dataesr/scientific-paragraphs-categorization huggingface dataset:
MELD contains multi-party conversations that are more challenging to classify. It uses seven emotions for the annotation, such as: anger, disgust, fear, joy, neutral, sadness, and surprise. In order to use IEMOCAP, a pre-processing task was required. Since our model uses static images, we had to split each video into individual frames. The audio files then had to be synced up with each individual frame that was used for training; the text file used did not have each utterance uniquely identified which led to confusing results at first. Even after having the data correctly formatted, we still had the problem of imbalanced data, making our networks struggle to classify some emotions. In our experience, this is a common issue in multi-label emotion classification (i. e. , multimodal, multisource), when selecting too many emotions, this lead us to the decision of removing the underrepresented emotions entirely from the dataset.
For future studies that require different modalities of data, such as audio and visual data, it is a must the creation of new datasets which takes into consideration the sync among different modalities from the start. We expect that researchers that have these possibilities, can share their datasets for the community interested. Another issue is regarding the transcription of speech to text in IEMOCAP, as it was done by professionals. Although this professional translation does not represent the robotic scenario, in which it is done by specialized speech-to-text software, the IEMOCAP is a well known and highly used dataset, that made it possible to perform the comparison of our approach with state of the art approaches. Nevertheless, it is also a must to generate datasets with data gathered from the robotic perspective.
For example, with the use of our system in a robot, it is possible to use speech-to-text machine translation techniques, for a fast automatic production of new data that could be used for comparison in new experiments. Another way to solve this issue would be the use of off-theshelf transcription services, such as the IBM Watson Speech-To-Text service. In summary, to have a better support in the developing of multimodal emotion recognition systems for social robots, more appropriate datasets are need, that consider multiple co-related modalities and generated from a robotic perspective.
The proposed system is composed of modules for: the data capturing from real-world; pre-processing strategy of the data inputs that need it, such as transforming audio into text, detecting faces in images; individual processing of each modality (e. g. , text, audio, images/faces); and the fusion method. Individual processing of modalities are performed by well-established methods specialized for each modality, that can be easily integrated into the system. To aggregate the individual results, we use the EmbraceNet+ fusion method to merge these modalities and to generate the final prediction, taking into account only present modalities, different levels of data quality, and missing data. The fusion method is evaluated in four tests combining different modalities: Face-Text (F+T), Face-Audio (F+A), Audio-Text (A+T), and Face-Audio-Text (F+A+T).
We show that existing approaches are not appropriate for social robots, since they are unable to be adapted according robots' sensory capacity and quality of data. Thus, our approach represent an important contribution in this area. We are currently working on optimization and tuning the learned models and the pre-processing of inputs for the synchronous emotion detection. The new tests include performance measures of robot responses in real time and real scenarios, with more variety of modalities and considering time-varying data, in which emotions change in time. We also plan to make a formal analysis of time complexity and experiments to measure the total execution time in such real time scenarios; as well as more experiments with other available datasets that consider several modalities, such as CMU-MOSEAS  [70]  and MELD  [71].
This work was supported in part by the  Fondo Nacional de Desarrollo CientíFico ,  Tecnológico y de Innovación Tecnológica-Fondecyt as Executing Entity of Concytec  through the  Project RUTAS-Robots for Urban Tourism Centers, Autonomous and Semantic-Based  under Grant  01-2019-FONDECYT-BM-INC. INV; and in part by the  Universidad de Valparaiso  under Grant  INICI UVA20993.
Acknowledgments: An earlier version of this paper was originally delivered to the  Departamento de Filosofía of the Universidad Panamericana, Ciudad de México, Mexico. I warmly thank all those present for their constructive criticisms on that occasion. A version of this paper was presented to the  Department of Philosophy at the Universidad Pamericana, Mexico City , and it was largely written while I was  Visiting Scholar  at the  Max Planck Institute for the History of Science, Berlin. I thank all my colleagues, students, and staff at these institutions for their helpful comments, as well as the Guest Editor  M. -H. Deroche  and my three anonymous referees.
EPP0541 Effects of a brief psychodynamic intervention on depressive patients. The "unfreezing" of psychic activity. Introduction: While psychotherapy is an essential aspect of the treatment of depression, there are few studies focusing on the effectiveness of psychoanalytic and psychodynamic group therapies for depressed patients. Objectives: In this presentation, we will study the effects of a brief, 4-session psychodynamic intervention (BPI) led by a group of therapists, as inspired by the Lausanne model. Methods: The patients were recruited in a therapeutic setting. A free consent form was completed and the ethics of research explained to each participant. Our sample consisted of 32 patients (average age = 43. 81 years, sex ratio: 1M/ 4F). The therapists gathered data by completing several assessment scales after each therapy session: MADRS, ESM, EFP, HAQ-IT, EDICODE, Counter-Transfer Scale.
The SPPS software (V21) was used to analyze the data. Results: The patients' mean MADRS score dropped by more than 11 after the four sessions. This improvement matches a more positive and committed self-reported counter-transference of the therapists towards the patients. As their insight increases, patients show greater behavioral and psychic activity. We name this exit of the depressive inhibition the "unfreezing" process. It enables more satisfactory human interactions and a more focused and structured self-narrative. Introduction: Depressive disorders are one of the most frequent psychiatric comorbidity in epilepsy and they have a negative impact on the quality of life. Depression often requires antidepressant treatment. However, it is often left untreated in people with epilepsy, in part due to fear that antidepressants could cause seizures.
Objectives: The goal of this study was to do a review and describe the evidence of the efficacy and safety of pharmacological treatment for depression in epilepsy. Methods: Review of literature sources were obtained through electronic search in PubMed database with special focus in papers published in the last 5 years. Results: The existing evidence of the effectiveness of antidepressants in treating depressive symptoms associated with epilepsy is still limited and response rate was highly variable. It is essential first to optimize seizure control and minimize unwanted antiepileptic drug-related side effects. As the first line of treatment you should consider the use of SSRI or IRSN. The improvement in depressive symptoms ranged from 25% to 82% according to the different studies and depending on the antidepressant administered.
Procedure This study reports data from one of several tasks that participants performed during the same experimental session, including a battery of questionnaires on gambling, gaming, personality traits, sleep habits, sensory processing sensitivity, and positive/negative affect; four computerized cognition tasks; two interviews on substance and behavioural addictions; and three computerized gambling tasks. Psychophysiological responding was recorded throughout the session. Participants received a gift card of 1000 SEK (≈ 100 €) for participation in the entire session, with the possibility of additional 600 SEK (≈ 60 €, maximum of 200 SEK/gambling task ≈ 20 €) depending on their performance on the gambling tasks. The examiner provided detailed information about the procedures when informed consent was solicited, after which electrodes for recording HR and SCRs were attached and the first part of the session was administered (questionnaires, cognition tasks, and diagnostic interview on addictions).
Participants had a short break with refreshments after the first part of the session and before starting the slot machine task. Electrodermal activity (EDA) and electrocardiograms (ECG) were recorded with a Biopac System MP150 (Biopac Systems, Goleta, CA, USA). The task computer was connected to the Biopac system and to a second computer running Acqknowledge v5. 0. 1 to event-mark the psychophysiological data using digital channels. For the skin conductance recording, two grounded Ag-AgCl electrodes were used (Biopac EL507 with a BN-PPGED amplifier module, sample rate 62. 5, constant voltage 0. 5 V, low-pass filter 3. 0 Hz, high-pass filter DC) attached to the thenar and hypothenar eminences of the non-dominant hand. Electrode sites were cleaned using a dry cloth prior to the application. To facilitate recording of the EDA signal, 0.
05 M NaCl electrolyte paste GEL101 was used. The signal was transformed into microsiemens (µS) in Acqknowledge. The ECG was recorded with three disposable grounded Ag-AgCl electrodes (Biopac EL504, with a BN-RSPEC module, sample rate 2000, low-pass filter 35 Hz, high-pass filter 1 Hz) attached to the right shoulder and grounded to the eighth rib on the left and right side. The electrodes contained liquid hydrogel (4% NaCl). The ECG data were converted into HR in Acqknowledge. Although room temperature and humidity were regulated, because of seasonal changes, the temperature ranged from 20 to 32 °C (M = 25. 5 °C) and the humidity ranged from 20 to 58% (M = 41%) across the sessions.
Given that a proportion of healthy individuals have zero SCRs to emotional stimuli  (Venables & Mitchell, 1996) , participants were excluded if they had zero SCRs to ≥50% of the winning outcomes. Using this threshold, 23 participants were identified as nonresponders and excluded, leaving a total of 178 participants in the SCR analysis. In the HR analysis, two participants were excluded because of heart arrythmia, and four were identified as outliers based on high bpm compared with the group mean (> 2. 5 SD). This left 264 participants in the HR analysis. One participant was excluded from all analyses because of technical failure in all recordings, leaving 269 participants in the SR analysis. The final step in the processing of the skin conductance data was a low-pass filter to remove any frequencies above 1.
0 Hz. The data were then exported for analysis in MAT-LAB, using the Ledalab software package (www. ledalab. de). Ledalab was used to perform a continuous decomposition analysis of the EDA signal, which separates skin conductance data into continuous signals of tonic and phasic activity. This type of decomposition is recommended when measurements of event-related sympathetic activity that reflect the original properties of the underlying sudomotor nerve activity are of interest  (Benedek &  Fig. 3  Inclusion procedure for separate analysis of SCR, HR and subjective ratings (SR)  Kaernbach, 2010). An SCR was defined as the maximum SCR amplitude in the 1-4 s poststimulus onset (gambling outcome) time window, minus the 1 s pre-stimulus onset baseline value. The minimum amplitude criterion for SCRs was set at 0.
Statistical Analyses All analyses were performed using IBM SPSS Statistics (v26). A repeated-measures analysis of variance (ANOVA) was used to analyse summary measures for the SRs and physiological responses, with significance threshold set at p < 0. 05, and Huynh-Feldt and Greenhouse-Geisser corrections used. Post hoc tests included Bonferroni corrections. Three sets of analyses were performed. The first set of analyses had three within-subject factor levels to explore differential responses to the gambling outcomes (wins, near-misses and full-misses) for SCR, ECG and SRs. The second set of analyses focused on the different subtypes of near-misses, using three within-subject factor levels (near-misses before payline, near-misses after payline and full-misses) for SCR, ECG and SRs. Finally, analysis investigated sex differences in the responses to gambling outcomes (wins, near-misses and full-misses) in two steps.
Cohen (2013)  guidelines were followed to assess the magnitude of the effect (η p 2 ) in the ANOVA models: small = 0. 01, medium = 0. 06 and large = 0. 15. A sensitivity power analysis for sex differences was performed using G*Power software  (Faul et al. , 2007). With an alpha = 0. 05 and power = 0. 80, the group sample sizes of N = 140/130 allowed a minimum detectable effect (MDE) of d = 0. 30, or η p 2 ≈ 0. 02  (Cohen, 2013). Because of smaller group sample sizes of N = 94/84, the SCR analysis allowed an MDE of d = 0. 37, or η p 2 ≈ 0. 03.
Such theories view near-misses as uniform events, describing them simply as almost hitting the jackpot, without considering different subtypes of near-misses. However, the data from this study suggest that this distinction is highly relevant to the understanding of near-miss psychology and the effects of such events on gambling behaviour. The female and male participants in this study responded differently to wins, both physiologically and via self-reports. Gender is known to influence patterns of gambling behaviour, underlying motivations to gamble and the development of problem gambling. Gambling is still more prevalent among men than women. Consequently, most of the experimental research on gambling has used male samples and gendered approaches are still limited. Results from the current study emphasize the importance of considering sex differences in experimental research on autonomic responses to gambling events, the mechanisms of gambling and the role that autonomic arousal plays in problematic gambling behaviour among women and men.
Acknowledgements The current study was conducted within the  Centre for Clinical Research, Västmanland Hospital Västerås ,  Region Västmanland ,  Uppsala University, Västerås, Sweden. The authors wish to thank  Simon Liljeström  for consulting on artifact management and statistics;  Andreas Ljungberg  and  Adrian Parnéus  for technical assistance and programming of Acqknowledge and the slot machine task; and  Luke Clark  for helpful comments on an early draft of the article. We are also grateful to all study participants.
e. , log-transformed nestling T/S ratio) as response variable and tested for the effect of fathers' telomere length (father T/S ratio) and mothers' telomere length (mother T/S ratio). As explanatory variables, we also accounted for other potentially effects such as that of number of hatchlings, nestling sex, hatching date, and nestling body condition (calculated in the same way as adult body condition). In all aforementioned models we accounted for the potential non-independence of the data within plots and nest boxes by including plot-ID and nest-box-ID as random factors. The only Posterior probabilities (PP; from 0. 5 to 1) represent the likelihood that the differences between factor levels are true. exception was the model where we tested the effect of disturbance on the number of hatchlings, in which there was no need to include nest-box-ID as random term since there is only one value per nest.
All analyses were done using the "lme4" package  (Bates et al. , 2014) in R v. 3. 6. 0 (R Core Team, 2019) using a Bayesian framework. Parameter estimates (and their 95% credible intervals; CrI) were calculated by simulating random samples (n = 10,000) from the joint posterior distribution of the model parameters using the function sim from the R-package arm  (Gelman and Su, 2015). In order to assess the impact of the explanatory variables we calculated the posterior probability (PP) of the hypotheses that these explanatory variables had either a positive or a negative influence on the response variables, based on the posterior distributions of the model parameters. The higher the probability (from 0. 5 to 1), the stronger the support for a given effect. For the categorical variables treatment, sex and road, we calculated the PP of the hypotheses that there were differences between disturbed and control treatment, between females and males, and between split-plots with or without bordering gavel road, respectively.
ACKNOWLEDGMENTS First of all we thank all the collaborators in the field which did the hard disturbance-work in the forest (in alphabetical order):  C. Arnace ,  M. Crespo Ballester ,  B. Garde ,  L. Gillespie ,  H. Lemke ,  F. Leugger ,  N. Mortier ,  J. Nouri ,  M. Quetstroey ,  A. Rouméas ,  J. Vasseur. We thank  M. Romanski  and  J. L. Dessolin  from the  Office National des Forêts (ONF) , as well as  B. Dorbani  from the  DREAL (Direction Régionale de l'Environnement, de l'Aménagement et du Logement de Franche-Comté ) for allowing us to do this study in the  Forêt domaniale de Chaux. We also thank  B. Almasi  and  J. Olano-Marin  for the support in the laboratory in Sempach. For the telomere analyses, we were financially supported by the  Observatoire des Sciences de l'Univers Terre Homme Environnement Temps Astronomie (OSU THETA Bourgogne-Franche Comté ).
Goal appraisals have been examined in relation to goal content (e. g. , goals focused on education are appraised as low in control), and well-being (e. g. , persons with high levels of depression appraised their goals as low in achievement)  [36]. In this study, associations between vertical goal conflict or facilitation and goal appraisals will be explored (e. g. , do individuals who score high in vertical goal conflict appraise their goals as more difficult?). • A measure of the participants' self-concordance will be included as a method of data triangulation to validate the findings of the vertical facilitation measure. Self-concordance represents the degree to which a goal matches a person's values  [20, 37, 38]. Individuals with higher self-concordance pursue goals that reflect their values and interests  [20, 37, 38].
Data analysis Data will be analyzed using R version 4. 1. 2 [48]. Descriptive analysis of the data will be completed. We will report the average number of goals reported by participants and calculate the percentages of goal categories to obtain a frequency of the goal types. We will also count the number of times a PA or diet goal was chosen for a personal value to obtain frequencies for the type of health goal chosen for a personal value. We will also complete a qualitative thematic analysis describing the participants' objectives.
5 to 37 cycles per faces (for a review see  Jeantet et al. , 2018)  and authors who investigated SF in face processing previously used similar cutoffs (e. g. ,  Goffaux and Rossion, 2006; Goffaux et al. , 2011; Beffara et al. , 2015). Filtered images were obtained by fast Fourier transform and by multiplying the Fourier energy with Gaussian filters. Images were normalized to obtain a mean luminance of 0. 5 (for luminance values between 0 and 1) with a standard deviation of 0. 075 (root mean square contrast). SF filtering and normalization were elaborated using MATLAB (Mathworks Inc. , Sherborn, MA, USA). The target stimuli (p = 0. 05 among standard stimuli) were also the same photograph but colored, so that it did not favor HSF or LSF processing. Saturation of the colored image was lowered to reduce the salience among stimuli and maintain attention.
16. In this sequence, target stimuli were the same stimuli but colored (p = 0. 05; p ≈ 0. 01 each). This sequence comprised 835 stimuli presented in one session of 10 min. Oddball and equiprobable sequences order was counterbalanced, as well as the two oddball sequences. While participants sat comfortably in an armchair, stimuli were displayed centrally on a CRT screen (37 × 29. 6 cm; refresh rate = 75 Hz; resolution = 1,280 × 1,024 pixels) at a viewing distance of 87 cm so that the faces corresponded to 5. 8 • of visual angle. Stimuli were presented using Presentation R software (Neurobehavioral Systems, Inc. , Berkeley, CA, www. neurobs. com) for 150 ms with a 550 ms inter-stimulus interval (Figure  1C ). Participants were instructed to look at the fixation cross and to press a button as quickly as possible when they saw a colored face.
Behavioral Data Collection and Analysis Hit rate, false alarm, miss, and correct rejection of the target detection were recorded during the experiment. The sensitivity index d ′ = (z-score hit rate) -(z-score false alarm rate) was calculated with the psycho package  (Makowski, 2018)  on R version 4. 0. 3 (R Core Team, 2020) and R studio version 1. 3. 1075 (RStudio Team, 2019)  to evaluate the involvement of the participants in the task.
EEG Recording EEG data were recorded using a 96 active electrodes system (BrainAmp amplifiers and EasyCaps, Brain Products GmbH, Germany) following the 10-5 standard system. Electrooculographic (EOG) activity was recorded using two electrodes on the left and right outer canthi of the eyes and two above and below the left eye for spotting horizontal and vertical eye movements respectively (hEOG and vEOG). The ground electrode for the EOG was placed on the left base of the neck. Impedance were adjusted and kept below 25 k before and during the recording. Signal was recorded with a sampling rate of 1,000 Hz, using an anti-aliasing filter at 500 Hz. FPz and FCz were defined as the ground and reference electrodes, respectively.
EEG Pre-processing EEG pre-processing and analysis were performed using Brainstorm software  (Tadel et al. , 2011) , and other custom scripts developed in MATLAB (The MathWorks Inc. ). First, bad channels were visually inspected and removed during the recording and during the pre-processing for each participant, based on both temporal (deviants dynamics, flat signals) and frequency (deviant Welch's power spectrum density) characteristics. Time periods contaminated by high-frequency muscular artifacts were discarded manually. We then rereferenced the signal using average reference. Both horizontal and vertical eyes movements artifacts were targeted by analyzing the corresponding EOG recording, and corrected by applying a specific signal-space projection (SSP, a spatial decomposition method to be compared with independent component analysis,  Uusitalo and Ilmoniemi, 1997).
ERPs Analyses In order to assess the sensory response to filtered and unfiltered equiprobable stimuli (eHSF, eLSF, and eBSF), we investigated ERP components. We extracted peak amplitude for each participants using MATLAB scripts (based on findpeaks function) and visual inspection over the latency range of 60-140 ms on O1 and O2 and on PO7 and PO8 for the P100. However, as a negative peak was observed for P100 in the HSF condition (Figure  2A ), we performed P100 analysis on PO7 and PO8 only. For the N170, P100-N170 peak-to-peak difference was performed by measuring peaks in the latency ranges of 60-140 and 130-200 ms on PO7 and PO8. Then, data were analyzed with repeated measure analyses of variance (ANOVA) on RStudio Team (2019) using the afex package  (Singmann et al. , 2021)  with Huynh-Feldt correction in case of departure of sphericity (tested with Mauchly tests).
Analyses included SF (eBSF, eLSF, eHSF) and channels/hemisphere (PO7 vs. PO8) as within subject factors. Post-hoc tests were performed with the emmeans package  (Lenth, 2021 ) by applying a Bonferroni correction. However, in case of strong violation of assumptions (normality and sphericity; which was the case for most analyses), we ran non-parametric tests, i. e. , Friedman rank sum test for SF (with Durbin-Conover test for pairwise comparison, Holm corrected) and Wilcoxon signed-rank test for channels.