Big data has gotten too big. Now, a research team with statisticians from Cornell has developed a data representation method inspired by quantum mechanics that handles large data sets more efficiently than traditional methods by simplifying them and filtering out noise.
This method could spur innovation in data-rich but statistically intimidating fields, like health care and epigenetics, where traditional data methods have thus far proved insufficient.
The paper is published in the journal Scientific Reports.
The interactions between light and nitroaromatic hydrocarbon molecules have important implications for chemical processes in our atmosphere that can lead to smog and pollution. However, changes in molecular geometry due to interactions with light can be very difficult to measure because they occur at sub-Angstrom length scales (less than a tenth of a billionth of a meter) and femtosecond time scales (one millionth of a billionth of a second).
The relativistic ultrafast electron diffraction (UED) instrument at the Linac Coherent Light Source (LCLS) at SLAC National Accelerator Laboratory provides the necessary spatial and time resolution to observe these ultrasmall and ultrafast motions. The LCLS is a Department of Energy (DOE) Office of Science light source user facility.
In this research, scientists used UED to observe the relaxation of photoexcited o–nitrophenol. Then, they used a genetic structure fitting algorithm to extract new information about small changes in the molecular shape from the UED data that were imperceptible in previous studies. Specifically, the experiment resolved the key processes in the relaxation of o-nitrophenol: proton transfer and deplanarization (i.e., a rotation of part of the molecule out of the molecular plane). Ab-initio multiple spawning simulations confirmed the experimental findings. The results provide new insights into proton transfer-mediated relaxation and pave the way for studies of proton transfer in more complex systems.
The key to this development is an AI-powered streaming method. By decoding brain signals directly from the motor cortex – the brain’s speech control center – the AI synthesizes audible speech almost instantly.
“Our streaming approach brings the same rapid speech decoding capacity of devices like Alexa and Siri to neuroprostheses,” said Gopala Anumanchipalli, co-principal investigator of the study.
Anumanchipalli added, “Using a similar type of algorithm, we found that we could decode neural data and, for the first time, enable near-synchronous voice streaming. The result is more naturalistic, fluent speech synthesis.”
