Abstract: Machine learning architectures may predict the likelihood of interaction by users with content items that are accessible using a client application. The machine learning architectures may include one or more feature interaction layers that are coupled with one or more extraction layers. Content items may be selected to provide to users of the client application based on probabilities of users performing one or more actions with respect to the content items, where the probabilities for each action are determined by the machine learning architectures.

Abstract: Watersport board fins with fin retention systems and associated watersport boards and methods. A fin includes a hydrodynamic blade and a fin base extending from the hydrodynamic blade. The hydrodynamic blade defines a fin plane and includes a leading edge, a trailing edge, and a foil surface. The fin further includes a fin retention system with a selective retention system that includes a retainer and an actuator coupled to the retainer via a pivot shaft. The actuator forms a portion of the hydrodynamic blade when the selective retention system is in a locked configuration and is configured to rotate away from the fin plane when the selective retention system transitions from the locked configuration to an unlocked configuration. The retainer is configured to rotate away from the fin plane when the selective retention system transitions from the unlocked configuration to the locked configuration.

Abstract: A machine learning engine identifies training data that includes historical user data and historical content data. A machine learning classifier is trained on the training data to generate a relevancy value for each of a plurality of given content items associated with a given user. The relevancy value for each given content item is indicative of a likelihood that the given user will perform a first user device input action and of a likelihood that the given user will perform a second user device input action, in response to being presented with the given content item. The machine learning classifier receives a plurality of candidate content items associated with a first user. The machine learning classifier generates a relevancy value for each candidate content item. At least one of the candidate content items is identified for inclusion in a first content collection based on the generated relevancy values.

Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.

Abstract: Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.

Abstract: Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.

Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.

Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.

Abstract: A surgical assembly and system are disclosed for use in performing a surgical procedure on a patient. The assembly comprises a surgical tool, the tool being arranged to receive a first signal for use in cutting or cauterizing tissue of the patient during the surgical procedure and an electrode disposed upon the tool. The assembly further comprises an electrical generator communicatively couplable with the electrode, for generating a second signal for use in generating an electrical field from the electrode proximate a site of the surgical procedure, for removing particles suspended proximate the surgical site. The assembly further comprises a controller for controlling the application of the second signal to the electrode, a sensing arrangement for sensing an activation status of the first signal, the sensing arrangement being communicatively coupled with the controller and arranged to output a sensing signal to the controller in dependence of the activation status of the first signal.

Abstract: Watersport board fins with fin retention systems and associated watersport boards and methods. A fin includes a hydrodynamic blade and a fin base extending from the hydrodynamic blade. The hydrodynamic blade defines a fin plane and includes a leading edge, a trailing edge, and a foil surface. The fin further includes a fin retention system with a selective retention system that includes a retainer and an actuator coupled to the retainer via a pivot shaft. The actuator forms a portion of the hydrodynamic blade when the selective retention system is in a locked configuration and is configured to rotate away from the fin plane when the selective retention system transitions from the locked configuration to an unlocked configuration. The retainer is configured to rotate away from the fin plane when the selective retention system transitions from the unlocked configuration to the locked configuration.

Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Abstract: Compounds of formula (I), wherein R1, R2, R3, R5, R6, R7, A1, A2, A3, A4, X1, and X2 have any of the values defined in the specification and pharmaceutically acceptable salts thereof, which are useful as agents in the treatment of diseases and conditions, including inflammatory diseases, cancer, and AIDS are provided. Pharmaceutical compositions comprising of compounds of formula (I) are also provided.

Abstract: Watersport board fins with fin retention systems and associated watersport boards and methods. A fin includes a hydrodynamic blade and a fin base extending from the hydrodynamic blade. The hydrodynamic blade defines a fin plane and includes a leading edge, a trailing edge, and a foil surface. The fin further includes a fin retention system with a selective retention system that includes a retainer and an actuator coupled to the retainer via a pivot shaft. The actuator forms a portion of the hydrodynamic blade when the selective retention system is in a locked configuration and is configured to rotate away from the fin plane when the selective retention system transitions from the locked configuration to an unlocked configuration. The retainer is configured to rotate away from the fin plane when the selective retention system transitions from the unlocked configuration to the locked configuration.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a respective embedding for each entity in a set of entities that are accessible through the social networking system, wherein each embedding is learned based at least in part on one or more sessions of connections formed between users and entities of the social networking system. One or more candidate entities that are related to a first entity can be determined based at least in part on the respective embeddings for the candidate entities and the first entity. At least a first candidate entity from the one or more candidate entities can be provided as a recommendation to a user that formed a connection with the first entity.

Abstract: Particular embodiments detect an indication of an event that is initiated on an online social network by a user and that is related to an entity associated with an activity that the user has engaged in. Based on the received indication, a social-networking system sends the user a request for follow-up information about the activity. After the follow-up information is received, the social-networking system retrieves contacts of the user and identifies contacts that have engaged in social activities on the online social network related to the entity. Recommendations for the entity are then sent to those identified contacts.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a plurality of candidate entities for recommendation to a user of a social networking system. A predicted activity objective value model configured to calculate activity stores for candidate entities is established. The activity score is indicative of the probability of future activity on the social networking system by a candidate entity. A first activity score is determined for each of the plurality of candidate entities based on the predicted activity object value model and a first set of feature values. A second activity score is determined for each of the plurality of candidate entities based on the predicted activity object value model and a second set of feature values that is different from the first set of feature values. A first entity is selected of the plurality of candidate entities based on the first and second activity scores.

Abstract: A surgical assembly and system are disclosed. The system comprises a first generator for generating a first signal for use in generating an electrical field proximate a site of a surgical procedure for removing particles suspended proximate the surgical site. The assembly further comprises a surgical tool comprising a tool-piece communicatively couplable with the first generator and a second generator, the second generator being arranged to generate a second signal for use in cutting or cauterizing biological tissue of the patient during the surgical procedure, the assembly further comprising a switching assembly for switching the application of the first signal and second signal to the tool-piece and a controller for controlling the application of the first signal and second signal to the tool-piece.

Abstract: Particular embodiments detect events associated with information about events and activities that a user has engaged in. The events may be of a particular type. An entity associated with an event may request that the user provide further information on the event and, based on the received information, the social-networking system sends the user a request for follow-up information after an appropriate time delay. The time delay may vary based on the user activity and the type or context of the event that triggered the request. After the follow-up information is received, such information is stored in the social-networking system and may be used to determine recommendations, sponsored stories, advertisements, etc. to send to friends of the user. The information may also be used for ranking or filtering recommendations.

Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.