Quantum Anomalous Hall State in Ferromagnetic SrRuO3_3 (111) Bilayers

SrRuO$_3$ heterostructures grown in the (111) direction are a rare example of thin film ferromagnets. By means of density functional theory plus dynamical mean field theory we show that the half-metallic ferromagnetic state with an ordered magnetic moment of 2$\mu_{B}$/Ru survives the ultimate dimensional confinement down to a bilayer, even at elevated temperatures of 500$\,$K. In the minority channel, the spin-orbit coupling opens a gap at the linear band crossing corresponding to $\frac34$ filling of the $t_{2g}$ shell. We demonstrate that the respective state is Haldane's quantum anomalous Hall state with Chern number $C$=1, without an external magnetic field or magnetic impurities.Comment: 5 pages, 3 figure

Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video

Temporally language grounding in untrimmed videos is a newly-raised task in video understanding. Most of the existing methods suffer from inferior efficiency, lacking interpretability, and deviating from the human perception mechanism. Inspired by human's coarse-to-fine decision-making paradigm, we formulate a novel Tree-Structured Policy based Progressive Reinforcement Learning (TSP-PRL) framework to sequentially regulate the temporal boundary by an iterative refinement process. The semantic concepts are explicitly represented as the branches in the policy, which contributes to efficiently decomposing complex policies into an interpretable primitive action. Progressive reinforcement learning provides correct credit assignment via two task-oriented rewards that encourage mutual promotion within the tree-structured policy. We extensively evaluate TSP-PRL on the Charades-STA and ActivityNet datasets, and experimental results show that TSP-PRL achieves competitive performance over existing state-of-the-art methods.Comment: To appear in AAAI202

Optimizing Performance of Hadoop with Parameter Tuning

Optimizing Hadoop with the parameter tuning is an effective way to greatly improve the performance, but it usually costs too much time to identify the optimal parameters configuration because there are many parameters. Users are always blindly adjust too many parameters and are sometimes confused about which one could be changed at a higher-priority. To make optimization easier, classifying the parameter based on different applications will be helpful. In this paper, we will introduce a method that can classify these parameters in order that users can optimize performance more quickly and effectively for different applications