Apple Inc.

Ventana Medical Systems, Inc.

Aveo Pharmaceuticals, Inc.

Huami Inc.

International Business Machines Corporation

Anhui Huami Information Technology Co., Ltd.

Microsoft Technology Licensing, LLC

Providence Health & Services - Oregon

Boe Technology Group Co., Ltd.

Uber Technologies, Inc.

Peking University

Xiaomi Inc.

Fuzhou Boe Optoelectronics Technology Co., Ltd.

Everpro (wuhan) Technologies Company Limited

Ting Chen

Stephen Olivier Lemay

Jeno Gyuris

William M Winston, Jr

Solly Weiler

Srinivas Chukka

Haitao Guo

Fei C Wang

Christophe Chefd'hotel

Kristan Meetze

Lyne Breault

David Daming Kuo

Lorena Lerner

Sylvie Vincent

Steve Bottega

Qing Liu

Michael S Barnes

Ailin Bai

Zhigang Weng

Alisa Tubbs

Suk Hwan Lim

Timothy John Millet

D Amnon Silverstein

Bin Ni

Shun Wai Go

Andrew Yanowitz

Yajun Zhao

Maria Isabel Chiu

Laura L Poling

Sandra Abbott

Steven Tyler

Nianjun Tao

Fang Wang

Michael DePrima

Christina Fleet

Jin-Kyeung Woo

Kien Nguyen

Yongfang Liang

Joerg Bredno

Marius Tico

Jixiang Su

Carlo Bifulco

Richard Lawrence Baer

Paul M Hubel

Anindya Sarkar

Shivakumar Vaithyanathan

Rajasekar Krishnamurthy

Wu Song Fang

Jay Zipnick

May Han

Kerry Whalen

Su Liu

Yan Li

Hsi-Jung Wu

Yang Wang

Elizabeth Caroline Cranfill

Xiaosong Zhou

Tanveer Fathima Syeda-Mahmood

Ralph T Brunner

Joe S Abuan

Hyeonkuk Jeong

Li Zhou

Qiang Fu

James O Normile

Jian Jun Wang

Si Bin Fan

Yan Yang

Tao Hong

Roberto Garcia

Dazhong Zhang

Sumit Chawla

Berkat S Tung

Yi Wu

David James Beymer

Frank Doepke

William Kenneth Day

Jim F Martin

Hui Jiang

Roberto Garcia, Jr

Douglas Scott Price

Jie Huang

Yao Nie

David S Gere

Michael Ross Malone

Ritwik K Kumar

Karen Wolfe Brannon

Noemi Sebastiao

Chad Andrew Bronstein

Munehiro Mori

Shalini Singh

Yu Luo

Anita Nariani Schulze

Junxiang Lu

Yufeng Cheng

Xinliang Zhou

Benjamin Ross

Jing Xia

Hongda Mao

Kirk Rodrigues

Yidan Lei

Embodiments of the disclosed technologies create a term frequency-inverse document frequency (tf-idf) model of interactions of user accounts with an online system, and, using the tf-idf model, identify a subset of the user accounts as being involved in a malicious use of the online system. The tf-idf model is created by, for a user account, storing a sequence of requests received by the online system from the user account over a time interval as a document, where a request includes a digital communication from the user account to the online system, and generating a feature embedding for the sequence of requests, where the feature embedding is based on a relationship between a frequency of occurrence of a request in the document and a number of documents that include the request.

Identifying coordinated malicious activities using sequences of requests

Systems and methods for compressing and querying data for real-time analytics. The system can receive log data and generate an intermediate representation by parsing a log template and variables from the log data into a columnar format. The method includes generating a compressed intermediate representation associated with an index type and storing the compressed intermediate representation in the columnar format based on the index type. The method includes receiving a search query and analyzing the search query to identify a user defined function. The method includes parsing the search query to convert the search query into one or more predicates that satisfy the search query. The method includes filtering the compressed log data based on the one or more predicates and providing a query result.

Data compression for real-time analytics

Monoclonal antibodies that bind and inhibit the activity of human GDF15 are disclosed. The antibodies can be used to treat body weight loss, including cachexia, associated with the over-expression of human GDF15.

Anti-GDF-15 antibodies

Systems and methods described herein relate, among other things, to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated.

Group sparsity model for image unmixing

In an example embodiment, a deep learning algorithm is introduced that operates on a transition matrix formed from user activities in an online network. The transition matrix records the frequencies that particular transitions between paths of user activity have occurred (e.g., the user performed a login activity, which has one path in the network, and then performed a profile view action, which has another path in the network). Each transition matrix corresponds to a different user's activities.

Deep learning using activity graph to detect abusive user activity in online networks

A first content and a second content of a first user are obtained. The second content is configured by the first user to be displayed to a second user instead of the first content. A familiarity degree between the first user and the second user are determined. A third content is generated based on the first content, the second content, and the familiarity degree. The third content is displayed to the second user.

Content display based on user relationship

Provided are a flexible body and a method for controlling the flexible body to deform. The flexible body comprises one or more flexible units, wherein each of the flexible units comprises: a first electrode, a second electrode, an electroactive polymer layer, and a thin film transistor, wherein a source electrode or a drain electrode of the thin film transistor is electrically connected to the second electrode. The first electrode and the second electrode are configured to provide an electric field acting on the electroactive polymer layer, and the electroactive polymer layer is configured to deform in response to the electric field provided by the first electrode and the second electrode.

Flexible body and method for controlling flexible body to deform

Monoclonal antibodies that bind and inhibit activation of epidermal growth factor receptor related member ErbB3/HER3 are disclosed. The antibodies can be used to treat cell proliferative diseases and disorders, including certain forms of cancer, associated with activation of ErbB3/HER3.

Anti-ErbB3 antibodies

Techniques are disclosed for managing image capture and processing in a multi-camera imaging system. In such a system, a pair of cameras each may output a sequence of frames representing captured image data. The cameras' output may be synchronized to each other to cause synchronism in the image capture operations of the cameras. The system may assess image quality of frames output from the cameras and, based on the image quality, designate a pair of the frames to serve as a 'reference frame pair.' Thus, one frame from the first camera and a paired frame from the second camera will be designated as the reference frame pair. The system may adjust each reference frame in the pair using other frames from their respective cameras. The reference frames also may be processed by other operations within the system, such as image fusion.

Still image stabilization/optical image stabilization synchronization in multi-camera image capture

Described herein are computer-implemented methods for analysis of a tissue sample. An example method includes: annotating the whole tumor regions or set of tumorous sub-regions either on a biomarker image or an H&E image (e.g. from an adjacent serial section of the biomarker image); registering at least a portion of the biomarker image to the H&E image; detecting different cellular and regional tissue structures within the registered H&E image; computing a probability map based on the different detected structures within the registered H&E image; deriving nuclear metrics from each of the biomarker and H&E images; deriving probability metrics from the probability map; and classifying tumor nuclei in the biomarker image based on the computed nuclear and probability metrics.

Computer scoring based on primary stain and immunohistochemistry images related application data

According to an embodiment, a source object presented in a source video is identified. Attribute information of the source object in respective frames of a sequence of source frames in the source video is identified. The attribute information represents an animation effect associated with the source object across the sequence of source frames. The attribute information is provided for use in reproducing the animation effect in a target video.

Animation effect reproduction

A transmission unit, receiving unit, active transmission device and active transmission system applicable to USB signal, and is used for addressing the problem that the ping.LFPS signal is easy to lose; setting a detection threshold value to distinguish ping.LFPS signals from other LFPS signals to ensure that no ping.LFPS signals are missed; optimizing the USB3.X signal with a proxy module to generate proxy signals for ping.LFPS signals and other LFPS signals respectively; supporting and managing the Link training to improve the success rate of high-speed signal transmission by modifying circuit configuration and re-training.

Transmitting unit, receiving unit, active transmission device and active transmission system suitable for USB signals

The subject disclosure presents systems and computer-implemented methods for automatic immune cell detection that is of assistance in clinical immune profile studies. The automatic immune cell detection method involves retrieving a plurality of image channels from a multi-channel image such as an RGB image or biologically meaningful unmixed image. A cell detector is trained to identify the immune cells by a convolutional neural network in one or multiple image channels. Further, the automatic immune cell detection algorithm involves utilizing a non-maximum suppression algorithm to obtain the immune cell coordinates from a probability map of immune cell presence possibility generated from the convolutional neural network classifier.

Systems and methods for detection of structures and/or patterns in images

Growing community of inventors

Sunnyvale, CA, United States of America

Ting Chen