Abstract: In some instances, a method is provided. The method comprises obtaining one or more care gap machine learning-artificial intelligence (ML-AI) models; obtaining individual information of an individual, wherein the individual information indicates one or more medical conditions of the individual and personal information of the individual; determining care gap information of the individual based on using the one or more care gap ML-AI models and the individual information, wherein the care gap information indicates one or more predictions for expectancy of a care gap of the individual, and wherein the care gap is associated with a gap in time that the individual has a lapse in receiving medical care for the one or more medical conditions; and performing one or more care gap interventions based on the care gap information.

Abstract: In an aspect, a data processing system includes a computer-readable memory comprising computer-executable instructions, and at least one processor configured to execute executable logic including at least one artificial neural network trained to predict one or more responses to a chemical substance by identifying one or more discrete biological tissue components in a biological image. When the at least one processor is executing the computer-executable instructions, the at least one processor is configured to carry out operations including: receiving spatially arranged image data representing a biological image of a patient; generating spatially arranged image tile data representing a plurality of image tiles; processing the spatially arranged image tile data through one or more data structures storing one or more portions of executable logic included in the artificial neural network to predict one or more responses of a patient.

Abstract: In an aspect, a data processing system includes a computer-readable memory comprising computer-executable instructions, and at least one processor configured to execute executable logic including at least one artificial neural network trained to predict one or more responses to a chemical substance by identifying one or more discrete biological tissue components in a biological image. When the at least one processor is executing the computer-executable instructions, the at least one processor is configured to carry out operations including: receiving spatially arranged image data representing a biological image of a patient; generating spatially arranged image tile data representing a plurality of image tiles; processing the spatially arranged image tile data through one or more data structures storing one or more portions of executable logic included in the artificial neural network to predict one or more responses of a patient.

Abstract: In some instances, a method is provided. The method comprises obtaining one or more care gap machine learning-artificial intelligence (ML-AI) models; obtaining individual information of an individual, wherein the individual information indicates one or more medical conditions of the individual and personal information of the individual; determining care gap information of the individual based on using the one or more care gap ML-AI models and the individual information, wherein the care gap information indicates one or more predictions for expectancy of a care gap of the individual, and wherein the care gap is associated with a gap in time that the individual has a lapse in receiving medical care for the one or more medical conditions; and performing one or more care gap interventions based on the care gap information.

Abstract: A system and a method for detecting a moving target based on multi-frame point clouds. The system comprises a voxel feature extraction module; a transformer module used for matching and fusing the feature tensor sequence, fusing a first feature tensor with a second feature tensor, fusing the fused result with a third feature tensor, fusing the fused result with a fourth feature tensor, and repeating the fusing steps with a next feature tensor to obtain a final fused feature tensor; and an identification module used for extracting features from the final fused feature tensor and outputting detection information of a target. The method comprises the following steps: S1, constructing each system module; S2, training the model by the data in a training set; S3, predicting by the trained model.

Abstract: An efficient across-camera target re-identification method based on similarity, which obtains a plurality of matching pairs and similarity scores thereof through two groups of targets to be matched; wherein for the matching pairs that are not matched by both parties, only a part of the matching pairs with higher similarity scores are selected each time, and the matching pairs are traversed according to the order of the similarity scores thereof from large to small, and the matching pairs and the similarity scores thereof are output as a matching result; when any target to be matched in a matching pair already appears in the matching result, the target cannot be output as the matching result; unmatched matching pairs are repeated traversed until the matching result reaches the expectation. The method firstly solves the multi-target matching problem based on similarity, and greatly reduces the time complexity and improves the efficiency.

Abstract: A system and a method for detecting a moving target based on multi-frame point clouds. The system comprises a voxel feature extraction module; a transformer module used for matching and fusing the feature tensor sequence, fusing a first feature tensor with a second feature tensor, fusing the fused result with a third feature tensor, fusing the fused result with a fourth feature tensor, and repeating the fusing steps with a next feature tensor to obtain a final fused feature tensor; and an identification module used for extracting features from the final fused feature tensor and outputting detection information of a target. The method comprises the following steps: S1, constructing each system module; S2, training the model by the data in a training set; S3, predicting by the trained model.

Abstract: An efficient across-camera target re-identification method based on similarity, which obtains a plurality of matching pairs and similarity scores thereof through two groups of targets to be matched; wherein for the matching pairs that are not matched by both parties, only a part of the matching pairs with higher similarity scores are selected each time, and the matching pairs are traversed according to the order of the similarity scores thereof from large to small, and the matching pairs and the similarity scores thereof are output as a matching result; when any target to be matched in a matching pair already appears in the matching result, the target cannot be output as the matching result; unmatched matching pairs are repeated traversed until the matching result reaches the expectation. The method firstly solves the multi-target matching problem based on similarity, and greatly reduces the time complexity and improves the efficiency.

Abstract: In an aspect, a data processing system includes a computer-readable memory comprising computer-executable instructions, and at least one processor configured to execute executable logic including at least one artificial neural network trained to predict one or more responses to a chemical substance by identifying one or more discrete biological tissue components in a biological image. When the at least one processor is executing the computer-executable instructions, the at least one processor is configured to carry out operations including: receiving spatially arranged image data representing a biological image of a patient; generating spatially arranged image tile data representing a plurality of image tiles; processing the spatially arranged image tile data through one or more data structures storing one or more portions of executable logic included in the artificial neural network to predict one or more responses of a patient.

Abstract: The present invention discloses a method for combining entry structures, by which similar entry structures can be automatically combined. By this method, the efficiency in both retrieving entries and acquiring knowledge is improved. In a technical solution, this method comprises: converting an entry structure into a text; combining, in the format of text and based on a cosine similarity between entries and a cosine similarity between entries and texts, a plurality of texts into one text; and converting the combined text back to the entry structure.

Abstract: A computer-implemented method for authenticating an access point attempting to access a wireless network is presented. The computer-implemented method includes receiving an authentication challenge from the access point, analyzing the authentication challenge at a wireless node by using an optical chaos generation module having an optical section and a radio frequency (RF) section to facilitate reproducibility, unpredictability, and unclonability of challenge-response pairs, and transmitting an authentication response to the access point.

Abstract: The present invention discloses a method for combining entry structures, by which similar entry structures can be automatically combined. By this method, the efficiency in both retrieving entries and acquiring knowledge is improved. In a technical solution, this method comprises: converting an entry structure into a text; combining, in the format of text and based on a cosine similarity between entries and a cosine similarity between entries and texts, a plurality of texts into one text; and converting the combined text back to the entry structure.

Abstract: A computer-implemented method for authenticating an access point attempting to access a wireless network is presented. The computer-implemented method includes receiving an authentication challenge from the access point, analyzing the authentication challenge at a wireless node by using an optical chaos generation module having an optical section and a radio frequency (RF) section to facilitate reproducibility, unpredictability, and unclonability of challenge-response pairs, and transmitting an authentication response to the access point.

Abstract: Disclosed are a neuron system based on an internet of things and a communication network, and a neuron device thereof, which integrate resources of the internet of things and the communication network, thereby improving the degree of utilization of data collected by a sensor, reducing manufacturing costs of mobile terminals, and improving the utilization efficiency of resources.