Artificial visual system of record-low energy consumption for the next generation of AI
A joint research led by
City University of Hong Kong (CityU) has built an ultralow-power consumption artificial visual system to mimic the human brain, which successfully performed data-intensive cognitive tasks. Their experiment results could provide a promising device system for the next generation of artificial intelligence (AI) applications.
The research team is led by
Professor Johnny Chung-yin Ho, Associate Head and Professor of the Department of Materials Science and Engineering (MSE) at CityU. Their findings have been published in the scientific journal
Science Advances, titled
Artificial visual system enabled by quasi-two-dimensional electron gases in oxide superlattice nanowires .
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IMAGE: A worker at the Daya Bay Reactor Neutrino Experiment site is perched near a water pool where four large detectors are submerged in this August 2012 photo. view more
Credit: Roy Kaltschmidt/Berkeley Lab
The Daya Bay Reactor Neutrino Experiment collaboration - which made a precise measurement of an important neutrino property eight years ago, setting the stage for a new round of experiments and discoveries about these hard-to-study particles - has finished taking data. Though the experiment is formally shutting down, the collaboration will continue to analyze its complete dataset to improve upon the precision of findings based on earlier measurements.
Home > Press > Stretchable micro-supercapacitors to self-power wearable devices
A team of international researchers, led by Huanyu Larry Cheng, Dorothy Quiggle Career Development Professor in Penn State s Department of Engineering Science and Mechanics, has developed a self-powered, stretchable system that will be used in wearable health-monitoring and diagnostic devices.
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Penn State College of Engineering
Abstract:
A stretchable system that can harvest energy from human breathing and motion for use in wearable health-monitoring devices may be possible, according to an international team of researchers, led by Huanyu Larry Cheng, Dorothy Quiggle Career Development Professor in Penn State s Department of Engineering Science and Mechanics.
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IMAGE: A team led by Nevan Krogan at Gladstone and UCSF has demonstrated that a large-scale and systematic genetic approach can indeed yield reliable and detailed information on the structure of. view more
Credit: QBI, UCSF
SAN FRANCISCO, CA December 10, 2020 One of biologists most vexing tasks is figuring out how proteins, the molecules that carry the brunt of a cell s work, do their job. Each protein has a variety of knobs, folds, and clefts on its surface that dictate what it can do. Scientists can visualize these features fairly easily on individual proteins. But proteins don t act alone, and scientists also need to know the shape and composition the structure, as they call it of the complexes that proteins form when working together.
Abstract
Single-cell whole-genome sequencing (WGS) is critical for characterizing dynamic intercellular changes in DNA. Current sample preparation technologies for single-cell WGS are complex, expensive, and suffer from high amplification bias and errors. Here, we describe Digital-WGS, a sample preparation platform that streamlines high-performance single-cell WGS with automatic processing based on digital microfluidics. Using the method, we provide high single-cell capture efficiency for any amount and types of cells by a wetted hydrodynamic structure. The digital control of droplets in a closed hydrophobic interface enables the complete removal of exogenous DNA, sufficient cell lysis, and lossless amplicon recovery, achieving the low coefficient of variation and high coverage at multiple scales. The single-cell genomic variations profiling performs the excellent detection of copy number variants with the smallest bin of 150 kb and single-nucleotide variants with allele dropout rat