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As Advanced Driver Assistance Systems (ADAS) become increasingly intelligent, facial expression recognition (FER) has become a significant requirement for the purpose of monitoring a driver’s emotional state as well as fatigue level. An automobile system with FER is very useful in improving transportation safety by recognizing the driver’s state to provide timely alerts and potentially reduce the likelihood of accidents. While deep neural networks (DNN) based systems have achieved high accuracy in FER in recent years based on data collected under good laboratory environments, recognizing real-world facial expressions remains challenging due to variations in lighting and head pose especially prevalent in the driving scenario. In this paper, we propose an attention-based multi-modal and multi-view fusion FER model that can accurately recognize facial expressions regardless of lighting conditions or head poses, using image data of various modalities including RGB, Near-infrared (NIR), and Depth Maps from different viewpoints. The model is developed on a novel facial expression dataset we collected that includes multiple modalities captured from multiple viewpoints, with varying lighting conditions and head poses. Our multi-modal and multi-view fusion approach shows superior performance compared with models that use data from a single modality/view. The model achieves an accuracy of over 95% when recognizing drivers’ facial expressions in real-world scenarios, even in poor lighting conditions and different head poses.

[INLINE:1] Neonatal hypoxic-ischemic encephalopathy is often associated with permanent cerebral palsy, neurosensory impairments, and cognitive deficits, and there is no effective treatment for complications related to hypoxic-ischemic encephalopathy. The therapeutic potential of human placental chorionic plate-derived mesenchymal stem cells for various diseases has been explored. However, the potential use of human placental chorionic plate-derived mesenchymal stem cells for the treatment of neonatal hypoxic-ischemic encephalopathy has not yet been investigated. In this study, we injected human placental chorionic plate-derived mesenchymal stem cells into the lateral ventricle of a neonatal hypoxic-ischemic encephalopathy rat model and observed significant improvements in both cognitive and motor function. Protein chip analysis showed that interleukin-3 expression was significantly elevated in neonatal hypoxic-ischemic encephalopathy model rats. Following transplantation of human placental chorionic plate-derived mesenchymal stem cells, interleukin-3 expression was downregulated. To further investigate the role of interleukin-3 in neonatal hypoxic-ischemic encephalopathy, we established an in vitro SH-SY5Y cell model of hypoxic-ischemic injury through oxygen-glucose deprivation and silenced interleukin-3 expression using small interfering RNA. We found that the activity and proliferation of SH-SY5Y cells subjected to oxygen-glucose deprivation were further suppressed by interleukin-3 knockdown. Furthermore, interleukin-3 knockout exacerbated neuronal damage and cognitive and motor function impairment in rat models of hypoxic-ischemic encephalopathy. The findings suggest that transplantation of hpcMSCs ameliorated behavioral impairments in a rat model of hypoxic-ischemic encephalopathy, and this effect was mediated by interleukin-3-dependent neurological function.

Giving a robot the ability to perceive emotion in its environment can improve human-robot interaction (HRI), thereby facilitating more human-like communication. To achieve emotion recognition in different built environments for adolescents, we propose a multi-modal emotion intensity perception method using an integration of electroencephalography (EEG) and eye movement information. Specifically, we first develop a new stimulus video selection method based on computation of normalized arousal and valence scores according to subjective feedback from participants. Then, we establish a valence perception sub-model and an arousal sub-model by collecting and analyzing emotional EEG and eye movement signals, respectively. We employ this dual recognition method to perceive emotional intensities synchronously in two dimensions. In the laboratory environment, the best recognition accuracies of the modality fusion for the arousal and valence dimensions are 72.8 and 69.3%. The experimental results validate the feasibility of the proposed multi-modal emotion recognition method for environment emotion intensity perception. This promising tool not only achieves more accurate emotion perception for HRI systems but also provides an alternative approach to quantitatively assess environmental psychology.

We provide a sufficient condition for constructing a class of compactly supported refinable functions with componentwise polynomial property in ℝ2. An iteration algorithm is developed to compute the polynomial on each component of the functions' support. Finally, two examples for constructing the symmetric refinable componentwise polynomial functions are given.