Yi L Zhou

US Patent:

8482330, Jul 9, 2013

Filed:

Oct 7, 2009

Appl. No.:

12/574815

Inventors:

Yi Zhou - San Ramon CA, US

Assignee:

Accusilicon Inc. - George Town, Grand Cayman

International Classification:

H03H 11/26

US Classification:

327262

Abstract:

A low cost, low power and low noise temperature sensor circuit is disclosed. A control circuit asserts a start signal and a stop signal, causes a pulse generating circuit to generate a finite number of pulses, whose pulse frequency varies with temperature. A counter counts the finite number of pulses and outputs the count which can be used to represents the temperature. Further, the pulse generating circuit includes a delay circuit, a pulse width controlling circuit, and a synchronizer with asynchronous clear.

US Patent:

20100294665, Nov 25, 2010

Filed:

Jul 11, 2008

Appl. No.:

12/668579

Inventors:

Richard Allen - San Carlos CA, US
Randall E. Burton - Billerica MA, US
Bryan Crane - San Diego CA, US
Jeffrey R. Krogmeier - Stoneham MA, US
Jonathan W. Larson - Chelsea MA, US
Vyacheslav Papkov - Waltham MA, US
Nicaulas Sabourin - Ottawa, CA
Qun Zhong - Lexington MA, US
Yi Zhou - Carlisle MA, US

International Classification:

G01N 27/447

US Classification:

204520, 204627

Abstract:

A system for transferring and/or concentrating a sample is provided. The system includes a chamber with a membrane brave positioned within the chamber. An electrode assembly is configured to create an electric field across the membrane to move a charged sample through the chamber such that the sample collects and may concentrate on the membrane. The system may include a plurality of membranes. The system may also include a plurality of microchannels outwardly extending from the channel, where the membrane extends along the plurality of microchannels. Aspects of the invention are also directed to a pipette which may be used to transfer and concentrate a sample on a membrane. Certain embodiments are directed to methods and systems for concentrating a nucleic acid sample.

US Patent:

20220414531, Dec 29, 2022

Filed:

Jun 25, 2021

Appl. No.:

17/358804

Inventors:

- Armonk NY, US
NATHALIE BARACALDO ANGEL - San Jose CA, US
ALY MEGAHED - San Jose CA, US
Ebube Chuba - San Jose CA, US
Yi Zhou - San Jose CA, US

International Classification:

G06N 20/00
G06N 5/02

Abstract:

An approach for providing prediction and optimization of an adversarial machine-learning model is disclosed. The approach can comprise of a training method for a defender that determines the optimal amount of adversarial training that would prevent the task optimization model from taking wrong decisions caused by an adversarial attack from the input into the model within the simultaneous predict and optimization framework. Essentially, the approach would train a robust model via adversarial training. Based on the robust training model, the user can mitigate against potential threats by (adversarial noise in the task-based optimization model) based on the given inputs from the machine learning prediction that was produced by an input.

US Patent:

20220392111, Dec 8, 2022

Filed:

Jun 3, 2021

Appl. No.:

17/338119

Inventors:

- Menlo Park CA, US
Kirk Erik Burgess - Newark CA, US
Lars Ola Axelsson - Brentwood CA, US
Yi Zhou - San Jose CA, US

International Classification:

G06T 7/80
H04N 5/232
G06K 9/32
G06F 3/01
H04N 13/246
H04N 13/239

Abstract:

An image capture system is configured to align a field of view of the image capture component with a field of view of a user of the system. In some cases, the image capture system may adjust the field of view of the image data based at least in part on orientation and position data associated with the capture device.

US Patent:

20220383132, Dec 1, 2022

Filed:

Aug 8, 2022

Appl. No.:

17/818132

Inventors:

- ARMONK NY, US
Yi Zhou - San Jose CA, US
Nathalie Baracaldo Angel - San Jose CA, US
Ali Anwar - San Jose CA, US
Simone Bianco - San Francisco CA, US

International Classification:

G06N 3/08
G06N 3/04

Abstract:

A method, a computer system, and a computer program product are provided for federated learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The cluster information may include centroid information per cluster. The aggregator may include a processor. The aggregator may integrate the cluster information to define data classes for machine learning classification. The integrating may include computing a respective distance between centroids of the clusters in order to determine a total number of the data classes. The aggregator may send a deep learning model that includes an output layer that has a total number of nodes equal to the total number of the data classes. The deep learning model may be for the distributed computing devices to perform machine learning classification in federated learning.

US Patent:

20220301262, Sep 22, 2022

Filed:

Mar 19, 2021

Appl. No.:

17/206813

Inventors:

- San Jose CA, US
Yi Zhou - Sunnyvale CA, US

International Classification:

G06T 17/20
G06T 15/08
G06T 7/20
G06N 3/08

Abstract:

Various disclosed embodiments are directed to estimating that a first vertex of a patch will change from a first position to a second position (the second position being at least partially indicative of secondary motion) based at least in part on one or more features of: primary motion data, one or more material properties, and constraint data associated with the particular patch. Such estimation can be made for some or all of the patches of an entire volumetric mesh in order to accurately predict the overall secondary motion of an object. This, among other functionality described herein resolves the inaccuracies, computer resource consumption, and the user experience of existing technologies.

US Patent:

20230067584, Mar 2, 2023

Filed:

Aug 24, 2022

Appl. No.:

17/821981

Inventors:

- Cupertino CA, US
Yi Zhou - San Jose CA, US

International Classification:

H04N 19/167
H04N 19/119
H04N 19/124
H04N 19/17
H04N 19/14

Abstract:

Encoding an extended-reality (XR) video frame may include obtaining an XR video frame comprising a background image and a virtual object; obtaining, from an image renderer, a first region of the background image over which the virtual object is overlaid; dividing the XR video frame into a virtual region and a real region, wherein the virtual region comprises the first region of the background image and the virtual object and the real region comprises a second region of the background image; determining, for the virtual region, a corresponding first quantization parameter based on an initial quantization parameter associated with virtual regions; determining, for the real region, a corresponding second quantization parameter based on an initial quantization parameter associated with real regions; and encoding the virtual region based on the corresponding first quantization parameter and the real region based on the corresponding second quantization parameter.

US Patent:

20210304062, Sep 30, 2021

Filed:

Mar 27, 2020

Appl. No.:

16/832809

Inventors:

- Armonk NY, US
Ali Anwar - San Jose CA, US
Yi Zhou - San Jose CA, US
Heiko H. Ludwig - San Francisco CA, US
Nathalie Baracaldo Angel - San Jose CA, US

International Classification:

G06N 20/00

Abstract:

One embodiment provides a method for federated learning across a plurality of data parties, comprising assigning each data party with a corresponding namespace in an object store, assigning a shared namespace in the object store, and triggering a round of federated learning by issuing a customized learning request to at least one data party. Each customized learning request issued to a data party triggers the data party to locally train a model based on training data owned by the data party and model parameters stored in the shared namespace, and upload a local model resulting from the local training to a corresponding namespace in the object store the data party is assigned with. The method further comprises retrieving, from the object store, local models uploaded to the object store during the round of federated learning, and aggregating the local models to obtain a shared model.