Abstract: A set of content items can be accessed by a community of users having a set of interests. A set of interest based clusters for the set of content items correspond to the set of interests. For a user, a recommendation system can determine a group of user interest clusters selected from the set of interest based clusters. A popularity score for each content item of the set of content items with respect to the community of users can be generated, and an interest based popularity score for a content item within the interest based cluster can be generated based on a rank of the content item based on the popularity score of the content item. Recommendation candidates for the user can be generated based on the interest based popularity score of the content item for each content item in the group of user interest clusters.

Abstract: Electrocatalysts and methods of forming the same are provided. A hybrid electrocatalyst can be a combination of a platinum (Pt)-based catalyst and a carbon-based non-precious-metal catalyst using a single atom approach. A fuel cell electrocatalyst can include a nitrogen-doped carbon support and a plurality of atoms of both Pt and of a non-precious-metal catalyst dispersed in the support. The dispersed atoms can be isolated from each other within the support.

Abstract: An embodiment of the present disclosure provides a leveling agent, which is specifically a polypyridine compound, where the polypyridine compound includes a structural unit shown in Formula (I) or a protonated product of the structural unit shown in Formula (I): In Formula (I), R1 and R2 are independently any one of the following: substituted or unsubstituted alkylene, substituted or unsubstituted arylene, substituted or unsubstituted arylene alkyl, substituted or unsubstituted alkylene aryl, alkylene containing an ether oxygen atom, ester, and/or imide, arylene containing an ether oxygen atom, ester, and/or imide, arylene alkyl containing an ether oxygen atom, ester, and/or imide, and alkylene aryl containing an ether oxygen atom, ester, and/or imide; and R3 is any one of groups such as a single bond, substituted or unsubstituted alkylene, substituted or unsubstituted arylene, substituted or unsubstituted arylene alkyl, and substituted or unsubstituted alkylene aryl.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for deep video understanding with large language models. An example embodiment operates by determining a relationship between respective first and second visual elements for each of a plurality of frames of a content item based on respective element types and respective locations for the respective first and second visual elements. For each of the plurality of frames, a respective visual prompt is generated describing the relationship between the respective first and second visual elements. Based on an audio-to-text conversion of audio content associated with the frame or classification of aural elements of the audio content, a respective audio prompt describing the audio content associated with each frame is generated.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating short-form content. An example aspect operates by analyzing a media file in a library using a machine learning model. To analyze the media file, the embodiment determines, using the machine learning model, a first portion of the media file that has a feature that satisfies a classification that the machine learning model is configured to identify. The embodiment tags the first portion using one or more position tags indicative of a beginning of the first portion of the media file or an end of the first portion of the media file. The embodiment then generates a segment from the media file based on the one or more position tags. The segment comprises the portion of the media file and excludes one or more second portions of the media file.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for personalizing a user interface (UI) of a media device and/or content presented thereby. An example embodiment operates by obtaining a first natural language user input, providing the first natural language user input to a personalization language model that is configured to interpret different natural language user inputs to respectively determine different update tasks invoked thereby, the different update tasks including a UI update task and a content update task, receiving from the model a first update task determined thereby based at least on the first natural language user input, generating one or more first application programming interface (API) calls based on the first update task, and placing the one or more first API calls to a service that implements the first update task based on the one or more first API calls.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content items. For example, a first content item unassociated with interaction-based data is determined. A description-based representation of the first content item, an image-based representation of the first content item, and/or a metadata-based representation of the first content item is obtained from machine learning model(s). Such representation(s) are provided as an input to a neural network. A first interaction-based representation of the first content item based on such representation(s) is received as an output from the neural network. A measure of similarity is determined between the first interaction-based representation and second interaction-based representation(s) of second content item(s).

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating a scene emotion value for a scene based on a sequence of frame emotion values for a sequence of frames within the scene of a content. The content can include multiple scenes, and a scene can include multiple frames, where a frame emotion value can be generated for each frame. A frame emotion value can be generated based on scene metadata related to the scene, content metadata related to the content, and a frame metadata related to the frame.

Abstract: A search method, a terminal and a server are provided. The server returns a similar image (namely, a second image) of a first image to the terminal. When displaying the second image, the terminal further displays controls of a plurality of attributes corresponding to the second image. When the terminal detects that a user operates a first control corresponding to the second image on an interface, the terminal triggers the server to obtain first attribute information of the second image, where the first attribute information corresponds to the first control.

Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for personalizing a user interface (UI) of a media device and/or content presented thereby. An example embodiment operates by obtaining a first natural language user input, providing the first natural language user input to a personalization language model that is configured to interpret different natural language user inputs to respectively determine different update tasks invoked thereby, the different update tasks including a UI update task and a content update task, receiving from the model a first update task determined thereby based at least on the first natural language user input, generating one or more first application programming interface (API) calls based on the first update task, and placing the one or more first API calls to a service that implements the first update task based on the one or more first API calls.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for personalizing a user interface (UI) of a media device and/or content presented thereby. An example embodiment operates by obtaining a first natural language user input, providing the first natural language user input to a personalization language model that is configured to interpret different natural language user inputs to respectively determine different update tasks invoked thereby, the different update tasks including a UI update task and a content update task, receiving from the model a first update task determined thereby based at least on the first natural language user input, generating one or more first application programming interface (API) calls based on the first update task, and placing the one or more first API calls to a service that implements the first update task based on the one or more first API calls.

Abstract: Provided in the present application are a composition for detecting occult blood in excreta, and a preparation method therefor and the use thereof. The composition comprises, in parts by weight, 20-80 parts of organic solvent, 0.01-50 parts of thickener, 0.01-2 parts of benzidine, 0.01-5 parts of peroxide, 0.001-0.5 parts of EDTA-2Na and 10-80 parts of water. The composition provided in the present application can quickly and effectively detect the occult blood condition of excreta, has fast color change, long color retention time and high sensitivity.

Abstract: The present invention relates to a new type pyrazolopyrimidine compound, a composition thereof, a preparation method therefor and the use thereof as an anti-cancer drug having anti-tumor activity. The new type pyrazolopyrimidine compound has structural general formula (I) as shown below, is used as an ATR inhibitor, has a good tumor inhibition activity, high selectivity, good water solubility and low toxicity, and can be used for oral administration or intravenous injection administration.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed are system, method and/or computer program product embodiments for improving the performance of a machine learning based algorithm used to provide a user experience to a user via a media device. An embodiment selects a first set of hyperparameter values, implements a first iteration of the algorithm based on the first set of hyperparameter values, utilizes the first iteration of the algorithm to provide a first user experience to the user, determines a response of the user to the first user experience, selects, by a hyperparameter tuning ML model implemented as a contextual multi-arm bandit model or a reinforcement learning model and based on at least the response of the user, a second set of hyperparameter values, implements a second iteration of the algorithm based on the second set of hyperparameter values, and utilizes the second iteration of the algorithm to provide a second user experience to the user.

Abstract: A box underframe end structure of a railway vehicle includes an underframe boundary beam, an end beam, a front draft sill, and a coupler mounting seat, and further includes a rear end cross beam located on one side in a rear of the front draft sill, an end floor fixed between the end beam and the rear end cross beam, and a coupler cross beam fixed with the coupler mounting seat into a whole. A draft sill includes the front draft sill and a rear draft sill respectively arranged on a front side and a rear side of the coupler mounting seat. The coupler cross beam, the rear end cross beam, and the end floor are welded and fixed with the underframe boundary beam. The front draft sill, the rear draft sill, and the coupler cross beam are welded and fixed on a lower surface of the end floor.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for demographic predictions for content items. An example embodiment operates by assigning weights representing demographics to a first plurality of nodes of a predictive model and assigning predictive values representing predicted demographics to a second plurality of nodes of the model. Pairwise distances between the predictive values for the nodes of the second plurality of nodes and the weighted values of the first plurality of nodes may be calculated and the shortest calculated pairwise distances may be used to assign demographics for content items corresponding to nodes of the first plurality of nodes to content items corresponding nodes of the second plurality of nodes. When content is requested, a content item for which the same demographic has been assigned may be recommended to the requestor.

Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.