The global epidemic of COVID-19 makes people realize that wearing a mask is one of the most effective ways to protect ourselves from virus infections, which poses serious challenges for the existing face recognition system. To tackle the difficulties, a new method for masked face recognition is proposed by integrating a cropping-based approach with the Convolutional Block Attention Module (CBAM). The optimal cropping is explored for each case, while the CBAM module is adopted to focus on the regions around eyes. Two special application scenarios, using faces without mask for training to recognize masked faces, and using masked faces for training to recognize faces without mask, have also been studied. Comprehensive experiments on SMFRD, CISIA-Webface, AR and Extend Yela B datasets show that the proposed approach can significantly improve the performance of masked face recognition compared with other state-of-the-art approaches.

Water electrolysis is one of the most promising processes for a hydrogen-based economy, so the development of highly active, durable, and inexpensive catalysts for the hydrogen evolution reaction (HER) is very important. IrO 2 is known to be one of the most active catalysts for the oxygen evolution reaction (OER) in a PEM electrolyzer, but the HER activity of IrO 2 is rarely studied because of its low cathodic current compared to platinum. Herein, an IrO 2 -Fe 2 O 3 composite oxide was prepared by a thermal decomposition method. The physical and electrochemical characterization of the material was achieved by scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Compared to that of IrO 2 , the CV curves of the IrO 2 -Fe 2 O 3 electrode reveal that hydrogen is more easily adsorbed on the surface, which would lead to the H underpotential deposition (H-UPD) redox current increasing significantly. Therefore, the IrO 2 -Fe 2 O 3 electrode exhibits higher HER activity than that of the IrO 2 electrode in 0.

Copper metal–organic polyhedra (Cu-MOP) nanorods exhibited high peroxidase-mimicking activity at a physiological pH value.

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The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER). To address the limitations in existing approaches (e.g., narrow receptive fields and homogenous supervisory signals) and further cement the capacity of FER tools, a novel multifarious supervision-steering Transformer for FER in the wild is proposed in this paper. Referred as FERformer, our approach features multi-granularity embedding integration, hybrid self-attention scheme, and heterogeneous domainsteering supervision. In specific, to dig deep into the merits of the combination of features provided by prevailing CNNs and Transformers, a hybrid stem is designed to cascade two types of learning paradigms simultaneously. Wherein, a FER-specific transformer mechanism is devised to characterize conventional hard one-hot label-focusing and CLIP-based text-oriented tokens in parallel for final classification. To ease the issue of annotation ambiguity, a heterogeneous domains-steering supervision module is proposed to make image features also have text-space semantic correlations by supervising the similarity between image features and text features. On top of the collaboration of multifarious token heads, diverse global receptive fields with multi-modal semantic cues are captured, thereby delivering superb learning capability. Extensive experiments on popular benchmarks demonstrate the superiority of the proposed FER-former over the existing state-of-the-arts.