dataesr/scientific-paragraphs-categorization huggingface dataset:
However, some limitations to this work are worth mentioning. The included population is a convenient sample, which limits the generalization of the results to the national population. Primiparity and educational attainment were higher in our sample than reported in the general French population  (43). These parameters are known to influence maternal feeding practices and therefore may have impacted the results. In addition, participating mothers were registered and recruited via the Bledina potential consumers database; however, they were not necessarily feeding their child (exclusively) with commercial baby food (only 14% of them were exclusively using this type of food to feed their child). Thus, future works should aim to extend the current study in a sample that is more representative of the national population, in terms of both parents' and children's characteristics.
Methods The studies were approved by the Institutional Review Board of the Centre de Recherche Multidisciplinaire Sorbonne Universites-INSEAD. All participants provided written consent. All the stimuli, additional pictures, R code, and datasets are accessible in the Supporting information. The data were collected in 2018, at a time where preregistration was not required for exploratory studies. For this reason, the two studies were not preregistered. We have deposited our laboratory protocols in protocols. io to ensure reproducibility of our results.
In addition, we measured interest in the discussion and self-reports of distraction. We included the PANAS scale to control for positive affect (α = 0. 77,  [69] ) slightly deviating from Przybylski and Weinstein  [12]  who used the Emmons Mood Indicator  [70]. The last part of the questionnaire included questions on phone ownership and usage, and ended with demographics questions (e. g. , gender, age, language) and mood measures. Participants were probed for suspicions, both in the written questionnaire and orally. None of the participants guessed the purpose of the study. Finally, participants were debriefed and thanked. Statistical analysis. As participants were nested into groups, our data structure is hierarchical, with the manipulation (smartphone vs. notebook) at level-2 and the dependent variables either at level-1 (e.
For level-2 toy creativity scores, we conducted t tests as both the manipulation and the dependent variables were at the group-level. In Study 1, we first conducted analyses on dyads to closely follow Przybylski and Weinstein  [12]. We then extended the analyses to triads, and finally to dyads and triads taken together. Study 2 included only triads. The level of significance was set at a two-tailed p value of 0. 05. Multilevel analyses were performed in R using the nlme package for the model estimations and the performance package for the ICCs. Results remain the same with SPSS and the R package lme4. T tests were performed in SPSS. Results. Relationship quality  (Przybylski and Weinstein [12] ). Tables  2  and 3  present the results of our multilevel analyses for dyads compared with results reported in Przybylski and Weinstein  [12].
g. , in populations where the use of the smartphone may not be as pervasive as in large European cities). Second, even though our sample sizes were much greater than those in Przybylski and Weinstein  [12] , they may not have provided enough power to detect an effect  [77]. Third, we measured relationship quality after the creative tasks and self-reports on the creative process, which might have wiped out the effect due to fatigue or contamination. Is there a way to reconcile our results with those of Przybylski and Weinstein  [12] ? We see several possibilities, all of which relate to the timing of our experiments. We collected our data in 2018, compared to Przybylski and Weinstein (in or before 2012)  [12]. The first possibility is that people might have simply gotten used to the presence of mobile devices, which could make them immune to their mere presence.
The frequent co-occurrence of post-traumatic stress disorder (PTSD) and substance use disorders (SUDs) leads to manifestations of both conditions that are more severe and more resistance to treatment than single disorders. One hypothesis to explain this synergy is the impact of intrusive memories on craving which, in turn, increases the risk of relapse among patients with substance use disorders. The aim of this systematic review is to examine this possibility by assessing the impact of PTSD and its symptoms on craving among dual disorder patients. Using PRISMA criteria, four databases were comprehensively searched up to June, 2021, in order to identify all candidate studies based on broad key words. Resulting studies were then selected if they examined the impact of PTSD or PTSD symptoms on craving, and if they used standardized assessments of PTSD, SUD, and craving.
Since the majority of the studies included in our review concern alcohol and tobacco, it would be also interesting to expand this research to other substances as well as to behavioral addictions. Specifically, no studies examined the association between craving and MDMA or psychedelic drugs, that is a major issue in view of novel treatment approaches of PTSD. Our data therefore challenge our current clinical practice in the treatment of patients suffering from dual diagnosis, and argue for the integration of an additional trauma-focused strategies into addiction facilities, notably including cognitive-behavioral therapies based on prolonged exposure. While all individuals suffering from SUD should be systematically assessed for trauma history and PTSD, the present data suggest that PTSD treatment should not be delayed until abstinence has been achieved.
In the second step, we build our model using a BiGRU network to ensure capturing contextual information from the input signals. To enhance our model's performance, we feed our model an additional input "difficulty indicator" on unimodal prediction and fusion stage. The difficulty indicator is obtained using an Auto-Encoder (AE) for each modality. The remainder of this article is organized as follows. In Section 2, we briefly present related work. In Section 3, we give a detailed explanation of our approach and describe the structure of our model. Then, in Section 4, we provide a description of the dataset, features, and model training settings. In section 5, we present and discuss the results of our experiments. Finally, we present our conclusions in Section 6.
Corpus Description The dataset used for the MuSe-Stress sub-challenge is the Ulm-TSST database  [34]. It consists of 69 German-speaking participants, aged between 18 and 39 years, in a stress inducing setup following the Trier Social Stress Test procedure  [15]. Each participant is asked to present orally for approximately five minutes in front of two interviewers. Video and physiological recordings are taken during the presentation. The total duration of the database is 5h: 47min: 27s. Arousal and valence are annotated by three raters and the fusion of the annotations is done using the RAAW method  [34]. The given modalities are audio, video, transcripts in addition to the physiological signals Electrodermal Activity (EDA), Electrocardiogram (ECG), respiration and heart rate (BPM).
According to this model, children and adolescents with insecure attachment, who present important peritraumatic reactions and a high level of behavioural and cognitive avoidance in the first months following the loss would be at high risk for PGD. This study has several limitations. First, the small and predominantly female (67. 6%) sample and longitudinal data that were limited to only three timepoints, both of which precluded examination of symptoms trajectories, adjustment for potential confounding variables such as parental or child's prior history of trauma, age of the children or the loss of father or mother, and the examination of the moderating effects of parental symptoms. We nevertheless found no significant association between age and PGD symptom severity. Second, we assessed the peritraumatic reactions retrospectively, and administered the PDI-C and PDEQ-C a few months after the loss.
Combination of facial muscle activations (e. g. , raised eyebrows, wrinkled nose, tightened lips) are differentiated as Action Units (AU) of micro expressions -i. e. , instantaneous facial movements hardly perceived by the naked eye -and are subsequently identifiable as an experienced emotion  (Ekman and Rosenberg, 2005). For instance, lip-corner raising is identified as an AU12 that, among others, is associated with joy. Thanks to progress in computational techniques and the increase in size of facial expression datasets, raw data rather than FACS or hybrid machine learning architectures are now used to let the mathematical models identify the relevant muscle patterns. For a detailed overview on the embodiment of emotions in facial expressions please refer to  Barrett et al. (2019). For an overview on automatic recognition of facial expression see  (Küntzler et al.
De Gelder and Poyo Solanas (2021, p. 1) define these features as middle level features and suggest that "behaviorally relevant information from bodies and body expressions is coded at the levels of mid-level features in the brain". A computational framework to model non-verbal emotions was proposed in the MEGA European project  (Camurri et al. , 2005)  and a more recent approach was proposed in  Camurri et al. (2016). By taking advantage of advanced machine learning architectures, there is today the tendency to use a more agnostic approach based on the temporal sequence of row data (e. g. ,  Wang et al. (2021)  from movement (e. g. , 3D position of each joint, angles between body segments) or muscle activity sensors (e. g. , intensity of muscle activity). Still, given the limited size of the datasets and also the complexity of body expressions when embodied in everyday activity, recognition performance may gain by a combination of low and middle level features.
This is again thanks to a technology that is more portable and acceptable for everyday use, enabling the extraction of continuous signals, extending the possibility for measuring a large set of statistical features and in particular features that characterize the variability of these signals  (Cho et al. , 2019). In a similar way to the work on non-verbal modalities, there is also the tendency to use advanced machine learning techniques that can work directly on raw data or on low-level statistical features extracted continuously over moving windows of the signals  (Wang et al. , 2021). However, this approach is still challenged by the limited size of the available datasets. While each modality carries emotion information, studies have shown that multimodal recognition systems tend to lead to better performances  (Al Osman and Falk, 2017; D'Mello and Kory, 2015; Poria et al.
g. ,  Cheng et al. (2016) ;  Rivas et al. (2021) ;  Wagner et al. (2011)). Transfer learning approaches have also been used to this purpose together with addressing the problem of limited dataset size (for a review see  Feng and Chaspari (2020) ). However, these are not the only critical questions that challenge the affective computing community. Most of the work so far has focused on mapping face, body and physiological features or their combination into emotion semantic concepts. As we move into real-world applications, such approaches are quite limited as affective experience, and its perception, are subjective processes shaped by various factors such as context  (Barrett et al. , 2019a)  and personality  (Komulainen et al. , 2014). Transfer learning approaches have been used to support the development of models between for example datasets built in the lab and smaller ecological dataset, or to compensate for the limited size of such datasets  (Feng and Chaspari, 2020).
While the work above is supported by the increasing number of multimodal and also multifactors datasets, e. g. , MAHNOB-HCI  (Soleymani et al. , 2012) ; DEAP  (Koelstra et al. , 2012) ; EMOPAIN  (Aung et al. , 2016) , ASCERTAIN  (Subramanian et al. , 2018) , there is the need for larger real-life datasets that are more inclusive and capture the variety of (social and activity) contexts, as well as the variety of emotional expression. While personality surely contributes to the experience, response and perception of emotions, there are many other personal factors (e. g. , cognitive and physical impairment) that are critical to these processes. Existing datasets are still largely lacking the investigations of the above questions. Multi-agent embodiment of emotion. One common characteristic of all the studies reviewed above, and of the-state-of-the art of embodied emotion in general, is that they all, almost exclusively, investigate the embodied manifestation of emotions in one individual in space and time (see  Niedenthal, 2007).
In general, captured data are used to infer human affective states to which social robots adapt in various semi-autonomous ways. Scholars in HRI have adapted and simplified the human emotional repertoire to social robots. For instance, the ASIMO, JUSTIN and NAO robots are programmed to express six basic emotions (and their various combinations): anger, disgust, fear, happiness, sadness, and Bridging the gap between emotion and joint action. | Bieńkiewicz et al. (in press, NBR)  surprise. In general, human participants correctly recognize all basic emotions from the upperbody movements with a success rate of 75%-100%, with some exceptions however (van de  Perre et al. , 2015). Other robots, such as the iCUB robot  (Metta et al. , 2008) , crawl or semiautonomously manipulate objects in various dyadic contexts, and learn by doing and imitating  (Billard and Dautenhahn, 1998; Boucenna et al.
Participants and clinical assessment. Included participants were older than 18. Participants with hand injuries, pathologies affecting tactile sensitivity (as diabetes) or neurological or cognitive disorders were excluded from the study. AN patients were assessed by a psychiatrist following DSM5 criteria. Healthy controls (HC) were recruited among volunteers taking part in another validation trial of the task. Exclusion criteria for HC were: self-declared psychiatric diagnosis, neurological or cognitive disorder with sensorimotor impairment, assessed by an interview with the participant. The clinical data collected for AN participants were: current BMI, minimal BMI, subtype of AN, disease duration. Participants were matched for age, as hand width representation reduces with age (Dupin et al.
Data analysis. The main measure for each participant was the ratio between normalized hand width and finger length (noted rWideness for relative-i. e. normalized-wideness of the hand). For each condition (VER-BAL, TACTILE, PHYSICAL) and each participant, the barycentre of each target (nail/metacarpophalangeal joint of each finger) repetition was computed. We used this barycentre as the responded location of the target. Normalized hand width. Hand width was computed as the distance between metacarpophalangeal joint of the little finger and the thumb. Hand width in TACTILE and VERBAL conditions was divided by PHYSICAL hand width in order to normalize data between participants. The length of each finger was computed as the distance between the nail and the metacarpophalangeal joint of each finger.
Then the mean of the five finger lengths was computed. Normalized finger length. Finger length in TACTILE and VERBAL was divided by PHYSICAL finger length in order to normalize data between participants. The relative wideness of the hand corresponds to the ratio between hand width and finger length of the representation both normalized over the corresponding physical hand extent. In other words, rWideness is the relative wideness of the representation compared to the one of the real hand. rWideness was computed so that a value of 1 means that the representation has the same ratio between hand width and length of the participant's physical hand. A rWideness lower than one means that the representation of the hand is narrower than the physical hand and a ratio greater than 1 means that the representation is wider.
PATIENTS AND METHODS Eligibility criteria Patients were eligible if they had received an allogeneic HSCT at least three months earlier, could speak and read French, and had signed an informed consent form. Patients could not be included if they presented an active GvHD, suffered from a progression of their hematological disease or presented with an active clinical or psychological complication needing active treatment. People unable to give their consent, or unable to follow the trial for geographical, social, or psychological reasons were not included. Thirty-two eligible adults patients were included in the study. The study (ClinicalTrials. gov Identifier: NCT03272789) was approved by the institutional and ethical review boards and the "Agence nationale de sécurité du medicament" (ANSM).
For confidentiality, the content of the sessions was not communicated to the researchers. Data collection. Time points protocol is shown in the supplementary table. Qualitative: Patients were questioned after coaching sessions in individual face-to-face semi-directive interviews with one of the researchers. Interviews to study the impact of the support program were scheduled (1) before the first session, (2) just after it, and (3) after the last session. The questions addressed satisfaction with the program, perception of its general characteristics (schedule, perception of the "sport coaching"), its consequences for the patient's well-being, the envisaged or initiated changes in behavior at the end of the program, and the relations established with the coach during the intervention. Quantitative: To evaluate the effects of the coaching and measure recovery progress.
Changes from the baseline of the different scores were analyzed using a linear mixed model. The principal criterion of the study was the patients' rate of acceptance of the offered program, measured by the empirical proportion of patients completing their first session with the coach, and was estimated by the proportion observed and its exact bilateral 90% confidence interval  [43]. The secondary criteria were the number of coaching sessions attended, the self-administered questionnaire completion rates at each visit, and changes in questionnaire scores over time (see supplementary material for a detailed explanation of the statistical analyses). Qualitative data were obtained from the semi-structured interviews. All interviews were recorded, transcribed verbatim, and analyzed by inductive qualitative analysis using the NVivo 12 software  [44]  on the methodological basis of framework analysis used in applied disciplinary fields and leading to coding by category  [44].
Stimuli Forty-eight syllables were created with the artificial speech synthesizer MBROLA (French voice database FR4), with phoneme duration of 120 ms and pitch of 200 Hz. We used 12 consonants (/b/, /d/, /g/, /p/, /t/, /k/, /v/, /f/, /s/, /l/, /m/, /n/) and 4 vowels (/a/, /i/, /o/, /u/). Each syllable was normalized to an intensity of 70 dB. These syllables were used to create standard sequences of four identical syllables (AAAA; e. g. , ba ba ba ba, di di di di, ku ku ku ku, etc. ) and deviant sequences of three identical and one different syllables (AAA¬A ; e. g. , fi fi fi lo). The video shown repeatedly on the screen of the eyetracker consisted in an animated video clip showing a smiling cartoon character jumping repeatedly (www. GoAnimate. com).
Procedure Infants sat on their parent's laps in front of a Tobii eyetracker T60XL. The presentation of stimuli and the recording of eye-tracking data at 60 Hz was controlled by PsyScope X (http://psy. cns. sissa. it). Parents' ears were covered, so that they could not hear the stimuli, and their eyes were close during the whole experimental session, so not to interfere with the eyetracker recordings. All lights in the room were switched off, except for those coming from the eyetracker screen. Each trial started automatically when infants fixated a central blinking cross presented on the screen. The central blinking cross disappeared and the jumping character appeared at the center of the screen. The pupil size is particularly sensitive to variations of luminance. To ensure that any observed effect could not be attributed to variations of luminance, contrast or any other visual features of the stimuli, we used the exact same video in all trials.
Analysis Fixations were identified by PsyScope X following the dwell-time algorithm  (Duchowski, 2007)  with the following parameters: WindowLength = 200, MinFixationLength = 100, DistanceFromMean = 0. 05. We defined an area of interest (660 pi x 432 pi) corresponding to the surface of the video played on the screen to attract infants' gaze. The pupil diameter for both eyes was recorded for fixations in that area of interest. For each trial, we considered a baseline time window beginning 500 ms before the onset of the fourth syllable (time 0) of the sequence. The average pupil diameter in the baseline window was subtracted from all data points. To analyze pupil dilation, we need first to select trials that provide data in the time window where an effect can be expected. In previous studies using our oddball paradigm, we found that pupil dilation in response to a change detection lasts about 500 ms and peaks around 1500 ms after the onset of the relevant stimulus  (Hochmann & Papeo, 2014).
Acknowledgements This work was funded by the  Agence Nationale pour la Recherche  grant  ANR-16-CE28-0006-TACTIC  awarded to JRH, the  H2020 European Research Council (ERC)   Starting Grant  agreement  312519  awarded to JMTS and the collaborative  McDonnell Foundation  Grant  220020449. We thank  Auriane Couderc ,  Roser Nicolau ,  Cinta Bertomeu  and  Claire Colsoulle  for their help in recruiting and testing babies;  Liuba Papeo ,  Luca Bonatti ,  Judit Gervain ,  Roman Feiman ,  Susan Carey  and  Liuba Papeo  for comments on previous versions of this manuscript. Finally, we would like to dedicate this work to the warm memory of our mentor Jacques Mehler.
Statistical Power A major strength of meta-analysis is the relatively high statistical power associated with compiling several studies (i. e. , independent RCTs may have few participants per group limiting their statistical power). The median number of studies in the Cochrane Database for Systematic Reviews is six, according to Borenstein et al. (2). This is a serious concern considering that (1) subgroup analysis and meta-regressions are routine procedures that require high levels of statistical power, and (2) many meta-analyses have high heterogeneity (I 2 > 75%), which negatively affects precision and thus statistical 2. Systematically run heterogeneity tests (Q statistic, the variance between studies (τ 2 ), and the relationship between the real heterogeneity and the total variation observed, I 2 ).
Publication Bias Publication bias affects both researchers conducting metaanalyses and physicians searching for primary studies in a database. If the missing studies are a random subset of all studies, excluding them will result in less evidence, wider confidence intervals, and lower statistical power, but will not have a systematic influence on the effect size (2). However, whenever there are systematic differences in unpublished and published studies included in the meta-analysis, the weighted effect sizes are biased (e. g. , a lack of studies reporting non-significant effects of HCQ in COVID-19 patients). Dickersin  (14)  found that statistically significant results are more likely to be published than non-significant findings, and thus when published studies are combined together, they may lead to overestimated effects.