Light-induced half-quantized Hall effect and axion insulator

Motivated by the recent experimental realization of the half-quantized Hall effect phase in a three-dimensional (3D) semi-magnetic topological insulator [M. Mogi et al., Nature Physics 18, 390 (2022)], we propose a scheme for realizing the half-quantized Hall effect and axion insulator in experimentally mature 3D topological insulator heterostructures. Our approach involves optically pumping and/or magnetically doping the topological insulator surface, such as to break time reversal and gap out the Dirac cones. By toggling between left and right circularly polarized optical pumping, the sign of the half-integer Hall conductance from each of the surface Dirac cones can be controlled, such as to yield half-quantized ($0+1/2$), axion ($-1/2+1/2=0$), and Chern ($1/2+1/2=1$) insulator phases. We substantiate our results based on detailed band structure and Berry curvature numerics on the Floquet Hamiltonian in the high-frequency limit. Our paper showcases how topological phases can be obtained through mature experimental approaches such as magnetic layer doping and circularly polarized laser pumping and opens up potential device applications such as a polarization chirality-controlled topological transistor.Comment: 24 pages, 11 figures, update references, published versio

GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models

Offline goal-conditioned RL (GCRL) offers a feasible paradigm to learn general-purpose policies from diverse and multi-task offline datasets. Despite notable recent progress, the predominant offline GCRL methods have been restricted to model-free approaches, constraining their capacity to tackle limited data budgets and unseen goal generalization. In this work, we propose a novel two-stage model-based framework, Goal-conditioned Offline Planning (GOPlan), including (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies. Specifically, the prior policy is based on an advantage-weighted Conditioned Generative Adversarial Networks that exhibits distinct mode separation to overcome the pitfalls of out-of-distribution (OOD) actions. For further policy optimization, the reanalysis method generates high-quality imaginary data by planning with learned models for both intra-trajectory and inter-trajectory goals. Through experimental evaluations, we demonstrate that GOPlan achieves state-of-the-art performance on various offline multi-goal manipulation tasks. Moreover, our results highlight the superior ability of GOPlan to handle small data budgets and generalize to OOD goals.Comment: Spotlight Presentation at Goal-conditioned Reinforcement Learning Workshop at NeurIPS, 202

How to Retain Consumers: A Trust-Commitment Model

Although studies on the determinants of consumers’ continuance intention in e-marketplaces have grown in recent years, the research is predominantly related to unidimensional trust and commitment. In this research, the authors focus on the distinct roles of different types of consumer trust and commitment on consumers’ continuance intention. Drawing upon organizational commitment and trust theories, we develop a continuance intention model that includes two types of trust and two types of commitments. We collected a sample of 287 online consumers to validate the theoretical model. Our data suggest that consumers’ trust and commitment positively affect their continuance intention. Our study also indicates that the psychological states underlying the commitments are different. Key findings and implications are discussed

Hybridized surface plasmon polaritons at an interface between a metal and a uniaxial crystal

The surface plasmonpolariton (SPP) at an interface between a metal and a uniaxial crystal is studied. A new class of hybridized SPP found in this work is quite different from the traditional SPP at the interface between a metal and an isotropic dielectric. In contrast to the two evanescent fields for the traditional SPP, the hybridized SPP involves four evanescent fields: transverse-electric-like and transverse-magnetic-like waves in the metal, and ordinary-light-like and extraordinary-light-like waves in the uniaxial crystal. The necessary conditions and the regimes for the existence of the hybridized SPP are presented. Some potential applications are also discussed.This work is supported in part by NSFC under Grant No. 10325417, by the State Key Program for Basic Research of China under Grant No. 2006CB921805, and by the 111 Project under Grant No. B07026

B\"uchi VASS recognise w-languages that are Sigma^1_1 - complete

This short note exhibits an example of a Sigma^1_1-complete language that can be recognised by a one blind counter B\"uchi automaton (or equivalently a B\"uchi VASS with only one place)

Atomic spatial coherence with spontaneous emission in a strong coupling cavity

The role of spontaneous emission in the interaction between a two-level atom and a pumped micro-cavity in the strong coupling regime is discussed in this paper. Especially, using a quantum Monte-Carlo simulation, we investigate atomic spatial coherence. It is found that atomic spontaneous emission destroys the coherence between neighboring lattice sites, while the cavity decay does not. Furthermore, our computation of the spatial coherence function shows that the in-site locality is little affected by the cavity decay, but greatly depends on the cavity pump amplitude.Comment: 4 pages, 5 figures, accepted by PR

Minimum Snap Trajectory Generation and Control for an Under-actuated Flapping Wing Aerial Vehicle

Minimum Snap Trajectory Generation and Control for an Under-actuated Flapping Wing Aerial VehicleThis paper presents both the trajectory generation and tracking control strategies for an underactuated flapping wing aerial vehicle (FWAV). First, the FWAV dynamics is analyzed in a practical perspective. Then, based on these analyses, we demonstrate the differential flatness of the FWAV system, and develop a general-purpose trajectory generation strategy. Subsequently, the trajectory tracking controller is developed with the help of robust control and switch control techniques. After that, the overall system asymptotic stability is guaranteed by Lyapunov stability analysis. To make the controller applicable in real flight, we also provide several instructions. Finally, a series of experiment results manifest the successful implementation of the proposed trajectory generation strategy and tracking control strategy. This work firstly achieves the closed-loop integration of trajectory generation and control for real 3-dimensional flight of an underactuated FWAV to a practical level

Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning

Support-query shift few-shot learning aims to classify unseen examples (query set) to labeled data (support set) based on the learned embedding in a low-dimensional space under a distribution shift between the support set and the query set. However, in real-world scenarios the shifts are usually unknown and varied, making it difficult to estimate in advance. Therefore, in this paper, we propose a novel but more difficult challenge, RSQS, focusing on Realistic Support-Query Shift few-shot learning. The key feature of RSQS is that the individual samples in a meta-task are subjected to multiple distribution shifts in each meta-task. In addition, we propose a unified adversarial feature alignment method called DUal adversarial ALignment framework (DuaL) to relieve RSQS from two aspects, i.e., inter-domain bias and intra-domain variance. On the one hand, for the inter-domain bias, we corrupt the original data in advance and use the synthesized perturbed inputs to train the repairer network by minimizing distance in the feature level. On the other hand, for intra-domain variance, we proposed a generator network to synthesize hard, i.e., less similar, examples from the support set in a self-supervised manner and introduce regularized optimal transportation to derive a smooth optimal transportation plan. Lastly, a benchmark of RSQS is built with several state-of-the-art baselines among three datasets (CIFAR100, mini-ImageNet, and Tiered-Imagenet). Experiment results show that DuaL significantly outperforms the state-of-the-art methods in our benchmark.Comment: Best student paper in PAKDD 202