Abstract: Technologies for monitoring performance of a machine learning model include receiving, by an unsupervised anomaly detection function, digital time series data for a feature metric; where the feature metric is computed for a feature that is extracted from an online system over a time interval; where the machine learning model is to produce model output that relates to one or more users' use of the online system; using the unsupervised anomaly detection function, detecting anomalies in the digital time series data; labeling a subset of the detected anomalies in response to a deviation of a time-series prediction model from a predicted baseline model exceeding a predicted deviation criterion; creating digital output that identifies the feature as associated with the labeled subset of the detected anomalies; causing, in response to the digital output, a modification of the machine learning model.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Future Connection Engine that generates a select pairing of member accounts for a potential social network connection. The Future Connection Engine predicts, according to the prediction model, a first number of subsequent social network connections for a first member account in the select pairing that will occur after establishing the potential social network connection and a second number of subsequent social network connections for a second member account in the select pairing that will occur after establishing the potential social network connection. The Future Connection Engine generates connection recommendations for display to the select pairing based on whether the first and/or the second number of subsequent social network connections satisfies a threshold.

Abstract: Technologies for monitoring performance of a machine learning model include receiving, by an unsupervised anomaly detection function, digital time series data for a feature metric; where the feature metric is computed for a feature that is extracted from an online system over a time interval; where the machine learning model is to produce model output that relates to one or more users' use of the online system; using the unsupervised anomaly detection function, detecting anomalies in the digital time series data; labeling a subset of the detected anomalies in response to a deviation of a time-series prediction model from a predicted baseline model exceeding a predicted deviation criterion; creating digital output that identifies the feature as associated with the labeled subset of the detected anomalies; causing, in response to the digital output, a modification of the machine learning model.

Abstract: Techniques are provided for optimization of user interactions based on connection value scores. A method may comprise receiving input that indicates a selection of one or more social network activities to optimize for a particular member of a social network. The method may also comprise, based on the selection of the one or more social network activities, calculating a connection value score for a social network connection between the particular member and each user in a plurality of other users. Calculating the connection value score may comprise identifying one or more attribute values that are correlated with the one or more social network activities. Further, the method may comprise, based on the connection value score for each user of the plurality of other users, determining a ranking of the plurality of other users. The method may also comprise determining content based on the ranking of the plurality of other users.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Future Connection Engine that generates a select pairing of member accounts for a potential social network connection. The Future Connection Engine predicts, according to the prediction model, a first number of subsequent social network connections for a first member account in the select pairing that will occur after establishing the potential social network connection and a second number of subsequent social network connections for a second member account in the select pairing that will occur after establishing the potential social network connection. The Future Connection Engine generates connection recommendations for display to the select pairing based on whether the first and/or the second number of subsequent social network connections satisfies a threshold.

Abstract: The disclosed embodiments provide a system for facilitating interaction within a social network. During operation, the system obtains a set of features associated with two members of a social network, wherein the features comprise a member feature and an activity feature. Next, the system analyzes the features to predict an effect of a potential interaction between the two members on subsequent interactions between the two members in the social network. The system then uses the predicted effect to generate output for modulating the subsequent interactions in the social network.

Abstract: The disclosed embodiments provide a system for facilitating interaction within a social network. During operation, the system identifies a first member of a social network with a first activity level that is lower than a threshold. Next, the system uses the first activity level to boost a score associated with recommending an interaction with the first member to a second member of the social network. The system then uses the boosted score to generate output for modulating subsequent interactions in the social network.

Abstract: In an embodiment, a method may comprise acquiring behavioral data of one or more users that suggests a causal relationship between source content and a request for destination content separated from the source content by one or more requests for intermediate content; and determining, based on the behavioral data, content to be placed on a web page. The method may also comprise determining, based on the behavioral data, a location on the web page to place the content. The method may comprise generating the behavioral data by associating a source URL with one or more subsequent URLs that are requested within a predetermined temporal proximity to a request for the source URL. The method may comprise generating the behavioral data by monitoring one or more user pseudonyms comprising a member identifier, a browser identifier, a computing platform identifier, a session identifier, a cookie, an IP address, or a geolocation.

Abstract: Techniques are provided for optimization of user interactions based on connection value scores. A method may comprise receiving input that indicates a selection of one or more social network activities to optimize for a particular member of a social network. The method may also comprise, based on the selection of the one or more social network activities, calculating a connection value score for a social network connection between the particular member and each user in a plurality of other users. Calculating the connection value score may comprise identifying one or more attribute values that are correlated with the one or more social network activities. Further, the method may comprise, based on the connection value score for each user of the plurality of other users, determining a ranking of the plurality of other users. The method may also comprise determining content based on the ranking of the plurality of other users.

Abstract: A flip-flop circuit with an internal level shifter includes an input stage, a clock input stage, an output stage and a level shifting stage. The output stage generates an output signal based on an input signal received by the input stage and a clock signal received by the clock input stage. The level shifting stage shifts-up the voltage level of the output signal.

Abstract: A flip-flop circuit with an internal level shifter includes an input stage, a clock input stage, an output stage and a level shifting stage. The output stage generates an output signal based on an input signal received by the input stage and a clock signal received by the clock input stage. The level shifting stage shifts-up the voltage level of the output signal.

Abstract: A flip-flop circuit with an internal level shifter includes an input stage, a clock input stage, an output stage and a level shifting stage. The output stage generates an output signal based on an input signal received by the input stage and a clock signal received by the clock input stage. The level shifting stage shifts-up the voltage level of the output signal.

Abstract: A flip-flop circuit with an internal level shifter includes an input stage, a clock input stage, an output stage and a level shifting stage. The output stage generates an output signal based on an input signal received by the input stage and a clock signal received by the clock input stage. The level shifting stage shifts-up the voltage level of the output signal.