dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research was supported by the  Research Federation  ( FR CNRS 2052 ) Visual Sciences and Cultures and funded by the  ANR-21-ESRE-0030 - Equipex + Continuum national Grant  from the  Program of Investments of Future. MFG was financed by the  Region Hauts-de-France  and the  University of Lille. We are grateful to SCALab members  Alicia Fasquel ,  Sarah Galisson ,  Lucie Lenglart  and  Mathis Colette  for their precious contribution to data collection, and to  Laurent Ott  for his support in coding and programming issues encountered during the study. Authors express their appreciation to the comments of the reviewers, which highlighted interesting points and expanded the interpretation of the present findings. We also thank  Estelle Chatelain  ( US 41-UAR 2014-PLBS  platform bilille) and  Guillemette Marot  ( ULR 2694  METRICS) for their help in answering statistical concerns of reviewers.
From the main sample, we selected 20 older adults for test-retest analysis. For practical reasons, only Belgian participants were selected. To ensure that the selection of participants was random, we selected one in every 5 participants in each setting until we had 6 NH residents and 14 PC patients. Those participants were contacted by phone (or, in the case of some NH residents, visited again) and asked to complete the questionnaire a second time, one to two weeks after the completion of the first questionnaire. Paper questionnaires were encoded using REDCap as an electronic case report form. 25 Online questionnaires were hosted and distributed via Qualtrics software, Version 01/2021 (Qualtrics, Copyright © 2021. Provo, UT, USA. Available at https://www. qualtrics. com; accessed on January 2021).
b. Construct validity An EFA was carried out on the data of the participants who answered all the items (n = 200). Overall, no item was systematically not answered and there were few missing data. Many items showed a floor or ceiling effect >30% (see Supplementary Table  1 ). The data showed   2 ). A new EFA was carried out on the data of the participants who answered all the retained items (n = 209). The data showed improved adequacy for factor analysis compared to the initial analysis (Kaiser-Meyer-Olkin test: 0. 79; Bartlett's test of sphericity p < 0. 001). The scree test, Kaiser-Guttman criteria, and parallel analysis indicated 2 to 3 factors to extract. Three factors were extracted. Loadings were significant for all items and, this time, no cross-loadings were observed. The final model includes 11 items and 2 global questions (see Table  2 ) and explains 47% of the variance.
Acknowledgments We would like to thank  Dr. Emily Reeve , for permitting us to adapt her questionnaire and for having provided feedback on our results. We are also grateful to  Prof. Gaëtane Caesens  ( UCLouvain ) and the  Support en Methodologie et Calcul Statistique (SMCS)  team for providing expertise and assistance during the data analysis. We thank  Mrs. Laura Jones  for having proofread the manuscript for English. We also thank all healthcare providers and NH that helped in recruiting participants for this study, which required extra effort in the COVID-19 pandemic context. Finally, we thank warmly all older adults that participated in our study.
Data collection tool and procedures In-depth interviews were conducted to get information on the awareness about and care seeking of long COVID symptoms. Nine researchers (PhD students) performed the interviews. All responders provided their informed consent. A structured interview guide that was written in Amharic (the local language) was used to conduct the interviews. Before beginning the actual data collecting, the interview guide was pre-tested. Two days of training for data collectors were given in order to harmonize the data collection process. Face to face interviews were conducted to assess the participants' feelings. The entire interview has been conducted under COVID 19 precautions.
Data analysis Individual interviews were transcribed word by word, and contextual translation was made. Open Cod 4. 03 software was used for coding and synthesizing. Thematic Analysis was used to analyze the transcripts. To ensure inter-rater reliability, all the investigators analyzed data, and interpretations of themes were shared and discussed to agree upon key themes that emerged from the data. Close and repeated reading of transcripts, regular discussion about emerging findings, and triangulation of findings were done to strengthen the quality of the analysis.
Dependability The study was undertaken by PhD students with different educational backgrounds. During data collection, since there was a mix in the understanding of the question, the researchers agreed to include a statement with a clear definition about long COVID-19 at the beginning of the question. During data analysis, the translated document of the 23 participants was first coded from P1-P23. The responses for each question were collected from 23 participants and compiled in a word document. Coding was done by displaying the compiled word document for all the researchers.
Procedures The present study has been conducted in accordance with the ethical standards established for human studies  [11] , and was approved by the Ethics Committee for Sports Sciences of the University of Coimbra (CE/FCDEF-UC/00182016). Participants were recruited from clubs in Portugal. Informed consent was signed by parents or legal guardians and, subsequently, swimmers were informed about the objectives, methods, benefits, and risks of the study and could withdraw from the experiment at any time. Data were collected following standardized conditions using the same equipment and by a single experienced investigator who accumulated >500 assessments.
Then, the initial five degrees of flexion were suppressed to allow the participant to exert at least 10% of the defined torque limit. The effect of gravity was corrected as recommended by the manufacturer using the Biodex Advantage software package (Biodex Medical Systems, Shirley, NY, USA). The preceding occurred after weighing the member relaxed at the angular position of 30˚  [18]. As previously published, participants were instructed to keep their upper arms crossed and hands on the opposite shoulder during the test  [17] [18] [19]. The protocol started with three continuous reciprocal concentric contractions of KE and KF for familiarization. After a 60-second interval, five reciprocal maximal concentric (cc) contractions of KE and KF were completed  [20, 21]. Then, participants rested for 60 seconds and the sequence was repeated for the reciprocal eccentric (ecc) contractions of KF and KE.
For each set (KEcc/KFcc; KFecc/ KEecc), the five isokinetic curves were visually inspected before using the software AcqKnowledge 4. 1 (Biopac Systems Incorporated, Goleta, CA, USA) to confirm data quality. Torque values were valid if obtained within 95% of the settled angular velocity, that is, between 57˚. s -1 and 63˚. s -1. The preceding routine allows the extraction of peak torque (PT) values, expressed in N. m, and respective angular position. PT refers to the highest value obtained in the range of motion of an isokinetic curve  [21].
B. Experiments with Synthetic Humans We used the method described in Sec. IV to compute the stochastic human FSCs associated to each human objective, and the method described in Sec. V to compute the robust robot policy with the POMDP solver SARSOP  [23]. To save resources, an action-threshold (here always set to 0. 1) is used to prune low-probability human actions when computing the stochastic human FSCs. We conducted experiments with 3 values for the softmax parameter T (0, 0. 3 and 0. 5) and several values for the maximum stochastic FSC size N max. 1) Qualitative Results: We first observed that the extracted human stochastic FSC can effectively encode possible trajectories of the human to solve the task if N max is large enough for the current T. For instance, when T = 0. 3 and N max = 100, the extracted human stochastic FSCs reach depths 2  of 22 and 18 (for each objective), which are sufficient for the human to finish the task if the robot collaborates.
Finally, females who occupy central positions in a group's social network typically interact more with their groupmates, potentially leading to higher spatial 'centrality', exposing them to greater coercion simply because they are more visible and encounter males more often than peripheral females. Here, we test the influence of female-male social bonds and female-female social integration on the intensity of male sexual coercion that females face, using a 9-year dataset on wild mandrills. Mandrills are seasonally breeding primates of the Cercopithecidae family, living in large polygynandrous groups. 44  Males are 3. 5-times heavier than females  45  and this physical asymmetry probably facilitates males to obtain mating opportunities through sexually coercive strategies rather than bribing females by offering services.
Individual traits Dominance ranks We used the function DS from the R package EloRating version 0. 46. 11  71  to compute intersexual hierarchies based on submissive dyadic interactions that were recorded between and within the sexes during focal or ad libitum observations, as per. 49  This function produces individual David's scores  78  using the proportion of wins of each individual in each dyad (corrected for the number of agonistic interactions; prop=''Dij''). Differences between individual scores correspond to dominance asymmetries, calculated for each female-male dyad. Previous results have shown that higher-ranking female mandrills receive significantly more male aggression and that higher-ranking males are more coercive. 47  In our models described below we therefore used both female and male rank as control variables, retrieved from the intersexual hierarchy (based on both within and between-sex interactions) because high-ranking females occasionally outrank some males.
Female-female bonds (female-only networks) We constructed female social networks including only grooming interactions because in many primates, grooming partners are likely to support each other during coalitions against conspecifics of either sex. 83  A female was observed grooming with another female on average 4. 91G5. 35 mins (GSD) per hour of observation. For each mating season, we constructed a network using data from the three last months of the preceding birth season (January-March), for two main reasons. First, female social interactions recorded just before the arrival of immigrant males are more likely to reflect the social landscape that these males face when entering the group and making decisions such as choosing which female to target. Second, the birth season may be more appropriate to measure stable patterns of female-female social bonds, independently of the stress and disruptions generated by sexual interactions, male-male competition, and male hierarchical instability in the mating season  83, 84  (for mandrills see also  85  ).
High values of both eigenvector centrality and grooming strength in other species have been associated with higher female fitness. 91, 92  We used the function graph_from_data_frame to create grooming and proximity networks and the functions centr_eigen and strength to compute eigenvector centrality and strength of each individual, respectively (R package igraph version 1. 4. 1  72  ). Finally, we used TikZ (TeX) and the R package ggplot2 76 to visualize our results.
ACKNOWLEDGMENTS We are grateful to past and present field assistants of the Mandrillus Project who collect daily data on the study population and to the  SODEPAL-COMILOG  society for their logistical support and contribution to the Mandrillus Project. We are also grateful to the handling editor and three anonymous referees for their useful suggestions. The silhouette icon of male mandrill was downloaded from http://phylopic. org (credit to  T. Michael Keesey  and  Owen Jones ) and the female icon was extracted from the logo of the Mandrillus Project. The Mandrillus Project is funded by several grants that allowed long-term collection of data:  SEEG Lekedi (INEE-CNRS) ,  Agence Nationale de la Recherche  ( ANR SLEEP 17-CE02-0002 ),  The Leakey Foundation  ( S202210309 ), and The  Max Planck Society  to MJEC.
The area of the ellipse was taken as a relative measure of pupil size in each imaging session. The data were z-scored for analysis. Imaging frames where the mouse closed its eye or where a pupil could not be detected were interpolated using adjacent frames. Pupil phase was calculated by z-scoring and filtering the pupil size using low pass filter (0. 1Hz) and then taking the angle of Hilbert transformed signal. Pupil dilation was defined as the rising phase from -120 degrees to 0 degrees where -180 and 180 degrees signify the trough, and 0 degree is the peak. For the analysis of facial motion, a region of interest was placed on the vibrissae pad. The absolute value of the mean difference in pixel intensity between adjacent frames was calculated for all adjacent frames (49, 51) and severed as a measure of facial movements.
Local field potentials (LFPs): LFP and reference electrodes were connected to a head stage (A-M Systems Model 1700) and connected to a differential amplifier (A-M Systems, Model 1700 Differential AC Amplifier). Signals were high pass filtered at 0. 1 or 1Hz and lowpass filtered below 1kHz and digitized at 30kHz on the Thorlabs system. LFPs were synchronized with calcium imaging using a binary pulse that was sent to the digitizer for every imaging frame. LFPs were analysed using the chronux signalling processing toolbox (82). Power spectrums were calculated on three second periods of data centered on each imaging frame, using the multitaper spectrogram function in chronux. Therefore, each imaging frame had an associated spectrum. The mean delta power (0. 5-4Hz), theta (6-11Hz), and gamma (30-100Hz) power were extracted, and the thetadelta and gamma-delta ratios used for analysis.
For this reason, our conclusions were arrived at through an analysis of single ROI, the mean claustrum population activity, and the averaged population activity within and across animals. All major claims were supported at all levels of analysis (ROI, sessions, mice). Control imaging with GFP labelled claustrum axons confirmed no observable z-drift on the imaging setup during wake and sleep (Figure  S1 ). For locomotion, we used auto fluorescence blebs that showed higher background fluorescence without observable calcium transients (47, 51), and measured their change in fluorescence across time, in order to ensure a lack of z-motion (Figure  S2 ). The correlation between claustrum ROI (or claustrum population activity) and other variables was performed using the 'corr' function in Matlab. The time-lag correlation analysis was performed by calculating the correlation between two variables, shifting the one variable by different positive or negative time lags.
The correlation asymmetry was calculated by taking the mean time-lag correlation coefficient in the from the positive time lags ranging from 0-5s minus the mean correlation coefficient within the -5s -0s time lag interval. Head-fixed sleep-wake scoring: Sleep-wake scoring in head-fixed mice was performed using LFPs, pupil, and face motion and was carried out similar to previous reports (43-45). We used an automated sleep-wake scoring procedure. The theta (6-11Hz) to delta (0. 5 -4Hz) spectral power ratio was calculated on 3s epochs centered on each imaging frame to obtain a theta-delta ratio value as a function of calcium imaging data. The theta-delta ratio, pupil, and face motion data were each smoothed using a 10s sliding window, and z-scored. NREM was defined as periods where pupil and face motion z-score were less than 0 and the z-score of the theta-delta ratio less than 0.
REM sleep was defined as periods of time when the z-scored pupil was less than 0, and z-scored theta-delta power greater than 2 standard deviations above the baseline theta-delta ratio. Baseline theta-delta was calculated using the lowest 95 th percentile of the data within a session. Rapid eye movements during the detected REM periods provided further evidence that the mouse was in REM sleep. All other periods were defined as quiet wakefulness. REM epochs lasting less than 15s and flanked by NREM were re-scored as NREM. REM epochs separated by less than 20s were interpolated as part of the same REM epoch. Likewise, periods of NREM less than 5s flanked by wake were rescored as wake. REM epochs were required to last at least 30s to be included in the analysis otherwise these periods were removed from analysis.
The signal was filtered between 1Hz and 1000Hz at the amplification stage and acquired at 1219Hz. A camera recorded pupil size and face movement as described above. Each mouse underwent 5-7 stimulation sessions separated by at least 48 hours. In vivo pharmacology: Pharmacological experiments were all performed on the head-fixed mice. The baseline period consisted of a 20-minute recording where mice spontaneously transitioned between locomotion and rest. Mice were then briefly anesthetized with isoflurane before receiving a dose of either atropine to block muscarinic receptors (10mg/kg), physostigmine, an inhibitor of acetylcholinesterase (0. 6mg/kg) or saline (0. 9% sodium chloride, subcutaneously). Post-injection data was recorded for 60-80 minutes. Throughout pre-and post-drug or saline recordings, the claustrum was continuously stimulated with 10-ms pulses of 470nm light at 2Hz as detailed above in blocks of 30s separated by 5 minutes.
LFP analysis: The putative monosynaptic post-synaptic potential was measured by first averaging the LFP response across all trials within a defined brain or behavioral state. This average trace was smoothed (5ms sliding window), and the absolute slope and amplitude were calculated on this final LFP waveform from 10-100ms following the start of the light pulse for each state. To compare across states measured during sleep experiments and experiments on the treadmill, we normalized the evoked response to the quiet wakefulness state, which was obtained in both awake and sleep experiments. Delta wave detection: Delta waves were detected as described previously (71), where field potential data were filtered between 0. 5-6Hz, and zero-crossings of the first derivative of this signal were detected corresponding to the start, peak, and end of each wave.
Thus, this index indicates at each time t, the phase difference of the oscillations (bounded between 0 and 1). For each mouse and each condition, we used the value of the maximum point of this index in the post-stimulation time period and assigned this as the phase locking index. To compare and determine the significance of the mean response between eYFP and ChETA mice, we compared the difference between the grand mean response in the two groups to a distribution of bootstrapped datasets that were resampled from the eighteen mice (n=8 eYFP, n=10 ChETA). The absolute value of the difference between two groups were compared. For each bootstrapped iteration (n=1000), the grand mean response for two surrogate data sets were calculated. Each surrogate set contained the mean response from ten randomly selected mice (5 eYFP mice and 5 ChETA mice).
Acknowledgements: We thank The  Faculty of Medicine & Dentistry Cell Imaging Center  for support, and  Anna Taylor  and  Jeremy Cohen  for comments on the manuscript. Funding was provided by the following:  Canada Foundation for Innovation John R. Evans Leaders Fund  (JELF), Grant/Award Number:  37931 ,  Canadian Institutes of Health Research , Grant/Award Number:  426485 ,  National Alliance for Research on Schizophrenia and Depression, Natural Sciences and Engineering Research Council of Canada , Grant/Award Number:  RGPIN2018-05212 , and  CNRS International associated laboratory ,  LIA JOURNEY. JJ holds a  Canada Research Chair award.
Working memory and attention are jointly needed in most everyday life tasks and activities. They have however mostly been studied separately. Here we investigate how auditory working memory and selective attention interact using a recently introduced paradigm (MEMAT) that combines a classic working memory paradigm, the delayed-matching-tosample task, and selective attention, with distractors presented during the encoding phase. All stimuli are four-tone melodies. Twenty-two participants performed the MEMAT task during MEG recordings. We manipulate the difficulty of the memory task and of attentional filtering. When memory task difficulty increases, the amplitude of the CNV in anticipation of the melody to encode increases and the decrease in alpha power during encoding and maintenance in a left fronto-temporal network is reduced.
Participants Twenty-two non-musician participants  (20 right-handed, 11 men and 11 women, aged 20-33  years, mean education level ± standard deviation, SD: 15. 5 ± 2. 0 years) participated in the experiment. Participants were considered as non-musicians when they practiced less than 1 year of instrument or singing outside compulsory educative programs (mean music education level ± SD: 0. 4 ± 0. 7 years). Note that these participants were selected to have low or high frequency of dream recall, in order to be able to study the relationship between dream recall frequency (DRF), attention, and short-term memory in analyses not presented here. Behavioral data collected in the present study were analyzed as a function of participants' DRF in  Blain, de la Chapelle et al. (2022). Detailed neuropsychological testing and questionnaire data can be found in  Blain, de la Chapelle et al.
Measurements Data are analyzed using Signal Detection Theory measures. dprime (d') is the difference between normalized "Hits" and normalized "False alarms". Hits are the proportion of correct "different" answer over all "different" trials, and False alarms are the proportion of incorrect "different" answer over all "same" trials. Criterion (c) indicates the response bias. Positive values indicate the tendency of answering "same" and negative values reflect the tendency of answering "different". Median Reaction Times (RTs) are computed out of correctly answered trials and correspond to the time between the end of the S2 and the button press.
Statistical analyses for the behavioral data All analyses are conducted using Bayesian statistics, which allow to test the similarity between measures and to estimate a degree of logical support or belief regarding specific results, as implemented in the JASP software (v0. 16. 3, JASP Team, 2022;  Marsman & Wagenmakers, 2017; Wagenmakers et al. , 2018; van den Bergh et al. , 2022). We report Bayes Factor (BF 10 ) as a relative measure of evidence (compared to the null model). To interpret the strength of evidence against the null model, we consider a BF between 1 and 3 as weak evidence, a BF between 3 and 10 as positive evidence, a BF between 10 and 100 as strong evidence, and a BF higher than 100 as a decisive evidence  (Lee & Wagenmakers, 2014). Similarly, to interpret the strength of evidence in favor of the null model, we consider a BF between 0.
Brain recordings MEG data are continuously recorded using a 275-channel whole-head axial gradiometer system (CTF-275 by VSM MedTech Inc. , Vancouver, Canada) with a sampling rate of 600Hz and a 0. 016-150Hz bandpass filter. Head movements are continuously monitored using 3 coils placed at the nasion and the two preauricular points. For each participant, a T1-weighted 3D MRI is obtained using a 1. 5T or 3T wholebody scanner (Magnetom Siemens Erlangen, Germany) after the MEG session. At the end of MEG session, locations of the nasion and the two preauricular points are marked using fiducials markers visible on the T1 acquisitions. The T1 images are used for reconstruction of individual head shapes to create forward models for the source reconstruction procedures. Data pre-processing MEG data are pre-processed offline using the software package for electrophysiological analysis (ELAN Pack) developed at the Lyon Neuroscience Research Center  (Aguera et al.