Abstract: A traffic signal phase and timing (SPaT) prediction system is disclosed. The system may include a transceiver configured to receive historical traffic signal information associated with a traffic light and real-time traffic signal information. The system may further include a memory configured to store a training data and a trained machine model. The trained machine model may be trained using the training data that includes the historical traffic signal information. The system may further include a processor configured to execute instructions stored in the trained machine model to predict traffic signal information (e.g., SPaT) associated with a future traffic signal cycle based on the real-time traffic signal information. The processor may further output the traffic signal information associated with the future traffic signal cycle to a vehicle.

Abstract: Intelligent process management is provided. A start time is determined for an additional process to be run on a worker node within a duration of a sleep state of a task of a process already running on the worker node by adding a first defined buffer time to a determined start time of the sleep state of the task. A backfill time is determined for the additional process by subtracting a second defined buffer time from a determined end time of the sleep state of the task. A scheduling plan is generated for the additional process based on the start time and the backfill time corresponding to the additional process. The scheduling plan is executed to run the additional process on the worker node according to the start time and the backfill time corresponding to the additional process.

Abstract: An artificial intelligence-based image processing method includes: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result; converting the first sample image into a target sample image, the target sample image carrying a corresponding target processing result; training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model; and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.

Abstract: A method for training an image recognition model includes: obtaining training image sets; obtaining a first predicted probability, a second predicted probability, a third predicted probability, and a fourth predicted probability based on the training image sets by using an initial image recognition model; determining a target loss function according to the first predicted probability, the second predicted probability, the third predicted probability, and the fourth predicted probability; and training the initial image recognition model based on the target loss function, to obtain an image recognition model.

Abstract: An artificial intelligence-based image processing method includes: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result; converting the first sample image into a target sample image, the target sample image carrying a corresponding target processing result; training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model; and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.

Abstract: This application relates to an endoscopic image display method, apparatus, computer device, and storage medium, and relates to the field of machine learning technologies. The method acquiring an endoscopic image; locating a target region image in the endoscopic image, the target region image being a partial image comprising a target region; inputting the target region image into a coding network to obtain a semantic feature of the target region image, the coding network being a part of an image classification network, and the image classification network being a machine learning network obtained through training with first training images; matching the semantic feature of the target region image against semantic features of image samples to obtain a matching result, the matching result indicating a target image sample that matches the target region image; and displaying the endoscopic image and the matching result in an endoscopic image display interface.

Abstract: Provided is a calculation method of line carrying capacity of regional rapid rail transit under mixed operation of express/local trains. Based on the theory of calculating the line carrying capacity by analytical method and direct calculation method, a framework and ideas for calculating the line carrying capacity including two modes of a following operation mode and an overtaking operation mode are established. Taking the train stop interval time, additional time for starting and stopping, following interval time, express and local train numbers, an express/local train overtaking operation mode, overtaking station number, overtaking station position and other factors that affect the line carrying capacity as input, the line carrying capacity of the two modes is deduced and calculated respectively under the conditions of stopping the express train and not stopping the express train at the intermediate station.

Abstract: A medical image processing method includes: determining a target mask of a target object in a medical image and a reference mask of a reference object in the medical image, the target mask indicating a position and a boundary of the target object, and the reference mask indicating a position and a boundary of the reference object; determining a feature size of the target object based on the target mask; determining a feature size of the reference object based on the reference mask; and determining a target size of the target object based on the feature size of the reference object, a preset mapping relationship between the feature size of the reference object and a reference size, and the feature size of the target object.

Abstract: Embodiments of this application disclose an image processing method performed by a computer device, and a computer-readable storage medium.

Abstract: A method for comprehensively analyzing and/or evaluating cigarette burning quality index is disclosed. The steps include collecting cigarette burning quality index data, filtering the cigarette burning quality index data, standardizing cigarette data, and measuring cigarette burning quality. The importance of the cigarette burning quality indicators can also be evaluated. The method for comprehensively analyzing and/or evaluating cigarette burning quality indicators can reflect general laws more accurately by maintaining the sample distribution through a singularity detection method, and analyzing correlations of each index with cigarette performance from multiple perspectives, to fuse them into a comprehensive measurement value. The importance ranking and weight of indicators can be obtained more completely and stably.

Abstract: A computer-implemented method, system and computer program product for identifying a root cause of failures in a CI/CD pipeline. Tags for tasks, templates and/or variables of the operator and the CI/CD pipeline are extracted. Code of the tagged tasks, templates and/or variables of the operator are mapped with the code of the tagged tasks, templates and/or variables of the Cl/CD pipeline forming mappings. Additionally, code of the tagged tasks, templates and/or variables between the roles of the operator are mapped forming mappings. Upon receiving a notification of a failure in the Cl/CD pipeline, a root cause of the failure is identified by searching such mappings for a mapped role or task in relation to the role or task involving the software product which failed in the CI/CD pipeline and searching the log file of the operator for an error in connection with such mapped role or task.

Abstract: A method for training an image recognition model includes: obtaining training image sets; obtaining a first predicted probability, a second predicted probability, a third predicted probability, and a fourth predicted probability based on the training image sets by using an initial image recognition model; determining a target loss function according to the first predicted probability, the second predicted probability, the third predicted probability, and the fourth predicted probability; and training the initial image recognition model based on the target loss function, to obtain an image recognition model.

Abstract: A sowing device changes a depth of wheat sowing includes an external skeleton, where one side of the external skeleton is fixedly connected with a connection part for articulated with a tractor; a main board slidably connected to an interior of the external skeleton and a seeding unit below the main board; a support frame fixedly connected to the main board; a plow blade for slotting land rotatably connected to a bottom of the support frame; a rotary roller below the external skeleton and rotationally connected thereto. The main board is moved downwards to drive a straight toothed plate fixedly connected to the main board to move downwards; the straight toothed plate is moved upwards through a meshing effect of a gear to cause L-shaped toothed plate to move upwards to squeeze a horizontal plate to move upwards.

Abstract: A method for testing burning performance of a dark-colored cigarette using a dark-colored cigarette paper is provided. The dark-colored cigarette paper has a grayscale less than 255. The method includes: simulating, by a robotic arm, a cigarette smoking process and environment; acquiring, by a full-vision camera system, an image of a burn line and ash column region of the dark-colored cigarette; and analyzing a burning performance indicator of the dark-colored cigarette according to coordinate information of the burn line and ash column region. The method is based on a surface reflection characteristic of the dark-colored cigarette paper and a principle of optical reflection to light and highlight an edge of the dark-colored cigarette sample by a light source at a certain angle from a side. In this way, the method forms a chromatic aberration to localize the dark-colored cigarette sample, thereby testing the burning performance of the dark-colored cigarette sample.

Abstract: This application relates to an image recognition model training method, an image recognition method, apparatus, and system. The method includes: obtaining a to-be-recognized image; extracting image feature information of the to-be-recognized image; and obtaining a lesion category recognition result of the to-be-recognized image by using the image feature information of the to-be-recognized image as an input parameter of a preset image recognition model, the image recognition model being trained by using a training image sample set comprising at least one strong-label training image sample, to determine the lesion category recognition result; and the strong-label training image sample representing an image sample having strong-label information, and the strong-label information comprising at least annotation information of a lesion category and a lesion position in the strong-label training image sample.