Abstract: Various embodiments a method and apparatus for unmanned driving behavior decision-making and model training, and an electronic device. The method includes: acquiring sample data, wherein the sample data includes a sample image; extracting a sample feature vector corresponding to the sample data, wherein a feature vector of the sample image is extracted by manifold dimension reduction; and based on the sample feature vector, training by semi-supervised learning to obtain a target decision-making model, wherein the target decision-making model is used for decision-making classification.

Abstract: An optical fiber adapter (10) comprises: a housing (1), a front portion of the housing (1) is provided with at least two accommodating cavities (19) which penetrate in a front-rear direction, are arranged sided by side, and are closed circumferentially, and a rear portion of the housing (1) is provided with a mounting base (14); at least two optical fiber plugs (2) provided on the mounting base (14), each optical fiber plug (2) comprises a positioning member (21), a ferrule (22) fixed to the positioning member (21) and extending forwardly, an optical fiber (23) exposed to a front end of the ferrule (22), a sleeve (24) fitted on an outer periphery of the ferrule (22) and a positioning cylinder (25) further sheathed on an outer periphery of the sleeve (24), a front end of the positioning cylinder (25) extends forwardly into the accommodating cavity (19), a rear end of the positioning cylinder (25) is fixed to the positioning member (21), an outer periphery of the positioning member (21) is provided with a first

Abstract: This application provides a method and apparatus for unmanned driving behavior decision-making and model training, and an electronic device. The method includes: acquiring sample data, wherein the sample data includes a sample image; extracting a sample feature vector corresponding to the sample data, wherein a feature vector of the sample image is extracted by manifold dimension reduction; and based on the sample feature vector, training by semi-supervised learning to obtain a target decision-making model, wherein the target decision-making model is used for decision-making classification.

Abstract: An optical fiber adapter (10) comprises: a housing (1), a front portion of the housing (1) is provided with at least two accommodating cavities (19) which penetrate in a front-rear direction, are arranged sided by side, and are closed circumferentially, and a rear portion of the housing (1) is provided with a mounting base (14); at least two optical fiber plugs (2) provided on the mounting base (14), each optical fiber plug (2) comprises a positioning member (21), a ferrule (22) fixed to the positioning member (21) and extending forwardly, an optical fiber (23) exposed to a front end of the ferrule (22), a sleeve (24) fitted on an outer periphery of the ferrule (22) and a positioning cylinder (25) further sheathed on an outer periphery of the sleeve (24), a front end of the positioning cylinder (25) extends forwardly into the accommodating cavity (19), a rear end of the positioning cylinder (25) is fixed to the positioning member (21), an outer periphery of the positioning member (21) is provided with a first

Abstract: Provided are a sub-totipotent stem cell product and epigenetic modification label thereof, a method for inducing the generation of the sub-totipotent stem cell product and identification for the epigenetic modification label of the differentiation potential of stem cells. Also provided is a use of histone modification states of sub-totipotent genes and/or differentiation related genes to predict the epigenetic modification label of the differentiation potentials of stem cells.

Abstract: Provided are a sub-totipotent stem cell product and epigenetic modification label thereof, a method for inducing the generation of the sub-totipotent stem cell product and identification for the epigenetic modification label of the differentiation potential of stem cells. Also provided is a use of histone modification states of sub-totipotent genes and/or differentiation related genes to predict the epigenetic modification label of the differentiation potentials of stem cells.