Abstract: The present disclosure relates to the technical field of surveying and mapping building structures, in particular to a built-in gap measuring device and a gap measurement method. The built-in gap measuring device includes an adjustment mechanism and measuring mechanisms disposed on both sides of the adjustment mechanism. The adjustment mechanism can adjust the distance between two groups of measuring mechanisms, and the measuring mechanism includes a panel structure and a measuring column. The panel structure includes a bottom cover close to the adjustment mechanism and a top cover away from the adjustment mechanism. A plurality of rows of elastic column holes are arranged on the top cover in array, column seats in one-to-one correspondence with elastic column holes are arranged inside the panel structure in array.

Abstract: Some embodiments of this specification provide face liveness detection computer-implemented methods, devices, and media. In an example, multiple face images continuously captured for a first user are obtained. Two face images are selected from the multiple face images for a target attribute in one or more predetermined image attributes, where a degree of difference between two attribute values of the target attribute corresponding to the two face images satisfies a predetermined criterion. Both of the two face images are input into a pre-trained face liveness detection model corresponding to the target attribute to obtain a detection result corresponding to the target attribute.

Abstract: Disclosed are computer-implemented methods, non-transitory computer-readable media, and systems for identity document face image quality recognition. One computer-implemented method includes pairing, for each user of a plurality of users and to form a pair of face images, an identity document (ID) face image and a live face image. For each pair of face images and based on a face similarity between the ID face image and the live face image, a similarity score for the ID face image is generated. Based on ID face images and similarity scores corresponding to the ID face images, a model for ID face image quality recognition is trained.

Abstract: Disclosed are computer-implemented methods, non-transitory computer-readable media, and systems for identity document face image quality recognition. One computer-implemented method includes pairing, for each user of a plurality of users and to form a pair of face images, an identity document (ID) face image and a live face image. For each pair of face images and based on a face similarity between the ID face image and the live face image, a similarity score for the ID face image is generated. Based on ID face images and similarity scores corresponding to the ID face images, a model for ID face image quality recognition is trained.

Abstract: A computer-implemented method comprising: training a pre-trained neural network that comprises: an input layer; a plurality of hidden layers, wherein each of the plurality of hidden layers has one or more nodes, wherein each of said one or more nodes has an associated weight trained based on data from a source domain; and an output layer. Training the pre-trained neural network comprises: introducing at least one additional layer to the plurality of hidden layers, wherein said additional layer has one or more nodes having associated weights; keeping weights of the nodes in the plurality of hidden layers of the pre-trained neural network unchanged; inputting data from a target domain to the input layer; and adjusting weights of the one or more nodes in the at least one additional layer based on features obtained at the output layer.

Abstract: A computer-implemented method comprising: training a pre-trained neural network that comprises: an input layer; a plurality of hidden layers, wherein each of the plurality of hidden layers has one or more nodes, wherein each of said one or more nodes has an associated weight trained based on data from a source domain; and an output layer. Training the pre-trained neural network comprises: introducing at least one additional layer to the plurality of hidden layers, wherein said additional layer has one or more nodes having associated weights; keeping weights of the nodes in the plurality of hidden layers of the pre-trained neural network unchanged; inputting data from a target domain to the input layer; and adjusting weights of the one or more nodes in the at least one additional layer based on features obtained at the output layer.

Abstract: Implementations include receiving an input image including a face, processing the input image through a global transformation network to provide a set of global transformation parameters, applying the set of global transformation parameters to the input image to provide a globally transformed image, processing the globally transformed image through a global representation learning network to provide a set of global features, processing the set of global features through a part localization network to provide a set of part localization parameters, applying the set of part localization parameters to the globally transformed image to provide a locally transformed image, processing the locally transformed image through a part representation learning network to provide a set of local features, and outputting a label representing at least one attribute depicted in the input image based on fusing global feature(s) from the set of global features, and local feature(s) from the set of local features.

Abstract: A computer-implemented method comprising: training a pre-trained neural network that comprises: an input layer; a plurality of hidden layers, wherein each of the plurality of hidden layers has one or more nodes, wherein each of said one or more nodes has an associated weight trained based on data from a source domain; and an output layer. Training the pre-trained neural network comprises: introducing at least one additional layer to the plurality of hidden layers, wherein said additional layer has one or more nodes having associated weights; keeping weights of the nodes in the plurality of hidden layers of the pre-trained neural network unchanged; inputting data from a target domain to the input layer; and adjusting weights of the one or more nodes in the at least one additional layer based on features obtained at the output layer.

Abstract: Implementations include receiving an input image including a face, processing the input image through a global transformation network to provide a set of global transformation parameters, applying the set of global transformation parameters to the input image to provide a globally transformed image, processing the globally transformed image through a global representation learning network to provide a set of global features, processing the set of global features through a part localization network to provide a set of part localization parameters, applying the set of part localization parameters to the globally transformed image to provide a locally transformed image, processing the locally transformed image through a part representation learning network to provide a set of local features, and outputting a label representing at least one attribute depicted in the input image based on fusing global feature(s) from the set of global features, and local feature(s) from the set of local features.

Abstract: The present invention relates to a Lactobacillus plantarum capsule and use thereof, and belongs to the technical field of microbe feed additives. The Lactobacillus plantarum capsule consists of a wall material, Lactobacillus plantarum, a cryoprotectant, stachyose, and Morchella esculenta zymolytic powders; wherein the wall material is composed of zymolytic soybean protein isolate, chitosan, xanthan gum, carrageenin, glycerin and trehalose, and when formulated into a mixed solution, the above substances have concentrations of 7 to 10% zymolytic soybean protein isolate, 1 to 2% chitosan, 1 to 3% xanthan gum, 0.1 to 0.5% carrageenin, 0.3 to 1% glycerin, and 0.5 to 2% trehalose. The Lactobacillus plantarum capsule of the present invention enhances resistance of the capsule to gastric acid, has very good enteric solubility, besides, it can effectively inhibit the growth of pathogenic germs to improve immunity, decrease incidence of diseases in livestock and poultry, and improve the breeding quality and benefit.

Abstract: The present invention relates to a Lactobacillus plantarum capsule and use thereof, and belongs to the technical field of microbe feed additives. The Lactobacillus plantarum capsule consists of a wall material, Lactobacillus plantarum, a cryoprotectant, stachyose, and Morchella esculenta zymolytic powders; wherein the wall material is composed of zymolytic soybean protein isolate, chitosan, xanthan gum, carrageenin, glycerin and trehalose, and when formulated into a mixed solution, the above substances have concentrations of 7 to 10% zymolytic soybean protein isolate, 1 to 2% chitosan, 1 to 3% xanthan gum, 0.1 to 0.5% carrageenin, 0.3 to 1% glycerin, and 0.5 to 2% trehalose. The Lactobacillus plantarum capsule of the present invention enhances resistance of the capsule to gastric acid, has very good enteric solubility, besides, it can effectively inhibit the growth of pathogenic germs to improve immunity, decrease incidence of diseases in livestock and poultry, and improve the breeding quality and benefit.