SRI International
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Effects of Nicotine Deprivation and Replacement on Bold-Fmri Response to Smoking Cues as a Function of DRD4 Vntr Genotype
INTRODUCTION: Reactivity to smoking cues is an important factor in the motivation to smoke and has been associated with the dopamine receptor 4 variable number tandem repeat (DRD4 exon III VNTR) polymorphism. However, little is known about the associated neural mechanisms. METHODS: Non-treatment-seeking Caucasian smokers completed overnight abstinence and viewed smoking and neutral cues during…
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Blended Learning’s Early Days: What We Learned; Where We’re Headed Next
In 2012, with funding from the Michael & Susan Dell Foundation, SRI Education researchers set out to examine changes unfolding in K-12 classrooms around the nation. We focused on the effectiveness of blended learning models in a sample of K-12 schools in California and Louisiana. We looked at the combination of traditional teacher-led instruction with online…
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Introducing Kasisto – A New SRI International Spin-off Offering Conversational Capabilities for Enterprise Mobile Applications
This week SRI International launched Kasisto, a new venture that integrates conversational capabilities into enterprise mobile products. This means companies can now create branded and configurable virtual personal assistants (VPAs) with deep domain knowledge to provide a fully automated, human-like interaction within their mobile applications. Kasisto will initially focus on the financial services market. The company unveiled…
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How Curriculum Materials Make a Difference for Next Generation Science Learning
SRI researchers found that when curriculum materials explicitly support the features and practices called for within the new science standards—such as including opportunities for students to engage in science practice—teachers can implement the new standards, and students learn more. The goal of Next Generation Science Standards (NGSS) is to focus students on doing science. The…
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National Cyberlearning Summit Features Major Advances in Learning with Technology
On June 9 and 10, 2014, more than 100 investigators, innovators, researchers, and educators convened for a summit at the University of Wisconsin, Madison to identify and communicate major advances in learning with technology.
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The EcoCyc Database
This review outlines the data content of EcoCyc and of the procedures by which this content is generated.
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New Phase 1 Clinical Trial Unit Nears Completion in Michigan
Since early 2013, SRI Biosciences, with support from the Michigan Economic Development Corporation, has been gearing up to establish a Phase 1 clinical trial facility in Plymouth, Michigan, located squarely in the state’s “Life Sciences Corridor.” The facility will meet an important translational need for government and industry clients and partners, particularly small biotech companies that need early-stage human…
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Multi-source anomaly detection: using across-domain and across-time peer-group consistency checks
We present robust anomaly detection in multi-dimensional data.
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New Food Recognition Technology Lets Devices See What’s on Your Plate—To Help You Decide Whether to Eat It
Computer vision—emulation of the human visual system—is enabling groundbreaking advances that allow computers to act and react like humans. Applications for computer vision range from monitoring activities at crowded events, to warning drivers of imminent danger, to self-steered robots that can navigate objects easily. For decades, SRI’s computer vision researchers have pioneered development of technologies…
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Methodology Considerations in School Mental Health Research
This paper reviews key steps needed to effectively study SMH research questions. Considerations around research designs, methods for describing effects and outcomes, issues in measurement of process and outcomes, and the foundational role of school and community research partnerships are discussed .
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Articulatory Features from Deep Neural Networks and Their Role in Speech Recognition
This paper presents a deep neural network (DNN) to extract articulatory information from the speech signal and explores different ways to use such information in a continuous speech recognition task.
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Application of Convolutional Neural Networks to Language Identification in Noisy Conditions
This paper proposes two novel frontends for robust language identification (LID) using a convolutional neural network (CNN) trained for automatic speech recognition (ASR).