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    METHOD AND SYSTEM FOR SENSING PLANT EXPRESSION
    272.
    发明申请
    METHOD AND SYSTEM FOR SENSING PLANT EXPRESSION 有权

    公开(公告)号：US20210116442A1

    公开(公告)日：2021-04-22

    申请号：US16767221

    申请日：2018-11-29

    Applicant: Ramot at Tel-Aviv University Ltd.

    Inventor： Yosi SHACHAM-DIAMAND ,  Adi AVINI ,  Richa PANDEY

    IPC: G01N33/50 ,  G01N27/327

    Abstract: A sensing system comprises an electrochemical chip having an arrangement of electrodes configured for electrochemical sensing; a microfluidic system having fluidic channels leading to ports on a surface of the sensing system, for delivering to a plant part a substrate for a reporter enzyme expressed by the plant; and an attachment system for attaching the surface of the sensing system to a surface of the plant part in a manner that the fluidic ports contact the surface of the plant part.

    TENSOR-BASED PREDICTIONS FROM ANALYSIS OF TIME-VARYING GRAPHS
    274.
    发明申请
    TENSOR-BASED PREDICTIONS FROM ANALYSIS OF TIME-VARYING GRAPHS 有权

    公开(公告)号：US20210090182A1

    公开(公告)日：2021-03-25

    申请号：US16579454

    申请日：2019-09-23

    Applicant: International Business Machines Corporation ,  TRUSTEES OF TUFTS COLLEGE ,  Ramot at Tel-Aviv University Ltd.

    Inventor： Lior Horesh ,  Osman Asif Malik ,  Shashanka Ubaru ,  Misha E. Kilmer ,  Haim Avron

    IPC: G06Q50/00 ,  G06F16/901 ,  G06N20/00 ,  G06F17/16

    Abstract: A computer-implemented method for analyzing a time-varying graph is provided. The time-varying graph includes nodes representing elements in a network, edges representing transactions between elements, and data associated with the nodes and the edges. The computer-implemented method includes constructing, using a processor, adjacency and feature matrices describing each node and edge of each time-varying graph for stacking into an adjacency tensor and describing the data of each time-varying graph for stacking into a feature tensor, respectively. The adjacency and feature tensors are partitioned into adjacency and feature training tensors and into adjacency and feature validation tensors, respectively. An embedding model and a prediction model are created using the adjacency and feature training tensors. The embedding and prediction models are validated using the adjacency and feature validation tensors to identify an optimized embedding-prediction model pair.

    SYSTEMS AND METHODS FOR THE SEGMENTATION OF MULTI-MODAL IMAGE DATA
    276.
    发明申请
    SYSTEMS AND METHODS FOR THE SEGMENTATION OF MULTI-MODAL IMAGE DATA 审中-公开

    公开(公告)号：US20200380687A1

    公开(公告)日：2020-12-03

    申请号：US16959693

    申请日：2019-01-03

    Applicant: Ramot at Tel-Aviv University Ltd. ,  Tel HaShomer Medical Research Infrastrastructure and Services Ltd.

    Inventor： Yitzhak AVITAL ,  Nahum KIRYATI ,  Eli KONEN ,  Arnaldo MAYER

    IPC: G06T7/11 ,  A61B5/055 ,  A61B5/00 ,  A61B34/32 ,  A61B34/20 ,  G01R33/56 ,  G06N3/08

    Abstract: There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output. The intermediate outputs are inputted into a single common decoding-expanding component for computing the indication of segmented 3D ROI(s).

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