dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Caroline Schuppli  (Suaq),  Erin Vogel  and  Suci Utami Atmoko (Tuanan) ,  Kerstin Bartesch  ( Tierpark Hellabrunn ),  Claudia Rudolf von Rohr  ( Zoo Zürich ),  Alexander Sliwa  ( Kölner Zoo ),  Simone Sheka  ( Allwetterzoo Münster ) and  Thomas Bionda  ( Apenheul Primate Park ) as well as all research staff and zoo keepers for a fruitful collaboration during this study. We gratefully acknowledge  Clemens Becker  for providing the EEP studbook, the  Indonesian State Ministry for Research and Technology (RISTEK) , the  Indonesian Institute of Science (LIPI) , the  Directorate General of Natural Resources and Ecosystem Conservation-Ministry of Environment & Forestry of Indonesia (KSDAE-KLHK) , the Ministry of Internal affairs, the  Nature Conservation Agency of Central Kalimantan (BKSDA) , the local governments in Central Kalimantan, the  Kapuas Protection Forest Management Unit (KPHL) , the  Bornean Orang-utan Survival Foundation (BOSF)  and MAWAS in  Palangkaraya.
a b s t r a c t Mutual information (MI) has been previously used to select the relevant features for the task of speech emotion recognition (SER). However, the procedure does not deliver the optimal number of relevant features. We propose MI based criterion for estimating this number defined as the minimum number of features that explains the variable of the class indices. In order to minimize the MI estimation errors, we also search the best histogram binning choice considering three formulas: Sturges, Scott and LMSE. Four selection strategies MMI, CMI, JMI and TMI have been implemented and applied on 39-features vectors and on large dimension vectors. The feature selection results have been validated on independent text SER system, based on GMM classifier and evaluated on EMO-db database.
In order to validate this proposal, an SER system has been carried out first using MFCC features with energy and their first and second derivatives and second using a combination of several types of spectral features (MFCC, LPCC, PLP) and prosodic features (pitch and energy). The latter validation investigates large features dimensions. Also, the system performance was evaluated using the Berlin Database of Emotional Speech (Emo-DB) that considers different emotion classes such as anger, boredom, disgust, fear, happiness, sadness and neutral  (Burkhardt et al. , 2005).
Emotional speech recognition system An automatic emotion recognition system is a pattern recognition system generally composed of two important phases, the training (learning) phase and the testing (classification) phase. Both of these phases require a features extraction step to transform each temporal signal into a sequence of short-term feature vectors. The training phase aims to learn the classes of emotion patterns from the occurrences of training database using a classes modeling approach. In the testing phase, the system uses a classifier to identify the class the unknown input signal belongs to. Generally, a testing database of signals is used to classify each signal and finally evaluate the system performance using accuracy criterion. Based on this general description, we hereafter present the architecture of the SER system we developed with a selection step useful for estimating the optimal number of selected features.
In order to take into account dynamic evolutions of the data, we used MFCC with energy and their 1st and 2nd derivatives which forms 39-components feature vectors  (Wu et al. , 2011). Each utterance of the database is preprocessed by suppression of silence of signals boundaries and is filtered by a high-pass filter with a pre-emphasis coefficient of 0. 97  (Wu et al. , 2011). Then, each obtained utterance signal corresponding to an emotion class is converted into a sequence of short-term vectors of 39 features computed each 10 ms on 30 ms hamming-windowed speech frame, using the 'Hcopy' command of HTK library. The vectors sequences of the training database are used to model each emotion class by a GMM model using the command 'HEREST'. Next, each sequence of vectors of the testing database is classified using the command 'HVITE'.
Speech database We have used the Berlin Database of Emotional Speech (EMO-DB) to evaluate the system performances  (Burkhardt et al. , 2005). The dataset is composed of 10 German sentences of different texts (5 short sentences constitute a set A and 5 longer sentences constitute a set B) pronounced by 10 actors (5 male, 5 female) who simulated seven primary emotion states (anger, boredom, disgust, fear, happiness, sadness) including neutral. The sentences come from everyday communication and can be interpretable in all applied emotions. The total collection consists of 800 utterances including some second versions but the final collection considered only 535 utterances because a human validation was performed for each utterance (20 listeners had to decide in which emotional state the speaker had been and the decision for dataset inclusion was made when the recognition rate was higher than 80% and also considering for more than 60% of the listeners sentences are natural).
This new selected feature Y P j grows the already selected subset S jÀ1 by appending it as S j ¼ Y P j [ S jÀ1 :  (Cover and Thomas, 1991) , (  2 ) can be reduced to: Y P j ¼ arg max Y i 2FÀS jÀ1 I C; Y i ; S jÀ1 À Á Â Ã ð2Þ Since I C; Y i ; S jÀ1 À Á ¼ I C; S jÀ1 À Á þ I C; Y i fS jÀ1 Y P j ¼ arg max Y i 2FÀS jÀ1 I C; Y i fS jÀ1 h i ð3Þ Eq. (  3 ) can also be expanded in a multivariate MI of order 3 between C, Y i and S jÀ1 as: Y P j ¼ arg max Y i 2FÀS jÀ1 I C; Y i ð ÞÀI 3 C; Y i ; S jÀ1 À Á Â Ã ð4Þ The I 3 term may be positive which corresponds to withdrawing redundancy introduced by the new feature. If this term is negative, this means that Y i and S jÀ1 are synergic  (Brown et al. , 2012). The evaluation of I 3 C; Y i ; S jÀ1 À Á becomes very difficult when j grows because this evaluation requires the estimation of highdimensional probability density functions that cannot be precise enough for fixed database sizes  (Drügman et al.
, 2007). Most of the algorithms propose a simplification of (4) following different    (Brown et al. , 2012; Hacine-Gharbi et al. , 2013). In  Brown et al. (2012) , the authors conclude that the JMI strategy provides a good compromise between precision, flexibility and stability when the database is small size. They also point out the MRMR and CMI strategies that perform better than other ones in terms of balance between high relevance and small redundancy. We give below the derivation of (4) for four selected strategies. & MMI (Maximum MI) Y P j ¼ arg max Y i 2FÀS jÀ1 I C; Y i ð Þ ½ ð 5Þ & TMI (Truncated MI) Y P j ¼ arg max Y i 2FÀS jÀ1 I C; Y i ð ÞÀ X jÀ1 k¼1 I 3 C; Y i ; Y P k À Á " # ð6Þ & JMI (Joint Mutual Information) Y P j ¼ arg max Y i 2FÀS jÀ1 I C; Y i ð ÞÀ 1 j À 1 X jÀ1 k¼1 I 3 C; Y i ; Y P k À Á " # ð7Þ & CMI (Conditional Mutual Information) Y P j ¼ arg max Y i 2FÀS jÀ1 I C; Y i ð ÞÀ max Y P k 2S jÀ1 I 3 C; Y i ; Y P k À Á " # ð8Þ For JMI and CMI strategies, the term I 3 C; Y i ; Y P k À Á is actually computed asI Y i ; Y P k À Á À I Y i ; Y P k fC À Á.
The features vectors were composed of the static of MFCC coefficients, the energy and their dynamic features D and DD. The SER system performance was evaluated using the EMO-db database. Also other results were obtained by taking large dimension vectors including spectral and prosodic features. The results demonstrate that LMSE choice gives the best estimation of MI, which succeeds in approximately reaching the expected plateau in the MI curve. The study has shown that the MI based criterion gives acceptable performance results compared to the criterion based on the curves of recognition rates. Practically, the CMI strategy combined with MI based criterion gives the high features reduction of 48. 72% (from 39 to 20 features) and of 67. 86% in a large dimension case (from 112 to 36) with a slight drop of performance results.
From these teachers, no demographic data were obtained. This results in a total sample of 136 Belgian teachers who filled out the SIS-PE questionnaire. A subsample of 69 Belgian PE teachers (31 men) filled out an optional questionnaire to determine convergent validity (see Measures). These 69 teachers were on average 38. 75 years old (SD = 11. 75, range = 21-61) and had 15. 71 years of experience (SD = 11. 38, range = 0-39). To determine concurrent validity, all teachers in the French sample filled out an additional questionnaire measuring their motivation to teach (see Measures). All participating teachers gave online consent after being informed that their participation was voluntary, that the collected data would be treated confidentially, and that they could withdraw from the study at any time, without having to give a reason and without any consequences.
Plan of Analysis To examine the dimensional structure of the SIS-PE items, that is whether the items could be organized along two dimensions (H1) and whether the four (de-)motivating styles could be segmented into two distinct teaching strategies (H2), Multidimensional Scaling (MDS;  [50] ) was conducted using the Proxscal procedure in SPSS 21. Specifically, MDS provides a graphical representation of (dis)similarities between elements (i. e. , items) in the form of distances between points in a geometric space. That is, items that are strongly positively correlated will be represented closely to each other in the geographical space (i. e. , responses are compatible), strongly negatively correlated items will be displayed in the opposite direction (responses are conflictual in nature) and items that are poorly correlated will be represented by larger distances.
This analysis will allow to examine whether the items are located in their expected area. MDS was performed on Belgian data and French data together and separately in order to obtain a country-specific configuration. Differences between the SIS-PE version and the original SIS-questionnaire were inspected. To investigate the stability of the dimensional structure across countries (H3), we subjected the obtained Belgian and French configurations to Generalized Procrustes Analysis (GPA;  [50] ). GPA calculates configurations from different samples in such a way that they correspond as closely as possible, without affecting the relative distances between items within each configuration. Further, to test the internal validity of the model, through correlational analyses, we examined the pattern of correlates between the subareas (H4).
Nineteen patients were considered by clinicians as hypomanic and 8 had a mixed hypomanic episode. Among predominantly depressed patients, 17 were clinically assessed as mixed depressive and 12 had a pure depressive episode (cf. Table  1 ). Subjects provided written informed consent prior to inclusion in the study in accordance with the Declaration of Helsinki (ClinicalTrials. gov registration number: NCT02036606). This study was approved by the regional ethics committee of the East of France.
Materials and procedure Following the aforementioned diagnostic clinical assessment, participants completed the Beck Anxiety Inventory (BAI)  [26]  prior to the administration of the VFT. Verbal fluency tasks. Nine different trials belonging to four conditions of VFT were administered: (i) one free fluency trial, (ii) six associational fluency trials, (iii) one letter fluency trial, and (iv) one category fluency trial. The four conditions of the VFT were administered in a fixed order starting with the most unrestrictive condition  [29] : first the free condition, followed by the six associational conditions, the letter condition, and the category condition. Participants' oral production was recorded using the Audacity© software (44,100 Hz sampling frequency, 24-bit pulse code modulation). A microphone connected to a laptop was used.
e. , manic versus mixed manic and depression versus mixed depression. The leave-one-subject-out (LOSO) strategy was used to train the classifier: LOSO consists of removing all the observations related to a subject (validation set) and train the classifier on the remaining observations (training set). The subject is then classified, and the predicted label is compared with the clinical scoring. This operation is repeated for the remaining subjects in the dataset until a predicted label is obtained for each subject. Before the training and the test steps, each speech feature underwent a subject-specific normalization step, to remove subjectspecific speech characteristics, such as gender or vocal tract signature. The MadF0, i. e. the median absolute deviation of F0, obtained for each task was normalized through the use of the median value of F0.
Among predominantly hypomanic patients, mixed mania was correctly classified in 6 cases, and misclassified in 2. Pure manic/ hypomanic states were correctly classified in 17 cases and misclassified in 2. The accuracy and F1 scores were high, i. e. , 0. 86 and 0. 75, respectively. The good performance of the classifier, as applied to the unbalanced dataset, was confirmed by the MCC whose result was 0. 57. Correctly classified mixed manic/hypomanic, but also pure manic/hypomanic states, had, on average, higher anxiety scores on the BAI, compared to misclassified cases. Dysphoric psychotic features were part of the ongoing episode in the two misclassified mixed manic cases. Classification and descriptive results on the clinical measures are presented in Tables  5  and 6. The analyses were repeated using information about antipsychotic medication (chlorpromazine equivalent dose) as a predictor.
Methods: The detailed content of BlueZberry and its promotional plan were determined via a literature review and 26 individual and group interviews with experts and youth with StopBlues as a starting part. Results: The literature review and interviews confirmed the need to adapt the tool according to age of the user since the context and source of psychological pain vary rapidly at this time of life. Blue-Zberry consists of three modules for age groups 12-14, 15-17 and 18-25 years with specific graphics and messages. Its locally organized promotion should include youth hangouts on top of usual places. Conclusions: This adaptation of StopBlues will reach a larger audience by offering a more suitable solution for this vulnerable population. A web-portal will serve as an entry point for both StopBlues and BlueZberry where users will be redirected to one of the tools/modules according to their profile and respective needs.
Data acquisition This study measured the neck ROM as a combination of rotational and linear displacements in healthy volunteers using an optoelectronic motion capture system (Codamotion, Charnwood Dynamics Ltd. ). The 3D coordinates of six optoelectronic markers placed on the helmet (SHARK, S800, size L) were measured to calculate the subject's neck ROM. The rotational neck movement was computed from the rotation matrix calculated from the initial and final positions of the markers resulting from the movement. The translational neck movement along three axes (X-axis aligned toward the subject's anterior direction, Y-axis aligned toward the subject's left side, and Z-axis aligned toward the  translations in the X, Y, and Z directions. This analysis was performed in a coordinate system defined for each session.
For each testing condition, the testing sequence described (Figure  4 ) was used. A total of 120 complete testing sequences were performed (12 volunteer subjects and 10 different testing conditions). With 3 consecutive movement sequences during recording sessions 2 and 4, a total of 720 (120 x 6) datasets were acquired. An average value of 6 acquisitions per testing conditions was calculated. For each session, ROM were calculated for the following six movements: axial rotation, right and left lateral bending, flexion/extension, and   (12)  with each set of testing conditions  (10) , for a total of 120 tests EVS and KTM neck braces significantly reduced the neck mobility for axial rotation on the racing motorbike (YS=135±21°, YB=74±20° (p=0. 001) and YC=109±23° (p=0,010)) (Figure  5 ). No significant difference was found between the two driving postures for lateral bending physiological ROM (SS=94±16°, YS=100±30°, p=0.
, 2010). The Portuguese IRI has only 24 items because four items were excluded to improve the adjustment of the data to the expected factorial structure of the scale  (Limpo et al. , 2010). Item scores are averaged to produce scores for each facet of empathy. Internal consistency values were acceptable-to-good in the current dataset (Cronbach's α =. 75 for perspective taking, α =. 88 for fantasy, and α =. 81 for personal distress, and α =. 81 for empathic concern). As in  Neves et al. (2018) , we focused our analyses on affective empathy, namely on the Empathic Concern subscale, which measures trait levels of affective reactions to others' emotions. Personal Distress also focuses on affective reactions, but is self-oriented.
Psychophysiological Measurement Psychophysiological data were acquired via BIOPAC MP150 (Biopac Systems Inc. ), using the EDA100C Electrodermal response and EMG100C Electromyogram amplifiers, and the recording software AcqKnowledge (version 4. 1). Both facial EMG and electrodermal activity were recorded with a sampling rate of 1000 Hz. Electrodermal activity was measured on the palmar surface of the middle phalanges of the index and middle fingers of the nondominant hand. A constant voltage of 0. 5 V was maintained between two disposable 6mm Ag/AgCl snap electrodes filled with NaCl electrolyte paste gel. Facial data were measured at the left corrugator, zygomatic and orbicularis muscles, using six disposable 4mm Ag/AgCl electrodes filled with conductive gel and placed according to common recommendations  (Fridlund & Cacioppo, 1986; Lapatki et al.
, 2003). The experimental setup is illustrated in Figure  1A  and the effects of muscle activity on facial features in Figure  1B. The resulting raw measurements were preprocessed offline using AcqKnowledge (versions 4. 3 and 5). Based on visual inspection, the quality of the data was generally good, without significant interference induced by motion. Electrodermal data were first resampled to 62. 5 Hz, processed using a 62-sample median smoothing filter, and a finite impulse response (FIR) low-pass filter at 1 Hz with a Blackman (-61 dB) window (e. g. ,  Braithwaite et al. , 2013). For our analyses, we considered the phasic component of electrodermal activity, i. e. , skin conductance responses (SCRs) for each vocalization. These responses were obtained using an in-built routine in AcqKnowledge, by implementing a 0.
Behavioral data and questionnaires According to recent guidelines  (Bürkner, 2019) , we modeled the 1-7 authenticity ratings with ordinal logistic regression. Because each stimulus was rated repeatedly, we included a random intercept per sound. To account for possible differences between participants, we also allowed the effects of authenticity and vocalization type (laughter or crying) to vary across subjects, so the main model of authenticity ratings was of the form: Auth_rating ~ Authenticity * Vocalization + (Authenticity * Vocalization | Subject) + (1|Sound) Demographic data (sex and age) and questionnaire results (ECS and IRI scores) were added to these models as covariates for analyses focused on these variables.
Behavioral data The accuracy of sex discrimination during the implicit task was high (M = 92. 5% per participant; SD = 9. 6%; range = 44 -100), indicating that participants remained attentive to the vocalizations while physiological responses were being recorded. Regarding the explicit authenticity task, we evaluated inter-rater agreement by calculating the mean Pearson's correlation between individual responses and the overall authenticity ratings averaged across all participants. This correlation was r =. 70 for laughter As can be seen in Figure  2 , participants perceived authentic laughs and cries as more authentic than their posed counterparts, with a difference of 2. 2 points (95% CI [1. 8, 2. 5]). The magnitude of authenticity discrimination (i. e. , difference between authentic and posed) was slightly greater for laughter (2.
Data Analysis The data were analyzed using STATA 15. 0 software (StataCorp LP, College Station, TX, USA). Descriptive statistics were used to report characteristic data covering mean, SD, percentage, and frequency. The exploratory factor analysis (EFA) was applied to assess the construct validity and define interpretable underlying sub-domains of measurement regarding perceived impacts of COVID-19 on the life and work of health workers. We also employed principal component analysis to extract said domains. A threshold defined by the screen test was set at an eigenvalue of 1. 5. tTo increase the interpretability of subdomains of the measurement, we used Orthogonal Varimax rotation with Kaisers' normalization to reorganize items in scales. The minimum factor loading cut-off point of this study was set at 0.
Statistics The data collected from the monitoring device were extracted by a specialized software (Sensewear Software, Version 7. 0, Pittsburgh, PA, USA). The average value of weekdays (WD, Monday to Friday) for each child was calculated (Table  2 ). Results are given as mean ± standard deviation (SD). The first objective was to compare children with ASD to CONT (with a Student's t-test after having checked the conditions for the suitable application of parametric or non-parametric tests). An effect size (Cohen's d value) was also calculated and added for each significant result. A d value of around 0. 2 is described as a "weak" effect, 0. 5 as "medium" and 0. 8 as "strong". Significance was accepted when the p-value was lower than 0. 05. The second objective was to assess the effect of PA level on sleep quality in children with ASD.
• Total bed time (a1) A factor map was also proposed. This graphical representation shows the partition of our data into different clusters and classifies each child, using different colors, in order to indicate the membership of specific items from different clusters. Based on these observations, an average score was calculated in each cluster, using the following method for each variable: a score of 3 was assigned to the most favorable observation, and a score of 1 to the least favorable value. A subtotal was finally calculated for the sleep and PA variables. With these scores, it was possible to define a label to characterize each cluster (Table  3 ).
Subjects: Inter-hemispheric asymmetries of the IA sulcus' depth were quantified from anatomical T1w MRI images in 80 baboons Papio anubis born in captivity and free from developmental or anatomical abnormalities or brain disorders (generation F1, 52 females, 28 males, age range = 7 to 32 years, mean age (years): M = 17,7 SE = 5,9). Out of this sample, 4 subjects did not present any behavioural data and were thus excluded from further analyses, 53 baboons had individual measures for both hand preference for communicative gestures and for handedness for (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted February 9, 2021. ; https://doi. org/10. 1101/2021. 02. 09. 430276 doi: bioRxiv preprint 5 manipulative actions, 5 had only for communicative gestures and 18 only for manipulative actions.
Sulcal Parametrization: The IA sulcus was extracted from T1w images using the pipeline of the free BrainVisa software (see  13  for details of the procedure). The sulcus parametrization tool within the BrainVisa toolbox provides therefore sulcus-based morphometry by subdividing the IA sulcus of each hemisphere into 99 standardized positions from dorsal to ventral sulcus extremities in order to quantify the variation of sulcal depth all across the IA sulcus' 99 positions 22. To estimate asymmetries of the IA sulcus depth between the two hemispheres an asymmetry quotient (AQ) for each of the 99 sulcal positions (AQ = (R -L) / [(R + L) × 0. 5) was computed  12.