LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network.

Facial micro expressions are brief, spontaneous, and crucial emotions deep inside the mind, reflecting the actual thoughts for that moment. Humans can cover their emotions on a large scale, but their actual intentions and emotions can be extracted at a micro-level. Micro expressions are organic when compared with macro expressions, posing a challenge to both humans, as well as machines, to identify. In recent years, detection of facial expressions are widely used in commercial complexes, hotels, restaurants, psychology, security, offices, and education institutes. The aim and motivation of this paper are to provide an end-to-end architecture that accurately detects the actual expressions at the micro-scale features. However, the main research is to provide an analysis of the specific parts that are crucial for detecting the micro expressions from a face. Many states of the art approaches have been trained on the micro facial expressions and compared with our proposed Lossless Attention Residual Network (LARNet) approach. However, the main research on this is to provide analysis on the specific parts that are crucial for detecting the micro expressions from a face. Many CNN-based approaches extracts the features at local level which digs much deeper into the face pixels. However, the spatial and temporal information extracted from the face is encoded in LARNet for a feature fusion extraction on specific crucial locations, such as nose, cheeks, mouth, and eyes regions. LARNet outperforms the state-of-the-art methods with a slight margin by accurately detecting facial micro expressions in real-time. Lastly, the proposed LARNet becomes accurate and better by training with more annotated data.

Choquet Integral and Coalition Game-Based Ensemble of Deep Learning Models for COVID-19 Screening From Chest X-Ray Images.

Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. Though, some vaccines have now been authorized worldwide, it will take huge time to reach everyone, especially in developing countries. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus. The primary agenda of this proposed research study is to leverage the classification performance of the deep learning models using ensemble learning. Many papers have proposed different ensemble learning techniques in this field, some methods using aggregation functions like Weighted Arithmetic Mean (WAM) among others. However, none of these methods take into consideration the decisions that subsets of the classifiers take. In this paper, we have applied Choquet integral for ensemble and propose a novel method for the evaluation of fuzzy measures using coalition game theory, information theory, and Lambda fuzzy approximation. Three different sets of fuzzy measures are calculated using three different weighting schemes along with information theory and coalition game theory. Using these three sets of fuzzy measures, three Choquet integrals are calculated and their decisions are finally combined. Besides, we have created a database by combining several image repositories developed recently. Impressive results on the newly developed dataset and the challenging COVIDx dataset support the efficacy and robustness of the proposed method. Our experimental results outperform many recently proposed methods.

The Association between Smoking Status and Influenza Vaccination Coverage Rate in Korean Adults: Analysis of the 2010-2012 Korea National Health and Nutrition Examination Survey.

BACKGROUND: Globally, smoking is one of the biggest challenges in public health and is a known cause of several important diseases. Influenza is preventable via annual vaccination, which is the most effective and cost-beneficial method of prevention. However, subjects who smoke have some unhealthy behaviours such as alcohol, low physical activity, and low vaccination rate. In this study, we analyzed the relationship between smoking status and factors potentially related to the influenza vaccination coverage rate in the South Korean adult population. METHODS: The study included 13,565 participants aged >19 years, from 2010 to 2012 from the Korea National Health and Nutrition Examination Survey data. Univariate analyses were conducted to examine the association between influenza coverage rate and related factors. Multivariate analysis was obtained after adjusting for variables that were statistically significant. RESULTS: The overall vaccination rate was 27.3% (n=3,703). Older individuals (P<0.0001), women (P<0.0001), non-smokers (P<0.0001), light alcohol drinkers (P<0.0001), the unemployed (P<0.0001), and subjects with diabetes mellitus (P<0.0001), hypercholesterolemia (P<0.0001), and metabolic syndrome (P<0.0001) had higher influenza vaccination coverage than the others. In multivariate analyses, current smokers and heavy smokers showed lower vaccination rates (odds ratio, 0.734; 95% confidence interval, 0.63-0.854). CONCLUSION: In the current study, smokers and individuals with inadequate health-promoting behaviors had lower vaccination rates than the others did.

Quantum opacity, the RHIC Hanbury Brown-Twiss puzzle, and the chiral phase transition.

We present a relativistic quantum-mechanical treatment of opacity and refractive effects that allows reproduction of observables measured in two-pion Hanbury Brown-Twiss (HBT) interferometry and pion spectra at RHIC. The inferred emission duration is substantial. The results are consistent with the emission of pions from a system that has a restored chiral symmetry.