Yan Bin Liu

US Patent:

7793018, Sep 7, 2010

Filed:

Aug 30, 2005

Appl. No.:

11/215860

Inventors:

Tianbing Brian Teng - Beaverton OR, US
Yan Mark Liu - Beaverton OR, US

Assignee:

Pixelworks, Inc. - Portland OR

International Classification:

G06F 13/12
G06F 17/00
G06F 3/048

US Classification:

710 72, 715234, 715765

Abstract:

A display, possibly in combination with a computer, is used to manage the shape and position of various inputs, so that the inputs can be presented in combination. Possible combinations include displaying a subset of one input with another input, either overlapping or adjacent, displaying a temporal subset of one input, and using conditions to automatically control the selection of inputs.

US Patent:

8195851, Jun 5, 2012

Filed:

Jul 26, 2010

Appl. No.:

12/843767

Inventors:

Tianbing Brian Teng - Beaverton OR, US
Yan Mark Liu - Beaverton OR, US

Assignee:

Pixelworks, Inc. - Portland OH

International Classification:

G06F 13/12
G06F 17/00
G06F 3/48

US Classification:

710 72, 715234, 715765

Abstract:

A display, possibly in combination with a computer, is used to manage the shape and position of various inputs, so that the inputs can be presented in combination. Possible combinations include displaying a subset of one input with another input, either overlapping or adjacent, displaying a temporal subset of one input, and using conditions to automatically control the selection of inputs.

US Patent:

20120100100, Apr 26, 2012

Filed:

May 13, 2010

Appl. No.:

13/319828

Inventors:

Norman E. Sharpless - Chapel Hill NC, US
Patrick J. Roberts - Durham NC, US
Kwok Wong - Chapell Hill NC, US
Yan Liu - Austin TX, US
Soren Johnson - Chapel Hill NC, US

International Classification:

A61K 38/20
A61P 37/06
A61K 31/4155
A61K 31/497
A61K 38/19
A61K 38/18

US Classification:

424 852, 424 851, 514 78, 514 77, 514 76, 514 92, 514407, 51425211

Abstract:

The presently disclosed subject matter relates to methods and compositions for protecting healthy cells from damage due to DNA damaging agents. In particular, the presently disclosed subject matter relates to the protective action of selective cyclin dependent kinase 4/6 (CDK4/6) inhibitors administered to subjects that have been exposed to or that are at risk of exposure to DNA damage.

US Patent:

20120185415, Jul 19, 2012

Filed:

Jan 13, 2011

Appl. No.:

13/006245

Inventors:

Vijil E. Chenthamarakshan - Kerala, IN
Richard D. Lawrence - Ridgefield CT, US
Yan Liu - Elmsford NY, US
Dan Zhang - West Lafayette IN, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

System, method and computer program product provides a novel domain adaption/transfer learning approach applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The proposed method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain. Exemplary results provided for a Twitter dataset demonstrate that the method identifies meaningful hidden topics and provides useful classifications of specific tweets.

US Patent:

20130013539, Jan 10, 2013

Filed:

Sep 14, 2012

Appl. No.:

13/618603

Inventors:

Vijil E. Chenthamarakshan - Kerala, IN
Richard D. Lawrence - Ridgefield CT, US
Yan Liu - Elmsford NY, US
Dan Zhang - West Lafayette IN, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

System, method and computer program product provides a novel domain adaption/transfer learning approach applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The proposed method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain. Exemplary results provided for a Twitter dataset demonstrate that the method identifies meaningful hidden topics and provides useful classifications of specific tweets.

US Patent:

20220300855, Sep 22, 2022

Filed:

Sep 9, 2021

Appl. No.:

17/470368

Inventors:

- Grand Cayman, KY
Andrew Yan-Tak Ng - Vancouver WA, US
Pingyang He - Palo Alto CA, US
Zeqi Qiu - Mountain View CA, US
Camilo Iral - Guarne, CO
Mingrui Zhang - Beijing, CN
Aldrin Leal - Envigado, CO
Junjie Guan - Redmond WA, US
Ramesh Sampath - Fremont CA, US
Dillion Anthony Laird - San Francisco CA, US
Yu Qing Zhou - San Francisco CA, US
Juan Camilo Fernancez - Medellin, CO
Camilo Zapata - Medellin, CO
Sebastian Rodriguez - Medellin, CO
Cristobal Silva - Medellin, CO
Sanjay Bodhu - Aurora IL, US
Mark William Sabini - River Edge NJ, US
Seshu Reddy - Milpitas CA, US
Kai Yang - Fremont CA, US
Yan Liu - Palo Alto CA, US
Whit Blodgett - San Francisco CA, US
Ankur Rawat - Bothell WA, US
Francisco Matias Cuenca-Acuna - Cordoba, AR
Quinn Killough - Sonoma CA, US

International Classification:

G06N 20/00

Abstract:

A model management system adaptively refines a training dataset for more effective visual inspection. The system trains a machine learning model using the initial training dataset and sends the trained model to a client for deployment. The deployment process generates outputs that are sent back to the system. The system determines that performance of predictions for noisy data points are inadequate and determines a cause of failure based on a mapping of the noisy data point to a distribution generated for the training dataset across multiple dimensions. The system determines a cause of failure based on an attribute of the noisy datapoint that deviates from the distribution of the training dataset and performs refinement towards the training dataset based on the identified cause of failure. The system retrains the machine learning model with the refined training dataset and sends the retrained machine learning model back to the client for re-deployment.

US Patent:

20150030227, Jan 29, 2015

Filed:

Feb 1, 2013

Appl. No.:

14/376177

Inventors:

- Albany NY, US
Jianhua Ma - Stony Brook NY, US
Yan Liu - Stony Brook NY, US

International Classification:

G06T 11/00
A61B 6/03

US Classification:

382131

Abstract:

Provided herein is a computerized image reconstruction apparatus and method that includes recording projection path information and energy loss information of a plurality of particles traversing an object being imaged and determining an estimated image of the object based on the projection path information and the energy loss information sampled into a projection format. The estimated image includes an active volume defined by an enclosure border. Cost function minimization uses an Adaptive-weighted Total Variation cost function, a Penalized Weighted Least-Squares cost function, or an Alpha-Divergence cost function to update the estimated image. An iterative updating algorithm corresponding to the cost function updates the estimated image and produces a final image based on the estimated image according to a predetermined threshold.