Yu-Chuan Su

TL;DR: A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided in this paper, covering approximately the last seven years, including developments in density functional theory and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces.

TL;DR: The Q-Chem quantum chemistry program package as discussed by the authors provides a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, and methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques.

TL;DR: In this article, a spherical convolutional network is proposed to translate a planar CNN to process 360° imagery directly in its equirectangular projection, which yields the most accurate results while saving orders of magnitude in computation versus the existing exact reprojection solution.

TL;DR: The Kernel Transformer Network (KTN) is presented to efficiently transfer convolution kernels from perspective images to the equirectangular projection of 360° images and successfully preserves the source CNN’s accuracy, while offering transferability, scalability to typical image resolutions, and, in many cases, a substantially lower memory footprint.

TL;DR: In this paper, a spherical convolutional network is proposed to translate a planar CNN to process 360° imagery directly in its equirectangular projection, sensitive to the varying distortion effects across the viewing sphere.