Intel Corporation

Microsoft Technology Licensing, LLC

Facebook, Inc.

Snowflake Inc.

Yi Wu

Igor V Kozintsev

Kalpana Seshadrinathan

Oscar Nestares

Maha El Choubassi

Yong Jiang

Wei Sun

Jean-Yves Bouguet

Gabriel Takacs

Ara Victor Nefian

Yoram Gat

Carole Dulong

Horst Haussecker

Glen J Anderson

Giuseppe Beppe Raffa

Lama Nachman

Rahul C Shah

Joshua J Ratcliff

Gowri Somanath

Michael Phillip Salem

Ata E Husain Bohra

Tao Ma

Xingyao Ye

David Townsend Martin

Jie Xiong

Mark Teng Xia

Jiang Yong

Stephen R Atherton

Shaojun Yao

Ting Chen

Richmond Hicks

Wei Guang Lu

Adam Jordan

Scott Ettinger

Wendy March

Garth Shoemaker

Richard T Beckwith

Qi Guo

Ali Mehdizadeh

James Granger

Daniel Sairom Krishnan Hewlett

Lucas B Ainsworth

Philip Muse

Kenneth T Anderson

Dan Liu

Junkai Wu

Maria Bezaitis

Baolei Li

Xuhong Zhang

Mingzhou Zhou

Ana P Rosario

Anthony Hsu

Michael M Chu

Beibei Wang

James R Verbus

Wensheng Sun

Keqiu Hu

Chenya Zhang

Max T Stein

Ermal Dreshaj

Navneet Panda

Jason Paul Chang

Mariem Boujelbene

In an example embodiment, a deep learning algorithm is introduced that operates on a transition matrix formed from user activities in an online network. The transition matrix records the frequencies that particular transitions between paths of user activity have occurred (e.g., the user performed a login activity, which has one path in the network, and then performed a profile view action, which has another path in the network). Each transition matrix corresponds to a different user's activities.

Deep learning using activity graph to detect abusive user activity in online networks

The subject technology determines a derived encryption key using a cryptographic hash function applied to a hybrid tenant master encryption key and a local random generated identifier. The subject technology encrypts a record value and a key value associated with a transaction using the derived encryption key. The subject technology determines a non-leaf node using a tenant prefix of a tenant. The subject technology inserts the encrypted record value at a leaf node below a non-leaf node of a tree structure associated with the tenant. The subject technology receives a second transaction for performing a read operation on a distributed database. The subject technology retrieves a set of encryption keys based at least in part on an account and the tenant. The subject technology decrypts, using the set of encryption keys, data from the distributed database. The subject technology provides the decrypted data as a result of the second transaction.

Key prefix driven data encryption in tree structures

The subject technology receives a transaction for performing an operation on a distributed database, the transaction associated with an account. The subject technology identifies a tenant corresponding to the account associated with the transaction. The subject technology retrieves a set of encryption keys based at least in part on the account and the tenant. The subject technology determines a derived encryption key using a cryptographic hash function applied to a hybrid tenant master encryption key and a local random generated identifier. The subject technology encrypts a record value and a key value associated with transaction using the derived encryption key. The subject technology determines a tree structure associated with the tenant. The subject technology determines a non-leaf node using a tenant prefix of the tenant. The subject technology inserts the encrypted record value, and the encrypted key value at a leaf node below the non-leaf node of the tree structure.

Systems, apparatuses and methods to provide image data, augmented with related data, to be displayed on a mobile computing device are disclosed. An example mobile device includes a camera to provide images of a scene from different angles to a server, at least one sensor to sense a position and an orientation of the camera, and a screen to present augmented reality data over the scene based on the position and the orientation of the camera and on a three-dimensional representation of the scene based on the images.

Mobile augmented reality system

A system for sub-pixel disparity estimation is described herein. The system includes memory circuitry to store image data and at least one processor to execute instructions to calculate a first disparity for a set of reference views. The reference views correspond to a first subset of views among a plurality of sub-aperture views represented in the image data. The at least one processor is to refine the first disparity to a second disparity for the reference views. The second disparity has higher precision than the first disparity. The at least one processor is to map the second disparity from the reference views to a second subset of views among the plurality of sub-aperture views different than the first subset of views.

Efficient sub-pixel disparity estimation for all sub-aperture images from densely sampled light field cameras

In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are essentially combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. In a further example embodiment, embeddings used for the input features are modified during training to maximize an objective.

Embedding layer in neural network for ranking candidates

An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

Framework for evaluating targeting models

A system for sub-pixel disparity estimation is described herein. The system includes a plenoptic camera, a memory, and a processor. The memory is configured to store imaging data. The processor is \coupled to the memory and the plenoptic camera. The processor is to obtain a plurality of sub-aperture views, select a subset of sub-aperture views as reference views for a disparity calculation, and calculate an integer disparity for the reference views. The processor is also to refine the integer disparity to sub-pixel disparity accuracy for the reference views and propagate the sub-pixel disparity from the reference views to other views of the plurality of sub-aperture views.

Techniques are provided for calculating temporally coherent disparity values for pixels in a sequence of image frames. An example method may include calculating initial spatial disparity costs between a pixel of a first image frame from a reference camera and pixels from an image frame from a secondary camera. The method may also include estimating a motion vector for the pixel of the first reference camera image frame to a corresponding pixel from a second reference camera image frame. The method may further include calculating a confidence value for the estimated motion vector based on a measure of similarity between the colors of the pixels of the first and second image frames from the reference camera. The method may further include calculating temporally coherent disparity costs based on the initial spatial disparity costs weighted by the confidence value and selecting a disparity value based on those costs.

Calculation of temporally coherent disparity from sequence of video frames

Techniques related to disparity search range compression are discussed. Such techniques may include determining a combination of disparity search values that do not coexist in any search range of multiple search ranges each associated with pixels of an initial disparity map, compressing the combination of disparity values to a single disparity label, and performing a disparity estimation based on a disparity search label set including the single disparity label.

Disparity search range compression

Technologies for representing alternate reality characters in a real-world environment include receiving sensor data from sensors of a sensor network of a home location of an alternate reality character, determining available response to the stimuli represented by the sensor data, and determining an activity of the alternate reality character for a time period based on the available responses. The technologies may also include generating a video of the alternate reality character performing the determined activity superimposed on an image map of a real-world environment of the home location during the time period. Users may view the video in real time or during a time period subsequent to the time period represented in the video. Additionally, the alternate reality character may be transferred to remote computing devices in some embodiments.

Asynchronous representation of alternate reality characters

Systems, devices and methods are described including receiving a source image having a foreground portion and a background portion, where the background portion includes image content of a three-dimensional (3D) environment. A camera pose of the source image may be determined by comparing features of the source image to image features of target images of the 3D environment and using the camera pose to segment the foreground portion from the background portion may generate a segmented source image. The resulting segmented source image and the associated camera pose may be stored in a networked database. The camera pose and segmented source image may be used to provide a simulation of the foreground portion in a virtual 3D environment.

Networked capture and 3D display of localized, segmented images

Embodiments of the invention relate to systems, apparatuses and methods to provide image data, augmented with related data, to be displayed on a mobile computing device. Embodiments of the invention display a live view augmented with information identifying an object amongst other objects. Embodiments of the invention may utilize other related data, such as 3D point cloud data, image data and location data related to the object, to obtain a specific location of an object within the live view. Embodiments of the invention may further display a live view with augmented data three-dimensionally consistent with the position and orientation of the image sensor of the mobile computing device.

Growing community of inventors

San Jose, CA, United States of America

Yi Wu