Ping an Technology (shenzhen) Co., Ltd.

Huawei Technologies Co., Limited

Zte Corporation

Tencent Technology (shenzhen) Company Limited

Sino-swed Tongkang Bio-tech (shenzhen) Limited

Peng Gao

Jun Wang

Ge Li

Rui Wang

Desheng Sun

Tao Bai

Guotong Xie

Yijun Tang

Xindong Teng

Hongtao Guo

Dayong Guo

Shengtao Sun

Yixuan Qiao

Yuechao Guo

Guanju Cheng

Chan Zeng

Chenxi Hu

Meng Zhang

Ang Li

Lilong Wang

Zhenhai Li

Zhenxiao Liu

Ji Zhou

Hu Chen

Li Dang

Cong Fang

Ellen He

Tang Zhou

Liwen Liang

Huijun Li

Zuosheng Li

Sven Isac Skog

Senbo Liu

The application relates to the technical field of artificial intelligence, and provides a method, device, electronic equipment and storage medium for positioning macular center in fundus images. The method comprises: acquiring a detection result of the fundus image detection model, wherein the detection result includes an optic disc area, and a first detection block and a first confidence score corresponding to the optic disc area, and a macular area, and a second detection block and a second confidence score corresponding to the macular area; calculating a center point coordinate of the optic disc area according to the first detection block, and calculating a center point coordinate of the macular area according to the second detection block; identifying whether the to-be-detected fundus image is a left eye fundus image or a right eye fundus image, and correcting a center point of the macular area using different correction models.

Method, device, electronic equipment and storage medium for positioning macular center in fundus images

Disclosed are the expression of a recombinant chicken IgY scFv antibody and monoclonal antibody raised against human thymidine kinase 1 (hTK1) in prokaryotic cells and a preparation method and use thereof. The scFv antibody includes an amino acid sequence shown in SEQ ID NO: 1. The anti-hTK1 recombinant scFv antibody and monoclonal antibody have good immunoreactivity, high specificity and sensitivity, and are easy to separate and purify.

Development of recombinant chicken IGY SCFV antibody raised against human thymidine kinase 1 expressed in prokaryotic cells and use thereof

The present disclosure relates to an artificial intelligence field using a neural network, and publishes a method for confirming a cup-disc ratio based on a neural network, an apparatus, a computer device, and a computer readable storage medium. The method includes: obtaining a retinal image, and detecting an optical disc region in the retinal image to obtain the optical disc region; inputting the optical disc region into a pre-trained neural network to obtain a prediction cup-disc ratio and segment images of an optical cup and an optical disc; computing a cup-disc ratio based on the segment images of the optical cup and the optical disc; and confirming a practical cup-disc ratio based on the prediction cup-disc ratio and the computed cup-disc ratio.

Method for confirming cup-disc ratio based on neural network, apparatus, device, and computer readable storage medium

A voiceprint recognition method includes: obtaining a target speech information set to be recognized that includes speech information corresponding to at least one object; extracting target feature information from the target speech information set by using a preset algorithm, and optimizing the target feature information based on a first loss function to obtain a first voiceprint recognition result; obtaining target speech channel information of a target speech channel, where the target speech channel information includes channel noise information, and the target speech channel is used to transmit the target speech information set; extracting target feature vectors in the channel noise information, and optimizing the target feature vectors based on a second loss function to obtain a second voiceprint recognition result; and fusing the first voiceprint recognition result and the second voiceprint recognition result to determine a final voiceprint recognition result.

Voiceprint recognition method, apparatus and device, and storage medium

The present disclosure relates to a technology field of artificial intelligence and provides a method for selecting image samples and related equipment. The method trains an instance segmentation model with first image samples and trains a score prediction model with third image samples. An information quantum score of second image samples is calculated through the score prediction model and feature vectors extracted. The second image samples are clustered according to the feature vectors of the second image samples and sample clusters of the second image samples are obtained. Target image samples are selected from the second image samples according to the information quantum score of the second image samples and the sample clusters. Target image samples from the image samples are selected for labelling, improving an accuracy of sample selection.

Method for selecting image samples and related equipment

The present disclosure provides a method of positioning vertebra in a CT image, an apparatus, a computer device, and a computer readable storage medium. The method includes: pre-processing vertebra CT image data; inputting the pre-processed vertebra CT image data into a pre-trained neural network to obtain regression results of heat maps of key points corresponding to the pre-processed vertebra CT image data; regressing of 3D heat maps corresponding to the positions of the key points of the vertebra mass center based on the regression results of the heat maps of the key points and the pre-processed vertebra CT image data; serving 3D heat maps corresponding to the positions of the key points of the vertebra mass center as labels, and networked regressing 3D heat map information to position the vertebra. Effects caused by scanning machine difference and scanning noise are avoided, and the vertebra with complex forms is accurately positioned.

Method for positioning vertebra in CT image, apparatus, device, and computer readable storage medium

A method for voiceprint recognition of an original speech is used to reduce information losses and system complexity of a model for data recognition of a speaker's original speech. The method includes: obtaining original speech data, and segmenting the original speech data based on a preset time length to obtain segmented speech data; performing tail-biting convolution processing and discrete Fourier transform on the segmented speech data through a preset convolution filter bank to obtain voiceprint feature data; pooling the voiceprint feature data through a preset deep neural network to obtain a target voiceprint feature; performing embedded vector transformation on the target voiceprint feature to obtain corresponding voiceprint feature vectors; and performing calculation on the voiceprint feature vectors through a preset loss function to obtain target voiceprint data, where the loss function includes a cosine similarity matrix loss function and a minimum mean square error matrix loss function.

Method, apparatus and device for voiceprint recognition of original speech, and storage medium

Embodiments of the present invention disclose a virtual network device. The virtual network device is configured to implement a broadband network gateway BNG function, and the virtual network device includes a load balance unit and a first forwarding unit; the load balance unit is configured to: receive a first data packet from a first user terminal, determine the first forwarding unit according to the first data packet and a first correspondence, and send the first data packet to the first forwarding unit, where the first correspondence indicates mapping from the first user terminal to the first forwarding unit; and the first forwarding unit is configured to receive and forward the first data packet. In addition, the embodiments of the present invention further disclose a method for implementing load sharing in a virtual network device.

Virtual network device and related method

Methods and systems for centralizedly controlling server user rights are provided herein. In an exemplary method, a first verification server can receive an instruction sent by a control server. The instruction can include a user-right-processing instruction or a user-right-adding instruction. The first verification server can process stored information of user rights in response to the user-right-processing instruction sent by the control server to generate processed information of the user rights, or the first verification server can store newly added information of the user rights in response to the user-right-adding instruction sent by the control server. The first verification server can then synchronize the processed information of the user rights or the newly added information of the user rights with a second verification server. The second verification server can be in a communication connection with the first verification server.

System and method for centralizedly controlling server user rights

A joint route query method in ASON. After the route controller in each route domain has received a route query request, it calls a routing algorithm based on the request and the route calculation is performed based on the route database of the current node; if the route calculation succeeds, a route query result is returned to the requester, if no complete route can be obtained, said route query request will be forwarded to the RC in the parent domain. Based on the request, the RC in the parent domain interacts with RCs in other domain of the same layer, and the RCs in the sub-domains contained in the parent domain to complete the route query, and calculates to obtain a complete route, then a route query result will be returned to the RC that initiates the request and this RC will return the route query result to the requester.

United route query method in the automatic switched optical network

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Shenzhen, China

Peng Gao