dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants with AoS suffered from a stroke in the left hemisphere, with no or only very mild language impairment. The demographic and clinical characteristic of the patient groups are reported in Table  1  along with the severity of their MSD. Two measures of severity are provided in Table  1 : 1. individual scores from the MonPaGe-2. 0. s speech assessment protocol  (Laganaro et al. , 2020; Pernon et al. , 2020) , which evaluates different speech dimensions in various speech tasks and provides an overall severity score ranging from 0 to 32 and 2. The severity score from the BECD  (Auzou et al. , 2000)  which is a perceptive judgment of severity ranging from 0 to 4 on 5 characteristics (voice quality, phonetic realization, prosody, intelligibility and naturalness) assessed by 6 independent raters.
Procedure and overall scoring Participants were installed in a desk chair in a quiet room approximately 70 cm in front of a computer screen. The MonPaGe computerised tool  (Fougeron et al. , 2018)  was used for the stimuli presentation and data collection. The recording was done by a speech therapist specialised in motor speech disorders. Professional audio material was used for the recording (external audiocard Foscurite Scarlet and headmounted Shure SM35-LXR microphone). The stimuli were presented on the computer screen in both orthographic and prerecorded audio form simultaneously in order to prevent possible reading difficulties. The presentation time is set by the clinician who moves to the next item by pressing a button after the patient's production. Only one production is recorded for each item.
Results The results on the overall clusters production accuracy rated with the MonPaGe assessment tool and the acoustically detected vocoids are presented in Table  2. Perceptually mispronunciations of the clusters are observed in all patient groups and not in the control group. The non-parametric Kruskal-Wallis test 1 (run on IBM SPSS Statistics for Windows, Version 25. 0) show a significant difference across patient groups (H (df = 3) = 21. 116, p <. 001) on the number of perceptually detected mispronounciations, with only WD differing from PD in the multiple comparisons (p =. 007). Other comparisons are not statistically different (PD vs AoS: p = 0. 062; ALS vs WD: p = 0. 75; all other p > 0. 1). The types of perceptually detected errors were either distortions, substitutions, omissions, or insertions.
Data Reduction and Analysis Saccade onset and offset were defined by a fixed velocity threshold of 30 • /s. The direction of a saccade was determined by the eye position difference between the start and the end of the saccade. Trials containing anticipated saccades (less than 80 ms) or delayed saccades (more than 800 ms) at the target onset were excluded from the analysis. Trials were also excluded when the eye tracker failed to record the eye coordinates (e. g. , eye blink, loss of pupil, or corneal reflection). Saccades directed toward the targets in the PS task and in the opposite direction in AS task were defined as correct saccades. Saccades directed toward the targets in AS task were defined as AS errors. PS errors were not analyzed because their number was too low for an informative statistical analysis.
Sample and data collection procedure A stratified purposive sampling of knowledge-intensive SMEs was done based on authors' convenience and city preferences. Twenty-four organizations were randomly selected from four different metropolitan cities. Out of 24 firms surveyed during 2018-2019, 9 firms belonged to small-firm category and the rest were mid-sized organizations. Our sample excluded micro-level firms (<20 employees) and had considered only small (>20 but <100 employees) and mid-sized firms (>100 and <500 employees). The data sample caters to both service and product firms spanning across IT and IT-enabled services, R&D, advertising, electronic gadgets manufacturing and KPOs. All the employees surveyed possessed unique skillsets and work knowledge or information that are valuable for the team's output.
Our firm-level categorization was based on the firm's employee size as it is highly correlated with the degree of HR orientation in the firm. Work-groups from each organization were randomly selected and teams with <3 members were excluded from the sample. Voluntary participation in the survey was sought from team members and the manager/leader of the team. The first data sample 1 (N 1 = 303) consisted of responses from team members (junior and middle-level employees) of all the teams surveyed (in total there were 73 teams). The second data sample 2 comprised team leader's/manger's responses (N 2 = 73) about their team's creativity output. The average age of the employees in sample 1 was 29. 2 years (SD = 6. 2 years), and the average work-experience was 5. 27 years (SD = 4. 9 years). The average age of the team managers (sample 2) was 36.
Faultline strength determination Faultline analysis strength based on creative self-efficacy and tenure in the organization was calculated using asw. cluster package (version 2. 10) in statistical software R (version 3. 1. 2). Using a wrapper function faultlines() in the package, faultline strengths of group's faultline is calculated based on agglomerative and partitioning clustering algorithms as suggested by  Meyer and Glenz (2013).
The model Architecture Our model is based on CORnet-Z, the simplest network in the CORnet family  [28]. These convolutional neural networks (CNN) all share a common design of 4 spatially-organized modules meant to represent V1, V2, V4 and IT areas in the ventral visual pathway (topological modules hereafter), capped by a non-topological decoding structure (dense layer in Fig. 1 ). We built three variants of CORnet-Z networks. The illiterate network was trained only on the visual images in ImageNet (ILSVRC-2012 dataset, about 1 300 000 image exemplars distributed over 1000 classes such as dogs, cars etc). Two literate networks were additionally trained to recognize words. To this end, we generated 1 300 000 word exemplars distributed over 1000 "word classes", where each word class actually corresponded to a single word, while exemplars varied in location, size, font and scale.
The literate networks thus had 1000 additional output units (see Fig. 1 ). In the unbiased literate network, their afferent connections were distributed across all units in the dense layer. In the biased literate network, to simulate the hypothesis of a biased connectivity from a subregion of visual cortex to language areas, we restricted the output units' afferent connections to a subset of 49 units in the dense layer. These biased units will be dubbed "Dense+" hereafter (violet units in Figure  1C ), in opposition to unbiased units ("Dense-", white units in Figure  1A , 1B, 1C). The code for training the networks is available online at https://github. com/THANNAGA/Origins-of-VWFA along with pre-trained models for all conditions.
Training Deep learning models typically involve a single training phase on a single large dataset, whereas children learn to identify faces and objects long before they acquire reading. To simulate reading acquisition as the partial recycling of prior visual recognition abilities, we trained the networks in two stages: an "image" phase (phase 1) followed by an "image + word" phase (phase 2). Word units were only added to the output layer of the network during the latter phase. Throughout phases 1 and 2, the networks were trained with Stochastic Gradient Descent on a categorical cross-entropy loss, using a linear learning rate scheduling (see Supplementary Material). Neural networks are notoriously prone to the phenomenon of catastrophic interference, whereby learning of a new task, or even on a new dataset, can spectacularly deteriorate previously stored knowledge.
This well-studied effect can be alleviated in a number of ways, included interleaved learning and the use of dropout  [45, 46]. We used interleaved learning: during phase 2, output word units were added to the network and the new training set was obtained by concatenating and shuffling the original generic image set and the new word set. The sets were matched for number of classes and exemplars, and the presentation probabilities were 50% images and 50% words. An illiterate control network was trained only on ImageNet, for as many epochs as the literate networks. While it is common to freeze a network's lower-level weights during transfer of learning to new datasets or tasks, here we trained the full network during the first and second stages, mindful of the fact that an effect of literacy has been reported as early as V1 in the visual pathway  [17].
Nevertheless, a crucial missing ingredient is the presence of recurrent connections, which are omnipresent in the primate visual system. Several simulations have shown that recurrent neural networks provide better predictors of activity in the human visual system than purely feedforward networks  [35, 77, 81]. Intracranial recordings suggest that recurrent connections may be particularly important in order to simulate the late part of the neural response to words  [31] , which is likely to contribute massively to fMRI BOLD signals. In this respect, it is worth mentioning that the present model failed to fully capture an important fMRI dataset, by Vinckier et al. [29] , who found a progressive and continuous increase in fMRI activity for stimuli approaching the statistics of real words. Our simulations only captured the difference between stimuli with or without frequent letters.
Recent motion tracking systems allow faster and more reliable spatial and temporal data capture  [58]. Devices such as accelerometers, potentiometers, electrogoniometers, optical and magnetic capture systems have been used to record movements of people trained to perform tasks requiring synchronization with a referent (e. g. , a metronome, a video, confederate or another participant). Movements of markers attached to body parts or moveable objects (e. g. , pendulums) may also be tracked. Studies using such devices have not only deepened understanding of the physical characteristics of MA (temporal scales, velocities and angles), but have also allowed the parameters that underpin MA and resultant outcomes to be described in detail  [63] [64] [65]. For instance, amongst biomechanical variables that influence MA, synchronization increases when: in-phase movement is predominant  [40] ; the preferred periodicity of movement is similar between interactants  [66] ; dyads move pendulums of similar length  [66] ; and interactants have visual, auditory or haptic access to their partner  [59, 67].
Participants Participants were invited to take part in a face-to-face conversation with an unknown participant as partner through advertisements distributed in the Pontificia Universidad Catolica de Chile. Ninety-two undergraduate students volunteered for the study. None had had formal musical training or played an instrument. From this sample, 46 same-sex dyads of participants unknown to each other were generated and randomly assigned to one of two groups that differed in whether they took part in a joint musical improvisation (MI) or a 'hands-busy' task (HB), as explained in Design section below. This produced an initial set of 46 dyads, 26 in the MI group (15 female), and 20 dyads in the HB group (10 female). The final group size was 13 dyads per group (seven females in MI, six in HB). 17 dyads (10 MI, 7 HB) were discarded due to loss of data (either Mocap or microphone malfunction).
Kendall's coefficient of concordance corrected for ties was computed and yielded a high inter-rater reliability (Wt = 0. 738, chi-square = 55. 4, p <. 001). The average score was computed for each dyad, and the 13 highest-scoring dyads chosen for analysis, matching the number of selected HB dyads. The ratings of these 13 dyads were 3. 3. or higher. Motion capture. Participants' motion was recorded with an optical motion-capture system consisting of 36 NaturalPoint Prime-41 cameras and a computer running Motive optical motion capture software. This software package was used for calibration, 3D reconstruction, and motion capture recording. Cameras were installed close to the ceiling of the recording room, in a rectangular array above and surrounding the participants. Capture area was at least 3 meters x 4 meters x 2 meters (width, depth, height).
Analysis Motion capture (Mocap) data. Mocap data pre-processing. The conversation Mocap data (T1 and T2) was exported and then each marker was trajectorized over time. Next, we manually labelled markers with their corresponding body parts and identified the participant to which each marker belonged. Finally, we visually inspected the results to identify where data for a marker had been lost. Such gaps were filled by linear interpolation. Computation of speed cross-correlation curves. Our data comprised position x time series pairs, corresponding to the period between 0 s and 60 s. Each pair consisted of one time series for the positions of one participant and another one for the other participant. Following Cornejo et al. [62] , we first averaged back markers for each participant into a single 3D position and only kept 1D positions of the proximity axis (i.
Of the total 195 spontaneous Q+A pairs analysed, 78 (40%) were classed as having Periodic answers by at least one of the three criteria. A score of 1 was assigned to Q+A pairs that had periodic answers, and 0 to pairs with aperiodic answers. A number of analyses were conducted on various parts of these binary periodicity data. For each, we fitted a logistic regression model, with Group (HB or MI) as a between subject factor, Conversation (T1 or T2) as a within-subject factor, and Dyad as a random factor. All analyses were conducted in R (Version 4. 0. 2, R Development Core Team) using the lme4 package  [96]  with an ANOVA using a Type III Wald chi-square Analysis of Deviance test applied to the model output. Critical alpha level for significance was p = 0. 05. All t-tests are two-tailed. The first analysis was on the complete set of 195 Q+A pairs-that is, single interval and multi-interval pairs together.
Discussion The Introduction identified three general questions. All have been answered affirmatively. The periodicity across conversational turns observed by Ogden and Hawkins  [54]  for English is also found for (1) conversants speaking Chilean Spanish, a language whose prosody differs markedly from that of English, (2) when they do not know each other. (The influence of familiarity of interlocutors is reflected in the overall incidence of periodicity: about 40% and 70% for the present study and Ogden & Hawkins  [54] , respectively. ) (3) The nature of the particular 'joint-action' task-by hypothesis its inherent rhythmicity-does appear to affect results. The following sections consider the details, first for motion capture and speech separately, and then in relation to each other.
Acknowledgments The team would like to thank  Dr. Alejandra Torres-Araneda  for her role in coordinating the project. We are also thankful to  Martı ´n Miranda ,  Ivan Vial ,  Enzo Ca  ´ceres, and  Francisco Nocetti  for their help pre-processing data, and  Dr. David Leblond  for his kind help in formalizing speech data algorithms into mathematical formulae.
Activity-based anorexia (ABA) model At the beginning of the ABA procedure (d10), mice were placed individually in standard cage (CT and CT þ ATB groups) or cages with an activity wheel (ABA and ABA þ ATB groups). The ABA model has been performed as previously described  [26]. ABA model combines a free running wheel access and a progressively limited food access from 6 h/day to 3 h/day (Supplemental Fig. S1 ). Food was given at the beginning of dark phase (10:00 am). Animals had free access to water. Running wheel activity has been continuously recorded with Activity Wheel software (Intellibio, Seichamps, France). Moreover, food intake and body weight were daily measured. If body weight loss was higher than 20% on 3 consecutively days, animals were euthanized in accordance with the ethical procedure.
Intestinal permeability assessment Global intestinal permeability was assessed by administration of a solution of fluorescein isothiocyanate-Dextran (FITC-Dextran 4 kDa) at 40 mg/mL by oral gavage (10 mL/g of body weight), 3 h before euthanasia, as previously described  [31]. Then, FITC-Dextran level was then measured on plasma samples using a spectrofluorometer (Chameleon Lablogic, Sheffield, UK; excitation ¼ 485 nm; emission ¼ 535 nm; Mikro Win 2000 Software).
Statistical analysis All data were analyzed using GraphPad Prism 6. 0 software (GraphPad Software Inc. , San Diego, CA, USA). Data were expressed as mean ± standard error to mean. Shapiro test was used to assess distribution parameters. Data were analyzed by repeated two-way ANOVA (time Â group) or two-way ANOVA (ABA Â ATB) followed by Bonferroni posthoc tests, as appropriate. Results were considered significant when p-value was lower than 0. 05.
5I ). Hypothalamic TNFa and TLR4 mRNA levels were not modified (data not shown). In the hippocampus, we evaluated dopaminergic and serotoninergic systems by assessing dopamine and serotonin receptors. For DRD1, DRD2 and 5-HT1A (Htr1a gene), the ANOVA p-value for interaction was significant while there was no difference for 5-HT4 (Htr4 gene, Fig. 6AeD ). Our data suggest that ATB treatment blunted the increase of DRD1 and DRD2 mRNA in ABA mice compared to CT (Fig. 6A, B ). A similar pattern was also observed for TNFa mRNA (Fig. 6E ) By contrast, 5-HT1A mRNA level was only decreased in ATB-treated ABA mice compared to ATB-treated CT mice but not in untreated ABA mice (Fig. 6C ). Finally, BDNF mRNA levels remained unchanged (Fig. 6F ).
, 2018) , also showing robust categorization of a specific expression among each of the other basic expressions  (Luo & Dzhelyova, 2020)  or among mixed facial expressions rather than a neutral face  (Poncet, Baudouin, Dzhelyova, Rossion, & Leleu, 2019). Despite these impressive results, rapid automatic categorization of facial expressions across different identities has not yet been demonstrated with this approach. Most importantly, highly homogenous sets of visual stimuli, that is, full front faces of human adults presented out of context and with posed expressions, most often extracted from databases where the facial features are exaggerated (e. g. , "Radboud Faces Database":  Langner et al. , 2010 ; "Karolinska Directed Facial Expression Database": Lundqvist, Flykt, & € Ohman, 1998; "NimSTIM":  Tottenham et al.
Preprocessing The continuously recorded data were first filtered using a bandpass filter at. 1e120 Hz (Butterworth filter, fourth order) and then resampled to 256 Hz to save disk space and reduce computational load. The data were then cropped into 56 sec segments (4 sec before and 2 sec after each stimulation sequence corresponding to the 2 sec fading in, 2 sec fading out and 2 extra sec). For 5 participants who blinked on average above. 1 times/sec (average number of blinks across participants ¼. 1, SD ¼. 07), blinks were corrected by means of independent component analysis (ICA) using the runica algorithm  (Bell & Sejnowski, 1995; Makeig, Bell, Jung, & Sejnowski, 1996) , as implemented in EEGLAB. This algorithm outputs a square mixing matrix in which the number of components corresponds to the number of channels.
Frequency-domain analysis Preprocessed data segments were cropped down to an integer number of 1. 2 Hz cycles beginning immediately after the fade in, until approximately 49. 18 sec (z50 sec, 59 cycles, 12589 time bins in total) in order to separate each condition. Data were then averaged in the time domain, separately for each condition: Orientation (Upright, Inverted) and Facial Expression (Disgust, Fear, Happiness, Surprise, Anger, Sadness) and for each participant. A Fast Fourier Transformation (FFT) was then applied to these averaged segments and amplitude spectra were extracted for all channels. The FFT transformation yielded a spectrum ranging from 0 to 256 Hz with a high spectral resolution of about 1/49 sec, i. e. ,. 02 Hz. Z-scores were calculated on the FFT grand-averaged data pooled across all electrodes for each condition in order to assess the significance of the responses at different harmonics.
Data and code availability The study data is available at https://data. 4tu. nl. The EEG data were analyzed using the open source Letswave software package (https://github. com/NOCIONS/Letswave5), running in MATLAB R2013a (MathWorks, USA). All face stimuli used in the experiment were collected from the Internet. The identity of the copyright holders was unknown, thus preventing us from making the face stimuli publicly available or sharing them. EEG data, behavioral data and data analysis steps of the study are available at https://osf. io/tkrha/. The experimental design was implemented using custom software written in Java. The code is available at https://osf. io/ tkrha/.
MATERIALS AND METHODS Data were collected using a survey conducted with parents of French children aged 4-36 months, aiming to describe both parental feeding practices with regard to food texture introduction and texture acceptance by children. The cross-sectional survey was launched online through a large database of members of the web information programme of the Blédina brand [declared to the national data protection authority, the Commission Nationale Informatique et Liberté (CNIL), no. 1824320v0] from September to December 2015. The survey was approved by the local ethics committee (Comité de Protection des Personnes Est III, no. 2015-A00323-46).
Statistical Analysis Data were split into 14 age classes in agreement with  (31)  Statistical analyses were run using SAS 9. 4 (SAS Institute, Inc. , Cary, North Carolina). For each age class, we determined the ratio (%) of food-texture acceptance "spat out, " "eaten with difficulties, " "eaten without difficulties" over the total number of combinations offered within each texture level (T1, T2, T3). The evolution of these ratios with age was assessed using oneway analyses of variance (ANOVAs) and Student Newman-Keuls post-hoc analyses to compare mean values. The impact of texture on the ratio was studied for each age class using one-way ANOVAs and Student Newman-Keuls post-hoc analyses. The effect of age class on TextAcc was assessed using ANOVA and the Student-Newman-Keuls test post-hoc analysis.
Study Population A total of 3,771 respondents participated in the survey. Data from respondents other than mothers (fathers or grandmothers, n = 71), twins (n = 37), children born at a gestational age under 37 weeks of amenorrhea (n = 137), with severe gastroesophageal reflux (n = 247) or tube-fed at birth (n = 139), aged below 4 months or above 36 months (n = 131) and missing data with regard to food texture introduction (n = 10) were excluded, yielding a final sample of 2,999 children. Most of the mothers were born in France (94. 6%), their age was 31. 1 (SD 4. 7) years on average, and 65. 0% had attained an educational level of 2-3 years of university or more. The characteristics of the children are described in Table  1. Children were mainly first-born (77. 1%) and balanced in gender (48.
e. , rough purees at 6 months) and of foods with pieces and harder texture (2nd year of life). A relationship between gagging and texture has been reported earlier in 8month-old children: gagging in response to food pieces was more frequent than in response to pureed foods  (19). Gagging is a normal reflex due to the high tactile sensitivity of the inner sides of the cheeks (39) and usually decreases upon repeated exposure to (new) textures. However, gagging has been reported earlier as behavior occurring in children presenting eating difficulties  (40)  and in tactile defensive children  (22). Here, gagging was evaluated as a general behavior of the child and not as a specific behavior related to a given food, so unfortunately, these data do not enable a further understanding of which food texture may specifically provoke a gag response.