Efficient Implementation of Ab Initio Quantum Embedding in Periodic Systems: Density Matrix Embedding Theory

We describe an efficient quantum embedding framework for realistic ab initio density matrix embedding theory (DMET) calculations in solids. We discuss in detail the choice of orbitals and mapping to a lattice, treatment of the virtual space and bath truncation, and the lattice-to-embedded integral transformation. We apply DMET in this ab initio framework to a hexagonal boron nitride monolayer, crystalline silicon, and nickel monoxide in the antiferromagnetic phase, using large embedded clusters with up to 300 embedding orbitals. We demonstrate our formulation of ab initio DMET in the computation of ground-state properties such as the total energy, equation of state, magnetic moment, and correlation functions

Adaptive Policy Learning to Additional Tasks

This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which combines Bellman's principle of optimality with the policy gradient approach to improve the convergence rate. This paper provides theoretical analysis which guarantees the convergence rate and sample complexity of $\mathcal{O}(1/T)$ and $\mathcal{O}(1/\epsilon)$, respectively, where $T$ denotes the number of iterations and $\epsilon$ denotes the accuracy of the resulting stationary policy. Furthermore, several challenging numerical simulations, including cartpole, lunar lander, and robot arm, are provided to show that APG obtains similar performance compared to existing deterministic policy gradient methods while utilizing much less data and converging at a faster rate

TROSD: A New RGB-D Dataset for Transparent and Reflective Object Segmentation in Practice

Transparent and reflective objects are omnipresent in our daily life, but their unique visual and optical characteristics are notoriously challenging even for state-of-the-art deep networks of semantic segmentation. To alleviate this challenge, we construct a new large-scale real-world RGB-D dataset called TROSD, which is more comprehensive than existing datasets for transparent and reflective object segmentation. Our TROSD dataset contains 11,060 RGB-D images with three semantic classes in terms of transparent objects, reflective objects, and others, covering a variety of daily scenes. Together with the dataset, we also introduce a novel network (TROSNet) as a high-standard baseline to assist other researchers to develop and benchmark their algorithms of transparent and reflective object segmentation. Moreover, extensive experiments also clearly show that the proposed TROSD dataset has an excellent capacity to facilitate the development of semantic segmentation algorithms with strong generalizability