Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: A sample ratio mismatch (SRM) analyzer receives data from an online controlled experiment (OCE) and provides information to help determine a root cause of an SRM. The SRM analyzer may identify one or more segments in the data that include an SRM and may determine whether a triggered scorecard of the OCE includes an SRM. The data may include one or more scorecards. The SRM analyzer may determine whether each scorecard has an SRM. The SRM analyzer may test a difference in proportion of users assigned to treatment between a last scorecard without an SRM and a first scorecard with an SRM. If the difference in proportion is statistically meaningful, the SRM analyzer may determine that the SRM arose after the last scorecard. If the difference in proportions is not statistically meaningful, the SRM analyzer may determine that the SRM existed from a beginning of the OCE.

Abstract: Method of determining insulin injection amount, computer storage medium, and devices, the method includes including: obtaining characteristic information and a blood glucose content at a current time of a target user; and determining an insulin injection amount at each time of the target user based on the characteristic information of the target user, the blood glucose content at the current time of the target user, a predetermined blood glucose prediction model, and a predetermined insulin injection amount prediction model. The method can facilitate the determination of the insulin injection amount at each time.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: The present application relates to a preparation method for recycling inorganic salt in printing and dyeing wastewater and comprises the following process steps: S1, performing impurity removal, softening, COD removal and decoloration on reverse osmosis (RO) membrane concentrated water to obtain pretreated wastewater; S2, performing two-stage electrodialysis on the wastewater obtained in step S1: returning fresh water obtained in a first-stage electrodialysis desalination chamber to a front end of the RO process, and taking saline water obtained in a concentration chamber as raw water of a second-stage electrodialysis desalination chamber and a second-stage electrodialysis concentration chamber; and returning the fresh water obtained by the second-stage electrodialysis desalination chamber to the first-stage electrodialysis concentration chamber; and S3, dealkalizing the concentrated saline water obtained in the step S2 and then adjusting the pH value to obtain concentrated saline water capable of being reuse

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Patient biographic data may be estimated by receiving patient image data, applying the patient image data to a machine learned model, the machine learned model trained on second patient data and trained to map the second patient data to associated biographic data using machine learned features, generating the patient biographic data based on the applying and the machine learned features, and outputting the patient biographic data. The patient biographic data may include a patient weight, a patient height, a patient gender, and a patient age.

Abstract: Camera-based respiratory triggered medical scanning is provided. A camara captures an image or images of a patient. A processor detects breathing from the camera image or images. Detected breathing is used to automatically trigger the medical scan. An extra breathing sensor is not needed. Manual monitoring is not relied upon.

Abstract: Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

Abstract: Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

Abstract: For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

Abstract: For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

Abstract: Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

Abstract: A sample ratio mismatch (SRM) analyzer receives data from an online controlled experiment (OCE) and provides information to help determine a root cause of an SRM. The SRM analyzer may identify one or more segments in the data that include an SRM and may determine whether a triggered scorecard of the OCE includes an SRM. The data may include one or more scorecards. The SRM analyzer may determine whether each scorecard has an SRM. The SRM analyzer may test a difference in proportion of users assigned to treatment between a last scorecard without an SRM and a first scorecard with an SRM. If the difference in proportion is statistically meaningful, the SRM analyzer may determine that the SRM arose after the last scorecard. If the difference in proportions is not statistically meaningful, the SRM analyzer may determine that the SRM existed from a beginning of the OCE.

Abstract: The present application relates to a preparation method for recycling inorganic salt in printing and dyeing wastewater and comprises the following process steps: S1, performing impurity removal, softening, COD removal and decoloration on reverse osmosis (RO) membrane concentrated water to obtain pretreated wastewater; S2, performing two-stage electrodialysis on the wastewater obtained in step S1: returning fresh water obtained in a first-stage electrodialysis desalination chamber to a front end of the RO process, and taking saline water obtained in a concentration chamber as raw water of a second-stage electrodialysis desalination chamber and a second-stage electrodialysis concentration chamber; and returning the fresh water obtained by the second-stage electrodialysis desalination chamber to the first-stage electrodialysis concentration chamber; and S3, dealkalizing the concentrated saline water obtained in the step S2 and then adjusting the pH value to obtain concentrated saline water capable of being reuse

Abstract: The present application provides a fault risk analysis system, a fault risk analysis method, an air conditioner and a computer readable storage medium, and the fault risk analysis system comprises: a memory for storing computer programs; a processor for executing the computer programs to implement: obtaining debugging information and/or system operation information, and determining fault inducing factors corresponding to the debugging information and/or the system operation information; and adjusting a fault risk level of the fault category corresponding to the fault inducing factors according to the fault inducing factors. By collecting the debugging information, potential fault inducing factors of the current unit can be accurately reflected; by collecting the system operation information to analyze the fault inducing factors, the fault risk analysis system can obtain more accurate and reliable fault inducing factors.

Abstract: A control method, an air conditioner, and a computer readable storage medium. The control method is used for the air conditioner, and includes obtaining abnormal weather information of a region where the air conditioner is located, determining operating parameters of a draught fan of an outdoor unit in the air conditioner according to the abnormal weather information, and controlling the draught fan of the outdoor unit to operate according to the operating parameters.

Abstract: Flight control systems, flight control methods, and aircraft are provided. An aircraft including a system and a method for receiving, via a fuel switch, a first control signal indicative of an off position on a fuel switch, determining a throttle resolver angle of a throttle controller in response to the first control signal, comparing the throttle resolver angle to a threshold throttle angle, and generating a pilot warning in response to the first control signal and the throttle resolver angle exceeding the threshold throttle angle.

Abstract: Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

Abstract: Method of determining insulin injection amount, computer storage medium, and devices, the method includes including: obtaining characteristic information and a blood glucose content at a current time of a target user; and determining an insulin injection amount at each time of the target user based on the characteristic information of the target user, the blood glucose content at the current time of the target user, a predetermined blood glucose prediction model, and a predetermined insulin injection amount prediction model. The method can facilitate the determination of the insulin injection amount at each time.