Yan Liu

US Patent:

7277574, Oct 2, 2007

Filed:

Jun 27, 2005

Appl. No.:

11/169107

Inventors:

Yan Liu - New York NY, US
John Kender - Leonia NJ, US

Assignee:

The Trustees of Columbia University in the City of New York - New York NY

International Classification:

G06K 9/62

US Classification:

382159, 707 7, 712300

Abstract:

Methods and systems for feature selection are described. In particular, methods and systems for feature selection for data classification, retrieval, and segmentation are described. Certain embodiments of the invention are directed to methods and systems for complement sort-merge tree (CSMT), fast-converging sort-merge tree (FSMT), and multi-level (ML) feature selection. Accurate and fast results may be obtained by the feature selection methods and systems described herein.

US Patent:

8332379, Dec 11, 2012

Filed:

Jun 11, 2010

Appl. No.:

12/813608

Inventors:

Ning Duan - Beijing, CN
Pei-Yun S. Hsueh - White Plains NY, US
Yan Liu - Elmsford NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 17/30

US Classification:

707706, 707707, 707708, 707709, 707723, 707748, 707749, 707750, 707751, 707753

Abstract:

A method and system for identifying nodes with similar content. In one aspect, the method comprises determining a structure of a network of nodes, said structure defined by incoming links and outgoing links between nodes within said network, grouping said nodes within said network into a first set of modules, calculating a first modularity value between each of the modules within the first set, said modularity value indicating a degree of similar content within each module, calculating a topical relevance value for each of the modules, selecting those modules whose topical relevance value exceeds a threshold value and calculating an authority score for the selected modules.

US Patent:

20030167505, Sep 4, 2003

Filed:

Mar 11, 2002

Appl. No.:

10/094778

Inventors:

Brian Dilkes - Tucson AZ, US
William Gordon-Kamm - Urbandale IA, US
Paolo Sabelli - Tucson AZ, US
Brian Larkins - Tucson AZ, US
Keith Lowe - Johnston IA, US
Yuejin Sun - Westfield IN, US
Chunsheng Zhang - North Charleston SC, US
Yan Liu - Corona NY, US

International Classification:

A01H005/00
C07H021/04
C12N005/04

US Classification:

800/278000, 435/419000, 435/320100, 536/023600

Abstract:

The invention provides isolated Rb3 nucleic acids and their encoded proteins that are involved in cell cycle regulation. The invention further provides recombinant expression cassettes, host cells, transgenic plants, and antibody compositions. The present invention provides methods and compositions relating to altering cell cycle protein content, cell cycle progression, cell number and/or composition of plants.

US Patent:

20050010975, Jan 13, 2005

Filed:

Aug 27, 2004

Appl. No.:

10/928574

Inventors:

Brian Dilkes - Seattle WA, US
William Gordon-Kamm - Urbandale IA, US
Paolo Sabelli - Tucson AZ, US
Brian Larkins - Tucson AZ, US
Keith Lowe - Johnston IA, US
Yuejin Sun - Westfield IN, US
Chunsheng Zhang - North Charleston SC, US
Yan Liu - New York NY, US

International Classification:

C12Q001/68
C07H021/04
C12N009/22
A01H001/00
C12N015/82

US Classification:

800288000, 435006000, 435069100, 435199000, 435419000, 435468000, 536023200

Abstract:

The invention provides isolated Rb3 nucleic acids and their encoded proteins that are involved in cell cycle regulation. The invention further provides recombinant expression cassettes, host cells, transgenic plants, and antibody compositions. The present invention provides methods and compositions relating to altering cell cycle protein content, cell cycle progression, cell number and/or composition of plants.

US Patent:

20110320387, Dec 29, 2011

Filed:

Nov 2, 2010

Appl. No.:

12/938063

Inventors:

Jingrui He - Ossining NY, US
Richard D. Lawrence - Ridgefield CT, US
Yan Liu - Elmsford NY, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. A graph-based transfer learning framework propagates label information from a source domain to a target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bipartite graph. An iterative algorithm renders the framework scalable to large-scale applications. The framework propagates the label information to both features irrelevant to the source domain and unlabeled examples in the target domain via common features in a principled way.

US Patent:

20220288568, Sep 15, 2022

Filed:

Aug 19, 2020

Appl. No.:

17/636160

Inventors:

- Columbus OH, US
Yan Liu - Columbus OH, US
Lang Qin - Columbus OH, US
Zhuo Cheng - Columbus OH, US

International Classification:

B01J 23/745
B01J 35/00
B01J 35/10
B01J 21/08
C01B 3/40
C01B 32/40

Abstract:

Exemplary oxygen carrier particles may comprise a mesoporous support and a plurality of metal oxide-based nanoparticles immobilized on the mesoporous support. The plurality of metal oxide-based nanoparticles may comprise 10 volume percent to 80 volume percent of mesopores in the mesoporous support. A reactor may comprise a feedstock inlet in fluid communication with a carbonaceous feedstock source, a product stream outlet, and oxygen carrier particles. Exemplary reactors may be operated by providing a carbonaceous feedstock to an inlet of the reactor, providing oxygen carrier particles within the reactor, and collecting a product stream from an outlet of the reactor.

US Patent:

20180130008, May 10, 2018

Filed:

Nov 6, 2016

Appl. No.:

15/344556

Inventors:

Yan Liu - Verona NJ, US

International Classification:

G06Q 10/08
B64C 39/02
B64D 47/08
G06K 7/14
G06K 19/06

Abstract:

An aerial vehicle equipped with a barcode reading sensor can initiate a landing process or cargo delivery process after reading preconfigured data from a barcode. The data configured in the barcode contains various forms of information including but not limited to GPS coordinates, barcode directions, identifications, size of the barcode, batter charging capabilities. This information can be used by the aerial vehicle how and where to land or deliver the cargo carried by the aerial vehicle, the aerial vehicle may also use this information to perform other tasks.