Abstract: Embodiments of the disclosure provide a method, apparatus, device, and storage medium for content capturing. The method includes, in response to a capturing start instruction, presenting a capturing start page which including at least a capturing control; in response to detecting a trigger instruction for the capturing control, switching from the capturing start page to a capturing page for video capturing, the capturing page including at least a stop control and a pause control, and the stop control being presented in a more prominent way than the pause control; and in response to detecting a trigger instruction for the stop control, switching from the capturing page to a video editing page, the video editing page being used to edit a captured video. In this way, it is possible for users to quickly perform video capturing and directly enter the editing page to complete editing of a work.

Abstract: In an example embodiment, a first machine learned model is trained to produce output, and a second machine learned model is then trained using training data that has been labeled, at least partially, using the output of the first machine learned model. The first machine learned model is trained to output a measure of how strong a positive signal in the training data really is. Specifically, this measure indicates the level of intention of a user who has engaged in a first user interface action with respect to a piece of content to engage in a subsequent second user interface action with the same piece of content.

Abstract: Disclosed in the present invention are an antibody targeting LAG-3, a preparation method therefor and the use thereof. In particular, disclosed in the present invention is a novel monoclonal antibody targeting LAG-3. Also disclosed in the present invention is a method for the preparation of the monoclonal antibody. The monoclonal antibody of the present invention is capable of binding LAG-3 antigens with high specificity, and has very high affinity and significant activities such as anti-tumor activity.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system performs processing related to a first set of features for a first entity using a first series of embedding layers, wherein the processing includes applying each embedding layer in the first series of embedding layers to a concatenation of all outputs of one or more layers preceding the embedding layer. Next, the system obtains a first embedding as an output of a first final layer in the first series of embedding layers. The system then outputs the first embedding for use by a machine learning model.

Abstract: The disclosed embodiments provide a system for processing scoring requests. During operation, the system matches an identifier for an entity in a scoring request to a cache entry in a score cache. Next, the system retrieves, from the cache entry, a previous value of a score generated by a machine learning model from previous values of features for the entity and a first encoded representation of the previous values of a subset of the features with greater than a threshold effect on the score. The system then compares the first encoded representation with a second encoded representation of the most recent values of the subset of the features for the entity. When the comparison indicates that the most recent values match the previous values, the system outputs the previous value of the score for the entity in a response to the scoring request.

Abstract: Provided in the present invention are a 4-1BB antibody and a preparation method and the use thereof. In particular, provided in the present invention are a 4-1BB antibody, which has high affinity to 4-1BB protein, can effectively activate the signal downstream of the 4-1BB and significantly increase expression quantities of IFN-? and IL-2 in human mixed lymphocytes or T lymphocytes, and can be used to treat cancers and autoimmune diseases.

Abstract: Methods, systems, and computer programs are presented for presenting return-on-investment (ROI) information, for budgeted services that resulted in a successful service delivery, on a user interface for setting the budget for a service request. One method includes an operation for identifying daily budgets for budgeted services that resulted in a successful service delivery (BSSSD). Each daily budget indicates an amount for spending in promotion of the BSSSD in an online service. The method further includes receiving a request, in a graphical user interface (GUI) of the online service, for posting a daily budget for a first budgeted service. Further, a performance value, associated with the daily budgets of the BSSSD that are similar to the first budgeted service, is selected. Further, the method includes causing presentation, by the one or more processors, of the performance value in the GUI.

Abstract: Technologies for generating a graph containing clusters of feature attribute values for training a machine learning model for content item selection and delivery are provided. The disclosed techniques include, for each entity, of a plurality of entities, a system identifies transitions from one geographic location to another geographic location. A graph is generated based on the transitions associated with each entity. The graph comprises nodes representing geographic locations and edges connecting the nodes. Each of the edges connects two nodes, represents a transition from one geographic location to another geographic location, and each edge represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two connected nodes. The system generates a plurality of clusters from the nodes based upon the edge weight value of each edge. The system includes the plurality of clusters as features in a machine learning model.

Abstract: Provided are an antibody targeting NKG2A, a preparation method therefor and use thereof. Specifically, provided is a new mouse or humanized monoclonal antibody targeting NKG2A, and a method for preparing the monoclonal antibody. The monoclonal antibody can bind to an NKG2A antigen with high specificity, and has high affinity and significant activities such as anti-tumor activity.

Abstract: Disclosed are a GITR-targeting antibody, a preparation method therefor and the use thereof. In particular, disclosed is a novel GITR-targeting chimeric antibody or a fully humanized monoclonal antibody. Also disclosed is a method for preparing the monoclonal antibody. The monoclonal antibody of the present invention can bind to a GITR antigen with a high specificity, and has a very high affinity and a significant anti-tumor activity or the like.

Abstract: An antibody targeting CD73, a preparation method therefor and a use thereof. The provided monoclonal antibody can bind to a CD73 antigen with high specificity, and has high affinity and significant antitumor activity.

Abstract: Provided are a murine or fully human monoclonal antibody targeting SEMA4D and a preparation method therefor, wherein the antibody binds to the SEMA4D antigen and has activities such as combating tumors.

Abstract: Provided are a high-affinity full human monoclonal antibody highly specifically targeting to TIM-3 and a preparation method therefor. The monoclonal antibody has a remarkable antitumor activity, and can be used for preparing a related diagnostic reagent or a pharmaceutical composition preventing or treating diseases related to TIM-3 dysfunction.

Abstract: Provided are a murine or human-mouse chimeric antibody targeting OX40 and a preparation method therefor, wherein the antibody binds to the OX40 antigen and has an anti-tumor activity and other activities.

Abstract: An antibody targeting CD47, a preparation method therefor and uses thereof. A novel mouse or human-mouse chimeric antibody targeting CD47. A method for preparing the monoclonal antibody. The monoclonal antibody of the present invention can bind CD47 antigen with high specificity, has high affinity and significant activities, such as anti-tumor activity.

Abstract: Disclosed in the present invention are an antibody targeting IL-5, a preparation method therefor and use thereof. In particular, disclosed in the present invention is a novel murine-derived or chimeric monoclonal antibody targeting IL-5. Also disclosed in the present invention is a method for preparing said monoclonal antibody. The monoclonal antibody of the present invention is capable of binding IL-5 antigen with high specificity, has high affinity and can well alleviate a series of asthma symptoms caused by IL-5, thereby achieving the effect of treating asthma.

Abstract: In an example the output of a machine learned model is a score is then compared to a threshold, and if the score transgresses the threshold, the corresponding item is available to be recommended to the user via the graphical user interface. In an example embodiment, rather than a fixed (static) threshold, a dynamic threshold is utilized. This dynamic threshold is based on a harmonic mean of probabilities utilized in the GLMix model. Specifically, the GLMix model may calculate and utilize the probability that a user will engage with a particular item via a graphical user interface, and also a probability that a user will dismiss a particular item via a graphical user interface.

Abstract: Disclosed by the present invention are an antibody targeting CTLA-4, a preparation method therefor and a use thereof. In particular, disclosed by the present invention is a novel fully human monoclonal antibody that targets CTLA-4. Further disclosed by the present invention is a method for preparing the monoclonal antibody. The monoclonal antibody of the present invention is capable of binding a CTLA-4 antigen with high specificity, has a high affinity, remarkable anti-tumor activity, and so on.

Abstract: In an example embodiment, a first machine learned model is trained to produce output, and a second machine learned model is then trained using training data that has been labeled, at least partially, using the output of the first machine learned model. The first machine learned model is trained to output a measure of how strong a positive signal in the training data really is. Specifically, this measure indicates the level of intention of a user who has engaged in a first user interface action with respect to a piece of content to engage in a subsequent second user interface action with the same piece of content.