Google Inc.

Jpmorgan Chase Bank, N.a.

Prashant Papiahgari Reddy

Mark Rajan Malhotra

Jesse William Boettcher

Jeffery Theodore Lee

Jeffrey Alan Boyd

Sophie Le Guen

Maria Manuela Veloso

Jay Dare Logue

Grant Michael Erickson

Timo A Bruck

Gregory Rourk Nelson

Patrick M Lister

John P Kozura

Naftali Y Cohen

Robert Collier Barnes

Simran Lamba

Lann Martin

Josh Buffum

Robert Elliott Tillman

John G Macleod

Jerry Johns

Yoky Matsuoka

Evan Jarman Fisher

Lauren Von Dehsen

Christopher Anthony Boross

Samuel W Kortz

Igor Karp

Mark Andrew McBride

Todd Hester

James P Lyons

Lev Stesin

Leon Tan

Jason Lee

Andrew Urman

Carles Pi-Sunyer

David Horton

Vamsi Krishna Potluru

Sumitra Ganesh

Nelson Vadori

Pieter Joris Verhoeven

Alexander Dubman

Mengda Xu

Gregory J Hu

Amanda Surya

Aubrey Thelen

Michael Soucie

Tim Enwall

A method for creating a realistic agent-based simulation of an over-the-counter market and using data from the real market to calibrate the simulator is provided. The method includes: assigning, to each of a group of agents, a type value that relates to a state of the respective agent; receiving agent-specific data that relates to market-based observations, market-based actions, and market-based rewards; generating a market simulation based on the assigned type values and the received agent-specific data; and calibrating the market simulation based on actual market data.

Method and system for simulation and calibration of markets

A method for heuristics-based link prediction in multiplex networks may include a link prediction computer program: receiving a model for a multiplex network; defining a property matrix that specifies feature for cross-layer correlation; constructing a cross-correlation matrix from the property matrix; applying a count and weight correlation heuristic to the cross-correlation matrix, wherein the count and weight correlation heuristic counts a number of layers that contain a link between two nodes, and weights that count according to cross-layer correlations; applying a correlation weighted heuristic to the cross-correlation matrix; applying a count correlation-weighted heuristic to the cross-correlation matrix that counts the number of layers that contain a link between two nodes and weights that count according to both cross-layer correlations and values resulting from evaluating a monoplex heuristic applied to each layer in the multiplex network, resulting in output values; and identifying a missing link based on the output values.

Systems and methods for heuristics-based link prediction in multiplex networks

A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Method for accounting for impact of uncertainty in customer surveys

A distributed resource model is described that maintains traits of resources in a distributed computing system. The traits include properties, commands, and events that, along with protocols operating in the distributed computing system, provide real-time access to the traits of resources in the distributed computing system, as well as real-time command of controls for the resources. Controllers manage and publish the traits of resources and provide various types of functionality, such as arbitration, complexity management, fan-out of capabilities, coordination, adaptation, and resource proxying.

Distributed resource model

A method and a system for using a graph neural network framework to implement a link prediction in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network, each respective layer including a respective plurality of nodes; for each node included in at least a first layer, providing, by a structural node label and determining a common embedding across all of the plurality of layers and an individual embedding for each individual layer; using a k-nearest approach to select a subset of the plurality of layers for performing link prediction with respect to each layer based on the determined embeddings; and performing a link prediction by determining a respective feed-forward network with respect to each layer included in the selected subset.

Method and system for link prediction in large multiplex networks

In a method for detection of an anomaly related to information about a location of a mobile computing device, the anomaly related to the information about the location of the mobile computing device can be detected by a processor of the mobile computing device. A communication about the anomaly can be provided via a user interface of the mobile computing device. The anomaly can be caused by a bug in an operating system of the mobile computing device. Notifications about the anomaly can be provided to a user of the mobile computing device or other mobile computing devices registered to a shared account.

Detection of anomaly related to information about location of mobile computing device

Systems and Methods disclosed herein relate to providing a message to an application programming interface (API). The message includes a request for data from a data model, a submission of data to the data model, or both; and a host selection between: a representational state transfer (REST) host and a subscription-based application programming interface (API) host, wherein the REST host receives REST-based messages and the subscription-based API host receives messages in accordance with a standard of the subscription-based API host; wherein the request for data, the submission of data, or both are configured to create, delete, modify, or any combination thereof data related to a smart-device environment structure, a thermostat, a hazard detector, or any combination thereof stored in a data model accessible by the API.

Methods and apparatus for using smart environment devices via application program interfaces

Systems and techniques are provided for online occupancy state estimation. Signals may be received from sensors distributed in an environment. The signals may include reports from the sensors. Occupancy state estimates may be generated for a ground-truth estimate sequence for the environment. Each occupancy state estimate may correspond to an interval of a time period. An online occupancy model may be generated. based at least partially on the ground-truth estimate sequence for the environment. A current occupancy state estimate may be generated for the environment using the online occupancy model and sensor states of the sensors. The sensor states may be based on the reports from the sensors.

Online occupancy state estimation

An electronic device may that communicates with a plurality of electronic devices may include a network interface and a processor. The network interface may receive a first set of data from a first electronic device and a second set of data from a second electronic device. The processor may then determine a first trust value for the first set of data based on a first corroboration of the first set of data using the second set of data. The first corroboration may include using the second set of data to verify the first set of data.

Crowd-based device trust establishment in a connected environment

A controller is provided for coordinated control of various subsystems, such as a heating, ventilation, and air conditioning (HVAC) subsystem, a camera subsystem, and a security subsystem, in a structure such as a home, by allowing automatic as well as manual changes to be made to the subsystems within their respective constraints. The controller may change the states or settings of the subsystems automatically upon occurrences of certain conditions, or allow the user to change them by switching a structure mode for the subsystems between a home mode and an away mode. A change in the structure mode may effect changes to some or all of the states or settings for some or all of the subsystems.

Structure modes for controlling various systems in closed environments

Systems and techniques are provided for automatic security system mode selection. A set of signals may be received from sensors distributed in an environment with a security system. The security system may be in a first mode. An occupancy model may be received. An occupancy estimate may be generated for the environment based on the set of signals from the sensors and the occupancy model. Mode rules may be received. The mode rules associate occupancy estimates with modes of the security system. A second mode for the security system may be determined based on the occupancy estimate and mode rules. The second mode may be different from the first mode. The mode of the security system may be automatically changed from the first mode to the second mode.

Automatic security system mode selection

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Madison, NJ, United States of America

Prashant Papiahgari Reddy