Vmware, Inc.

Ca, Inc.

Cisco Technology, Inc.

Other

Loon LLC

Google Inc.

Verily Life Sciences LLC

Softbank Corporation

Aalyria Technologies, Inc.

Leland Stanford Junior University

Lam Research Corporation

Government of the United States of America, As Represented by the Secretary of Commerce

Decho Corporation

Jin Zhang

Sharath Ananth

Radhika Vullikanti

Brian Barritt

Chi Zhang

Eric Shannon Knauft

Pascal Renauld

Ye Chen

Wenguang Wang

Jorge Guerra Delgado

Peter Wubbels

Kira Whitehouse

Tyler Rhodes

Zheng Chen

Wei Chen

Steven Lloyd Greenspan

Christos Karamanolis

Abhishek Gupta

Zhao Xia Jin

Maria C Velez-Rojas

Mansi Shah

Xin Liang Li

Christian Dickmann

Feng Tian

Liping Julia Zhu

Gopala Suryanarayana

Haoran Zheng

Jiajun Wang

Zheng Yang

Rohan Ganeshananthan

Bryan Fink

Jonathan Lemon

Shan Zhong

Lei Wu

Zhonghua Zhu

John T Chapman

Alon Shlomo Bernstein

Peter D Olcott

Kiran R Joshi

Craig Steven Levin

Varun A Shah

Srinath Premachandran

Sandeep Rangaswamy

Sunil Khaunte

Edward Joseph Goggin

Serge Mankovskii

Radu Berinde

Yong Lu

Kartik S Chandran

William Dean Thompson

Shengyou Zeng

Albert A Slane

William Guckel

David B Fox

Yue Xiao

Osman Sorkhabi

Regis Joseph Kline

Daniel F Sunday

Lan Xu

Wenli Wu

Xiaoshu Chen

The disclosure provides a method of operating a communication network. The method includes receiving input data related to a state of the communication network and operation of the communication network. The method then includes determining a policy for the communication network based on the input data. The policy is a set of features for forming a plurality of communication links in the communication network over a time interval. The plurality of communication links provides one or more paths through the communication network. Determining the policy is based at least in part on utility values of previous policies. The utility values of previous policies are derived using simulation and/or real-world implementation of the previous policies. The communication network is then operated to implement the policy in the time interval.

Operation of sectorized communications from aerospace platforms using reinforcement learning

Machine learning models used in medical diagnosis should be validated after being deployed in order to reduce the number of misdiagnoses. Validation processes presented here assess a performance of the machine learning model post-deployment. In post-deployment validation, the validation process monitoring can include: (1) monitoring to ensure a model performs as well as a reference member such as another machine learning model, and (2) monitoring to detect anomalies in data. This post-deployment validation helps identify low-performing models that are already deployed, so that relevant parties can quickly take action to improve either the machine learning model or the input data.

Validating a machine learning model after deployment

The technology relates to simulating a dynamic hybrid network. A method for simulating a dynamic hybrid network includes modeling a transmission supply of a mesh network of moving stations on aerial vehicles in flight at each time step in a time series, modeling capacity demand as a function of user distribution data and user behavior data, and computing an aggregate network metric for the dynamic hybrid network including throughput. A method for optimizing throughput of a dynamic hybrid network includes modeling a dynamic hybrid network, including a transmission supply of a mesh network of moving stations, modeling capacity demand as a function of user distribution data and user behavior data, and computing an aggregate network metric; then providing feedback to a controller and causing a change to a station in the network to increase throughput.

Simulating a dynamic hybrid network

The disclosure provides a method of operating a communication network. The method includes receiving input data related to a state of the communication network and determining an implementation policy for the communication network based on the input data. The implementation policy is a set of features for forming one or more communication links in the communication network over a time interval. The one or more communication links includes at least one communication link between a terrestrial terminal and a high-altitude platform terminal. Determining the implementation policy is based at least in part on utility values of previous policies. The utility values of previous policies are derived using simulation and/or real-world implementation of the previous policies. The communication network is then operated to implement the implementation policy in the time interval.

Parameter selection for network communication links using reinforcement learning

The present invention discloses an air energy boiler and belongs to the technical field of energy conversion. A crankshaft of the boiler is fixed at the output end of a rotating unit. The crankshaft is provided with at least one bulge. A driving piston of each conversion assembly is arranged in an air cavity in a reciprocating motion. The driving piston divides the air cavity into a first cavity and a second cavity. A wrist pin is arranged in the second cavity, and the wrist pin is fixed and connected with the driving piston. Both ends of a piston rod is rotatably connected with the wrist pin and the corresponding bulge respectively. The first cavity is provided with an air intake. Multiple heating pipes are in communication with the first cavity at one end and stretch into the boiler body at the other end. The boiler body is sealed and stores water, and the boiler body is provided with a steam outlet, wherein the steam outlet of the boiler body is connected to various terminals through the use of pipelines. Since air is used as the heat source, no conditional limitations exist for installation, and it is safe to use and saves energy and electricity.

Air energy boiler

Provided herein are methods and apparatus for characterizing high aspect ratio (HAR) structures of fabricated or partially fabricated semiconductor devices. The methods involve using small angle X-ray scattering (SAXS) to determine average parameters of an array of HAR structures. In some implementations, SAXS is used to analyze symmetry of HAR structures in a sample and may be referred to as tilted structural symmetry analysis-SAXS (TSSA-SAXS) or TSSA. Analysis of parameters such as tilt, sidewall angle, bowing, and the presence of multiple tilts in HAR structures may be performed.

Determining tilt angle in patterned arrays of high aspect-ratio structures by small-angle x-ray scattering

In accordance with the present disclosure, files may be deduplicated in a distributed storage system having a plurality of storage volumes. A uniqueness metric for each file may indicate a degree of deduplication of the respective data files in the given storage volume. The uniqueness metric may be used to identify files for rebalancing in the distributed storage system. The uniqueness metric may be efficiently calculated with enough accuracy using a sampling methodology.

File granular data de-duplication effectiveness metric for data de-duplication

The present disclosure relates to a FMCW Light Detection and Ranging system, a wave train of carrier frequency is modulated in narrow-band sequence, an delay interferometer with in-phase and quadrature outputs extracts the phase of this frequency modulation, and a coherent receiver with in-phase and quadrature outputs detects the phase of reflected light from a remote object, the ratio between two phases determines the distance of the remote object.

Multi-line, high-definition LiDAR device and method with integrated direct spatial reference

A logical group of data blocks stored in a first node is migrated to a second node according to a method that includes determining a first metric for each logical group of data blocks stored in the first node, the first metric representing a total size of the data blocks in the logical group, determining a second metric for each logical group of data blocks stored in the first node, the second metric representing a total size of the data blocks in the logical group that are uniquely stored in the first node, and selecting a logical group of data blocks for migration from the first node to the second node based on the first metric and the second metric.

Capacity based load balancing in distributed storage systems with deduplication and compression functionalities

Machine learning models used in medical diagnosis should be validated before being deployed in order to reduce the number of misdiagnoses. Validation processes presented here assess a performance of the machine learning model pre-deployment. In one or more examples, prior to the deployment of the machine learning model, the validation process assesses (1) whether a model achieves high enough performance to be deployed, and (2) that the process by which the performance metrics were computed was both sanitary and comprehensive. This pre-deployment validation helps prevent low-performing models from being deployed.

Validating a machine learning model prior to deployment

A tenant's clear text data in a multi-tenant storage system can be encrypted using the tenant's cryptographic key to produce encrypted yet compressible data ('cryptographic data'). The cryptographic data can be encrypted using a system cryptographic key that is managed by the multi-tenant storage system and then stored. Use of the system cryptographic key allows for subsequent maintenance activities such as deduplication and compression to be performed on data stored in the multi-tenant storage system without having to access any of the tenants' cryptographic keys.

Multi-tenant encryption on distributed storage having deduplication and compression capability

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Palo Alto, CA, United States of America

Jin Zhang