The era of the pandemic has brought about significant changes in human life and societal operations, particularly in vibrant dining spaces, which now exhibit extreme differences. The prohibition of physical contact, avoidance of communal dining, installation of partitions, and restrictions on entry to exhibition venues have led to gradual changes in emotional interactions, culinary cultures, and modes of sharing and communication among people. Experts foresee a world that will continuously combat infectious diseases, and in the face of such a reality, innovative approaches are necessary to address the relationships between individuals and life, culture, and art, potentially fostering new modes of value.

In recent years, the rapid development of depth cameras and wide application scenarios. The depth image information becomes more influential in face identification. In the proposed architecture, we implement the networks in dual CNN paths for color and depth images separately. Moreover, we design innovative loss functions to strengthen the discrimination and the complementary features between color and depth modalities. To preserve the strengthened color and depth features, we fuse both features by concatenation before classification. The experimental results show that our multi-modal learning method achieve 4.3381% EER, 0.27 FMR1000, and 0.33 ZeroFMR on IIIT-D Kinect RGB-D Face dataset for face verification and 99.7% classification accuracy, which exceeds the most state-of-the-art methods. Moreover, the global descriptors of model output are designed to be binarized. Our method requires less memory and computation time.

Memristor is a resistive device which is considered as an alternative non-volatile device for future non-volatile memories. For a memristor memory, a reference current is needed for discriminating the high-resistance (ROFF) state from low-resistance (RON) state. The reference current has an impact on the yield and reliability of the memristor memory. In this paper, we propose a test method in associate with a current comparing circuit for finding the boundary currents of ROFF and RON states. Therefore, the user can set a good reference current according to the boundary currents. Simulation results show that if our test method is used to identify the boundary currents, 2.05% and 3.68% memristor cells which may be read incorrectly due to process variation for ROFF/RON = 50 and 3 can be eliminated, respectively.

In this paper, the design and implementation of a corpus-based singing voice synthesis (SVS) system for Mandarin Chinese was introduced. The design rules of three corpora for singing voice synthesis were proposed. After that, two distance functions were defined and the Viterbi search algorithm was applied to identify the optimal combinations of synthesis units from the three corpora. For better performance, several sound effects with synthesized outputs were combined. Finally, we conduct a listening experiment to demonstrate the feasibility of this system.