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In this paper, the problem of audio semantic communication over wireless networks is investigated. In the considered model, wireless edge devices transmit large-sized audio data to a server using semantic communication techniques. The techniques allow devices to only transmit audio semantic information that captures the contextual features of audio signals. To extract the semantic information from audio signals, a wave to vector (wav2vec) architecture based autoencoder is proposed, which consists of convolutional neural networks (CNNs). The proposed autoencoder enables high-accuracy audio transmission with small amounts of data. To further improve the accuracy of semantic information extraction, federated learning (FL) is implemented over multiple devices and a server. Simulation results show that the proposed algorithm can converge effectively and can reduce the mean squared error (MSE) of audio transmission by nearly 100 times, compared to a traditional coding scheme. 

SARS-CoV-2 has been mutating since it was first sequenced in early January 2020. Here, we analyze 45,494 complete SARS-CoV-2 geneome sequences in the world to understand their mutations. Among them, 12,754 sequences are from the United States. Our analysis suggests the presence of four substrains and eleven top mutations in the United States. These eleven top mutations belong to 3 disconnected groups. The first and second groups consisting of 5 and 8 concurrent mutations are prevailing, while the other group with three concurrent mutations gradually fades out. Moreover, we reveal that female immune systems are more active than those of males in responding to SARS-CoV-2 infections. One of the top mutations, 27964C > T-(S24L) on ORF8, has an unusually strong gender dependence. Based on the analysis of all mutations on the spike protein, we uncover that two of four SARS-CoV-2 substrains in the United States become potentially more infectious.