Unmanned Aerial Vehicles (UAVs) are an indispensable core component of low altitude economic networks. It is very critical for achieving efficient UAV image transmission of air-to-ground communication. Due to the changes in flight area and unstable channel conditions, the signal-to-noise and transmission rate change rapidly. To adapt to these changes, we propose a channel adaptive encoding and decoding method for UAV image transmission. The proposed method includes a lightweight feature extraction module, a channel-wise feature enhance module, a transmission rate adaptive module, and the corresponding decoding module. The lightweight feature extraction module can quickly extract local detailed features and long-range spatial dependencies via residual block and mobile mamba. The channel-wise feature enhance module can enhance channel wise useful features via the involution operation and the SNR adjustment block according to channel state SNRs. The transmission rate adaptive module can further adaptively adjust the size of transmission features according to the transmission rate via the rate adjustment block and the rate mask block. The extensive experimental results on the SIRI-WHU, WHU-RS19, AID, and UCMerced Land Use datasets demonstrate that our method obtains higher PSNR, MS-SSMI, LPIPS and ACC than state-of-the-art methods.

Artificial intelligence (AI) has been widely adopted across various service industries. However, consumers often feel that current AI lacks the capability to understand emotions, which can impede its effectiveness in service recovery following service failures. To explore this issue, we examine customer reactions to two compensation strategies—symbolic and utilitarian—provided by different recovery agents (employee vs. AI). A survey and three scenario-based experiments revealed that customers prefer symbolic compensation when it is provided by an employee rather than an AI. Interestingly, this preference gets reversed when utilitarian compensation is provided. Moreover, our research verified that perceived empathy and perceived fairness serve as mediators for this effect, while failure attribution acts as a moderator. These findings enhance our understanding of service recovery and offer important implications for the integration of AI in service industries.

Abstract Resolution and superposition provers rely on the given clause procedure to saturate clause sets. Using Isabelle/HOL, we formally verify four variants of the procedure: the well-known Otter and DISCOUNT loops as well as the newer iProver and Zipperposition loops. For each of the variants, we show that the procedure guarantees saturation, given a fair data structure to store the formulas that wait to be selected. Our formalization of the Zipperposition loop clarifies some fine points previously misunderstood in the literature.