This paper makes a simple increment to state-ofthe-art in sarcasm detection research. Existing approaches are unable to capture subtle forms of context incongruity which lies at the heart of sarcasm. We explore if prior work can be enhanced using semantic similarity/discordance between word embeddings. We augment word embedding-based features to four feature sets reported in the past. We also experiment with four types of word embeddings. We observe an improvement in sarcasm detection, irrespective of the word embedding used or the original feature set to which our features are augmented. For example, this augmentation results in an improvement in F-score of around 4% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used. Finally, a comparison of the four embeddings shows that Word2Vec and dependency weight-based features outperform LSA and GloVe, in terms of their benefit to sarcasm detection.

This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a sequence of utterances. We create a manuallylabeled dataset of dialogue from TV series 'Friends' annotated with sarcasm. Our goal is to predict sarcasm in each utterance, using sequential nature of a scene. We show performance gain using sequence labeling as compared to classification-based approaches.Our experiments are based on three sets of features, one is derived from information in our dataset, the other two are from past works. Two sequence labeling algorithms (SVM-HMM and SEARN) outperform three classification algorithms (SVM, Naive Bayes) for all these feature sets, with an increase in F-score of around 4%. Our observations highlight the viability of sequence labeling techniques for sarcasm detection of dialogue.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

At the onset of 2020, the world saw the rise and spread of a global pandemic named COVID-19 which caused numerous deaths and affected millions of people around the world. Due to its highly contagious nature, this disease spread across the world within a short span of time. It forced almost all the nations to implement strict social distancing rules along with use of face masks to reduce the risk of getting infected. While the virus is still on loose, markets and business firms have reopened to keep the economy alive. This calls for modification of existing technological models to cater for the safety of individuals and stop the spread of virus in public places. One such stringent implementation to achieve this safety would be deployment of a mask detection model. The proposed mask detection models can serve as a vital utility in the coming years for ensuring proper enforcement of safety protocols. This research paper explores the use of state of the art YOLOv3 model, a deep transfer learning object detection technique, to develop a mask detection model. Along with the implementation of a standard approach of any object detection algorithm, this paper has proposed the use of a data augmentation approach for mask detection. The proposed model focuses on generating an augmented dataset from the standard dataset with the help of data augmentation done by using image filtering techniques such as grayscale and Gaussian blur. This augmented dataset is used for training the object detection model for mask detection. The mean average precision for the Data augmentation based mask detection model is observed to be 99.8% while training. Finally, a comparison on the model performance is evaluated for the standard and proposed augmented data approach. The experiment conducted showed that the average confidence level for Standard mask detection model was 0.94, 0.93, 0.91 for images of individuals (type A), images with groups of people (type B) and video with the group of people (type C) respectively. The average confidence levels for the Data augmentation based mask detection model for types A, B and C are 0.97, 0.96 0.93 respectively. This paper therefore concludes that the proposed Data augmentation based mask detection model performs better than the Standard mask detection model.

Context: Respiration is known to modulate neuronal oscillations in the brain and is measured by electroencephalogram (EEG). Sudarshan Kriya Yoga (SKY) is a popular breathing process and is established for its significant effects on the various aspects of physiology and psychology. Aims: This study aimed to observe neuronal oscillations in multifrequency bands and interhemispheric synchronization following SKY. Settings and Design: This study employed before- and after-study design. Subjects and Methods: Forty healthy volunteers (average age 25.45 ± 5.75, 23 males and 17 females) participated in the study. Nineteen-channel EEG was recorded and analyzed for 5 min each: before and after SKY. Spectral power for delta, theta, alpha, beta, and gamma frequency band was calculated using Multi-taper Fast Fourier Transform (Chronux toolbox). The Asymmetry Index was calculated by subtracting the natural log of powers of left (L) hemisphere from the right ® to show interhemispheric synchronization. Statistical Analysis: Paired t -test was used for statistical analysis. Results: Spectral power increased significantly in all frequency bands bilaterally in frontal, central, parietal, temporal, and occipital regions of the brain after long SKY. Electrical activity shifted from lower to higher frequency range with a significant rise in the gamma and beta powers following SKY. Asymmetry Index values tended toward 0 following SKY. Conclusions: A single session of SKY generates global brain rhythm dominantly with high-frequency cerebral activation and initiates appropriate interhemispheric synchronization in brain rhythms as state effects. This suggests that SKY leads to better attention, memory, and emotional and autonomic control along with enhanced cognitive functions, which finally improves physical and mental well-being.

Meditation experience has previously been shown to improve performance on behavioral assessments of attention, but the neural bases of this improvement are unknown. Two prominent, strongly competing networks exist in the human cortex: a dorsal attention network, that is activated during focused attention, and a default mode network, that is suppressed during attentionally demanding tasks. Prior studies suggest that strong anti-correlations between these networks indicate good brain health. In addition, a third network, a ventral attention network, serves as a “circuit-breaker” that transiently disrupts and redirects focused attention to permit salient stimuli to capture attention. Here, we used functional magnetic resonance imaging to contrast cortical network activation between experienced focused attention Vipassana meditators and matched controls. Participants performed two attention tasks during scanning: a sustained attention task and an attention-capture task. Meditators demonstrated increased magnitude of differential activation in the dorsal attention vs. default mode network in a sustained attention task, relative to controls. In contrast, there were no evident attention network differences between meditators and controls in an attentional reorienting paradigm. A resting state functional connectivity analysis revealed a greater magnitude of anticorrelation between dorsal attention and default mode networks in the meditators as compared to both our local control group and a n = 168 Human Connectome Project dataset. These results demonstrate, with both task- and rest-based fMRI data, increased stability in sustained attention processes without an associated attentional capture cost in meditators. Task and resting-state results, which revealed stronger anticorrelations between dorsal attention and default mode networks in experienced mediators than in controls, are consistent with a brain health benefit of long-term meditation practice.

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Meditation has been practised for millennia but the neuroscientific understanding of the dynamics is still lacking. Sudarshan Kriya Yoga (SKY) is an evidence based breathing based meditation technique that utilizes rhythmic breathing to induce a deep state of relaxation and calm. Multiple studies have found benefits of the SKY technique from genetic, physiological, psychological to behaviour levels. We collected Electroencephalographic (EEG) data in 43 subjects who underwent the SKY technique and analysed the brain rhythms at different stages of the technique namely preparatory breathing (Pranayama), rhythmic breathing (Kriya) and meditation (Yoga Nidra) using newly developed methods to analyse periodic and aperiodic components. Alpha waves amplitude in the parieto-occipital region decreased as the rhythmic breathing progressed and dropped sharply during the meditation period. Theta amplitudes and peak frequency increased in the centro-frontal region during the rhythmic breathing period but were marked by sustained low theta waves during the meditation period. The delta wave amplitude was not affected by breathing but both delta band power and peak frequency increased during the meditation period in the centro-frontal region. We also saw a decrease in the 1/f aperiodic signal across the brain during the meditation period suggesting a modification of excitation-inhibition balance. We see an overall slowing down of brain oscillations from alpha to theta to delta as the meditation progressed. The paper studies in depth the transitional dynamics of the SKY technique analysing the alpha, theta, delta waves and aperiodic signals and demonstrates that each phase in a breathing based meditation has a unique electrophysiological signature.