Microsoft Technology Licensing, LLC

Altera Corporation

Souvik Ghosh

Shaunak Chatterjee

Yijie Wang

Timothy Paul Jurka

Myunghwan Kim

Qiang Wu

Wei W Wang

Yang Li

Yu Shi

Wei Guang Lu

Aastha Jain

Mitul Tiwari

Ankit Gupta

Hung Hing Anthony Pang

Yang Chen

Jing Wang

Romer E Rosales-Delmoral

Lokesh Pooranmal Bajaj

Binh Vo

John P Moore

Chencheng Wu

Xiaojing Chen

Karan Ashok Ahuja

Haifeng Zhao

Haowen Cao

Jiashuo Wang

Shilpa Gupta

Paul Fletcher

Hema Raghavan

Bonnie Barrilleaux

Henry Heyburn Pistell

Keyi Yu

Mira Thambireddy

Befekadu Ayenew Ejigou

Ningfeng Liang

Wei Deng

Wei Xue

Sergei Tolmanov

Techniques for a flexible data security and machine learning system for merging third-party data are provided. In one technique, the system receives a data set from a third-party entity and receives selection data that indicates that the third-party entity selected a set of data security policies that includes an encryption option and a data mixing option from among multiple data mixing options. In response to receiving the selection data, the system stores data that associates the set of data security policies with the data set, encrypts the data set according to the encryption option, and persistently stores the encrypted data set. Later, the system decrypts the encrypted data set in volatile memory, generates, based on the data mixing option, training data based on the decrypted version of the data set, trains a machine-learned model based on the training data, and stores the machine-learned model in association with the data set.

Flexible data security and machine learning system for merging third-party data

In an example, a plurality of potential feed objects are obtained. An identification of a user performing a navigation command in a user interface is also obtained, the navigation command causing a feed to be displayed or updated. The identification of the user and the plurality of potential feed objects are fed to a machine learned feed object ranking model, the feed object ranking model having been trained via a machine learning algorithm to calculate a score for each of the potential feed objects, the score being based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihoods that the user's interaction will cause one or more downstream events by other users, and a value of the one or more downstream events to a social networking service. The plurality of feed objects are ranked by their scores.

Feed optimization

A plurality of potential feed objects and corresponding identifications of actors who performed a user interface action that caused a corresponding potential feed object to be generated are obtained. The plurality of potential feed objects and corresponding actor identifications are then fed into a machine learned feed object ranking model, with the machine learned feed object ranking model having been trained via a machine learning algorithm to calculate a score for each of the potential feed objects. The score is based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihood that the user's interaction will cause one or more downstream events by other users, and likelihood that a response from a viewer will cause the actor corresponding to the potential feed object to perform an additional user interface action to generate another potential feed object.

Feed actor optimization

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.

Future connection score of a new connection

In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.

Machine learned models for contextual editing of social networking profiles

The disclosed embodiments provide a system for facilitating interaction within a social network. During operation, the system obtains a set of attributes of a social network of a first member and a set of historic interactions in the social network. Next, the system analyzes the attributes and the historic interactions to predict an effect of a potential interaction between the first member and a second member of the social network on subsequent interactions in the social network. The system then uses the predicted effect to generate output for modulating the subsequent interactions in the social network.

Characterizing and managing social network interactions

A method for generating automatic design characterization patterns for integrated circuits (IC) is provided. The method includes selecting a routing scheme from a file containing the device description of the routings of the IC. The routing scheme may be of a phase locked loop, clock tree, delay element, or input output block in one embodiment. Resource types for the routing scheme are identified and a path is defined, within constraints, between the resources. Once a path is defined, alternate paths are defined by retracing the path within constraints from an end of the path to the beginning of the path. An alternative path is then built and the alternative path shares a portion of the path previously defined. A computing system providing the functionality of the method is also provided.

Method and system for semiconductor device characterization pattern generation and analysis

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Saratoga, CA, United States of America

Souvik Ghosh