Deep-learning techniques appear to be poised to play very important roles in our processing
flows for inversion and interpretation of seismic data. The most successful seismic …

Ab‐initio molecular dynamics (AIMD) is a key method for realistic simulation of complex
atomistic systems and processes in nanoscale. In AIMD, finite‐temperature dynamical …

A hybrid quantum–classical approach to model continuous classical probability distributions
using a variational quantum circuit is proposed. The architecture of this quantum generator …

Background Liver segmentation is a fundamental step in the treatment planning and
diagnosis of liver cancer. However, manual segmentation of liver is time-consuming …

Abstract Artificial Intelligence (AI) and Computer Vision (CV) have rapidly permeated various
industries, increasing demand for professionals well-versed in these disciplines. In response …

Finding efficient decoders for quantum error correcting codes adapted to realistic
experimental noise in fault-tolerant devices represents a significant challenge. In this paper …

An efficient transportation system can substantially benefit our society, but road intersections
have always been among the major traffic bottlenecks leading to traffic congestion …

We tackle the problem of texture synthesis in the setting where many input images are given
and a large-scale output is required. We build on recent generative adversarial networks …

Artificial intelligence (AI) is poised to revolutionize many aspects of science, including the
study of evolutionary morphology. While classical AI methods such as principal component …

This article implements a Convolutional Neural Network (CNN)-based deep-learning model
for solar-wind prediction. Images from the Atmospheric Imaging Assembly (AIA) at 193 Å …