Search NASA⌕ Search

SEARCH · Search NASA

Results for “self-motion”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Real-space local self-motion of protonated and deuterated water

Here, we report on the self-part of the Van Hove correlation function, the correlation function describing the dynamics of a single molecule, of water and deuterated water. The correlation function is determined by transforming inelastic scattering spectra of neutrons or x rays over a wide range of momentum transfer Q and energy transfer E to space R and time t. The short-range diffusivity is estimated from the Van Hove correlation function in the framework of the Gaussian approximation. The diffusivity has been found to be different from the long-range macroscopic diffusivity, providing information about local atomic dynamics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd

Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Real-Space Local Dynamics in 1,2,3-Triazole Using Inelastic Neutron Scattering

Enhancing proton transport in polymer electrolytes is crucial for advancing next-generation solid-state batteries, yet our understanding of proton conductivity in nonaqueous environments remains limited due to a lack of atomic-scale insights. Here, in this study, we investigated the atomic-scale dynamics of 1,2,3-triazole, a small molecule capable of dynamic hydrogen bonding, as a model system for proton hopping in nonaqueous environments. Using the real-space correlation function determined by the double Fourier transformation of inelastic neutron scattering spectra, we identified that the self-motion of protons and intermolecular dynamics occur on comparable time scales. Furthermore, we observed that the activation energy associated with the intermolecular dynamics matches the energy barrier for molecular rotations determined through Density Functional Theory calculations. These findings underscore the importance of controlling molecular dynamics at the atomic scale to control proton transport. Additionally, we demonstrated that intermolecular dynamics in systems involving protons can be studied using inelastic neutron scattering even without deuteration, thereby providing a broader avenue for studying atomic-scale dynamics in soft matter systems.

Shinohara, Yuya [Oak Ridge National Laboratory (OR↗