Anthropomorphic design in intelligent vehicle agents (IVAs) is crucial for driving safety and user experience. Cultural background may shape user preferences, as evidenced by Chinese car manufacturers offering more anthropomorphic IVAs (e.g., physical robots, human-like virtual agents) than their Western counterparts. While prior research has examined cross-cultural differences in visual anthropomorphism, behavioral anthropomorphism remains understudied. Here we evaluated the performance of eight IVAs (five from Chinese brands, three from Western brands) in responding to user requests and their social repair behaviors (e.g., apology and promise) following request failures. Overall, Chinese and Western IVAs did not differ in their corrective responses or likelihood of employing repair behaviors. However, Chinese IVAs were more likely to use combined behaviors rather than single ones and to incorporate intimacy expressions in their repair behaviors. Our findings highlight cultural design nuances in behavioral anthropomorphism, with implications for the culturally adaptive design of IVAs.

Neural Architecture Search (NAS) can discover the optimal neural network architecture within a given SuperNet through automated search, which can improve model performance and reduce computational overhead on resource-constrained devices. Due to the vast SuperNet requiring substantial computational resources for training and evaluation, the search process is costly and difficult to apply directly on End Devices (EDs) with limited computational resources. Moreover, existing methods utilize zero-cost proxies to reduce computational costs in NAS, but overlook limited computational resources on EDs and waste a large amount of computational resources on the cloud server. Deploying NAS on the cloud server can effectively address this issue. The cloud server is used to search for the optimal Subnet, and EDs only need to train the Subnet based on local data. To this end, we propose a nonlinear aggregation-based Neural Architecture Search method based on Feature and Gradient zero-cost proxies (FG-NAS). Specifically, EDs upload local data characteristics to the cloud server, and then the cloud server uses FG-NAS to obtain an optimal SubNet model from the SuperNet based on the uploaded data characteristics. Finally, the cloud server sends the optimal SubNet to EDs, which can reduce the computational burden on EDs. Furthermore, FG-NAS evaluates the accuracy of neural architectures by considering both feature proxies during forward propagation and gradient proxies during backward propagation. Experiments on three datasets demonstrate that compared to current mainstream zero-cost proxy methods, FG-NAS can improve evaluation accuracy by an average of 1.04% and reduce single-network evaluation time by up to 2.45%.

Semantic segmentation plays an important role in biomedical image analysis applications. Convolutional neural networks (CNN) have become a promising approach to segment biomedical images. Nevertheless, the accuracy of these methods is highly dependent on the training data. These models may not generalize well to unseen image domains due to the phenomenon of domain shift. We propose a self-ensembling attention networks to address domain shift for biomedical image segmentation. There are two main components in the proposed network: a student network which plays a role of the base networks and a teacher network which plays a role of the ensemble networks. As the iteration goes on, the student network becomes more accurate, and the ensemble predictions in the teacher network also get closer to the correct labels in the target domain. In this way, domain-invariant features can be learnt correspondingly. We use the DRIVE, STARE, HRF and CHASEDB eye vasculature segmentation datasets and show that our approach can significantly improve results where we only use labels of one domain in training and test on the other domain.

Although the wide deployment of convolutional networks has greatly promoted the progress in the field of medical image segmentation, the performance of these method on tiny tissues, such as cell and fundus vessel, still needs to be improved. Most approaches focus on modifying the network architecture to overcome the problem of missing details in segmented images. In this paper, we try to solve this problem from a new perspective, that is, introducing self-distillation mechanism to fully utilize the features extracted from the network. Our method can be viewed as a combination of a novel loss function and a specific training strategy. It can be easily integrated into most existing encoderdecoder structured networks with few additional computational cost. We conduct experiments on four datasets, which are DRIVE, CHASEDB, GlaS and TNBC, and serval commonly used models to prove the effectiveness of our method. Experiments show that the performance of these models has been improved, which proves that our method is a general method and can be widely used in the field of m edical image segmentation.