Two-dimensional rare-earth Janus 2H\textit{H}-GdXY\textit{XY} (X\textit{X},Y\textit{Y}=Cl, Br, I, X\textit{X}≠\neqY\textit{Y}) monolayers: Bipolar ferro-magnetic semiconductors with high Curie temperature and large valley polarization

Two-dimensional (2D) ferromagnetic semiconductors show great interest due to their potential applications for the nanoscale electronic devices. In this work, the Janus 2$H$-Gd$XY$ ($X$, $Y$=Cl, Br, I, $X$$\neq$$Y$) monolayers with rare-earth element Gd (4$f^{7}$+5$d^{1}$) are predicted by the first-principles calculations. Small exfoliation energy of less than 0.25 J/m$^{2}$ and excellent dynamical/thermal stabilities can be confirmed for the Janus 2$H$-Gd$XY$ monolayers, which exhibit the bipolar magnetic semiconductor character with high Curie temperatures above 260 K and large spin-orbit coupling effect, and can be further transformed into the half-semiconductor phase under proper tensile strains (5-6\%). In addition, the in-plane magnetic anisotropy can be observed in the 2$H$-GdICl and 2$H$-GdIBr monolayers. On the contrary, the 2$H$-GdBrCl monolayer exhibits perpendicular magnetic anisotropy character, which originates from the competition between Gd-$p$/$d$ and halogen atom-$p$ orbitals. Calculated valley optical actions of the Janus 2$H$-Gd$XY$ monolayers exhibit distinguished valley-selective circular dichroisms, which is expected to realize the special valley excitation by polarized light. Spontaneously valley-Zeeman effect in the valance band for the Janus 2$H$-Gd$XY$ monolayers induces a giant valley splitting of 60-120 meV, which is also robust against various external biaxial strains. Tunable valley degree of freedom in the Janus 2$H$-Gd$XY$ systems is very necessary for encoding and processing information.Comment: 8 pages, 8 figure

Attention-Aware Face Hallucination via Deep Reinforcement Learning

Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations

2-D Compass Codes

The compass model on a square lattice provides a natural template for building subsystem stabilizer codes. The surface code and the Bacon-Shor code represent two extremes of possible codes depending on how many gauge qubits are fixed. We explore threshold behavior in this broad class of local codes by trading locality for asymmetry and gauge degrees of freedom for stabilizer syndrome information. We analyze these codes with asymmetric and spatially inhomogeneous Pauli noise in the code capacity and phenomenological models. In these idealized settings, we observe considerably higher thresholds against asymmetric noise. At the circuit level, these codes inherit the bare-ancilla fault-tolerance of the Bacon-Shor code.Comment: 10 pages, 7 figures, added discussion on fault-toleranc

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012, a novel state estimation methodology called {\it composite disturbance filtering} (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances