Abstract: A device and a method for detecting cigarette fly ash by a gray-scale difference based on machine vision (MV) are provided. A manipulator holds a cigarette as a detection sample to simulate a human smoking action, and multiple groups of cameras track a simulated smoking process of the detection sample synchronously in real time. It is determined whether fly ash appears based on a gray-scale difference of burning ash columns, produced without being subjected to flicking, in acquired images. A fly ash area of the burning ash columns of the cigarette is calculated by an area with the gray-scale difference of the burning ash columns of the cigarette in two sequential images of the burning ash columns of the cigarette, and a fly ash amount is further determined. The detection of the amount of fine fly ash is converted into the detection of the gray-scale difference.

Abstract: A method, computer program product, and computer system for processing instances of message sequences. Two or more instances are received. Each instance has an instance number and an associated instance Id. Each instance includes a stream of one or more messages. Each message in each stream is associated with a respective task. All messages in the two or more instances whose respective tasks have been completed are distributed into partitions in the broker. Different messages in each instance of at least one instance are distributed into different partitions. The messages distributed in the partitions include parallel task messages. The parallel task messages are sequenced by being grouped by the instance number or instance Id and are sequentially ordered in each group in an ascending order of a timestamp of arrival of each parallel task message at the broker. The parallel task messages in each instance are dispatched to a service.

Abstract: A device and a method for detecting cigarette fly ash by a gray-scale difference based on machine vision (MV) are provided. A manipulator holds a cigarette as a detection sample to simulate a human smoking action, and multiple groups of cameras track a simulated smoking process of the detection sample synchronously in real time. It is determined whether fly ash appears based on a gray-scale difference of burning ash columns, produced without being subjected to flicking, in acquired images. A fly ash area of the burning ash columns of the cigarette is calculated by an area with the gray-scale difference of the burning ash columns of the cigarette in two sequential images of the burning ash columns of the cigarette, and a fly ash amount is further determined. The detection of the amount of fine fly ash is converted into the detection of the gray-scale difference.

Abstract: A robotic arm includes a base, a first robotic arm, a second robotic arm, a robotic hand and a cigarette holder connected in sequence. The cigarette holder includes a cigarette insertion hole and a cigarette smoking tube. The cigarette smoking tube is connected to a smoking simulator. The robotic arm further includes at least one air exhausting hole. The air exhausting hole is located around the cigarette holder and communicated with an air exhauster through an air exhausting pipe. The robotic arm swings to control the cigarette holder to carry a cigarette from a smoking position to an ash flicking position. Airflow is formed near the air exhausting hole by means of air exhaustion, so as to simulate influences of a wind speed in an outdoor environment on the columnar ash in the cigarette smoking process of the human body.

Abstract: A video decoding method and a video decoding device are provided, and the video decoding method includes the following steps. A to-be-decoded bin string is received. A plurality of first current syntax elements in the to-be-decoded bin string are decoded based on a first context model until a decoding output value of the last one of the first current syntax elements matches a specific bin pattern. When the first current syntax elements are decoded, in response to that a state machine is switched from a first decoding operation mode to a second decoding operation mode, a second current syntax element that exists based on the last one of the first current syntax elements is decoded.

Abstract: A smoking path simulation system includes a case, the robotic arm fixedly provided in the case and a cigarette holder mounted at a working end of the robotic arm, and further includes a feeding device, a cigarette tapping device and a cigarette lighting device provided in the case and located at a periphery of the robotic arm, as well as an image acquisition device mounted at the working end of the robotic arm. With the smoking path simulation system, an action path of smoking a cigarette by a consumer may be simulated, and consistency of the simulated action paths may be guaranteed, thus effectively improving precision of test of indexes of a cigarette-combustion performance, such as cigarette-combustion ash integration characteristics, or the like.

Abstract: Provided are an anti-PD-L1 nanobody and an Fc fusion protein thereof, and an application thereof. The anti-PD-L1 nanobody and the Fc fusion protein thereof have strong specificity, high affinity, and weak immunogenicity to humans. In addition, same have high stability and a significant anti-tumor effect.

Abstract: Provided are an anti-CLDN18.2 antibody and a pharmaceutical composition thereof and a detection method therefor, wherein the heavy chain of the antibody is selected from any one of SEQ ID NOs: 1-7 or SEQ ID NOs: 15-30, and the light chain of the antibody is selected from any one of SEQ ID NOs: 8-14 or SEQ ID NOs: 31-46. The ability of the antibody to bind to cell lines and tumor tissue cells is more powerful than that of the existing antibody IMAB362, and the anti-tumor effect of the antibody is also more powerful than that of the existing antibody IMAB362.

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: An image analysis method includes: obtaining an image; performing image classification on the image by using an image classification network, to obtain an image classification result of an image category of the image, the image category including a first category and a second category different from the first category; performing object detection on the image by using an object detection network, to obtain an object detection result of a target object associated with the first category; and generating an image analysis result of the image based on the image classification result and the object detection result.

Abstract: A robotic arm includes a base, a first robotic arm, a second robotic arm, a robotic hand and a cigarette holder connected in sequence. The cigarette holder includes a cigarette insertion hole and a cigarette smoking tube. The cigarette smoking tube is connected to a smoking simulator. The robotic arm further includes at least one air exhausting hole. The air exhausting hole is located around the cigarette holder and communicated with an air exhauster through an air exhausting pipe. The robotic arm swings to control the cigarette holder to carry a cigarette from a smoking position to an ash flicking position. Airflow is formed near the air exhausting hole by means of air exhaustion, so as to simulate influences of a wind speed in an outdoor environment on the columnar ash in the cigarette smoking process of the human body.

Abstract: An object detection model training method includes: inputting an unannotated first sample image into an initial detection model of a current round, and outputting a first prediction result for a target object, transforming the first sample image and a first prediction position region within the first prediction result to obtain a second sample image and a prediction transformation result in the second sample image; inputting the second sample image into the initial detection model, and outputting a second prediction result for the target object; obtaining a loss value of unsupervised learning according to a difference between the second prediction result and the prediction transformation result; and adjusting model parameters of the initial detection model according to the loss value and returning to the operation of inputting a first sample image into an initial detection model of a current round to perform iterative training, to obtain an object detection model.

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: A smoking path simulation system includes a case, the robotic arm fixedly provided in the case and a cigarette holder mounted at a working end of the robotic arm, and further includes a feeding device, a cigarette tapping device and a cigarette lighting device provided in the case and located at a periphery of the robotic arm, as well as an image acquisition device mounted at the working end of the robotic arm. With the smoking path simulation system, an action path of smoking a cigarette by a consumer may be simulated, and consistency of the simulated action paths may be guaranteed, thus effectively improving precision of test of indexes of a cigarette-combustion performance, such as cigarette-combustion ash integration characteristics, or the like.

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 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: 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.

Abstract: A video decoding method and a video decoding device are provided, and the video decoding method includes the following steps. A to-be-decoded bin string is received. A plurality of first current syntax elements in the to-be-decoded bin string are decoded based on a first context model until a decoding output value of the last one of the first current syntax elements matches a specific bin pattern. When the first current syntax elements are decoded, in response to that a state machine is switched from a first decoding operation mode to a second decoding operation mode, a second current syntax element that exists based on the last one of the first current syntax elements is decoded.

Abstract: A data disaster tolerance method, device and system is disclosed. Each node in a logic unit including a single master node and two or more slave nodes is monitored. If the master node is abnormal, the server acquires log information of the plurality of two or more slave nodes separately, the log information of the two or more slave nodes includes respective time points of data synchronization between the slave nodes and the master node A respective slave node of the two or more slave nodes having the time point of data synchronization closest to a current time is selected as a target node. A master-slave relationship in the logic unit is updated to change a role of the target node to that of the master node.