Yan Xia Liu

7277574, Oct 2, 2007

Jun 27, 2005

11/169107

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

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

G06K 9/62

382159, 707 7, 712300

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.

8332379, Dec 11, 2012

Jun 11, 2010

12/813608

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

International Business Machines Corporation - Armonk NY

G06F 17/30

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

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.

20030167505, Sep 4, 2003

Mar 11, 2002

10/094778

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

A01H005/00
C07H021/04
C12N005/04

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

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.

20110320387, Dec 29, 2011

Nov 2, 2010

12/938063

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

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

G06F 15/18

706 12

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.

20210271553, Sep 2, 2021

Mar 11, 2021

17/198715

- Tinton Falls NJ, US
Amit MAHAJAN - Bangalore, IN
Yan LIU - Marlboro NJ, US
Jun H. AHN - Manalapan NJ, US

G06F 11/14
G06F 3/06
H04L 12/58
G06F 11/00
G06F 16/11

A system according to certain aspects improves the process of creating secondary copies of data (e.g., creating backup copies). The system can compute the score of the data (e.g., a computer file storing information) to be backed up, and determine whether the score satisfies one or more threshold criteria before backing up the data. In one example, a change in score indicates a change in the content of the data. The threshold criteria may be that the score be different from the score of the most recently backed up copy of the data.

20200356525, Nov 12, 2020

Apr 24, 2020

16/858316

- Tinton Falls NJ, US
Sesha N. Krishnan - Morganville NJ, US
Yan Liu - Marlboro NJ, US

G06F 16/11
G06F 21/62
H04L 12/58
H04L 29/06

Aspects of this disclosure relate to protecting email data. For example, email protection rules can instruct an email server to route an email to an email protection module. The one or more email rules can identify a group of one or more email addresses. The email protection module can receive an email from the email server that has an email address of the group as an intended receipt. The email protection module can route the email to secondary storage and store the email in secondary storage to create a secondary copy of the email. The secondary copy can be stored inline to persistent memory of secondary storage, according to certain embodiments. Access to the backup copy of the email stored to the persistent memory can be controlled, for example, based on whether a user had permission to access to the email when the email was sent.

20190129798, May 2, 2019

Dec 27, 2018

16/234468

- Tinton Falls NJ, US
Amit MAHAJAN - Bangalore, IN
Yan LIU - Marlboro NJ, US
Jun H. AHN - Manalapan NJ, US

G06F 11/14
G06F 11/00
H04L 12/58
G06F 16/11
G06F 3/06
H04L 29/08

A system according to certain aspects improves the process of creating secondary copies of data (e.g., creating backup copies). The system can compute the score of the data (e.g., a computer file storing information) to be backed up, and determine whether the score satisfies one or more threshold criteria before backing up the data. In one example, a change in score indicates a change in the content of the data. The threshold criteria may be that the score be different from the score of the most recently backed up copy of the data.

20180130008, May 10, 2018

Nov 6, 2016

15/344556

Yan Liu - Verona NJ, US

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

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.