Abstract: In an example embodiment, a generative artificial intelligence (GAI) model is used to generate embeddings, eliminating the need for a separately trained embedding model or layer. These embeddings may then be used as input to another machine learning model. In some example embodiments, these embeddings are generated on interaction data regarding one or more interactions between a user and digital content presented on one or more online platforms.

Abstract: Machine learning techniques to optimize user interface template selection are provided. In one technique, a first set of feature values pertaining to a first entity is identified. Multiple sets of feature values are also identified, each set of feature values pertaining to a different user interface (UI) template for rendering content items on a computer screen. For each set of feature values of the multiple sets, the set of feature values and the first set of feature values are inserted into a machine-learned model to generate a score, which is added to a set of scores, which set of scores is initially empty. Based on the set of scores, a particular UI template is selected for a content item. The content item is transmitted over a computer network to be presented on a screen of a computing device of the first entity according to the particular UI template.

Abstract: Techniques for controlling item frequency using machine learning are provides. In one technique, two prediction models are trained: one based on interaction history of multiple content items by multiple entities and the other based on predicted interaction rates and an impression count for each of multiple content items. In response to a request, a particular entity associated with the request is identified and multiple candidate content items are identified. For each identified candidate content item, the first prediction model is used to determine a predicted interaction rate, an impression count of the candidate content item is determined with respect to the particular entity, the second prediction model is used to generate an adjustment based on the impression count, and an adjusted entity interaction rate is generated based on the predicted interaction rate and the adjustment. A particular candidate content item is selected based on the generated adjusted entity interaction rates.

Abstract: Techniques are provided for using machine learning techniques to learn embeddings for content items. In one technique, training data is used to learn embeddings for each attribute value of multiple attribute values of multiple content items, embeddings for each attribute value of multiple attribute values of multiple entities, and weights for a set of contextual features. In response to receiving a content request, a content item that is associated with one or more targeting criteria that are satisfied based on the content request is identified. A first set of embeddings for the content item are identified, a requesting entity that initiated the content request is identified along with a second set of embeddings for the requesting entity, and a set of feature values for the set of contextual features is identified. The content item is selected based on the sets of embeddings, the set of feature values, and the weights.

Abstract: Techniques for controlling item frequency using machine learning are provides. In one technique, two prediction models are trained: one based on interaction history of multiple content items by multiple entities and the other based on predicted interaction rates and an impression count for each of multiple content items. In response to a request, a particular entity associated with the request is identified and multiple candidate content items are identified. For each identified candidate content item, the first prediction model is used to determine a predicted interaction rate, an impression count of the candidate content item is determined with respect to the particular entity, the second prediction model is used to generate an adjustment based on the impression count, and an adjusted entity interaction rate is generated based on the predicted interaction rate and the adjustment. A particular candidate content item is selected based on the generated adjusted entity interaction rates.

Abstract: A user terminal for multiple-user wireless communication is provided, comprising a transmit buffer configured to store uplink data for transmission. The user terminal comprises a processor configured to generate a request to transmit frame in response to uplink data being present in the transmit buffer, and initiate a transmit timer for determining when to transmit the request to transmit frame. The user terminal comprises a transmitter configured to transmit the request to transmit frame when the transmit timer expires or when the uplink data present in the transmit buffer exceeds a threshold amount. The user terminal comprises a receiver configured to receive a clear to transmit frame from an access point based on the transmitted request to transmit frame.

Abstract: A user terminal for multiple-user wireless communication is provided, comprising a transmit buffer configured to store uplink data for transmission. The user terminal comprises a processor configured to generate a request to transmit frame in response to uplink data being present in the transmit buffer, and initiate a transmit timer for determining when to transmit the request to transmit frame. The user terminal comprises a transmitter configured to transmit the request to transmit frame when the transmit timer expires or when the uplink data present in the transmit buffer exceeds a threshold amount. The user terminal comprises a receiver configured to receive a clear to transmit frame from an access point based on the transmitted request to transmit frame.

Abstract: Apparatus, methods, and computer program product for data-aided frequency estimation in time division synchronous code division multiple access (TD-SCDMA) include receiving, in a downlink time slot of a TD-SCDMA network, a first data burst before a midamble, the midamble, and a second data burst after the midamble; determining at least one data segment that includes symbols in one or both of the first data burst and the second data burst, where the at least one data segment includes a data segment with fewer symbols than a union of the first data burst and the second data burst; and determining a frequency estimate based on the data segment.