Adobe, Inc.

R2 Technology, Inc.

U-systems, Inc.

Altera Corporation

First Solar, Inc.

Qview Medical, Inc.

Vmware, Inc.

Arch Development Corporation

Hologic, Inc.

Sun Microsystems, Inc.

Facebook, Inc.

Leland Stanford Junior University

Cypress Semiconductor Corporation

Walmart Apollo, LLC

Mevis Medical Solutions, Inc.

Wei Zhang

Shih-Ping Wang

Thomas Neff

Harlan M Romsdahl

Alexander Caldwell Schneider

Zengpin Yu

Jiayu Chen

Jimmy R Roehrig

Deng Liu

Gang Xiong

Kunio Doi

Thomas A Phelan

Mayank Rawat

Sachit Grover

Li Zhou

Sandeep Madhav Uttamchandani

Kiran Prakash Madnani

Haiming Yu

Danhua Zhao

Sambasiva Rao Bandarupalli

Ryan A Rossi

Eunyee Koh

William Brandon George

Douglas D Lopata

Sunil Agrawal

John David Weld

Julian Marshall

Haili Chui

William Hullinger Huber

Jon Brian Ehlen

Xiaoyun Zhu

Kevin Kreeger

Sungchul Kim

Xiangwei Zhang

Shankar Sinha

Shiv Kumar Saini

Ritwik Sinha

Markus Gloeckler

Donald Chin

Tung Mai

Sergei Kniajanski

Shih-Lin S Lee

Christian Markus Petersen

Anandhavelu Natarajan

Ramkumar Vadivelu

Fangyi Rao

Christopher John Challis

Michael E Reed

Tommy Earl Cupples

Scott Allen Tomko

Xiaoping Li

Said Kobeissi

Xiaolan Zeng

Roger Malik

Shiladitya Bose

Hao-Yuan Howard Chou

Jeanine M Rader

Jason Kephart

Shahram Seyedmohammadi

Feng Liao

Tor Collins Anderson

Tong Yu

William Ie

Brian L Wong

Robert M Nishikawa

Douglas G Summers

Shunan Guo

Hiroyuki Yoshida

Andrei Los

Arlene Gabriana Murillo

Takeshi Doi

Dingyuan Lu

Arvind Heda

Chungho Lee

Victor Soares Bursztyn

Jordan Walker

Chris Challis

Chih-Ping Sun

Harlan Romsdahl

Nico Karssemeijer

Ray Ruey-Hsien Hu

Wen-Chau Wayne Hou

Jufang Wang

Prithvi Bhutani

Le Chen

Jianzhe Tai

Abhishek Bankey Behari Sharma

Chukka Srinivas

Ning Cao

Ron R Kopito

Stuart Irvine

Jeremy W King

Mark Hendryx

James Becker

Rajni Mallick

Wenming Wang

Fang Mei

Ilya Borisovich Reznik

Yanbing Shao

Methods and systems are provided for hallucination prevention for natural language insights. In embodiments described herein, a template-based insight with a set of facts is generated by a template-based insights engine. The set of facts are generated from a set of data and the template-based insight is generated based on a text template. A natural language insight is generated from the template-based insight through a language model. If a threshold number of facts of the template-based insight are missing from the natural language insight as determined by a hallucination gatekeeper engine then a new natural language insight is generated from the template-based insight through the language model.

Hallucination prevention for natural language insights

A method and system acquiring, processing and displaying breast ultrasound images in a way that makes breast ultrasound screening more practical and thus more widely used, and reduces the occurrence of missing cancers in screening and diagnosis, using automated scanning of chestwardly compressed breasts with ultrasound. Enhanced, whole-breast navigator overview images are produced from scanning breasts with ultrasound that emphasize abnormalities in the breast while excluding obscuring influences of non-breast structures, particularly those external to the breast such as ribs and chest wall, and differentiating between likely malignant and likely benign abnormalities and otherwise enhancing the navigator overview image and other images, thereby reducing the time to read, screen, and/or diagnose to practical time limits and also reduce screening or diagnostic errors.

Automated breast ultrasound equipment and methods using enhanced navigator aids

The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.

Facilitating efficient identification of relevant data

Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.

Learning-based method for generating visualizations via natural language

Photovoltaic devices having transparent contact layers are described herein.

Transparent conducting layers and photovoltaic devices including the same

A photovoltaic device includes a substrate, a semiconductor stack and a transparent tunnel junction. The semiconductor stack includes an n-type layer selected from a first transparent conductive oxide layer, or a window layer, or both; and a p-type absorber layer disposed on the n-type layer, wherein the absorber layer consists essentially of CdSexTe(1-x), wherein x is from 1 to about 40 at. %. The transparent tunnel junction comprises a transparent interface layer of CdZnTe doped to be p+type, and a transparent contact layer doped to be n+type, and the interface layer is disposed between the p-type absorber layer and the transparent contact layer. In bifacial embodiments, the tunnel junction forms a transparent back contact and electrode; and in multi-junction embodiments, the tunnel junction forms a diode-like connector between top and bottom cells. The transparent contact layer may comprise tin oxide or zinc oxide doped with aluminum, fluorine or indium. The photovoltaic device may also include an electron reflector layer and/or an optical reflector layer.

Photovoltaic device with transparent tunnel junction

The present disclosure relates to systems, methods, and non-transitory computer readable media that determine internet traffic data loss from internet traffic data including bulk ingested data utilizing an internet traffic forecasting model. In particular, the disclosed systems detect that observed internet traffic data includes bulk ingested internet traffic data. In addition, the disclosed systems determine a predicted traffic volume for an outage period from the bulk ingested internet traffic data utilizing an internet traffic forecasting model. The disclosed systems further generate a decomposed predicted traffic volume for the outage period. The disclosed systems also determine an internet traffic data loss for the outage period from the decomposed predicted traffic volume while calibrating for pattern changes and late data from previous periods.

Determining data loss for internet traffic data

According to the embodiments provided herein, a photovoltaic device can include a buffer layer adjacent to an absorber layer doped p-type with a group V dopant. The buffer layer can have a plurality of layers compatible with group V dopants.

Buffer layers for photovoltaic devices with group V doping

Systems and methods for facilitating updates to software programs via machine-learning techniques are disclosed. In an example, an application generates a feature vector from a textual description of a software defect by applying a topic model to the textual description. The application uses the feature vector and one or more machine-learning models configured to predict classifications and sub-classifications of the textual description. The application integrates the classifications and the sub-classifications into a final classification of the textual description that indicates a software component responsible for causing the software defect. The final classification is usable for correcting the software defect.

Software component defect prediction using classification models that generate hierarchical component classifications

The present disclosure includes methods and systems for generating digital predictive models by progressively sampling a repository of data samples. In particular, one or more embodiments of the disclosed systems and methods identify initial attributes for predicting a target attribute and utilize the initial attributes to identify a coarse sample set. Moreover, the disclosed systems and methods can utilize the coarse sample set to identify focused attributes pertinent to predicting the target attribute. Utilizing the focused attributes, the disclosed systems and methods can identify refined data samples and utilize the refined data samples to identify final attributes and generate a digital predictive model.

Selecting attributes by progressive sampling to generate digital predictive models

Systems and methods provide for feature selection that combines semantic classification and generative filtering with forward selection. Features from an original feature set are divided into feature subsets corresponding to ranked semantic classes. Additionally, low quality features are removed from consideration. Features are selected for a reduced feature set by iteratively processing the feature subsets using forward selection in an order corresponding to the ranking of the semantic classes. The reduced feature set is used to generate a predictive model.

Efficient feature selection for predictive models using semantic classification and generative filtering

Provided are structures and methods for doping polycrystalline thin film semiconductor materials in photovoltaic devices. Embodiments include methods for forming and treating a photovoltaic semiconductor absorber layer.

Doped photovoltaic semiconductor layers and methods of making

Embodiments of the present technology provide systems, methods, and computer storage media for facilitating anomaly detection. In some embodiments, a prediction model is generated using a training data set. The prediction model is used to predict an expected value for a latest (current) timestamp, which is used to determine that the incoming observed data value is an anomaly. Based on the incoming observed data value determined to be the anomaly or not, a corrected data value is generated to be included in the training data set. Thereafter, the training data set having the corrected data value is used to update the prediction model for use in determining whether a subsequent observed data value is anomalous. Such a process may be performed in an iterative manner to maintain optimized training data and prediction model.

Facilitating efficient and effective anomaly detection via minimal human interaction

Growing community of inventors

San Jose, CA, United States of America

Wei Zhang