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Facial emotions are expressed through a combination of facial muscle movements, namely, the Facial Action Units (FAUs). FAU intensity estimation aims to estimate the intensity of a set of structurally dependent FAUs. Contrary to the existing works that focus on improving FAU intensity estimation performance, this study investigates how knowledge distillation (KD) incorporated into a training model can improve FAU intensity estimation efficiency while achieving the comparable level of performance. Given the intrinsic structural characteristics of FAU, it is desirable to distill deep structural relationships, namely, DSR-FAU, using heatmap regression. Our methodology is as follows: First, a feature map-level distillation loss is applied to ensure that the student network and the teacher network share similar feature distributions. Second, the region-wise and channel-wise relationship distillation loss functions are introduced to penalize the difference in structural relationships. Specifically, the region-wise relationship can be represented by the structural correlations across the facial features, whereas the channel-wise relationship is represented by the implicit FAU co-occurrence dependencies. Third, we compare the model performance of DSR-FAU with the state-of-the-art models, based on two benchmarking datasets. It is shown that our model achieves comparable performance, with a lower number of model parameters and lower computation complexities. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) represents a global health challenge, with an estimated 55 million people suffering from the non-curable disease across the world. While amyloid-β plaques and tau neurofibrillary tangles in the brain define AD proteinopathy, it has become evident that diverse coding and non-coding regions of the genome may significantly contribute to AD neurodegeneration. The diversity of factors associated with AD pathogenesis, coupled with age-associated damage, suggests that a series of triggering events may be required to initiate AD. Since somatic mutations accumulate with aging, and aging is a major risk factor for AD, there is a great potential for somatic mutational events to drive disease. Indeed, recent data from the Gozes team/laboratories as well as other leading laboratories correlated the accumulation of somatic brain mutations with the progression of tauopathy. In this review, we lay the current perspectives on the principal genetic factors associated with AD and the potential causes, highlighting the contribution of somatic mutations to the pathogenesis of late onset Alzheimer's disease. The roles that artificial intelligence and big data can play in accelerating the progress of causal somatic mutation markers/biomarkers identification, and the associated drug discovery/repurposing, have been highlighted for future AD and other neurodegenerations, with the aim to bring hope for the vulnerable aging population. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is crucial for social communication. However, current studies present limitations when addressing facial expression difference due to demographic variation, such as race, gender, and age, etc. In this article, we first propose a deeply-supervised attention network (DSAN) to recognize human emotions based on facial images automatically. Based on DSAN, a two-stage training scheme is designed, taking full advantage of the race/gender/age-related information. In our DSAN framework, multi-scale features are leveraged to capture more discriminative information from the deep layers to the shallow layers. Furthermore, we adopt the attention block to highlight the essential local facial characteristics; it performs well when it is incorporated into the deeply-supervised framework. Finally, we combine the complementary characteristics of multiple convolutional layers in deeply-supervised manner and ensemble the intermediate predicted scores. Our experimental results have shown that our proposed framework can (i) effectively integrate demographic information in improving the performance of a variety of FER tasks, (ii) learn informative feature representations with a visual explanation by capturing the regions of interests (ROI), (iii) achieve superior performance for both the posed and the spontaneous FER databases, each containing pictures of human facial expressions varied in gender, age or race. [ABSTRACT FROM AUTHOR]

AI and big data technologies have been increasingly deployed to process complex, heterogeneous, high-resolution environmental data, and generate results at greater speeds and higher accuracies to facilitate environmental decision-making. However, current attempts to develop reliable AI and big data technologies for environmental decision-making are still inadequate. In this special issue, AI for Social Good: AI and Big Data Approaches for Environmental Decision-Making, we attempt to address the following important questions: What are the conditions for AI and big data technologies to facilitate environmental decision-making? How can AI and big data be used to facilitate environmental decision-making? Do AI and big data serve those most at risk of environmental pollution? Who should own and govern AI and big data? This special issue brings together researchers in relevant fields of AI and environmental science to address these pertinent questions. First, we will review the existing works which attempt to address these four questions. Second, we summarize the significance and novelty of six articles included in our special issue in addressing these four questions. Finally, we highlight the important principles of AI for Social Good, which can help distinguish good from bad environmental decisions based on AI and big data technologies. [ABSTRACT FROM AUTHOR]