Abstract: A sample processing apparatus (100) and a use method therefor. The sample processing apparatus (100) comprises a uniform mixing mechanism (130) and a magnetic attraction mechanism (140). The sample processing apparatus (100) further comprises at least one of a sample tube (110) and a reaction tube (120). The uniform mixing mechanism (130) is configured to perform uniform mixing processing on a biological sample in the sample tube (110) and/or a biological sample in the reaction tube (120). The magnetic attraction mechanism (140) comprises a magnet (141). The magnet (141) can move relative to the sample tube (110) and/or the reaction tube (120), so as to perform magnetic attraction processing on the biological sample in the sample tube (110) and/or the biological sample in the reaction tube (120).

Abstract: An automatic extraction device and a use method therefor. The automatic extraction device comprises a sample processing apparatus (100), a portal frame (200), a consumable assembly (300), and a pipetting apparatus (400). The sample processing apparatus (100) comprises a uniform mixing mechanism (130) and a magnetic attraction mechanism (140), which are respectively used for performing uniform mixing processing and magnetic attraction processing on a biological sample. The portal frame (200) comprises a base (201) and a moving beam (202) capable of moving relative to the base (201). The sample processing apparatus (100) and the consumable assembly (300) are provided on the base (201). The pipetting apparatus (400) is provided on the moving beam (202) for implementing a pipetting operation between the consumable assembly (300) and the sample processing apparatus (100).

Abstract: Provided is a method for moderating live streaming. The method includes: performing preliminary violation moderation on a current live streaming frame in a to-be-moderated live streaming room based on a high-accuracy moderation model and a high-recall moderation model that are cascaded with each other; and acquiring a target violation score of the current live streaming frame by inputting a first violation score of the current live streaming frame under the high-accuracy moderation model, a second violation score of the current live streaming frame under the high-recall moderation model, and a multi-dimensional behavior feature of the current live streaming frame in the to-be-moderated live streaming room to a pre-constructed behavior moderation model in response to the current live streaming frame passing the preliminary violation moderation.

Abstract: Provided is a method for moderating abnormal users. The method includes: acquiring history behavior data of a plurality of to-be-moderated users, wherein the history behavior data includes behavior data of interactions of the plurality of to-be-moderated users; extracting a plurality of predetermined valid features from the history behavior data for each of the plurality of to-be-moderated users, wherein the predetermined valid features are features predetermined in sample data; calculating, based on predetermined probabilities of events associated with the plurality of valid features, a probability that each of the plurality of to-be-moderated users is the abnormal user; establishing a total probability function based on probabilities that the plurality of to-be-moderated users are the abnormal users; determining candidate users by solving, based on predetermined conditions, a maximum value of the total probability function; and determining the abnormal users by moderating the candidate users.

Abstract: A method for training a multi-target image-text matching model and an image-text retrieval method are provided. The method for training the multi-target image-text matching model includes: obtaining a plurality of training samples that include sample pairs each including a sample image and a sample text, the sample image including a plurality of targets; obtaining, for each of the plurality of training samples, a heat map corresponding to the sample text in the training sample, the heat map representing a region of the target in the sample image that corresponds to the sample text; and training an image-text matching model based on a plurality of the sample texts and corresponding heat maps to obtain the multi-target image-text matching model.