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Abstract As one of the most important senses in human beings, touch can also help robots better perceive and adapt to complex environmental information, improving their autonomous decision-making and execution capabilities. Compared to other perception methods, tactile perception needs to handle multi-channel tactile signals simultaneously, such as pressure, bending, temperature, and humidity. However, directly transferring deep learning algorithms that work well on temporal signals to tactile signal tasks does not effectively utilize the physical spatial connectivity information of tactile sensors. In this paper, we propose a tactile perception framework based on graph attention networks, which incorporates explicit and latent relation graphs. This framework can effectively utilize the structural information between different tactile signal channels. We constructed a tactile glove and collected a dataset of pressure and bending tactile signals during grasping and holding objects, and our method achieved 89.58% accuracy in object tactile signal classification. Compared to existing time-series signal classification algorithms, our graph-based tactile perception algorithm can better utilize and learn sensor spatial information, making it more suitable for processing multi-channel tactile data. Our method can serve as a general strategy to improve a robot’s tactile perception capabilities.

Abstract Introduction Psychotic depression (PD) is characterized by the co-occurrence of emotional dysfunction and psychotic symptoms such as delusions and hallucinations with poor clinical outcomes. TSH may involve in the development of PD. This study aims to explore relationship between TSH and PD. Methods A total of 1718 outpatients diagnosed as FEDN MDD were recruited in this study. The relationship between PD and TSH was evaluated using multivariable binary logistic regression analysis. To assess the presence of non-linear associations, a two-piecewise linear regression model was employed. Furthermore, interaction and stratified analyses were conducted with respect to sex, education, marital status, comorbid anxiety, and suicide attempt. Results Multivariable logistic regression analysis revealed that TSH was positively associated with the risk of PD after adjusting for confounders (OR = 1.26, 95% CI: 1.11 to 1.43; p

Deep learning and reinforcement learning are limited by small sample data set, which is impossible to realize the strong generalization learning ability.Meta-learning can make up for their shortcomings effectively.The values formed by accumulated experience feedback the corresponding signals to promote the model to adjust itself.It allows the artificial intelligence to learn to complete complex tasks quickly, which implements true artificial intelligence.Firstly, the basic principles of meta-learning were outlined.Secondly, according to the different forms of meta-knowledge, the research status of various methods was analyzed in depth.Finally, the application potential and the future development trends of meta-learning was discussed .

Gait recognition is an advanced technology in the field of biometric identification.In view of the advantages of long-distance identification,uncontrolled identification and difficult camouflage,it has great potential to promote the development of pattern recognition,image processing,treatment and recovery.Firstly,the key technologies,research status and the common databases of gait recognition were introduced.Then,its application prospect was analyzed.Finally,the development trend of gait recognition was judged.

Heterogeneous transfer learning breaks through the boundary that the feature space of source domain and target domain must be the same.It realizes analysis mining and knowledge migration of heterogeneous data.It further promotes data reuse and opens up a wider range of applications for the field of machine learning.Firstly,the definition and classification of transfer learning was introduced.Then,the research status of heterogeneous transfer learning was elaborated and its application scenarios were analyzed.At last,the existing problems and the possible research direction in the future were pointed out.