 proximity scans in Michelin group (mean of 76 scans/ind. ) and 492 proximity scans in Utara group (mean of 70 scans/ind. ).
Social Network Data To build the female proximity and grooming networks, we compiled monthly undirected social matrices based on proximity frequencies, and directed social matrices based on duration of grooming interactions, respectively. We removed from the matrices all the non-focal individuals  [66]  and focal individuals who disappeared over the study. To take into account variation in sampling effort between focal individuals, we weighted the matrices with the total observation duration of each individual following Farine and Whitehead  [26]. From the monthly matrices, we constructed the ego matrices considering only interactions with direct female social partners or proximity associations within 5 m around the ego individual during the whole study period  [30]  for each focal individual according to its nursing condition.
All statistics were performed using R 4. 0. 3. software  [72]  and we checked for assumptions of model residual normality using Kolmogorov-Smirnov's test. When the residual normality of the model was not respected (i. e. , with Laplacian centrality and Strength metrics), we used the square root transformation. As inferential statistical techniques request, observations must be independent  [28, 73]. Since interaction data between samegroup members broke this rule, we used the ANTs package  [70]  to permute our social networks prior to run GLMMs with a confidence interval set at 95%, as recommended by Whitehead  [74]  and Croft et al. [75]  to obtain unbiased significance tests for the coefficients. More specifically, we used node label permutations (N = 10,000) on the metrics (argument 'labels' in function 'perm.
Acknowledgments: We are very grateful to the  Indonesian Ministry of Research and Technology , the  Ubud Monkey Forest Management Committee Nyoman Buana, Bagus  and  Wayan Buda  for support and permission to conduct this research in Indonesia. We thank  Fanny de Thier Nagelmackers  for assistance with data collection. We greatly thank the  Ubud Monkey Forest  staff and  Kadek Eva Suartini  for logistical assistance. We acknowledge both anonymous reviewers for providing constructive comments on a previous version of this manuscript.
Participants Twelve children with DCD and 20 TD children aged 8-12 years took part in this study. They were all right-handed, as assessed by the 10-question version of the Edinburgh Handedness Inventory  (Oldfield, 1971; mean laterality quotient: 88. 77 ± 20. 33; range: 20-100). Seven more children were examined for this study, but their MABC score did not meet the inclusion criteria of Movement Assessment Battery for Children (M-ABC) <5th percentile, so they were not included in the study. We did not include children with musical skills (more than 4 h a week of formal practice for over 1 year). Participants had corrected-tonormal vision and hearing, as verified by a pre-experimental questionnaire. The children were enrolled in the DYSTAC-MAP study (ANR-13-APPR-0010). Eleven DCD and 18 TD children who passed an MRI participated in the study by  Lê et al.
Practice and Reactivation Asynchrony and stability were calculated via a circular data processing method  (Fisher, 1995)  using CircStat. CircStat is a MATLAB Toolbox (MATLAB version 2015a) for circular statistics  (Berens, 2009) , recommended for cyclical data, particularly suited to synchronization data and sensitive to individual differences  (Dalla Bella and Sowiński, 2015). Circular data processing has been used in the literature during manual tapping on a synchronization task for an isochronous rhythmic sequence in healthy adults  (Sowiński and Dalla Bella, 2013) , children with or without a neuro-developmental disorder  (Puyjarinet et al. , 2017)  and patients with neurodegenerative diseases  (Martin et al. , 2017). Processing involves representing each finger tap with an angle (unitary vector) on a 360 • polar scale, where the circle represents the inter-beat interval of the stimuli.
The resultant angle of vector R represents synchronization accuracy  (Sowiński and Dalla Bella, 2013; Dalla Bella et al. , 2017). For each subject and each trial, we obtained a resultant vector angle that we transformed into an absolute value (in order to average the data across the trials). Subsequently, the vector angles were converted into a percentage of asynchrony to obtain data on a linear scale and for better understanding. To give an example of conversion: an angle of 0 • was converted to 0% asynchrony and an angle of 180 • was converted to 50% asynchrony. The higher the percentage, the lower the synchronization. The length of resultant vector R  (Sowiński and Dalla Bella, 2013; Dalla Bella et al. , 2017)  is referred to as synchronization stability and varies from 0 to 1: 0 corresponds to a uniform and random distribution of responses around the circle while 1 corresponds to a uniform distribution of responses in one direction.
In other words, the longer the vector length is close to 1, the greater the stability for the synchronization of responses within the trials. The vector angle and the vector length were obtained via circular statistics using CircStat  (Berens, 2009)  in MATLAB, based on temporal tapping data. For each subject and each trial, we obtained a percentage of absolute asynchrony representing accuracy and a vector length representing the stability of sensorimotor coordination synchronization. Please note that the first response was never taken into account in data processing because it was considered a "warm-up" step. Every three consecutive trials of the 30 practice trials were averaged to obtain 10 blocks of three practice trials for absolute asynchrony and vector length.
1 ). Based on these analyses and the attachment theory, the expert committee proposed a French crosscultural adaptation of the IPR, used for the remainder of the study (box 4, Fig. 1 ). Study of the metric properties of the French version of the IPR. Two independent groups were constituted: one for exploratory approach, further named development group, constituted from voluntary adolescents from schools and colleges; one for con rmatory approach further named validation group, constituted from school enrolled adolescents and adopted adolescents. All adolescents responded to the 62 items of the IPR. Socio-demographic data were collected from parents. Consent was deemed to have been given when the adolescent and his/her parents completed all of the questionnaires distributed. All questionnaires in the study were addressed to the parents and not to the adolescent.