Abstract: Systems and techniques are disclosed for a buffer management system that dynamically and automatically adjusts operational buffers in resources based on resource utilization based on buffer parameters. Buffer parameters for a resource can include a target utilization, a buffer capacity, a minimum buffer capacity, and a maximum buffer capacity. The buffer management detects changes in utilization, such as a number of operations performed in a time period and determines whether a candidate buffer capacity determined using the buffer capacity parameter and the current utilization is between the minimum buffer capacity and the maximum buffer capacity. If so, the buffer management system sets the buffer capacity to the candidate buffer capacity, otherwise the buffer management system sets the buffer capacity to the minimum or maximum buffer capacity as applicable.

Abstract: A generator is configured to generate a domain-independent representation of an input data sample, an encoder is configured to generate a domain-dependent representation of the input data sample, and a decoder is configured to ensure that a combination of the domain-independent representation and the domain-dependent representation contains sufficient information to reconstruct the input data sample. A discriminator is configured to attempt to determine an originating domain of the domain-independent representation and a classifier is configured to classify the input data sample based on the domain-independent representation of the input data sample.

Abstract: A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user's interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.

Abstract: A social data aggregator generates entries of action data describing actions taken by users of a social networking system. The entries are archived in a number of remote data hosts, with a portion of the entries stored in an action cache to expedite retrieval. The action cache is modified to include relevant entries of action data by selecting the most relevant entries from proxy data aggregators, which identify sets of entries from remote data hosts coupled to the proxy data aggregators based on the relevance of the entries in the remote data hosts. The relevance of an entry is based on an associated engagement score indicating a likelihood of a user requesting content interacting with a notification based on an entry.

Abstract: A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user's interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.

Abstract: A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user's interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.

Abstract: A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user's interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.

Abstract: A social data aggregator generates entries of action data describing actions taken by users of a social networking system. The entries are archived in a number of remote data hosts, with a portion of the entries stored in an action cache to expedite retrieval. The action cache is modified to include relevant entries of action data by selecting the most relevant entries from proxy data aggregators, which identify sets of entries from remote data hosts coupled to the proxy data aggregators based on the relevance of the entries in the remote data hosts. The relevance of an entry is based on an associated engagement score indicating a likelihood of a user requesting content interacting with a notification based on an entry.

Abstract: A social data aggregator generates entries of action data describing actions taken by users of a social networking system. The entries are archived in a number of remote data hosts, with a portion of the entries stored in an action cache to expedite retrieval. The action cache is modified to include relevant entries of action data by selecting the most relevant entries from proxy data aggregators, which identify sets of entries from remote data hosts coupled to the proxy data aggregators based on the relevance of the entries in the remote data hosts. The relevance of an entry is based on an associated engagement score indicating a likelihood of a user requesting content interacting with a notification based on an entry.

Abstract: A social data aggregator generates entries of action data describing actions taken by users of a social networking system. The entries are archived in a number of remote data hosts, with a portion of the entries stored in an action cache to expedite retrieval. The action cache is modified to include relevant entries of action data by selecting the most relevant entries from proxy data aggregators, which identify sets of entries from remote data hosts coupled to the proxy data aggregators based on the relevance of the entries in the remote data hosts. The relevance of an entry is based on an associated engagement score indicating a likelihood of a user requesting content interacting with a notification based on an entry.

Abstract: A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user's interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.