Abstract: An example may, at a first device, input an entity embedding for an entity and an item embedding for a plurality of items to a scoring function. The entity embedding and the item embedding are pre-computed using a first language model. At the first device, a retrieval score for the entity and a first item of the plurality of items is computed. The retrieval score is used to identify an item subset of the plurality of items. A ranking prompt is input to a second language model. The second language model and the first language model have a common parameter value. The second language model generates a ranking score for the entity and a second item in response to the ranking prompt. An online system uses the ranking score to include or exclude the second item from a presentation of digital content to the entity via a device.

Abstract: An example may train a cross encoder embedding model using a ranking instruction, a combined input, a pseudo label, and a combined loss. The combined loss includes a ranking loss and a first retrieval loss. A first entity embedding of an entity and a first item embedding of an item may be obtained from the trained cross encoder embedding model. A first input including the first entity embedding obtained from the trained cross encoder embedding model, a second input including the first item embedding obtained from the trained cross encoder embedding model, and a second retrieval loss, may be used to train a dual encoder retrieval model to produce a trained dual encoder retrieval model. A system may use output of the trained dual encoder retrieval model to include or exclude items from a presentation of digital content items to the entity via a device.

Abstract: The AR glasses include a power supply unit, a computing processing unit, a communication unit and a wired connector. The wired connector is connected to a host: when the host is not connected, the power supply unit supplies power to the AR glasses, the computing processing unit may receive and process AR glasses data, and the communication unit is configured to perform data communication with an external device; when the host is connected, a host power supply supplies power to the AR glasses and charges the power supply unit, and the computing processing unit is configured to receive AR glasses data, send the data to a processor of the host through the wired connector for processing, and receive the processed AR glasses data through the wired connector.

Abstract: Embodiments of the disclosed technologies are capable of providing a ranking of digital content using a machine learning model. The machine learning model is configured for multi-task learning for dependent multi-objective optimization. Embodiments configure a memory according to a machine learning model, where the machine learning model includes a shared backbone and multiple heads. Each of the multiple heads are trained to perform a task associated with a first objective of a second objective.

Abstract: Embodiments of the disclosed technologies receive, for a first machine learning task, a first set of raw features arranged according to a first schema, and, for a second machine learning task, a second set of raw features arranged according to a second schema different than the first schema. A multi-task raw feature set is created by storing, in a data store arranged according to a common schema, the first and second sets of raw features. A common feature that is common to both the first and second sets of raw features is identified. A multi-task transformed feature set and a model bundle are created. The multi-task transformed feature set is separated into first and second sets of transformed features. The first and second sets of transformed features and the model bundle can be used to create a trained multi-task machine learning model.

Abstract: The AR glasses include a power supply unit, a computing processing unit, a communication unit and a wired connector. The wired connector is connected to a host: when the host is not connected, the power supply unit supplies power to the AR glasses, the computing processing unit may receive and process AR glasses data, and the communication unit is configured to perform data communication with an external device; when the host is connected, a host power supply supplies power to the AR glasses and charges the power supply unit, and the computing processing unit is configured to receive AR glasses data, send the data to a processor of the host through the wired connector for processing, and receive the processed AR glasses data through the wired connector.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method described herein are directed to a Similar Profiles Engine. The Similar Profiles Engine generates an inverted index query based on one or more portions of profile data of a target member account of a social network service. The Similar Profiles Engine identifies respective profile data, of one or more candidate member accounts in the social network service, that maps to at least one inverted index filter, the at least one inverted index filter matching at least a portion of the inverted index query. The Similar Profiles Engine calculates a similarity score between each respective candidate member account and the target member account, and causes a display of identifiers of one or more candidate member accounts in a user interface of a client device based on respective similarity scores.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method described herein are directed to a Similar Profiles Engine. The Similar Profiles Engine generates an inverted index query based on one or more portions of profile data of a target member account of a social network service. The Similar Profiles Engine identifies respective profile data, of one or more candidate member accounts in the social network service, that maps to at least one inverted index filter, the at least one inverted index filter matching at least a portion of the inverted index query. The Similar Profiles Engine calculates a similarity score between each respective candidate member account and the target member account, and causes a display of identifiers of one or more candidate member accounts in a user interface of a client device based on respective similarity scores.

Abstract: A method for recycling rare earth from a wastewater from rare earth mining or from rare earth smelting, includes the steps of: removing oil from the wastewater to obtain a degreased wastewater; purifying the degreased wastewater to obtain a purified wastewater using a pretreatment system; recycling rare earth from the purified wastewater to obtain a rare earth slurry and a low rare earth wastewater using a rare earth recycling tank; treating the low rare earth wastewater to obtain ammonia gas using an ammonium degassing device; and converting the ammonia gas to obtain aqueous ammonia using an aqueous ammonium preparing system.

Abstract: A method for recycling rare earth from a wastewater from rare earth mining or from rare earth smelting, includes the steps of: removing oil from the wastewater to obtain a degreased wastewater; purifying the degreased wastewater to obtain a purified wastewater using a pretreatment system; recycling rare earth from the purified wastewater to obtain a rare earth slurry and a low rare earth wastewater using a rare earth recycling tank; treating the low rare earth wastewater to obtain ammonia gas using an ammonium degassing device; and converting the ammonia gas to obtain aqueous ammonia using an aqueous ammonium preparing system.